Advancing Life Cycle Comparisons of Future Alternative Light-duty Vehicles

Advancing Life Cycle Comparisons of Future Alternative Light-Duty Vehicles

by

Jason Ming Luk

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Civil Engineering University of Toronto

© Copyright by Jason Luk 2015

ii

Advancing Life Cycle Comparisons of Future Alternative Light-

Duty Vehicles

Jason Ming Luk

Doctor of Philosophy

Department of Civil Engineering

University of Toronto

2015

Abstract

The overall objective of this thesis is to systematically compare the life cycle energy use, air

emissions and costs of future alternative light-duty vehicles in a more robust manner than is done

in the literature. Models are developed using GREET (Greenhouse Gases, Regulated Emissions,

and Energy Use in Transportation), Autonomie vehicle simulation software, Vehicle Attribute

Model, Air Pollution Emission Experiments and Policy (APEEP) analysis model, and Crystal

Ball (Monte Carlo analysis). Four questions are investigated:

Should the transportation sector use ethanol or bio-electricity? Life cycle assessment

results indicate that neither has a clear advantage in terms of greenhouse gas (GHG) emissions

or energy use. This finding is in contrast to those in the literature that favor the use of bio-

electricity because this thesis develops pathways with comparable vehicle characteristics.

Do plug-in electric vehicles provide incremental life cycle air pollutant impact benefits

over internal combustion engine vehicles using the same primary energy source? The

results based on natural gas-derived fuels show that battery electric vehicles (BEV) may not

provide benefits, in terms of climate change and health impacts, over hybrid electric vehicles

iii

(HEV). This can be attributed to the many sources of uncertainty and stringent tailpipe

emissions regulations.

How can vehicles be designed to meet future CAFE (Corporate Average Fuel Economy)

standards? Case study results for a reference vehicle show that the 66% increase in fuel

economy targets between model years 2012 to 2025 can be met with a 10% vehicle price

increase (lightweight HEV powertrain), 31% increase in 0-96 km/h acceleration time (smaller

engine), 17% interior volume decrease (smaller body), or 94% driving range decrease (BEV

powertrain), while other attributes are maintained.

How might CAFE standards affect the ability for non-petroleum vehicles to mitigate

GHG emissions by displacing petroleum vehicles? Life cycle costing results indicate that

there is a financial incentive for automakers to produce CNG vehicles that could emit higher

well-to-wheel GHG emissions on a per kilometer basis than gasoline vehicles. This is

permitted by CAFE standards because non-petroleum fuel incentives allow vehicles using

CNG to be less efficient, and thus potentially more affordable, than those using gasoline.

iv

Acknowledgements

Dr. Heather MacLean for being an infuriatingly great supervisor. Her patience and trust gave me

the freedom to make my own mistakes, while her unrelenting expectations never allowed me to

become complacent. I am privileged to have the opportunity to continue to work with her.

Dr. Bradley Saville for going far beyond his official position as a committee member. Our high

energy/volume debates provoked me to realize the strengths and address the weaknesses of my

work.

Dr. Chris Kennedy, Dr. Gregory Keoleian, Dr. Matthew Roorda, Dr. Murray Thomson, Dr.

James Wallace for their roles on my examination committees. Their diverse insights helped

refine the direction and academic significance of my research.

Dr. Candace Wheeler, Ian Sutherland and Norm Brinkman for their contributions on behalf of

General Motors. Their industry prospective identified valuable resources and improved the real

world relevance of this thesis.

Dr. Clement Bowman, Marjorie Bowman, Paul Price and Suzana Price for generously

contributing to the scholarships that have funded my studies.

Kaye and Eleanor Yu for being sources of joy.

v

Table of Contents

Table of Contents ............................................................................................................................ v

List of Tables ............................................................................................................................... viii

List of Figures ................................................................................................................................ xi

List of Notations ........................................................................................................................... xv

Chapter 1 Introduction .................................................................................................................... 1

1.1 Thesis Objectives ................................................................................................................ 5

1.2 Publications contained in this thesis ................................................................................... 6

Chapter 2 Background .................................................................................................................... 8

2.1 Light-duty Vehicle Energy Use Policies ............................................................................. 8

2.2 Status of Light-Duty Vehicle Powertrains and Fuels ....................................................... 17

2.3 Life Cycle Comparisons of Alternative Light-Duty Vehicles .......................................... 28

Chapter 3 Methods ........................................................................................................................ 36

3.1 Life Cycle Assessment ...................................................................................................... 36

3.2 GREET Model .................................................................................................................. 38

3.3 Air Pollution Emission Experiments and Policy Analysis Model .................................... 39

3.4 Autonomie ......................................................................................................................... 41

3.5 Vehicle Attribute Model ................................................................................................... 44

3.6 Monte Carlo Analysis ....................................................................................................... 46

Chapter 4 Life Cycle Assessment of Bioenergy Use in Light-Duty Vehicles .............................. 48

4.1 Methods ............................................................................................................................. 50

4.2 Results and Discussion ..................................................................................................... 54

Chapter 5 Life Cycle Air Emissions Impacts and Ownership Costs of Light-Duty Vehicles

Using Natural Gas As A Primary Energy Source .................................................................... 66

5.1 Methods ............................................................................................................................. 67


vi

Chapter 6 Vehicle Design Options To Meet 2025 Corporate Average Fuel Economy

Standards .................................................................................................................................. 86

6.1 Methods ............................................................................................................................. 88


Chapter 7 Potential Impact of Corporate Average Fuel Economy Standards On The Ability

For Non-Petroleum Vehicle To Mitigate Greenhouse Gas Emissions .................................. 104

7.1 Methods ........................................................................................................................... 106

7.2 Results and Discussion ................................................................................................... 112

Chapter 8 Conclusion .................................................................................................................. 119

8.1 Chapter Conclusions ....................................................................................................... 119

8.2 Thesis Conclusions ......................................................................................................... 122

8.3 Limitations ...................................................................................................................... 123

8.4 Future Research .............................................................................................................. 126

References ................................................................................................................................... 129

Appendix A: Chapter 4 Supporting Information ........................................................................ 146

Methods Section Details ........................................................................................................ 146

Results .................................................................................................................................... 163

Scenario Analysis ................................................................................................................... 166

Appendix B: Chapter 5 Supporting Information ........................................................................ 169

Supplemental Methods ........................................................................................................... 169

Ownership Costs .................................................................................................................... 171

Uncertainty and Sensitivity Analysis ..................................................................................... 176

Supplemental Results ............................................................................................................. 178

Supplemental Scenarios ......................................................................................................... 183

Appendix C: Chapter 6 Supporting Information ........................................................................ 185

Methods Details ..................................................................................................................... 185

Results Details ........................................................................................................................ 199

vii

Appendix D: Chapter 7 Supporting Information ........................................................................ 201

Supplemental Methods ........................................................................................................... 201

Supplemental Results ............................................................................................................. 207

Copyright Acknowledgements .................................................................................................... 209

viii

List of Tables

Table 4-1: Reference and bioenergy pathways ............................................................................. 51

Table 5-1: Key assumptions used to develop fuel cycle and vehicle models ............................... 71

Table 7-1: Overview of base case assumptions used in this study ............................................. 107

Table A-1: Biomass production data from the GREET Fuel-Cycle model7 ............................... 148

Table A-2: Physical characteristics for hybrid poplar ................................................................ 148

Table A-3: Chemical production data from MacLean and Spatari175 ......................................... 149

Table A-4: Bioenergy production data ....................................................................................... 150

Table A-5: Aspen115 subroutines used to develop production models ....................................... 150

Table A-6: Base case ethanol production model material flow balance ..................................... 152

Table A-7: Future ethanol production model material flow balance .......................................... 153

Table A-8: Base case bio-electricity production model material flow balance .......................... 154

Table A-9: Future bio-electricity production model material flow balance ............................... 156

Table A-10: Ethanol delivery data from the GREET Fuel-Cycle model7 .................................. 157

Table A-11: Reference fuel production data from the GREET Fuel-Cycle model7 ................... 158

Table A-12: Grid-electricity resource mix from the GREET Fuel-Cycle model7 ...................... 158

Table A-13: Vehicle design and performance characteristics .................................................... 159

Table A-14: Mass and battery characteristics of vehicle models created in Autonomie96 ......... 160

Table A-15: Un-weighted fuel consumption results of vehicle models created in Autonomie96 160

Table A-16: Vehicle emissions and fuel consumption ............................................................... 161

Table A-17: Vehicle cycle results for vehicle models based on GREET Vehicle-Cycle model7162

ix

Table A-18: Life cycle pathway results ...................................................................................... 164

Table A-19: GHG emissions, fossil energy and petroleum mitigation results ........................... 165

Table B-1: CNG fuel tank and BEV battery cost and mass parameters ..................................... 173

Table B-2: CV and BEV powertrain cost, mass and efficiency parameters ............................... 174

Table B-3: Vehicle maintenance cost and frequency parameters ............................................... 175

Table B-4: Key life cycle inventory assumptions used to develop Monte Carlo and sensitivity

analyses ....................................................................................................................................... 176

Table B-5: Key ownership cost and emissions impact assumptions used to develop Monte Carlo

and sensitivity analyses ............................................................................................................... 177

Table B-6: Specific costs of CAC emissions impacts used to develop Monte Carlo and sensitivity

analyses ....................................................................................................................................... 177

Table B-7: Incremental life cycle ownership and emissions impact cost 90% confidence intervals

for supplementary scenarios ....................................................................................................... 183

Table C-1: Chevy Equinox-like and Honda Accord-like components ....................................... 187

Table C-2: Gasoline Chevy Equinox-like and Honda Accord-like vehicle specifications for a

range of engine power ratings ..................................................................................................... 188

Table C-3: Plug-in electric Chevy Equinox-like vehicle specifications for range of motor power

ratings and battery capacities ...................................................................................................... 189

Table C-4: Model Year 2012 vehicle manufacturing costs ........................................................ 191




x

Table C-8: Parameters for calculating price of added fuel efficiency technologies from Vehicle

Attribute Model3 ......................................................................................................................... 196

Table C-9: 2012 Reference vehicle model specifications ........................................................... 199

Table C-10: Vehicle design option model specifications ........................................................... 200

Table D-1: Fuel economy and price of base vehicle models ...................................................... 203

Table D-2: Incremental fuel economy and price from added fuel efficiency technologies ........ 204

Table D-3: Incremental fuel economy and price from CNG modifications ............................... 205

Table D-4: Monte Carlo and sensitivity analyses assumptions .................................................. 206

xi

List of Figures

Figure 2-1: Corporate Average Fuel Economy standards over time (adapted from National

Highway Traffic Safety Administration)9 ....................................................................................... 9

Figure 2-2: Historical average US light-duty vehicle market attributes2 (adapted from An and

DeCicco)33 ..................................................................................................................................... 10

Figure 2-3: Fuel economy improvement and cost increases from fuel efficiency technologies2 . 11

Figure 2-4: Simplified schematic of vehicle powertrain configurations ...................................... 13

Figure 2-5: Current Zero Emission Vehicle program sales requirements10 .................................. 14

Figure 2-6: Renewable Fuel Standard11 volume targets ............................................................... 15

Figure 2-7: Low Carbon Fuel Standard carbon intensity target for gasoline substitute fuels12 .... 16

Figure 2-8: US 2012 light-duty vehicle energy use by fuel type2 ................................................. 18

Figure 2-9: US model year 2012 light-duty vehicle sales by fuel and powertrain type2 .............. 19

Figure 2-10: Recent US transportation fuel prices2 ...................................................................... 19

Figure 2-11: Historical oil price1 and average US light-duty vehicle market attributes2 ............. 20

Figure 2-12: US 2012 non-petroleum transportation fuel use1 ..................................................... 21

Figure 2-13: Non-petroleum transportation fuel stations62 ........................................................... 22

Figure 2-14: US domestic natural gas and crude oil production forecast2 ................................... 23

Figure 2-15: US model year 2013 HEV sales60 ............................................................................ 25

Figure 2-16: US model year 2013 PHEV sales60 .......................................................................... 26

Figure 2-17: US model year 2013 BEV sales60 ............................................................................ 27

Figure 2-18: GREET comparison of GHG emissions from model year 2020 vehicles using 100-

and 20-year Intergovernmental Panel on Climate Change (IPCC) global warming potentials7 ... 33

xii

Figure 3-1: Simplified overview of life cycle stages modelled within GREET7 .......................... 38

Figure 3-2: Simplified overview of components modelled within Air Pollution Emission

Experiments and Policy analysis model97 ..................................................................................... 40

Figure 3-3: Simplified overview of Autonomie conventional and battery electric vehicle model

components96 ................................................................................................................................. 42

Figure 3-4: Illustrated example of the relationship between vehicle price and incremental fuel

economy improvements from the Vehicle Attribute Model3 ........................................................ 44

Figure 3-5: Example of frequency distribution graph produced from a Monte Carlo analysis .... 46

Figure 4-1: a) Lignocellulosic biomass use, b) total energy use, and c) GHG emissions for

reference and bioenergy pathways ................................................................................................ 56

Figure 4-2: GHG emissions mitigation resulting from displacing reference fuels with bioenergy

alternatives: a) comparing mitigation potential of ethanol with that of bio-electricity, and b)

sensitivity of mitigation potential of ethanol to ethanol yield ...................................................... 59

Figure 5-1: Base case life cycle (a) energy use, (b) CO2, (c) NOx, (d) SOx, (d) PM2.5 and (d) VOC

emissions inventory results ........................................................................................................... 76

Figure 5-2: Base case life cycle (a) GHG climate change impacts, (b) CAC health impacts and

(c) ownership costs ....................................................................................................................... 78

Figure 5-3: Life cycle incremental (a) benefit-cost Monte Carlo analysis, (b) benefit sensitivity

analysis, and (c) cost sensitivity analysis results .......................................................................... 79

Figure 6-1: a) CAFE standards29 for different model years and a 4.5 m2 Chevy Equinox-like

vehicle footprint63, b) potential incremental fuel economy improvements and vehicle price

increases2 from example added fuel efficiency technologies, and c) illustration of the vehicle

price model used to develop the 2012 reference vehicle .............................................................. 90

Figure 6-2: Overview of mid-price scenario models used to develop the; a) vehicle price option,

b) vehicle acceleration option, c) vehicle size option and, d) vehicle driving range option .. Error!

Bookmark not defined.

xiii

Figure 6-3: New vehicle price curves representing, a) the Vehicle Price Option and vehicles with

constant fuel economy, b) the Vehicle Acceleration Option and vehicles with constant 0-96 km/h

acceleration times, c) the Vehicle Size Option and vehicles with constant interior volumes, and

d) the Vehicle Driving Range Option and vehicles with different driving ranges. ...................... 98

Figure 7-1: illustrative comparison of how petroleum and non-petroleum vehicle fuel economy

can evolve to meet or exceed CAFE standards ........................................................................... 106

Figure 7-2: Relationship between vehicle price and fuel economy for a) internal combustion

engine vehicles and b) battery electric vehicles .......................................................................... 109

Figure 7-3: Base case results and Monte Carlo and sensitivity analyses of the incremental results

relative to the Gasoline High-Efficiency ICEV for a) ownership costs and, b) well-to-wheel GHG

emissions ..................................................................................................................................... 114

Figure A-1: Pathway System Boundaries ................................................................................... 147

Figure A-2: Flow diagram of the ethanol production process .................................................... 152

Figure A-3: Flow diagram of the base case bio-electricity production process ......................... 154

Figure A-4: Flow diagram of the high efficiency bio-electricity production process ................ 155

Figure A-5: Fuel production energy balance .............................................................................. 163

Figure A-6: Life cycle sensitivity of total energy use to co-product scenarios .......................... 166

Figure A-7: Life cycle sensitivity of net GHG emissions to co-product .................................... 166

Figure A-8: Life cycle total energy use results for bioenergy production efficiency scenarios . 167

Figure A-9: Life cycle net GHG emissions results for bioenergy production efficiency scenarios

..................................................................................................................................................... 167

Figure A-10: Life Cycle total energy use results for vehicle efficiency scenarios ..................... 168

Figure A-11: Life Cycle net GHG emissions results for vehicle efficiency scenarios ............... 168

xiv

Figure B-1: Incremental costs of changes in relative fuel economy ........................................... 172

Figure B-2: Life cycle CH4 and N2O emissions disaggregated by life cycle stage and life cycle

GHG and CAC impacts disaggregated by emission ................................................................... 179

Figure B-3: Life cycle energy use, CO2, CH4 and N2O emission Monte Carlo analysis results,

including 90% confidence intervals in the legend. ..................................................................... 180

Figure B-4: : Life cycle NOx, SOx, VOC and PM2.5 emission Monte Carlo analysis results,


Figure B-5: : Life cycle air emissions impacts and ownership costs Monte Carlo analysis results,


Figure B-6: Incremental life cycle ownership and emissions impact cost results for

supplementary scenarios ............................................................................................................. 184

Figure C-1: Flow chart depicting base vehicle model development with Autonomie96 ............. 186

Figure C-2: PSFI projected to 2025 based on 1977-2005 data33 (adapted from An and DeCicco)33

..................................................................................................................................................... 198

Figure D-1: Overview of vehicle models .................................................................................... 201

Figure D-2: Histogram and 90% confidence intervals (CI) of incremental ownership costs and

well-to-wheel GHG emissions relative to gasoline use .............................................................. 207

Figure D-3: Histogram and 90% confidence intervals (CI) of incremental well-to-wheel GHG

emissions relative to gasoline use for vehicles using renewable compressed natural gas, biomass-

derived electricity or coal-derived electricity ............................................................................. 208

xv

List of Notations

APEEP Air Pollution Emissions Experiments and Policy Analysis model

BEV Battery electric vehicle

Bio-e Biomass-derived electricity

CAC Criteria air contaminant

CAFE Corporate Average Fuel Economy

CNG Compressed natural gas

CV Conventional vehicle

E85 85% ethanol and 15% gasoline by nominal volume

FCEV Fuel cell electric vehicle

GREET Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation

model

Grid-e Grid-derived electricity

HEV Hybrid electric vehicle

ICE Internal combustion engine

ICEV Internal combustion engine vehicle

LCFS Low Carbon Fuel Standard

NGCCe Natural gas combined cycle-derived electricity

NOx Nitrogen oxides

PHEV Plug-in hybrid electric vehicle

PM2.5 Particulate matter with a diameter of less than 2.5 micrometers

SOx Sulphur oxides

SUV Sport utility vehicle

VKT Vehicle kilometer travelled

VOC Volatile organic compound

1

Chapter 1 Introduction

The global transportation sector relies on petroleum fuels for 92% of its energy requirements.1

This dependency is, in part, because the sector is characterized by decentralized, mobile loads

that demand energy dense fuels that can be conveniently and affordably distributed and stored.

Although there are concerns over volatile oil prices,2 the use of petroleum fuels continues

because vehicle powertrains based on an internal combustion engine and petroleum fuel tank are

relatively affordable.3

Petroleum use in the transportation sector also has negative environmental and social

implications. Petroleum fuels are carbon dense, which has led to the transportation sector

comprising 35%1 of US and 28%4 of Canadian greenhouse gas (GHG) emissions. The

combustion of petroleum fuels has also resulted in the transportation sector being responsible for

54%1 of US and 55%5 of Canadian nitrogen oxide (NOx) emissions. Compared to other sectors,

the health impact of criteria air contaminant emissions from the transportation sector are also

disproportionately high because they tend to be concentrated in populated areas.6 In Canada, the

increasing use of petroleum fuels worldwide has led to oil sands development, which raises

concerns over the potential for increased emissions.7 In the US, despite an increase in domestic

petroleum production, imported crude is still forecasted to be approximately 40% of the US

petroleum supply for decades to come.2 This dependency on petroleum fuels has energy security

implications that threaten economic and political stability.8

The severity of the concerns over petroleum use has resulted in an array of public policies. Many

of these target light-duty vehicles (i.e., passenger cars, light trucks, sport utility vehicles and

minivans), which comprise the majority of transportation sector energy use.2 Policies include

Corporate Average Fuel Economy (CAFE) standards,9 Zero Emission Vehicle programs,10

Renewable Fuel Standards11 and Low Carbon Fuel Standards.12 Both automakers and fuel

producers have responded to regulations by developing and implementing new technologies.13

Consumers have responded by purchasing a gradually increasing variety of vehicle powertrains

and transportation fuels.13

2

US CAFE standards9 require automakers to improve the efficiencies of petroleum use in light-

duty vehicles. This policy was first developed in response to the Arab oil embargoes of the

1970’s.9 Since model year 1978, automakers have been required to meet fleet average fuel

economy targets.9 These targets increased annually through the 1980s before largely stagnating.9

Recent amendments, which are also motivated by climate change concerns, increase the targets

for model years 2012 through to 2025.9 Automakers have responded with the development and

implementation of fuel efficiency technologies, including hybrid electric vehicle (HEV)

powertrains.13 In Canada, this policy has been adopted in the form of Corporate Average Fuel

Consumption standards, which were originally voluntary but are now mandatory.14

The California Zero Emission Vehicle program10 effectively requires automakers to produce

vehicles that do not use any petroleum fuels at all. The program began in model year 1998 before

being suspended due to a legal challenge by automakers on the basis of technological limitations

and consumer interests.10 Battery technological developments allowed the program to be revived

starting in model year 2012 with increasing targets through 2025.10 Automakers have responded

with the development and sales of plug-in hybrid electric vehicles (PHEV) and battery electric

vehicles (BEV) powered by grid-electricity,15 which is not typically generated with petroleum.2

Canada does not have similar legislation; however, as with California and the rest of the US,16

plug-in electric vehicle sales in Ontario17 and Quebec18 are directly supported by government

financial incentives.

The US Renewable Fuel Standard11 requires obligated parties (fuel producers) to sell renewable

fuels.11 Fuel producers have responded by blending biofuels, particularly corn ethanol to meet

the targets since 2006. Recent revisions increase volume targets until 2022 and have resulted in

virtually all US gasoline vehicles now consuming gasoline blended with, on average, 10%

ethanol fuel.11 The new legislation also requires the production of advanced biofuels, such as

ethanol produced from lignocellulosic biomass instead of corn starch.11 Canada has a less

stringent Renewable Fuel Regulation, which requires 5% renewables within gasoline fuels and

2% renewables within diesel fuels (with some exceptions).19

The California Low Carbon Fuel Standard12 required fuel producers to reduce the carbon

intensity, on a life cycle basis, of the state’s transportation fuels beginning in 2011 and through

to 2020. Fuel producers have responded by developing infrastructure for alternative fuels, such

3

as compressed natural gas (CNG), in addition to ethanol and electricity.2 In Canada, British

Columbia also has a Low Carbon Fuel Standard.20

The policies and technologies discussed above collectively are assisting to reduce the petroleum

fuel use in light-duty vehicles. Although an important objective, there can be unintended

negative environmental and economic consequences associated with some of these actions. The

environmental and financial implications of the policies and associated technologies should be

understood by taking a systems level (i.e., life cycle) approach to avoid unintended consequences

and the inefficient utilization of resources.

Life cycle assessments21 can be used to evaluate the environmental impacts of the technologies

supported by the aforementioned policies. Life cycle assessment involves identifying the life

cycle stages of a product or process (e.g., fuel production and consumption, in addition to vehicle

production, maintenance and disposal) and its functional unit (e.g., vehicle kilometer travelled).21

A life cycle inventory of environmental inputs and outputs is then compiled for the individual

life cycle stages (e.g., quantities of GHG emissions).21 The life cycle inventory results can then

be weighted to estimate environmental impact (e.g., global warming potential).21 These data can

be compared against other metrics (e.g., land and water use).

Life cycle costing can be used to supplement life cycle assessments. Vehicle purchase price and

ongoing fuel costs are particularly important factors to consider when evaluating transportation

technologies. This information is essential for understanding the cost-effectiveness of using

alternative technologies to achieve societal objectives.

Evaluating alternative vehicles on a life cycle basis is not a novel concept but analyses in the

literature can be improved or expanded upon. In particular, many life cycle studies are focused

on the characteristics of vehicle fuels, but not the vehicles themselves. For example, Choudhary

et al.22 analyzes GHG emissions from different transportation fuels produced from biomass;

while the study includes an uncertainty analysis based on probability distribution functions for

many fuel production variables, it uses a single point estimate for non-plug-in-vehicle fuel

economy and neglects to provide any description or specifications for the plug-in vehicle it is

compared with. Campbell et al.23 conducts a similar analysis and does provide vehicle

descriptions; however, the choice of vehicles conflate many design factors because of substantial

differences in vehicle characteristics, including size and highway capability.

4

There are many life cycle assessments that do make comparisons of similar vehicles. However,

these studies are generally focused on vehicle characteristics and do not clearly distinguish

between the environmental merits that can be directly attributed to the vehicles themselves

versus those of the primary energy sources they can use. For example, the National Research

Council6 compares the life cycle air emissions impacts of alternative vehicles and shows BEVs

can result in higher “damages” than gasoline vehicles because much of the electricity in the US

is generated from coal; however, electricity is produced from many different fuels and BEV sales

are concentrated in regions24 that use little coal.25 Lewis et al.26 and Michalek et al.8 show the life

cycle GHG emissions and air emissions impacts, respectively, of BEVs can be higher or lower

than those of non-plug-in vehicles depending on the source of electricity, but neither analyze the

ability for many sources of electricity to also be used to produce fuels for non-plug-in vehicles.

Therefore, it is unclear if the benefits quantified are due to the primary energy source or the

alternative vehicle powertrain.

Life cycle assessments of future vehicles neglect to consider the influence of financial and policy

considerations. For example, Laser and Lynd27 makes conclusions regarding the life cycle energy

use of future vehicles by correcting for differences in driving range between BEVs and non-plug-

in vehicles, because BEVs with longer driving ranges have higher mass and thus lower fuel

economy; however, BEV driving range is (and will be for the foreseeable future)3 limited by

high battery prices. Curran et al.28 analyzes future life cycle GHG emissions of alternative fuels

based on the assumption that dedicated CNG vehicle fuel economy improvements will exceed

the improvement in gasoline vehicles; however, while gasoline vehicle fuel economy must

increase to meet future CAFE standards, current dedicated CNG vehicles already exceed future

requirements because of non-petroleum fuel incentives. Therefore, dedicated non-petroleum fuel

vehicles can avoid the use of potentially costly fuel efficiency technologies.

Analyses on the impact of policies on future vehicle designs arrive at findings based on a narrow

scope. For example, the National Highway Traffic Safety Administration29 assumes vehicle

characteristics, such as size and acceleration performance, will remain constant when concluding

2025 CAFE standards will increase vehicle price. Conversely, Knittel30 concludes vehicle size

and acceleration performance must be reduced to meet 2020 CAFE standards, based on the

assumption that consumers will not pay more for fuel efficient vehicles. The findings from the

5

National Highway Traffic Safety Administration29 and Knittel30 exclude dedicated non-

petroleum fuel vehicles.

1.1 Thesis Objectives


emissions and costs of alternative light-duty vehicles in a more robust manner than is done in the

literature. In particular, there is an emphasis on distinguishing among the technological and

policy limitations and opportunities. The focus is on the US market, which is similar in many

ways to Canada in terms of product offerings and light-duty vehicle policies; however, the US is

larger and thus has the benefit of greater data availability. The findings in this thesis are aimed at

contributing to the scientific literature as well as informing public policy. This will be done by

investigating the following four questions in Chapters 4 through 7:

Should the transportation sector use ethanol or bio-electricity? The Renewable Fuel

Standard11 promotes the use of ethanol, which has become the dominant non-petroleum

fuel used in US light-duty vehicles. However, ethanol is produced from biomass, whose

production, and thus ability to mitigate greenhouse gas emissions and petroleum use, is

limited by feedstock and land availability. The development of plug-in electric vehicles

provides another means of utilizing biomass as a transportation energy source. Thus, a

life cycle energy use and GHG inventory analysis is conducted for biomass use in both

plug-in and non-plug-in vehicle powertrains in Chapter 4.

Do plug-in electric vehicles provide incremental life cycle air pollutant impact

benefits over internal combustion engine vehicles using the same primary energy

source? The Zero Emission Vehicle program10 promotes the use of BEVs, which lack

tailpipe emissions and are increasingly fuelled by natural gas-derived electricity. The

ability for automakers to meet Zero Emission Vehicle program10 requirements by

producing low emission CNG vehicles is being phased out. However, CNG vehicles may

have lower upstream emissions and ownership costs than BEVs. Thus, the life cycle air

emissions impacts and ownership costs of a range of vehicles are evaluated, using natural

gas as a common primary energy source in Chapter 5.

6

How can vehicles be designed to meet future CAFE standards? The costs and benefits

of non-petroleum fuelled vehicles are typically quantified in comparison to petroleum

fuelled vehicles. However, recently amended CAFE standards9 will require substantial

design changes to light-duty vehicles. Thus, alternative vehicle design options are

evaluated for their ability to meet future fuel economy standards in Chapter 6.

How might CAFE standards affect the ability for non-petroleum vehicles to mitigate

GHG emissions by displacing petroleum vehicles? Chapters 4 and 5 compare the

potential environmental merits of using alternative fuels in leading edge vehicle

technologies; however, these technologies may not be utilized by real world vehicles

because of financial and policy considerations. In particular, CAFE standards provide

credits for non-petroleum fuel use, which means dedicated non-petroleum fuel vehicles

do not require fuel efficiency improvements to meet future fuel economy targets. This

can affect low carbon fuel standards, which promote the sale of alternative fuels that are

less carbon intensive than petroleum fuels after the relative fuel economy ratings of

different vehicles are accounted for. These relative fuel economy ratings can change over

time if improvements in fuel efficiency differ between petroleum and non-petroleum fuel

vehicles. Thus, in Chapter 7, the life cycle GHG emissions and ownership costs of

(potentially) lower efficiency non-petroleum fuel vehicles are compared with petroleum

fuelled vehicles designed to meet stringent future CAFE standards.9

1.2 Publications contained in this thesis

I personally conducted the majority of the research, analysis and writing as first author of each of

the studies described below. Dr. Heather L. MacLean provided guidance in her role as a co-

author of the individual publications and as supervisor of the overall thesis research.

Luk, J., Pourbafrani, M., Saville, B., MacLean, H. Ethanol or Bio-electricity? Life cycle

assessment of bioenergy use in light-duty-vehicles, Environmental Science &

Technology, 2013, 47 (18) 10676-10684.

Chapter 4 has been published in Environmental Science & Technology, with the citation

information above. Dr. Mohammad Pourbafrani is co-author for his development of ethanol and

bio-electricity production models in AspenPlus software. Dr. Bradley Saville is a co-author for

7

his ongoing feedback throughout the research process and help with manuscript revisions. This

research was also awarded with a 2013 Outstanding Energy Paper award by the University of

Toronto’s Institute of Sustainable Energy and an invited presentation at the 2014 Canada-Korea

Scientific Conference.

Luk, J., Saville, B., MacLean, H. Life cycle air emissions impacts and ownership costs of

light-duty vehicles using natural gas as a primary energy source, Environmental Science

& Technology, 2015, 49 (8) 5151-5160.

Chapter 5 has been accepted for publication in Environmental Science & Technology, with the

citation information above. Dr. Bradley Saville is a co-author for his ongoing feedback

throughout the research process and help with manuscript revision. This research was awarded

with a third place overall finish at the 2014 AUTO21 Network Centre of Excellence Poster

Competition.

Luk, J., Saville, B., MacLean, H. Vehicle design options to meet 2025 Corporate Average

Fuel Economy standards, Energy Policy, in preparation for submission.

Chapter 6 is being prepared for publication in Energy Policy, with the pending citation

information above. Dr. Bradley Saville is a co-author for his ongoing feedback throughout the

research process and help with manuscript revisions.

Luk, J., Saville, B., MacLean, H. Potential impact of CAFE standards on the ability for

non-petroleum vehicles to mitigate GHGs. Environmental Research Letters, in

preparation for submission.

Chapter 7 is being prepared for publication in Environmental Research Letters, with the pending

citation information above. Dr. Bradley Saville is a co-author for his ongoing feedback

throughout the research process and help with manuscript revisions.

8

Chapter 2 Background

This thesis focuses on alternative light-duty vehicles in the US. This chapter first discusses the

key US light-duty vehicle energy use policies that shape the vehicles and fuels consumers can

choose from. Secondly, the significance of different powertrains and fuels are presented in the

form of high-level market share statistics along with specific examples to illustrate how these

alternatives are manifested in the real world. Finally, a review of life cycle comparisons of

alternative light-duty vehicles in the US is conducted to provide insights into the state of our

knowledge on the environmental and financial implications of the technologies promoted by the

light-duty vehicle energy use policies.

2.1 Light-duty Vehicle Energy Use Policies

A range of US public policies are aimed at reducing petroleum use in light-duty vehicles. Four

key policies are discussed here. These are divided into those that regulate automakers and those

that regulate fuel producers.

2.1.1 Automaker Regulations

Automakers are regulated by US CAFE standards9 and the California Zero Emission Vehicle

program.10

2.1.1.1 US Corporate Average Fuel Economy Standards

CAFE standards are designed to improve the efficacy of petroleum use in light-duty vehicles

beginning in 1978.9 This legislation was passed in response to the Arab Oil Embargo, which

restricted oil imports to the US, thus increasing oil prices and interrupting economic growth. A

committee of the National Research Council31 was formed in 2001 to review the literature on the

historical impact of the CAFE standards,9 which by then had plateaued. They found that CAFE

standards only reinforced the trend of vehicle size and weight reductions initiated by high oil

prices, but helped maintain fuel economy after oil prices began to fall.31 However, when CAFE

standards plateaued in the 1990’s (Figure 2-1) there was a slight decrease in fuel economy when

light-duty trucks, including sport utility vehicles (SUVs), were increasingly used as consumer

9

vehicles.31 Light-duty trucks were considered work vehicles, and thus were subjected to less

stringent fuel economy targets when CAFE standards were established.31 The committee found

the shift towards these heavier vehicles was beneficial in terms of reducing fatalities from

collisions.31 However, credits for flex fuel vehicles (which can consume gasoline blended with

up to 85% ethanol by nominal volume) were found to increase petroleum consumption because

these vehicles primarily used gasoline fuel and allowed automakers to avoid fuel economy

improvements.31 Anderson and Sallee32 concluded that meeting CAFE standards by adding flex

fuel capability was more affordable for automakers than increasing fuel economy alone. The

overall increase in fuel economy was found to contribute to the “rebound effect,” which resulted

in an increase in driving demand as consumers took advantage of fuel cost savings.31

CAFE standards have recently been amended for model years 2012 through 2025.9 As shown in

Figure 2-1, fuel economy targets for both cars and trucks will increase throughout this

timeframe.9 The definition of a truck was narrowed to exclude many SUVs, thus limiting the

ability for automakers to meet fuel economy targets by classifying consumer vehicles as trucks.9

The targets are now scaled by vehicle footprint (wheelbase multiplied by track) to limit the

ability for automakers to meet fuel economy targets by reducing vehicle size (and potentially

safety).9 Credits for flex fuel vehicles are being phased out, but the incentives will remain for

dedicated non-petroleum fuelled vehicles.9

Figure 2-1: Corporate Average Fuel Economy standards over time (adapted from National

Highway Traffic Safety Administration)9

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An and DeCicco33 analyzed the historical trends in light-duty vehicle characteristics to provide

insights into future CAFE standards Figure 2-2 shows that on average, current light-duty vehicles

are more fuel efficient, while being larger and more powerful than in the past. An and DeCicco33

found that the product of vehicle power-to-weight ratio, interior volume, and fuel economy

increased relatively linearly over time. They concluded that trade-offs among these factors

showed aggregate energy efficiency improvements that could be used to reduce fuel

consumption or to improve acceleration performance or increase size.33 Bandivakar et al.34

extrapolated the historical trends and termed the trade-off as an emphasis on reducing fuel

consumption versus improving performance and/or size. Cheah et al.35 applied this method to

evaluate 2016 CAFE standards and Knittel30 used a similar approach to analyze 2020 CAFE

standards. Both concluded that using future energy efficiency improvements to improve fuel

economy (instead of vehicle size or performance) would not be sufficient to meet future CAFE

standards. Cheah et al.35 suggested that plug-in electric vehicles could be introduced to meet

standards while Knittel,30 who did not evaluate plug-in vehicles, concluded that reducing average

vehicle size and/or performance to historical levels would be necessary.

Figure 2-2: Historical average US light-duty vehicle market attributes2 (adapted from An

and DeCicco)33

The linear improvement in energy efficiency determined in the aforementioned studies considers

several factors but excludes the price of further increases. The Energy Information

Administration2 has detailed the prices of a range of fuel efficient technologies automakers can

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utilize to improve vehicle fuel economy, examples of which are shown in Figure 2-3. These

include not only technologies that improve powertrain efficiency, but also those that reduce the

load applied on these powertrains to improve the efficacy of energy use. Makino36 has discussed

in detail Toyota’s efforts to reduce vehicle mass, aerodynamic drag and accessory loads

independently of vehicle powertrain to meet future regulations.

Figure 2-3: Fuel economy improvement and cost increases from fuel efficiency

technologies2

The price of these fuel efficiency technologies are the focus of the Regulatory Impact Analysis of

the amended CAFE standards.29 The analysis estimated the average 2025 US light-duty vehicle

will be $1870-$2120 higher in price than if CAFE standards, in addition to vehicle size and

performance, were held constant at 2012 levels.29 However, the historical trends in Figure 2-2 do

not suggest that vehicle size and performance will hold constant.

Recent research on CAFE standards by Shiau et al.37 and Whitefoot and Skerlos38 combined

financial and engineering modelling. Shiau et al.37 highlighted the need to balance fuel economy

targets with penalties for violations, while Whitefoot and Skerlos38 warned of the potential for

footprint-based targets to be a moral hazard that encourages the production of larger vehicles.

Both studies arrived at their conclusions by modelling the sensitivity of consumer demand for

vehicle size and power to vehicle price and fuel economy. Neither study considered changes to

technology nor fuel economy targets over time; therefore, the studies do not provide insight into

how stringent future year CAFE standards can be met.

Continuously Variable Transmission

Engine Start-Stop

Cylinder Deactivation

Aerodynamic Improvements

Low Resistance Tires

Direct Injection

Improved Accessories

8-speed Automatic

Low Friction Lubricants

Engine Friction Reduction

7-speed Automatic

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2.1.1.2 California Zero Emission Vehicle Program

Whereas CAFE standards subject US automakers to performance targets, California’s Zero

Emission Vehicle program establishes technology requirements.10 The different technologies are

discussed below and illustrated in Figure 2-4. (Detailed descriptions of these different vehicle

powertrains are provided in Section 2.2.2.) The program was established in response to air

quality concerns and required large automakers to produce increasing quantities of BEVs

(battery electric vehicles) and/or FCVs (fuel cell vehicles) beginning in 1998.10 Bedsworth and

Taylor39 reviewed the evolution of this policy, which was originally based on the projection that

battery prices could be reduced sufficiently to be competitive with conventional vehicles (CVs)

in an eight to 13 year time frame. Unfortunately, the nickel metal hydride and lead acid battery

price did not improve as expected and automakers and battery packs that were projected by

regulators to cost $1350 were still around $20,000 by 2000.39 The program was amended in 2003

to allow automakers to temporarily comply by selling low emission vehicles, including PHEVs

(plug-in hybrid electric vehicles), HEVs (hybrid electric vehicles), and fuel efficient CVs.39

PHEVs and HEVs use smaller, and thus more affordable, batteries and benefited from the

development of electric vehicle technologies by automakers.39 Automakers would eventually win

a lawsuit to have Zero Emission Vehicle requirements temporarily eliminated.39

13

Figure 2-4: Simplified schematic of vehicle powertrain configurations

Parallel Hybrid Electric Vehicle (HEV)

Conventional Vehicle (CV)

Battery Electric Vehicle (BEV)

Series-Parallel Split Hybrid Electric Vehicle (HEV)

Series Plug-in Hybrid Electric Vehicle (PHEV)

Series-Parallel Split Plug-in Hybrid Electric Vehicle (PHEV)

Wheels

Wheels Wheels

Wheels

Wheels

Internal Combustion

Engine

Battery Battery

Battery

Battery

Electric Motor

Electric Motor

Electric Motor

Internal Combustion

Engine

Internal Combustion

Engine

Internal Combustion

Engine

Electric Motor

Wheels

Battery Electric Motor

Internal Combustion

Engine

External Energy Source Internal Energy Flow

14

The policy has since been revised and major automakers in California are again required to sell

Zero Emission Vehicles beginning in model year 2012.10 There are also requirements for

transitional Zero Emission Vehicles (e.g., PHEV), Advanced Technology Partial Zero Emission

Vehicles (e.g,, HEV), and Partial Zero Emission Vehicles (e.g., fuel efficient CV), as outlined in

Figure 2-5. There is less industry opposition to the current policy, as compared to the original, in

part because automakers now benefit from advances in the development of battery technology.40

Figure 2-5: Current Zero Emission Vehicle program sales requirements10

Unfortunately, the costs of modern batteries remain relatively high, particularly for BEVs.

Kromer and Heywood41 estimated that BEV battery packs will still cost $8,600-$12,000 by 2030.

Argonne National Laboratory42 estimated that BEV battery packs will cost $11,000-$20,000 in

2015 and still be $6,000-$9,000 by 2045. A committee for the National Research Council43

tasked with assessing the costs of fuel economy technologies, excluded a quantitative analysis of

BEVs altogether because its expectation was that there would not be significant numbers of them

produced during the study’s 15 year timeframe. A committee for the National Petroleum Council

found estimates for 2020 battery packs to range from $5000-$15000.3 Fiat-Chrysler has claimed

losses of $14,000 on every Fiat 500e sold to meet Zero Emission Vehicle program

requirements.44 Sales are aided by federal and state level tax credits provided to plug-in electric

vehicles.16, 45

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Zero Emission Vehicles(e.g., BEV)

15

2.1.2 Fuel Producer Regulations

Fuel producers are regulated by the US Renewable Fuel Standard and the California Low Carbon

Fuel Standard.

2.1.2.1 US Renewable Fuel Standard

The US Renewable Fuel Standard11 originally mandated that 2.78% of gasoline sold in calendar

year 2006 be comprised of renewable fuels. There is controversy regarding of the feedstock of

this ethanol, which is largely corn starch.46 In response, Farrell et al.46 conducted a meta-analysis

and found that there are environmental benefits, in terms of a reduction in fossil fuel use and

GHG emissions from displacing petroleum fuel with corn ethanol use, but they may be minor.

Wang et al.47 provided a more nuanced study that showed that any environmental benefits from

corn ethanol use are highly sensitive to the production technologies and types of fossil fuels

used.

The standard has since been revised to require the production of corn ethanol with life cycle

GHG emissions at least 20% lower than those of gasoline. Additionally, increasing volumes of

renewable fuels are to be produced over time, as shown in Figure 2-6. A portion of these fuels

must be advanced biofuels, such as cellulosic biofuels produced from non-food crops and have

life cycle GHG emissions 60% lower than those of gasoline. The scope of these calculations

must include land use change, which Searchinger et al.48 suggested could substantially contribute

to GHG emissions.

Figure 2-6: Renewable Fuel Standard11 volume targets

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Advanced biofuel provisions can help reduce GHG emissions to a greater extent than corn

ethanol but fuel producers have not met volume targets. Brown and Brown49 recently reviewed

the state of cellulosic ethanol in the US and highlight the continued lack of commercial

production (<20 million gallons per year) despite government mandates and incentives, though

some facilities have since opened. In response, annual changes to renewable volume obligations

have been made to reduce advanced biofuel targets.11

2.1.2.2 California Low Carbon Fuel Standard

The California Low Carbon Fuel Standard12 establishes performance targets for fuel producers to

who must reduce the life cycle carbon intensity (g CO2e/MJ) of the state’s transportation fuels

beginning in calendar year 2011. The carbon intensity targets for gasoline replacement (as

opposed to diesel replacement) fuels are illustrated in Figure 2-7. Early studies by Zhang et al.50

and Kaufman et al.51 concluded that the introduction of cellulosic ethanol fuels could meet the

targets, while Yeh et al.52 and Andress et al.53 identified that the use of electricity would also be

sufficient.

Figure 2-7: Low Carbon Fuel Standard carbon intensity target for gasoline substitute

fuels12

Sperling and Yeh54 commended the Low Carbon Fuel Standard for being a carbon intensity

based policy that does not suffer from “fuel du jour phenomenon.” However, the policy is

complicated by the use of fuel carbon intensities that are adjusted with “somewhat arbitrary”53

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energy economy ratios to address the fact that not all fuels can be used interchangeably. For

example, the carbon intensity of electricity is divided by an energy efficiency ratio of 3.4 because

plug-in electric vehicles are more fuel efficient than gasoline internal combustion engine

vehicles.12 Unfortunately, vehicles change over time and there is no single objectively correct

energy economy ratio.53

The precision of the carbon intensity calculations required by the Low Carbon Fuel Standard

may overstate the precision of our understanding of real world emissions. Venkatesh et al.55

analyzed natural gas fuels, while Mullins et al.56 and Kocoloski et al.57 evaluated ethanol, and

each found that the high uncertainty in estimating life cycle emissions can result in conditions

whereby ostensibly low carbon fuels can have higher GHG emissions than gasoline. In the case

of ethanol fuels, indirect land use change is a particularly substantial source of uncertainty.

DeCicco58 criticized the low carbon fuel policies (including the Renewable Fuel Standard) for

using life cycle carbon intensities as if they were a fuel property, as opposed to being a product

of complex systems.

2.2 Status of Light-Duty Vehicle Powertrains and Fuels

Light-duty vehicles are defined as having a gross vehicle weight rating of 8500 lbs (3900 kg) or a

curb weight less than 6000 lbs (2700 kg) and a base vehicle frontal area of 45 ft2 (4.2 m2) or

less.59 These include passenger cars, light trucks, sport utility vehicles, and minivans.9 They are

discussed here in terms of powertrain type: either conventional vehicles (CVs) or electric

vehicles.

2.2.1 Conventional Vehicles (CVs)

CVs comprise 96% of the US light-duty vehicle market.2 The wheels of these vehicles are driven

exclusively by an internal combustion engine. Both petroleum and non-petroleum fuels can be

used by internal combustion engines.

18

2.2.1.1 Petroleum fuels

Almost all US light-duty vehicle energy use is from petroleum fuels, as shown in Figure 2-8.

Gasoline fuels (including those containing up to 10% ethanol) comprises of 99% of light-duty

vehicle energy use.2 The remaining 1% is divided among diesel, liquefied petroleum gas

(propane) and non-petroleum fuels.2

Figure 2-8: US 2012 light-duty vehicle energy use by fuel type2

The majority of new light-duty vehicles in the US are gasoline-fuelled CVs, as shown in Figure

2-9.2 Gasoline is also used in 12% and 3% of the market classified by flex fuel vehicles and

hybrid electric vehicles, respectively.2 Flex fuel and hybrid electric vehicles are further discussed

in Sections 2.2.1.2 and 2.2.2.1, respectively.

Gasoline (including blends with low ethanol concentrations)

Diesel

Liquefied Petroleum Gas

Non-Petroleum Fuel

19

Figure 2-9: US model year 2012 light-duty vehicle sales by fuel and powertrain type2

Diesel-fuelled CVs comprise 2% of new light-duty vehicles. Diesel fuel has generally been more

affordable than gasoline in the US, on an energy equivalent basis, as shown by the recent history

illustrated in Figure 2-10.2 Diesel vehicles also benefit from the use of compression ignition

engines, which are 30%-35% more efficient than a comparable gasoline spark ignition engine.15

Consumer diesel vehicles are available, however, interest among US consumers has been harmed

by past concerns over noise, poor acceleration, cold weather starting issues, and high

emissions.15

Figure 2-10: Recent US transportation fuel prices2

Gasoline Conventional

Vehicles

Flex-fuel Vehicles

Gasoline Hybrid Electric

Vehicles

Diesel Conventional

Vehicles

Other

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Liquid petroleum gas (propane) is used in less than 1% of new light-duty vehicles.2 This fuel has

recently had a lower price than both gasoline and diesel.2 However, compared to other petroleum

fuels, propane has a low energy density and thus requires large storage tanks and vehicles using

the fuel suffer from short driving ranges.60 Propane vehicles are not widely available and are

either converted from gasoline vehicles or special order products for commercial fleets.60

The dependency on petroleum fuels appears to have influenced light-duty vehicle characteristics.

Figure 2-11 shows that the real (inflation adjusted) oil price mostly increased in the late 1970’s,

before falling in the 1980’s and 1990s, and increasing in the 2000’s.1 In general, increasing fuel

efficiency has been correlated with increasing oil prices, and vice versa, though fuel efficiency is

a much less volatile variable.2 The market share of trucks, as opposed to cars, is inversely

correlated with oil price.2 However, some of these shifts are also due to CAFE standards

changes, as discussed in Section 2.1.1.1.

Figure 2-11: Historical oil price1 and average US light-duty vehicle market attributes2

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2.2.1.2 Biofuels

The vast majority (97%) of non-petroleum fuel use in the US transportation sector is comprised

of biofuel use, as seen in Figure 2-12.1 This is a result of the Renewable Fuel Standard (Section

2.1.2.1) requirements and CAFE standards (Section 2.1.1.1) incentives. Both ethanol and

biodiesel are produced from biomass feedstock. Biomass is organic material, including

agricultural crops or even organic waste. Biofuels are blended with petroleum fuels and there are

no dedicated ethanol or biodiesel vehicles (i.e., vehicles that can use biodiesel but cannot use

petroleum diesel) sold in the US.15

Figure 2-12: US 2012 non-petroleum transportation fuel use1

Most biofuels are consumed in gasoline vehicles, which typically operate on E10 (gasoline

blended with 10% ethanol by nominal volume).60 E85 (85% ethanol and 15% gasoline by

nominal volume) flex fuel vehicles are largely similar to gasoline vehicles, but are capable of

operating on high concentration ethanol blends, but generally operate on gasoline.60 The

concentration of ethanol is limited by issues with cold-weather starting, and the ethanol content

in E85 fuelling stations may be even reduced to 51% in colder seasons.60 As shown in Figure

2-13, there are also less than 3,000 E85 fuelling stations, as compared to the approximately

168,000 gasoline/E10 fuelling stations in the US.60, 61 E85 fuelling stations are also concentrated

in the US Midwest, near where corn feedstock is grown, as opposed to more highly populated

areas.62

Ethanol in low concentration

blends

Ethanol in E85

BiodieselCNG

LNGElectricity

22

Figure 2-13: Non-petroleum transportation fuel stations62

Diesel vehicles can also use biodiesel.60 Biodiesel is typically consumed in the form of B20

(petroleum diesel blended with 20% biodiesel by nominal volume).60 The relative lack of diesel

vehicles, as compared to gasoline vehicles, in the US limits the current market for this biodiesel.

2.2.1.3 Natural Gas Fuels

Natural gas comprises the largest share of non-petroleum, non-biofuel transportation energy use.

Natural gas has recently been more affordable than both petroleum and E85 fuels, on an energy

equivalent basis, as shown in Figure 2-10.2 While both US crude oil and natural gas production

have increased in recent years, natural gas production is forecasted to continue to increase for

decades to come, as shown in Figure 2-14.2 Its use in the transportation sector is encouraged by

incentives within CAFE standards (2.1.1.1) and Low Carbon Fuel Standard (2.1.2.2)

requirements.

The only natural gas fuelled consumer light-duty vehicle for sale is the Honda Civic Natural

Gas.15 It is a dedicated compressed natural gas (CNG) vehicle, which utilizes a high pressure fuel

tank that increases both vehicle mass and price. Soon there will also be CNG/gasoline bi-fuel

versions of the Chevy Impala, Chevy Silverado and GMC Sierra available.15

CNG suffers from a relatively low energy density compared to petroleum fuels. This results in

vehicles using CNG having a relatively short driving range and/or requiring a large storage tank.

There is also a lack of public fuelling stations (less than 1000 in the US) but this is less of an

0 2000 4000 6000 8000 10000 12000

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impediment for some commercial fleet vehicles, which can return to private fuelling stations.62

There are nearly as many private CNG fuelling stations as there are public.62

Figure 2-14: US domestic natural gas and crude oil production forecast2

Some medium- and heavy-duty vehicles also use liquefied natural gas (LNG).60 Natural gas is

liquefied under high pressure, low temperature conditions and thus requires costly storage

systems.60 This results in higher energy densities than CNG, and thus LNG can be more

attractive for applications that require longer driving distances.60

2.2.2 Electric Vehicles

Electric vehicles consist of vehicles whose wheels are propelled by an electric motor, either

exclusively or along with an internal combustion engine. They are promoted by CAFE standards

(Section 2.1.1.1) and the Zero Emission Vehicle program (Section 2.1.1.2). Non-plug-in electric

vehicles use electricity generated via the internal combustion engine or regenerative braking but

cannot be charged by an external source of electricity. Plug-in electric vehicles can utilize

electricity generated onboard and from an external source. An overview of different electric

powertrain configurations is provided in Figure 2-4.

2.2.2.1 Hybrid Electric Vehicles (HEVs)

HEVs are non-plug-in vehicles that have a 3% market share of model year 2012 US light-duty

vehicles.2 There are different variations of HEV powertrains illustrated in Figure 2-4. These

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variations involve pairing an internal combustion engine with an electric motor. The addition of

the electric motor can be used to improve the acceleration performance of the vehicle (e.g., first

generation Honda Accord Hybrid), or allow the internal combustion engine to be downsized as a

means to improve fuel economy (e.g., second generation Honda Accord Hybrid).63

The parallel configuration requires the fewest additional components over a conventional

vehicle.63 This configuration utilizes regenerative braking to charge a battery. This energy is later

provided to a relatively small electric motor that helps the internal combustion engine propel the

wheels during acceleration. The relative simplicity of this powertrain allowed the Honda Insight,

which uses this configuration, to be the lowest priced model year 2013 HEV.63

The series-parallel split configuration is a more complex configuration. This configuration is

used in the dominant Toyota lineup of HEVs, as shown in Figure 2-15.63 As with the parallel

configuration, the wheels are driven in this powertrain by both an internal combustion engine and

an electric motor, which uses electricity generated from regenerative braking. The series-parallel

split configuration also has the capability to allow the internal combustion engine to power a

generator to charge the battery. This added flexibility has the benefit of allowing the internal

combustion engine to operate at speeds closer to its optimum efficiency for greater periods of

time and thus improve fuel economy. The relative efficiency of this powertrain allowed the

Toyota Prius and Prius C vehicles to be the most fuel efficient model year 2013 HEVs.15

HEVs may also be referred to as mild or full hybrids.63 This is a distinction independent of the

powertrain configuration. If the motor has insufficient power to propel the wheels independently

of the internal combustion engine, as is the case with the Honda Insight, the vehicle is also

25

referred to as a mild hybrid. The Toyota Prius is considered a full hybrid because it is capable of

driving, at low speeds, using the electric motor only.

Figure 2-15: US model year 2013 HEV sales60

2.2.2.2 Plug-in Hybrid Electric Vehicles (PHEVs)

PHEVs are able to utilize electricity generated from an external source. A larger battery is

generally required than those in HEVs, which increases both vehicle price and mass. This has, in

part, limited PHEV market share to less than 1% of model year 2012 US light-duty vehicles.

PHEVs can have lower emissions than HEVs because of the greater use of the high efficiency

electric motor. The efficiency of electric motors can also allow PHEVs to have lower fuel costs

than HEVs, despite the relatively high price of electricity, on an energy equivalent basis, as

shown in Figure 2-10.

The Toyota Prius Plug-in and Chevy Volt comprise the majority of PHEV sales, as shown in

Figure 2-16.60 The Toyota Prius Plug-in has a parallel-series split powertrain similar to its non-

plug-in counter part, but with a larger battery.63 The Chevy Volt has a series hybrid powertrain,

which is also referred to as an extended range electric vehicle.63 This vehicle’s wheels are

propelled by an electric motor and an internal combustion engine is mainly used to charge the

Toyota Prius

Toyota Prius C

Other ToyotaFord

Hyundai

GM

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Other

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26

plug-in battery only as required to extend driving range. This can be advantageous because

batteries suffer from relatively low energy densities, in comparison to gasoline.

Figure 2-16: US model year 2013 PHEV sales60

2.2.2.3 Battery Electric Vehicles (BEVs)

BEVs use electric motors to propel their wheels and utilize electricity as its only external energy

source. The lack of an internal combustion engine results in driving ranges that are generally

shorter than those of a PHEV or HEV, which has helped limit the BEV market share to less than

1% of model year 2012 US light-duty vehicles. Advantages of these vehicles include reduced

fuel expenses and complete lack of tailpipe emissions. Compared to other dedicated non-

petroleum vehicles, BEVs also have the benefit of being able to utilize a relatively widespread

fuelling infrastructure in the form of almost 9000 public and an additional 2000 private electric

vehicle charging stations in the US, more than double all of the alternatives combined, as shown

in Figure 2-13.62

The Tesla Model S and the Nissan Leaf make up the majority of US BEV sales, as shown in

Figure 2-17.60 Many of the other BEVs can be considered “compliance cars” with limited

availability.64 These are modified versions of gasoline CVs that are sold or leased to reach the

minimum targets required to comply with the Zero Emission Vehicle program.64

Chevrolet Volt

Toyota Prius Plug-in Ford Fusion

Energi

Ford C-MAX Energi

Other

Other

27

Figure 2-17: US model year 2013 BEV sales60

2.2.2.4 Other Electric Vehicles

Fuel cell vehicles are a variety of electric vehicle that are not yet for sale in the US.15 Honda and

Hyundai have leased a limited number of vehicles, which help meet Zero Emission Vehicle

program requirements.15 This type of vehicle utilizes hydrogen as the external energy source,

which is stored in a high pressure storage tank and converted into electricity by a fuel cell

system. As with plug-in electric vehicles, fuel cell vehicles suffer from having relatively high

costs and a low energy density storage system. However, fuel cell vehicles also suffer from

having only 100 fuelling stations in the US, half of which are private.60

Micro hybrids are another variation of electric vehicle that are categorized as CVs in the market

share statistics provided in Section 2.2.1, but comprise less than 1% of model year 2012 light-

duty vehicle sales.2 Micro hybrids are propelled exclusively by an internal combustion engines,

just like other CVs, but utilize select electric vehicle technologies to improve fuel efficiency. In

particular, engine start-stop technology shuts down the internal combustion engine to prevent

idling and uses an upgraded alternator to restart the engine once the accelerator is pressed.

Regenerative braking can also be utilized to provide energy to the alternator.

Nissan LEAF

Tesla Model SSmart for Two EV

Ford Focus Electric

Fiat 500E

Chevrolet Spark EV

Other

Other

28

2.3 Life Cycle Comparisons of Alternative Light-Duty Vehicles

This section reviews the scientific literature on life cycle comparisons of alternative light-duty

vehicles. The review provides an overview of the recent literature most relevant to this thesis, but

is not exhaustive due to the broad scope of alternative light-duty vehicles. In particular, this

thesis focuses on vehicles using electricity, ethanol and CNG, which are fuels promoted by the

policies described in Section 2, available in existing fuelling infrastructure and used in vehicles

currently available for consumer purchase.15

Life cycle assessments21 can be used to evaluate the environmental impacts of the technologies

supported by the aforementioned policies. The literature typically divides the life cycle of a

vehicle into fuel cycle and vehicle cycle components. The fuel cycle consists of fuel production

and use (i.e., well-to-wheel processes), while the vehicle cycle consists of vehicle production,

maintenance and end-of-life processes. Life cycle assessments of vehicles typically use

functional unit of a vehicle lifetime or a vehicle kilometer travelled, and present life cycle

inventory results for GHG emissions and/or energy use. GHG emissions are commonly weighted

by 100-year global warming potential to estimate environmental impact. Life cycle cost analyses

are sometimes conducted to supplement discussions of environmental impacts. Life cycle

assessment is further discussed in Section 3.

2.3.1 Conventional Gasoline Vehicles

Early life cycle assessments of light-duty vehicles used different approaches but arrived at

similar conclusions. USAMP65 produced an early LCA of a conventional gasoline vehicle using

detailed vehicle component level data obtained from industry participation. MacLean and Lave66

analyzed a conventional gasoline vehicle by developing an economic input-output (EIO) LCA,

which uses relationships among economic sectors to expand the system boundary to the entire

US economy at the expense of technical details regarding specific vehicle components. The

results from both approaches agree that gasoline use/combustion during vehicle operation

accounts for the vast majority of energy use and GHG emissions, and thus life cycle results are

particularly sensitive to vehicle fuel economy.

Many later studies on conventional gasoline vehicles analyzed how material substitutions would

impact life cycle results. Kim and Wallington67 conducted a literature review that revealed that

29

disagreement over whether the use of lightweight materials increases or decreases life cycle

energy use and GHG emissions resulted from differering assumptions regarding the impact of

mass reduction on fuel consumption, the energy intensity of different materials and recycling

rates. More recently, Colett et al.68 found that the impact of lightweight material use on life cycle

results are sensitive to indirect assumptions regarding electricity allocation.

Studies have also compared conventional gasoline vehicles to emerging alternatives. MacLean

and Lave69 conducted a review of early comparisons and found that no alternative

fuel/technology was clearly superior on a life cycle basis as each came with tradeoffs. This

finding would later be supported by the GREET model,7 which many subsequent studies6, 8, 28

would be based on. The following subsections discuss unique aspects of more recent

comparisons of alternative vehicles.

2.3.2 Plug-in Electric Vehicles

Plug-in electric vehicles, consisting of PHEVs and BEVs, require battery systems that are not

required in CVs. There are questions regarding the production of these batteries and whether the

environmental impacts offset the benefits of improved vehicle fuel economy (on an energy

equivalent basis). Much concern arguably originates from work produced by CNW Marketing

Research that claimed vehicle (production and disposal) cycle energy use was higher than fuel

use on a per mile basis, and that additional energy requirements for battery production and

disposal more than offset the reduction in vehicle fuel use, while comparing a Hummer H3 CV

and a Toyota Prius (non-plug-in) HEV.70 Hauenstein and Schewel70 argued that these findings,

which received wide media coverage, are not supported by the scientific literature. For example,

the National Research Council has used the GREET model, which agrees that battery production

does increase vehicle cycle energy use but finds vehicle (production, maintenance and disposal)

cycle energy use is relatively minor compared to fuel (production and consumption) cycle energy

use.6, 7

The emissions from electricity production for plug-in electric vehicles are another environmental

concern. Samaras and Meisterling71 illustrated how the use of carbon-intensive electricity from

coal when used in plug-in vehicles can result in life cycle GHG emissions comparable to those of

gasoline-fuelled vehicles, whereas the use of low-carbon electricity from natural gas can reduce

GHG emissions. MacPherson et al.72 and Kelly et al.73 posit that the particular source(s) of

30

electricity used by a plug-in electric vehicle depends on the geographic location and time (of day

and year) charging occurs, respectively. Kennedy74 estimated the GHG emissions for gasoline

and plug-in electric vehicles were similar when electricity production results in 600 g

CO2e/kWh. Tessum et al.75 found that the health impacts from life cycle criteria air contaminants

(CACs) from plug-in electric vehicles use were higher than those from gasoline use if the

electricity was produced from coal – despite emissions largely occurring away from populated

areas - or lower if it was produced from natural gas.

The fuel cycle energy use of plug-in electric vehicles is sensitive to battery size. Shiau et al.37

analyzed the trade-off between electric vehicle battery size and GHG emissions. Larger batteries

can allow PHEVs to operate on potentially low carbon sources of electricity more often, instead

of gasoline, which can reduce life cycle GHG emissions. Conversely, larger batteries also

increase vehicle price and mass, which reduces fuel economy. Michalek et al.8 determined that

smaller batteries were a more cost-effective means of reducing life cycle emissions impacts

(from both GHGs and CACs). Lewis et al.26 showed that the ability to reduce battery size is a co-

benefit of manufacturing electric vehicles with lightweight materials to further improve fuel

economy.

Driving patterns affect the fuel economy ratings of electric vehicles and CVs differently. CV fuel

economy is much higher in steady highway driving than in city driving conditions. Conversely,

BEVs, PHEVs and HEVs benefit from regenerative braking, which captures some of the energy

otherwise lost during deceleration, and thus leads to better fuel economy in city driving

conditions. Thus, Raykin et al.76 and Karabasoglu et al.77 found that the potential emissions

reductions from electric vehicles are highly dependent on driving conditions (in addition to the

source of electricity for PHEVs and BEVs).

Climate is another variable that can impact the fuel economy ratings of CVs and electric vehicles

differently. In cold climates, CVs utilize otherwise wasted heat from the internal combustion

engine for cabin heating. Conversely, electric motors are more efficient and do not generate

sufficient heat. In HEVs and PHEVs, this can result in a greater reliance on the internal

combustion engine, than under ideal conditions. In BEVs, this results in energy from the battery

being depleted to provide heat. Additionally, battery efficiency can suffer in cold weather.

Yuksel and Michalek78 found that BEV life cycle GHG emissions can vary by up to 22% within

31

a US grid-electricity sub-region (e.g., CAMX – California Mexico Power Area) due to

temperature differences.

The performance of electric vehicles relative to CVs is forecasted to change over time.2 As

discussed in Section 2.1.1.1, CAFE standards require automakers to improve the fuel economy of

petroleum-fuelled vehicles. Figure 2-3 provides examples of technologies that automakers can

use to meet CAFE standards, which would reduce the difference between CV and electric vehicle

fuel economy ratings. Nordelof et al.79 reviewed scientific literature assessing the life cycle

environmental impacts of electric vehicles and found studies often neglected to provide temporal

assumptions.

The literature does not clearly distinguish between the environmental merits that can be

attributed to plug-in electric vehicles versus those that are the result of particular primary energy

sources. This is important because despite the lack of tailpipe emissions and use of high

efficiency electric motors, BEV life cycle air emissions impacts can be higher than those from

petroleum fuelled vehicles if coal is used to generate electricity, or lower if other sources are

used. However, non-plug-in vehicles can also use non-petroleum energy sources (e.g., natural

gas) and benefit from the high efficiency of electric motors (i.e., HEVs). This is an important

distinction because given the high price of BEVs, as discussed in Sections 2.1.1.2 and 2.2.2.3,

non-plug-in vehicles using non-petroleum energy sources may be a more cost-effective means of

reducing life cycle air emission impacts (among other environmental impacts).

2.3.3 Lignocellulosic Ethanol Vehicles

As with gasoline, the use of ethanol results in the release of air pollutants. Hill et al.80 found that

lignocellulosic ethanol can reduce PM2.5 emissions by displacing gasoline. However, Jacobson81

and Nopmongcol et al.82 evaluated a wider range of air pollutants and found that air quality can

be similar or even worse in some areas than if gasoline continued to be used.

Some researchers have investigated whether the economic and land use constraints of biomass

feedstock better justify its use to produce ethanol or bio-electricity. Laser et al.83 found that both

alternatives are similarly capable of displacing GHG emissions. Searcy and Flynn84 found that

the relative cost-effectiveness of displacing GHG emissions depends on how the different fuels

are used.

32

Ethanol and bio-electricity can also be compared for use in light-duty vehicles, and thus a

common end use. Campbell et al.23 concluded that the superior efficiency of BEVs would result

in bio-electricity production being favorable in terms of energy use and GHG mitigation.

However, their analysis compared vehicles with greatly differing vehicle attributes that

influenced the results. These vehicles were was also the basis of a study by Clarens et al.85 that

found bio-electricity to be favorable over biodiesel. Choudhary et al.22 also arrived at the same

finding as Campbell et al.,23 by citing an ethanol flex fuel concept vehicle and a database of all

light-duty vehicles in the US as the source of the electric vehicle fuel economy, without any

mention of the vehicle or value assumed.

Laser and Lynd27 compared the use of ethanol and bio-electricity in vehicles and found neither

had an clear technological advantage in terms of life cycle energy use, and thus ability to

mitigate GHG emissions. However, the results are based on comparisons of vehicles with some

characteristics that may be unnecessarily dissimilar (Toyota Camry sedan versus Nissan Leaf

hatchback) and others that are unrealistically similar (driving range). The finding that ethanol

and bio-electricity use can result in similar life cycle energy use is based on the caveat that

driving range is similar, which requires a scaling BEV battery size (and thus vehicle mass and

fuel consumption) far beyond what can be found in real world vehicles15 and what is targeted by

automakers.86

The literature comparing the use of biomass-derived fuels in vehicles comprehensively examines

fuel production processes, while relying on simplified vehicle modelling. This is notable because

there is disagreement in the literature regarding whether ethanol or bio-electricity use is

favorable in terms of life cycle GHG emissions and energy use. These results depend on

assumptions regarding the relative fuel economy ratings of plug-in and non-plug-in vehicles, and

thus a systematic examination of vehicle technologies is required to produce a fair comparison.

2.3.4 Compressed Natural Gas Vehicles

Natural gas is an increasingly available energy source in the US.2 It is also less carbon intensive

than gasoline and diesel, on an energy equivalent basis.7 However, shale gas extraction

techniques that have helped increase supplies may result in higher methane emissions than

conventional natural gas extraction. Howarth et al.87 provided an early analysis that suggested

shale gas could have GHG emissions similar to coal and petroleum, on an energy equivalent and

33

100-year global warming potential (GWP) basis, as a result of leakages. More recent analyses by

Allen et al.88 and Laurenzi and Jersey89 using direct measurements of leakages have found that

these emissions are less than the estimate of Howrath et al.87 and that life cycle GHG emissions

from shale gas are significantly less than those from coal; however, Darrah et al.90 found that

water contamination from well leaks can be a concern. Venkatesh et al.55 and Burnham et al.91

concluded that compressed natural gas use in light-duty vehicles, even if sourced from shale gas,

can mitigate GHG emissions by displacing gasoline fuelled vehicles. A committee for the

National Research Council6 goes further by analyzing air pollutant impacts, and also found that

CNG use can be an improvement over petroleum use. Howarth et al.87 also examined the GHG

emissions of different fuels on a 20-year GWP basis. Their life cycle GHG emissions results for

shale gas under these conditions were higher than those for coal and petroleum. This is because

shale gas/natural gas leakages are primarily methane, which is more efficient at trapping

radiation (but resides in the atmosphere for a shorter period of time) than carbon dioxide.

Although this is an aspect not analyzed by the other studies cited above, GREET7 is regularly

updated based on the most recent scientific literature and does include the capability to compare

emissions on a 20-year GWP basis. As shown with the GREET7 results in Figure 2-18, shale gas

as a transportation fuel has similar life cycle GHG emissions than gasoline, on a per mile

travelled basis, using a 20-year GWP. This suggests that the use of shale gas can reduce GHG

emissions, but may not in the short term.

Figure 2-18: GREET comparison of GHG emissions from model year 2020 vehicles using

100- and 20-year Intergovernmental Panel on Climate Change (IPCC) global warming

potentials7

0

100

200

300

100 Year 20 YearWel

l-to

--w

hee

l GH

G E

mis

sio

ns

(g

CO

2eq

./km

)

Gasoline CNG (comprised of shale gas)

34

Although CNG is not widely used directly as a vehicle fuel, 29% of US electricity is generated

from natural gas (second only to coal) and is thus a primary energy source for plug-in electric

vehicles.2 Wang et al.92 compared the use of these two natural gas-derived fuels and found that

the use of a BEV would result in the least GHG emissions and urban air pollutants, even if CNG

was used in a HEV. Dai and Lastoskie93 agreed that natural gas-derived electricity use in a BEV

would be favorable in terms of GHG emissions (though did not evaluate HEVs) but found that

CNG would be favorable based on seven environmental impacts (e.g., human toxicity), in part,

because of battery production impacts. Curran et al.28 found the life cycle energy use and GHG

emissions from natural gas-derived electricity can be similar to those from CNG use, even in a

CV. Curran et al.28 attributed this to the variability in natural gas electricity generation efficiency.

Although Dai and Lastoskie93 also analyzed the use of different electricity generation

efficiencies, their analysis was distorted by a comparison of vehicles with fuel economy

estimates derived from greatly differing means and no apparent attempt to control for vehicle

attributes or fuel economy test conditions. Unlike Wang et al.92 and Dai and Lastoskie93, Curran

et al.28 did not examine air pollutants.

The literature comparing the use of natural gas-derived fuels in future light-duty vehicles does

not analyze financial or policy considerations. A common underlying assumption is that

dedicated CNG vehicle fuel economy improvement will exceed the improvement in gasoline

vehicles; however, while gasoline vehicle fuel economy must improve to meet future CAFE

standards, current dedicated CNG vehicles already exceed future requirements because of non-

petroleum fuel incentives. This is important because dedicated non-petroleum fuel vehicles,

which also include BEVs, can avoid the use of potentially costly fuel efficiency technologies.

2.3.5 Life Cycle Costing

Life cycle costing can be used to supplement life cycle assessments. Vehicle purchase price and

ongoing fuel costs are particularly important factors to consider when evaluating transportation

technologies. However, the literature has typically found vehicle price to the largest contributor

to life cycle costs. Thus, the discussion here is focused on the different approaches used in the

literature to estimate vehicle price.

Data can be a limitation when assessing the costs of alternative vehicles, which may not be

commercially available. Thus, Granovskii et al.94 used disparate sources to estimate the life cycle

35

costs of a CV, HEV, FCV and BEV. This includes the citing a (now defunct) magazine for a

$100,000 estimate for the price of a hypothetical, future FCV. While the CV, HEV and FCV fuel

costs were based on the fuel economy of small cars, the BEV specifications used were based on

an SUV. Granovskii et al.94 is not alone in relying on unofficial, non-academic data sources. Lee

et al.95 estimated the cost-effectiveness of a BEV at reducing life cycle GHG emissions by citing

a blog post, which quoted an unnamed source for the approximate price difference between a

BEV and CV powertrain.

Kromer and Heywood41 systematically compared the life cycle costs of CV, HEV, PHEV, FCV

and BEV powertrains. Technical differences among the different alternatives were first

identified. A literature review was then conducted to estimate the costs of each of the component

level differences. This incremental approach avoided conflating other vehicle design factors

(e.g., car versus SUV).

Argonne National Laboratory42 also systematically estimated the life cycle costs of vehicles with

CV, HEV, PHEV, FCV and BEV powertrains. Total costs were determined, rather than focusing

only on differences among powertrains. Forecasted component level costs were based on a set of

US Department of Energy development goals.

The National Petroleum Council3 utilized a different approach to forecast the life cycle costs of

vehicles with CV, HEV, PHEV, FCV and BEV powertrains. The research is based on model year

2008 vehicle characteristics and predicted changes over time. The focus of the analysis was on

the trade-off between vehicle price and fuel economy as fuel efficiency technologies are added to

vehicles with model year 2008 characteristics. A wide range of forecasted technologies were

aggregated together to model continuous relationships (as opposed to only analyzing discrete

data points as in the above studies) to determine the vehicle price and fuel economy that resulted

in the minimum life cycle cost (once fuel costs are added) for each powertrain type at different

points in time.

This thesis utilizes systematic means of estimating vehicle price developed in the literature to

analyze the questions introduced in Section 1.1. The work by Argonne National Laboratory42 is

incorporated into Autonomie96 vehicle simulation software and its used is discussed below in

Section 3.4. This work by National Petroleum Council3 is publically available in the form the

Vehicle Attribute Model3 and its use in this thesis is described in Section 3.5.

36

Chapter 3 Methods

This thesis conducts life cycle assessments to analyze future alternative light-duty vehicles. Life

cycle inventory analyses are primarily based on the US Department of Energy’s Greenhouse

Gasses, Regulated Emissions and Energy Use in Transportation Model (GREET).7 The human

health impacts of criteria air contaminants are estimated with the Air Pollutant Emission

Experiments and Policy (APEEP) analysis model.97 Vehicle price and performance

characteristics are estimated with Autonomie96 and the Vehicle Attribute Model.3 Finally, Monte

Carlo analyses are conducted using Crystal Ball software.98 An overview of these tools and how

they are used in this research is provided below. Additional details of how these models are used

is discussed within the Methods sections of Chapters 4-7.

3.1 Life Cycle Assessment

Life cycle assessments21 quantify the environmental impacts of products or activities. This

method involves identifying the life cycle scope of a product or process (e.g., fuel production and

consumption, in addition to vehicle production, maintenance and disposal) and its functional unit

(e.g., vehicle kilometer travelled).21 A life cycle inventory of inputs and outputs is then compiled

for the individual life cycle stages (e.g., quantity of greenhouse gas emissions).21 The life cycle

inventory results can then be weighted to estimate environmental impact (e.g., global warming

potentials).21

3.1.1 Types of Life Cycle Assessment

Life cycle assessments may be classified as attributional or consequential.99 Attributional life

cycle assessments quantify the average environmental impacts that can be directly attributed to a

product life cycle. Consequential life cycle assessments quantify the change in environmental

impacts from the use of a product or service, which requires modelling of the marginal products

or services displaced based on economic relationships. Some contain aspects of both types of life

cycle assessments, such as models that account for indirect land use change but are otherwise

attributional life cycle assessments.

37

Both consequential and attributional life cycle assessments have their strengths and weaknesses.

Plevin et al.100 argues that the scope of attributional life cycle assessment may be misleading to

policy makers because not all relevant environmental impacts are captured. For example,

assuming perfect substitution of a reference product with an alternative does not take into

account scale and indirect effects. However, consequential life cycle assessments are less

transparent, more complex and thus introduce additional sources of uncertainty because of the

greater scope of the study, including economic assumptions (e.g., supply and demand

interactions) that must be made to define the indirect relationships.99

3.1.2 Use of Life Cycle Assessment

This thesis uses aspects of both attributional and consequential life cycle assessments. It is

largely based on the GREET model, which is further discussed in Section 3.2.

Chapter 4 is a life cycle inventory analysis of GHG emissions and total, biomass and fossil

energy use for model year 2015 vehicles. The functional unit is 100 vehicle kilometers travelled,

which is a common functional unit used in vehicle life cycle studies as it captures the key vehicle

use component, fuel consumption, and is selected based on the emphasis on comparing energy

use in this chapter. The system boundary includes the life cycle stages of vehicle production, fuel

production and vehicle/fuel use. Also included is indirect energy use and GHG emissions from

secondary processes, such as fertilizer production. It also analyzes co-product GHG emission

credits for excess electricity generation from the production of lignocellulosic ethanol.

Chapter 5 is a life cycle assessment of air emissions impacts from model year 2020 vehicles. The

functional unit is one vehicle lifetime, to facilitate the presentation of financial results (monetary

quantification of environmental impacts) on a common net present value basis. The system

boundary includes vehicle production, fuel production and vehicle/fuel use. It assumes the

displacement of a reference vehicle on a one-to-one basis.

Chapter 7 is a life cycle assessment of GHG emissions for model year 2025 vehicles. The

functional unit is one vehicle kilometer travelled, which is typical in the literature comparing

vehicle GHG emissions.28, 91, 101 The system boundary includes fuel production and use. Vehicle

cycle activities are excluded to facilitate more direct comparisons with light-duty vehicle GHG

standards and LCFS, in addition to the uncertainties regarding which vehicle technologies will be

38

used in 2025, as a result of ambiguity within the Vehicle Attribute Model.3 It assumes the

displacement of a reference vehicle on a one-to-one basis.

Chapter 6 does not use life cycle assessment. However, it does develop vehicle models that are

used in the life cycle assessments in Chapter 7.

3.2 GREET Model

GREET7 estimates the life cycle energy and emissions of a variety of vehicles and fuels. It is a

frequently updated (annually in recent years) model developed by Argonne National Laboratory.

It is commonly used in scientific literature and was used to develop California’s Low Carbon

Fuel Standards.12

3.2.1 Overview of GREET Model

GREET7 is divided into the fuel and vehicle cycle components, as shown in Figure 3-1. The fuel

cycle is consists of feedstock production (e.g., oil extraction), fuel production (e.g., gasoline

refining) and operation (e.g., gasoline consumption); the former two are also referred to as well-

to-pump processes, the latter as the pump-to-wheel stage, and collectively they are also known as

well-to-wheel processes. The vehicle cycle consists of battery production, other parts production,

fluids production (e.g., motor oil), vehicle assembly and disposal. Default parameters are

included within the models, which can be customized by the user.

Figure 3-1: Simplified overview of life cycle stages modelled within GREET7

Fuel Production

Vehicle Operation Vehicle Assembly

Feedstock Production

Other Parts Production

Battery Production Fluids Production

Vehicle Disposal

Vehicle Cycle Process

Fuel Cycle Process

39

3.2.2 Use of GREET Model

The research in this thesis uses GREET7 in both direct and indirect means. GREET7 is used

directly to model energy use and emissions from particular life cycle stages (e.g., CO2 emissions

from E85 production). GREET7 is also used indirectly as a source of particular assumptions (e.g.,

relative fuel economy of dedicated E85 vehicles compared to gasoline vehicles) to create custom

spreadsheet models for Chapters 4, 5 and 7.

GREET7 is used in Chapter 4 to help compare the life cycle energy use and GHG emissions of

vehicles with different powertrains, but otherwise comparable characteristics. GREET7 is used

directly to model vehicle cycle and feedstock production energy use and GHG emissions for

model year 2015 vehicles. GREET7 is also cited for fuel cycle energy use and GHG emissions

assumptions for the creation of a custom spreadsheet model.

GREET7 is used in Chapter 5 to help compare the life cycle air emissions of vehicles with

different powertrains, but otherwise comparable characteristics. GREET7 is used directly to

model both fuel and vehicle cycle GHG emissions of model year 2020 vehicles. GREET7 is also

cited for probability distribution factors for the creation of a custom Monte Carlo analysis

spreadsheet model, further discussed in Section 3.6.

GREET7 is used in Chapter 7 to help compare the fuel cycle GHG emissions of vehicles using

different fuels and with different fuel economy performances, but otherwise similar

characteristics. GREET7 is used directly to model fuel cycle GHG emissions of model year 2025

vehicles. GREET7 is also cited for probability distribution factors for the creation of a custom

Monte Carlo analysis spreadsheet model, further discussed in Section 3.6.

3.3 Air Pollution Emission Experiments and Policy Analysis Model

The APEEP97 model is an integrated air emissions assessment model, which estimates the

monetary cost of damages from exposure to criteria air contaminant emissions in the US. The

model was developed by environmental economist Dr. Nicholas Muller, who helped the National

Research Council6 to analyze life cycle air emissions impacts of alternative vehicles. The

National Research Council6 noted that other models were considered, but that the use of

40

APEEP97 was “clearly appropriate for the task” and “had received sufficient prior use and

performance evaluation.”

3.3.1 Overview of Air Pollution Emission Experiments and Policy Analysis Model

A simplified overview of the components within APEEP97 is shown in Figure 3-2. APEEP97

analyzes the impacts of PM2.5, PM10, NOx, SOx, and VOC emissions. Users can input the

quantity of each emission type released in each US county, from ground sources and from stacks.

The default is based on the release of 1 ton of each emissions at each location and elevation.

These emissions are added to the inventory of background emissions levels across the US. The

movement and interactions of these emissions are estimated with an internal dispersion model.

Finally, dose-response relationships are used to estimate the effect of the released emissions on

economic costs of mortality, morbidity, agricultural crop damage, building material degradation

and visibility. Muller and Mendohlson102 discuss additional details regarding APEEP.

Figure 3-2: Simplified overview of components modelled within Air Pollution Emission

Experiments and Policy analysis model97

3.3.2 Use of Air Pollution Emission Experiments and Policy Analysis Model

APEEP97 is used in Chapter 5 to help compare the life cycle air emissions health impacts of

vehicles with different powertrains, but otherwise comparable characteristics. The marginal

Emissions release of each criteria air pollutant,

geographic location and elevation

Background Emissions Levels

Emissions Dispersion

Model

Human Health Dose-Response Model

Non-Human Dose-Response Model

Model Process

User Input

41

emissions impacts are calculated from a release of 1 ton of PM2.5, NOx, SOx, and VOC

emissions, which is consistent with work from the National Research Council,6 Michalek et al.8

and Mashayekh et al.103. These marginal results were scaled linearly to facilitate the creation of a

custom Monte Carlo analysis spreadsheet model for analyzing uncertainty (further discussed in

Section 3.6). The linear scaling of emissions impacts is valid for relatively small changes in air

emissions (consistent with a Taylor Series expansion). Tessem et al.75 analyzed the air emissions

impacts resulting from 10% market penetration of alternative light-duty vehicles by 2020, and

found that the air quality impacts scaled in an approximately linearly with changes in the size of

the functional unit. Therefore, the assumption of linear air emissions impacts is supported both

mathematically and by literature results, and can be expected to be a reasonable approximation

for near-term changes in the light-duty vehicle market.

3.4 Autonomie

Autonomie96 is a vehicle modelling and simulation software package. It is the successor to the

Powertrain System and Analysis Toolkit (PSAT), which was developed by Argonne National

Laboratory with input from General Motors, Ford, and Daimler Chrysler.104 Autonomie96 was

developed by Argonne National Laboratory in partnership with General Motors.96

3.4.1 Overview of Autonomie

Vehicles are modelled within Autonomie96 at the component level, as shown in Figure 3-3. For

example, a conventional vehicle (CV) model includes a specific glider (vehicle without

powertrain), wheels, internal combustion engine and transmission, while a battery electric

vehicle (BEV) model includes a specific glider, wheels, electric motor and battery. There are

component templates within Autonomie, many of which are based on actual production vehicle

(e.g., seventh generation Honda Accord sedan and first generation Chevy Equinox SUV)

components, and their specifications are customizable. For example, glider specifications include

mass, aerodynamic drag coefficient, frontal area and manufacturing cost. Complete vehicle

model templates are also included within Autonomie.96 Argonne National Laboratory105 provides

additional detail on modelling the capabilities of Autonomie.96

42

Figure 3-3: Simplified overview of Autonomie conventional and battery electric vehicle

model components96

3.4.2 Use of Autonomie

Manufacturing cost estimates for individual components are included within Autonomie.96

Autonomie96 provides manufacturing costs that correspond to the level of risk in achieving that

cost (e.g., lower cost estimates are associated with higher risk of not achieving them); the high

risk case is “aligned with aggressive technology advancement based on the U.S. DOE

[Department of Energy] Vehicle Technologies program”, while the low risk case is “aligned with

original-equipment-manufacturer improvements based on regulations”.105 Cost estimates for each

model year are included because the cost of producing a particular component (when assuming

all is else is equal) is reduced in subsequent model years.

Vehicle performance tests can be simulated within Autonomie.96 Standardized test parameters

are included for different performance metrics, such as the US Environmental Protection

Agency’s fuel economy rating and 0-96 km/h acceleration time. For vehicles to be designed to

particular performance specifications, vehicle component modifications and performance tests

can be iteratively conducted.

The research in this thesis uses vehicle template models available within Autonomie.96 These

templates are modified according to the objectives of Chapters 4, 6 and 7. Modifications include

Conventional Vehicle

Glider (Vehicle without Powertrain)

Powertrain

Wheels Transmission Internal

Combustion Engine

Battery Electric Vehicle

Glider (Vehicle without Powertrain)

Powertrain

Wheels Electric Motor

Plug-in Battery

43

the scaling of component sizing (e.g., internal combustion engine power rating) and substitution

of components (e.g., glider type).

Autonomie96 is used in Chapter 4 to help compare the life cycle energy use of vehicles with

different powertrains, but otherwise comparable characteristics. The CV, HEV, PHEV and BEV

templates are modified. Aerodynamic drag, rolling resistance and component mass are adjusted

to represent the use of lightweight materials in leading edge, model year 2015 midsize sedans.42

The powertrain components are scaled to maintain consistent 0-96 km/h acceleration

performance. US Environmental Protection Agency laboratory test fuel economy performance is

simulated for each vehicle.

Autonomie96 is used in Chapter 6 to help compare the different vehicle design options that can be

used to improve fuel economy. The CV template is modified with Chevy Equinox-like

components to produce a reference vehicle. The reference small crossover SUV body is replaced

with a smaller vehicle glider to examine the trade-off between vehicle size and fuel economy.

The reference vehicle engine power rating is modified to examine the trade-off between vehicle

0-96 km/h acceleration time and fuel economy. The reference vehicle CV powertrain is replaced

with BEV powertrain with different battery sizes to examine the trade-off between vehicle

driving range and fuel economy. The reference vehicle is modified using the Vehicle Attribute

Model,3 as discussed in Section 3.5, to examine the trade-off between vehicle price and fuel

economy.

Autonomie96 is used in Chapter 7 to help compare alternative fuel vehicles models that can be

used to meet or exceed model year 2025 CAFE standards.9 The reference Chevy Equinox-like

vehicle model developed in Chapter 6 is also used in Chapter 7. The reference vehicle CV

powertrain is replaced with BEV powertrain with different battery sizes to examine the trade-off

between vehicle driving range and fuel economy, though larger battery sizes are examined than

in Chapter 6. The reference vehicle is also modified using the Vehicle Attribute Model,3 as

discussed in the following subsection, to examine the use of CNG use in internal combustion

engine vehicles.

44

3.5 Vehicle Attribute Model

The Vehicle Attribute Model3 is a spreadsheet model that estimates the price and fuel economy

of future vehicles. This work was part of the National Petroleum Council’s analysis of future

vehicle technologies. The model was developed by General Motors.

3.5.1 Overview of Vehicle Attribute Model

The Vehicle Attribute Model3 is based on data from the US Energy Information Administration2

and General Motors vehicle assumptions. Existing vehicle characteristics are based on average

model year 2008 models in different vehicle classes. Estimates of future characteristics are based

on relative efficiencies of different vehicle powertrains, technology cost reductions over time and

the price of incremental fuel economy improvements. The latter is modelled as a continuous

range of technologies (as opposed to discrete) based on the aggregation of individual

technologies with different degrees of use and combinations of them (see Figure 3-4). Examples

of the added fuel efficiency technologies are provided in Figure 2-3, but the specific price and

fuel economy improvement values are from based on 2014 data,2 whereas the Vehicle Attribute

Model3 cites 2008 data. The Vehicle Attribute Model3 estimates future vehicle characteristics

(fuel economy and price) based on a minimization of vehicle ownership costs (vehicle price plus

fuel costs based on forecasted fuel prices2).

Figure 3-4: Illustrated example of the relationship between vehicle price and incremental

fuel economy improvements from the Vehicle Attribute Model3

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45

3.5.2 Use of Vehicle Attribute Model

The Vehicle Attribute Model3 is structured to evaluate the trade-off between vehicle price and

fuel economy, as fuel efficiency technologies are added, to minimize the cost of the vehicle and

fuel (over different time frames). In contrast, this thesis requires price estimates for vehicles with

specific fuel economy characteristics. As such, this model is not used directly in this thesis but

instead underlying assumptions and equations from the Vehicle Attribute Model3 are used to

create custom spreadsheet models for Chapters 5, 6 and 7.

The Vehicle Attribute Model3 is used in Chapter 5 to help compare the prices of model year 2020

vehicles with different powertrains, but otherwise comparable characteristics. The price of an

average model year 2008 midsize sedan is adjusted for midsize sedans with gasoline CV, CNG

CV, CNG HEV and BEV powertrains with fuel economy performances from the GREET model,

which is further discussed in Section 3.2. The vehicle price is then reduced to reflect lower

manufacturing costs in model year 2020.

The Vehicle Attribute Model3 is used in Chapter 6 to help compare the prices of different model

year 2012-2025 vehicle design options that can be used to improve fuel economy. The prices of

incremental fuel economy improvements is added to Autonomie96 vehicle models to examine the

trade-off between vehicle price and fuel economy. The model is also used to examine the

potential for price-neutral improvements in fuel economy over time, made possible by utilizing

the savings from manufacturing cost reductions to add fuel efficient technologies.

The Vehicle Attribute Model3 is used in Chapter 7 to help estimate the price of model year 2025

vehicles using different fuels and with different fuel economy performances, but otherwise

similar characteristics. The price of powertrain modifications for CNG fuel use are added to

Autonomie96 vehicle models. The price of incremental fuel economy improvements are added to

gasoline, CNG, and electricity-fuelled vehicle models to examine the trade-off between vehicle

price and fuel costs. Additional detail on all methods are included in Chapter 4 to 7.

46

3.6 Monte Carlo Analysis

Monte Carlo analyses are conducted in this thesis to characterize model uncertainties. This helps

illustrate the significance of the results and is particularly important when analyzing variables,

such as the future price of fuels or immature technologies, which are highly uncertain. Crystal

Ball is used, which a spreadsheet-based predictive modelling application developed by Oracle.98

There are many examples in the literature of life cycle assessments using Crystal Ball to conduct

Monte Carlo analyses, including Mullins et al.,56 Venkatesh et al.106 and Spatari and MacLean.107

3.6.1 Overview of Monte Carlo Analysis

Monte Carlo analysis is a method of quantifying uncertainties when other mathematical means

are difficult or even impossible. Probability distribution factors (e.g., normal distribution) are

assigned to model variables. Multiple trials are simulated based on a random sampling of values

for these variables (within the specified probability distribution functions). The results from the

simulations are compiled so that percentiles (e.g., 95th percentile result) can be quantified. The

percentiles can then be used to estimate confidence intervals (e.g., 90% confidence interval

ranges from 5th to 95th percentile results). The results are often illustrated in the form of a

frequency distribution graph or histogram, an example of which is shown in Figure 3-5.

Figure 3-5: Example of frequency distribution graph produced from a Monte Carlo

analysis

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3.6.2 Use of Monte Carlo Analysis

This thesis conducts Monte Carlo analyses on life cycle assessment and life cycle costing results.

Incremental results (i.e., differences between two results) are analyzed to capture correlations.

For example, the uncertainty in natural gas price could result in the fuel costs of two compressed

natural gas (CNG) vehicles to have overlapping confidence intervals. However, if the vehicles do

not have the same fuel economy, an incremental analysis would reveal that the more fuel

efficient vehicle would have lower fuel costs, regardless of natural gas price.

A Monte Carlo analysis is conducted in Chapter 5 to estimate the life cycle incremental

ownership costs and air emission benefits of a set of model year 2020 vehicles. A gasoline CV is

compared with a CNG CV to analyze the impact of fuel switching. A CNG CV is compared with

a CNG hybrid electric vehicle (HEV) to analyze the impact of improving the efficiency of CNG

use. A CNG HEV is compared with a BEV using natural gas-derived electricity to analyze the

impact of shifting emissions from a vehicle tailpipe to a power plant. Variables analyzed include

the fuel price, the geographic location in which emissions occur, and the economic costs of

climate change from GHG emissions.

A Monte Carlo analysis is conducted in Chapter 7 to estimate the life cycle incremental

ownership costs and GHG emissions of a set of model year 2025 vehicles. A set of vehicles

fuelled by CNG or electricity are each compared to a reference gasoline vehicle that meets CAFE

standards, to analyze the impact of using non-petroleum fuels. Variables analyzed include fuel

price, vehicle price and natural gas power plant efficiency.

Monte Carlo analyses are not conducted in Chapters 4 or 6. Chapter 4 analyzes the use of

lignocellulosic biomass-derived fuels produced with leading edge technologies, for which there

is a relative lack of data to produce probability distribution functions. Chapter 6 has a relatively

narrow scope with relatively few variables to quantify with probability distribution functions, but

the work is used contribute to the Monte Carlo analysis in Chapter 7. In lieu of Monte Carlo

analyses, sensitivity analyses are conducted in both Chapter 4 and 6.

48

Chapter 4 Life Cycle Assessment of Bioenergy Use in Light-Duty Vehicles

*Adapted with permission from Luk, J., Pourbafrani, M., Saville, B., MacLean, H. Ethanol or Bio-electricity? Life

cycle assessment of bioenergy use in light-duty-vehicles, Environmental Science & Technology, 2013, 47 (18)

10676-10684. http://pubs.acs.org/articlesonrequest/AOR-JzIibBUXwnhzN6Exqysg. Copyright 2015 American

Chemical Society.

The automotive industry has developed alternative powertrains to reduce petroleum use in light-

duty vehicles. Conventional vehicles (CV) with an internal combustion engine (hereafter referred

to as 'engine') have been modified to operate with alternative fuels. Hybrid electric vehicles

(HEV) primarily utilize an engine for propulsion, supplemented with an electric motor that

operates on electricity generated on-board and stored in a battery. Similarly, plug-in hybrid

electric vehicles (PHEV) use both an engine and electric motor, but the battery can also be

charged by an external electricity source. Battery electric vehicles (BEV) operate exclusively on

a battery charged by an external electricity source.

Bioenergy can be utilized for these alternative vehicle powertrains. Ethanol produced from

biomass is, for the most part, compatible with the existing fuelling infrastructure, and various

ethanol mandates or Renewable Fuels Standards11 have been enacted in several jurisdictions.

Biomass can also be used to produce a dispatchable source of electricity (bio-electricity), and can

deliver a stable, on-demand supply of electricity, unlike renewable electricity produced from

intermittent sources. While bio-electricity may not be explicitly generated for use in light-duty

vehicles, through Renewable Portfolio Standards108 and electric vehicle incentives, bio-electricity

may be indirectly incentivized for use in vehicles.

Although bioenergy may be able to reduce petroleum use, it may not alleviate other concerns.

Competition for feedstock and cropland may limit development.48 Lignocellulosic biomass, such

as crop and forest residues, and fast-growing woody biomass, can avoid some of these concerns.

Nonetheless, these multiple demands dictate a rational utilization of biomass resources, to reduce

unintended negative environmental, social and economic impacts.

Recent life cycle-based studies have examined the use of liquid biofuels and bio-electricity in

light-duty vehicles.23, 85, 109 The studies focused on GHG emissions, total energy use and/or land

use required to produce the energy. The studies that compared the environmental performance of

http://pubs.acs.org/articlesonrequest/AOR-JzIibBUXwnhzN6Exqysg

49

the liquid biofuel vs. bio-electricity pathways both concluded that the bio-electricity pathways

were more effective in reducing GHG emissions, energy and land use. 23, 78

Campbell et al.23 studied the use of corn and switchgrass to produce ethanol and bio-electricity

for use in light-duty vehicles. Ethanol production was largely based on a meta-analysis by Farrell

et al.46 The study focused on deployed technologies, considering vehicles from Model Years

2000-2003, and in some cases, demonstration vehicles. In all cases comparing BEV and gasoline

vehicles, the BEV were less powerful than the gasoline vehicles (which were assumed to operate

on 100% ethanol (E100) on an energy equivalent basis).

Clarens et al.78 analyzed the use of algae-derived bioenergy in light-duty vehicles. Although the

study compared biodiesel to bio-electricity, the study utilized fuel consumption data for gasoline-

powered vehicles and BEV from Campbell et al.23 For the former, data for diesel vehicles, would

have been more appropriate (see Results and Discussion for further detail). Campbell et al.23 and

Clarens et al.78 compared alternative liquid biofuel and bio-electricity scenarios. In contrast,

Pacca and Moreira100 evaluated a single scenario where ethanol and bio-electricity are co-

products in a facility that uses sugarcane and sugarcane bagasse to produce ethanol and bio-

electricity for light-duty vehicles. The resulting liquid fuel and electricity are then co-consumed

in a PHEV. Pacca and Moreira100 also cited Campbell et al. 23 for certain vehicle fuel

consumption data.

While these studies have made significant contributions to the literature, various aspects can be

refined and updated. The bio-electricity and ethanol production models in these studies can be

updated based upon recent technology improvements, with a transparent discussion of

underlying process yields and assumptions. In addition, Campbell et al. 23 and Clarens et al. 78

compared bio-electricity and biofuel pathways using different classes of vehicles; however, in

some classes comparisons included vehicles that differed based on passenger capacity, and in all

classes, differed on performance metrics, which influenced fuel consumption. As noted by Lave

et al.,101 life cycle comparisons of fuels/powertrains would ideally be based upon vehicles with

similar performance and operational characteristics. Thus, the conclusions of the studies may

have been impacted by the vehicles chosen for analysis. Additionally, they may have been

impacted by the fact that none of the studies accounted for realistic environmental impacts from

50

vehicle production and end-of-life processes, due to exclusion78 of this life cycle stage or

simplified assumptions.23

Our study aims to evaluate the life cycle energy use and GHG emissions of lignocellulosic

ethanol and bio-electricity use in light-duty vehicles, and compare these results with reference

fossil fuel/vehicle pathways, all within the United States. The work adds to the literature by

comprehensively modeling the well-to-pump (fuel production) and pump-to-wheel (vehicle fuel

consumption), and vehicle cycle (production/disposal) stages, while basing comparisons on

similar vehicles. Life cycle results are analyzed in five main components: (1) a comparison of

biomass and total energy use among the bioenergy pathways; (2) a comparison of fossil energy

use and GHG emissions among both bioenergy and reference pathways; (3) a comparison of total

energy use and net GHG emissions results with those in literature, while identifying sources of

differences; (4) a scenario analysis of the impact of regional characteristics on results; (5) a

comparison of petroleum use among all pathways. Finally, future developments and policy

implications of these findings are discussed.

4.1 Methods

4.1.1 Research Scope

The reference and bioenergy pathways developed in our study are specified in Table 4-1. Details

about pathway specifications and assumptions are discussed in subsequent sections and in the

Appendix A. The year 2015 is used as a near-term timeframe, allowing for implementation of

currently feasible technologies. Values of parameters applicable to the United States are utilized

and presented. The study includes a scenario analysis that examines the impact of variations in

key input parameters upon the results.

Life cycle energy use and GHG emissions are examined for the pathways. Lignocellulosic

biomass (referred to hereafter as ‘biomass’), petroleum, fossil (which includes petroleum) and

total energy use are quantified on a higher heating value (HHV) basis (see Appendix A for

details of the fuels included in each category). The cumulative impact of CO2, CH4 and N2O

emissions based on 100-year global warming potential CO2 equivalents (CO2eq.) is reported.7

Results are presented based on a functional unit of 100 vehicle kilometers travelled (VKT).

51

Table 4-1: Reference and bioenergy pathways

Reference Pathways Bioenergy Pathways

Pathway Name Gasoline CV

Gasoline HEV

Grid-e/ Gasoline

PHEV Grid-e BEV E85 CV

E85 HEV

Bio-e/ E85

PHEV Bio-e BEV

Liquid Fuel Production Conventional gasoline

(2015 U.S. average) n/a

85% ethanol by nominal volume (313 L/dry t)

n/a

Electric “Fuel” Production n/a Grid-electricity

(2015 U.S. average) n/a

Bio-electricity (27% HHV)

Powertrain Name CV HEV PHEV BEV CV HEV PHEV BEV

Liquid Fuel Consumption (L/100 VKT)

9.0 6.2 7.3 n/a 11.2 7.6 9.0 n/a

Electric “Fuel” Consumption (kWh/100 VKT)

n/a n/a 23.0 23.1 n/a n/a 23.0 23.1

Notes: Grid-e = grid-electricity, Bio-e = bio-electricity, CV = conventional vehicle, HEV = hybrid electric vehicle,

PHEV = plug-in hybrid electric vehicle, BEV = battery electric vehicle, VKT = vehicle kilometers traveled,

HHV = higher heating value, n/a = not applicable to pathway

Additional detail on these processes can be found in the Appendix A.

4.1.2 Reference Fuels

Well-to-pump values for U.S. average gasoline and grid-electricity are from the GREET Fuel-

Cycle model (version 1 2012 rev 2).7 Additional details on gasoline and grid-electricity

production, including the grid electricity mix, are in Appendix A. The sensitivity of the life cycle

GHG emissions results to grid-electricity characteristics is examined.

4.1.3 Lignocellulosic Biomass-based Fuels

Hybrid poplar, a short-rotation forestry feedstock, is the lignocellulosic biomass feedstock used

in each pathway. Hybrid poplar has a high yield and could be grown on marginal land.110 Based

on attractive attributes such as these, hybrid poplar has been discussed in the literature as an

energy crop,111 and analyzed in life cycle studies of ethanol112 and bio-electricity113 production.

Key characteristics of hybrid poplar are summarized in the Appendix A.

Hybrid poplar production data are based on the GREET Fuel-Cycle model7 poplar farming data.

This includes the production of agricultural inputs and biomass delivery. Biogenic carbon

absorption during growth and emission during combustion are included. The GREET Fuel-Cycle

model7 does not have emissions due to direct or indirect land use change48, 114 (LUC) associated

with tree farming (although the model does include these for some other biomass crops, e.g.,

switchgrass), and there is significant uncertainty in the LUC data for hybrid polar;112 therefore,

52

the current study does not include LUC. However, the potential impact of LUC is discussed

qualitatively in the Results and Discussion.

Process models are developed in Aspen Plus115 for ethanol and bio-electricity production from

hybrid poplar. This is in contrast to utilizing (and comparing) models developed externally,

which may have dissimilar assumptions (e.g., different boundary conditions). Key performance

characteristics are in Table 4-1; additional details are provided in the following sections, and

block flow diagrams of processes are provided in the Appendix A.

4.1.3.1 Ethanol

The ethanol production model used in our study is based on an auto-hydrolysis pre-treatment and

enzymatic hydrolysis process similar to that developed by Mascoma Canada and other

organizations, and examined in prior research.112 Currently, a pilot-scale system is in operation

and a commercial demonstration plant is in development.116 Lignocellulosic ethanol could also

be produced by other biochemical117 and thermochemical118 methods. Biochemical processes

require a pretreatment technology (e.g., auto-hydrolysis), which is ideally suited for different

classes of feedstock. The effectiveness of auto-hydrolysis as a pretreatment for hybrid poplar is

established in the literature.119 Thermochemical technologies are based on feedstock gasification

rather than pretreatment, and could be effective for hybrid poplar.120

In the ethanol production model, the feedstock is pretreated via auto-hydrolysis in its delivered,

un-dried state and without addition of chemicals. Enzymatic hydrolysis of pre-treated material

converts cellulose and hemicellulose into sugars. Glucose and xylose are fermented to produce

dilute ethanol, which is distilled to produce fuel-grade ethanol. The remaining unfermented

material, which includes lignin, is combusted to generate process heat and electricity. Excess

electricity is exported as a co-product to displace grid-electricity.

Additional processes are developed outside of Aspen Plus, and integrated within a spreadsheet

model. Enzymes are assumed to be produced “off-site” and have environmental impacts obtained

from Spatari and MacLean.107 Ethanol produced is denatured with gasoline and blended with

additional gasoline to produce E85 (85% nominal ethanol or 80.75% of pure ethanol, by volume,

dictated by cold weather starting requirements).7 Distribution data are based on ethanol produced

and consumed within the U.S.7 The electricity co-product is accounted for with a system

53

expansion approach.21 Consequently, ethanol production receives a co-product credit for

displacing U.S. average grid-electricity. The sensitivity of life cycle results to both ethanol yield

and alternative co-product scenarios is examined (See Appendix A).

4.1.3.2 Bio-electricity

Bio-electricity production is a commercial process, commonly employing direct combustion of

wood.121 The bio-electricity production model is based on a Rankine cycle system. Delivered

feedstock is combusted within a biomass boiler, generating steam to drive a steam turbine

electrical generator, and flue gas to dry delivered feedstock. The Aspen Plus model developed is

based on process efficiencies from the National Renewable Energy Laboratory.122 Downstream

electricity transmission and distribution losses are calculated within a spreadsheet model. Losses

are assumed to be 8%, the same as for U.S. average grid-electricity, based upon the GREET

Fuel-Cycle model.7 The sensitivity of life cycle results to bio-electricity generation efficiency is

examined (See Appendix A).

4.1.4 Vehicle Models

The four vehicle powertrains modeled are commercially available. In order of increasing vehicle

electrification (defined here as utilization of electricity, rather than a liquid fuel for propulsion),

they are conventional vehicle (CV), hybrid-electric vehicle (HEV), plug-in hybrid electric

vehicle (PHEV) and battery electric vehicle (BEV) powertrains. Gasoline and E85 fuels can be

used in the first three, while grid-electricity (grid-e) and bio-electricity (bio-e) are used with the

latter two. The CV lacks regenerative braking, which is utilized by the other three vehicles.

Detailed specifications are in Appendix A.

Autonomie (version 1210)96 is used to simulate pump-to-wheel vehicle performance for each

vehicle. Common gliders (vehicles without a powertrain) are assumed, in an effort to distinguish

fuel consumption differences among the powertrains. Vehicle component specifications are

based on Argonne National Laboratory42 projections for a “leading edge” mid-sized sedan.

Specifications represent a “medium” degree of optimism for model year 2015 vehicles, based on

literature and industry consultation. 42

Compared to the CV, there is greater uncertainty in modeling the other powertrains because of

alternative hybrid configurations and battery capacity considerations. The HEV was created with

54

a series-parallel split hybrid powertrain, in which the engine is able to both power the wheels and

act as an electric generator. The PHEV model utilizes a range extending series hybrid

powertrain, in which the engine powers an electric generator once the battery is depleted. The

PHEV and BEV battery capacities are sufficient for approximately 60 km and 250 km of driving

range on a single charge, respectively. The PHEV charge depleting driving range is estimated to

be sufficient for an average of 63% (i.e., fraction of vehicle kilometers travelled that do not

require the use of the engine). 42 These specifications are obtained from literature that evaluated

vehicle powertrains.8

Fuel consumption values of the vehicles are determined based on the U.S. EPA’s standardized 5-

cycle test.123 Ethanol is assumed to be consumed in dedicated E85 vehicles, with an E85

optimized engine. Ethanol has a higher octane content than gasoline, enabling a higher engine

compression ratio and improved energy efficiency.124 E85 fuels are assumed to achieve 7%

greater energy efficiency than gasoline.7 Although hybrid vehicles are not currently certified for

use with E85 fuels, E85 capable hybrid vehicles are under development.125 Thus, E85 hybrid

vehicles are included in our study. The sensitivity of life cycle results to vehicle fuel

consumption is examined and details are provided in Appendix A.

Vehicle cycle impacts include raw material extraction, vehicle manufacturing and end-of-life

processes. Total vehicle and lithium ion battery mass characteristics from Autonomie96 are used

within the GREET Vehicle-Cycle model (version 2 2012 rev 1).7 Energy use and GHG emissions

are estimated for “conventional” materials, and described in Appendix A.

4.2 Results and Discussion

The results for biomass, petroleum, fossil and total energy use, and GHG emissions for the

bioenergy and reference pathways are shown in Figure 4-1. Total life cycle results are shown,

disaggregated by life cycle stage or energy use contribution, per 100 VKT. All bioenergy

pathways have similar life cycle fossil energy use and GHG emissions (Figure 4-1 b and c),

indicating no clear advantage for the ethanol or bio-electricity pathways based on these metrics.

The E85 HEV, Bio-e/E85 PHEV and Bio-e BEV also have similar life cycle biomass and total

energy use. The minor differences in these metrics among these pathways are insignificant, as

illustrated by the scenario analysis. Only the E85 CV has considerably higher life cycle biomass

55

and total energy use than the other bioenergy pathways. We frame the following discussion

around five key insights and provide additional details in Appendix A.

4.2.1 A hybrid vehicle using ethanol (E85 HEV) and a fully electric vehicle using bio-electricity (Bio-e BEV) have similar life cycle biomass and total energy use.

Ethanol and bio-electricity pathways can have similar biomass and total energy use (Figure 4-1a

and 1b). The E85 HEV, Bio-e/E85 PHEV and Bio-e BEV have 340-390 MJ/100 VKT of

biomass energy use and 400-490 MJ/100 VKT of total energy use. The results are essentially the

same for these pathways when considering the precision of the estimates (additional discussion

below and in Appendix A), and are ~30%-40% lower than results for the E85 CV (biomass

energy use of 580 MJ/100 km and total energy use of 700 MJ/100 km). The well-to-pump and

pump-to-wheel stages represent the majority of life cycle biomass and total energy use. Although

the well-to-pump vs. pump-to-wheel efficiencies are substantially different between the E85

HEV and battery-powered vehicles (Bio-e/E85 PHEV and Bio-e BEV), these differences largely

offset each other, leading to similar biomass energy and total energy use. The well-to-pump

efficiency for electricity generation (27%) is lower than for producing ethanol (40% when

including the co-product). In contrast, an electric motor has a much higher peak efficiency than

an internal combustion engine (91% vs. 38%, excluding losses in power electronics,

transmissions, etc). Consequently, with increased vehicle electrification (i.e., increased use of an

electric motor), well-to-pump energy use increases, while pump-to-wheel energy use decreases.

Energy use for the vehicle cycle (production/disposal) represents less than 20% of life cycle total

energy use and is primarily comprised of fossil energy(see Appendix A for detail). The E85 CV

has higher biomass and total energy use because it relies entirely on a comparatively low

efficiency engine and lacks regenerative braking. However, inclusion of regenerative braking,

similar to that in the PHEV and BEV, closes this gap and improves the performance of an

ethanol-powered vehicle (E85 HEV) to a level comparable to its battery-powered counterparts

(Bio-e/E85 PHEV and Bio-e BEV).

56

Figure 4-1: a) Lignocellulosic biomass use, b) total energy use, and c) GHG emissions for

reference and bioenergy pathways

Note: The charts represent total life cycle results for the stated metrics. Values for all life cycle stages are included

in Appendix A. Biomass refers to poplar lignocellulosic biomass only. Well-to-pump processes include feedstock

and fuel, production and delivery. Pump-to-wheel process account for vehicle operation. Vehicle cycle comprises of

vehicle production and disposal, and does not have any significant quantity of lignocellulosic biomass energy input.

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4.2.2 All bioenergy pathways have similar life cycle fossil energy use and net GHG emissions, which are considerably lower than those of the reference pathways.

There are no substantial differences in fossil energy use or GHG emissions among the ethanol

and bio-electricity pathways (Figure 4-1b and 1c). Fossil energy use for the bioenergy pathways

is ~100 MJ/100 VKT, ~75% lower than that of the Gasoline CV pathway (430 MJ/100 VKT),

and ~65% lower than calculated for the HEV, PHEV and BEV reference pathways (~320

MJ/100 VKT). Fossil energy use in the bioenergy pathways is associated primarily with three

aspects of the life cycle: (i) in the vehicle cycle (production/disposal) stage, coal and natural gas

are used extensively. Vehicle electrification also affects vehicle cycle energy use because battery

manufacture is energy intensive, and larger, more powerful batteries require more energy to

manufacture; (ii) fossil energy is used during combustion (pump-to-wheel stage) and, to a lesser

extent, production (well-to-pump stage) of the gasoline contained in E85. Increasing vehicle

electrification reduces gasoline-related fossil energy use due to less (or no) consumption of E85;

(iii) the ethanol co-product credit reduces fossil energy use by displacing grid-electricity.

However, increasing vehicle electrification reduces ethanol use, and correspondingly, the

magnitude of the co-product credit.

The life cycle GHG emissions associated with the bioenergy and reference pathways are

presented in Figure 4-1c. The bioenergy pathways’ net GHG emissions are ~5 kg CO2eq./100

VKT, ~85% lower than emissions from the Gasoline CV pathway (30 kg CO2eq./100 VKT) and

~75% lower than emissions from the HEV, PHEV and BEV reference pathways (~20 kg

CO2eq./100 VKT). For all pathways, the well-to-pump and pump-to-wheel stages of the life

cycle are responsible for the majority of emissions, whereas the vehicle cycle stage is associated

with a smaller portion of emissions. For the bioenergy pathways, well-to-pump emissions are

higher for bio-electricity than for ethanol because the latter contains biogenic carbon not released

until the fuel is consumed in the vehicle. Thus, pump-to-wheel emissions are higher for ethanol

because electricity does not create emissions at the point of use. The emissions due to biomass

use are largely offset by the CO2 absorbed during feedstock (hybrid poplar) growth (termed

biogenic sequestration in Figure 4-1c). Therefore, net GHG emissions predominantly result from

fossil energy use, and are considerably lower for bioenergy pathways than for reference

pathways. In terms of both mass and 100-year global warming potential, CO2 is the dominant

58

GHG resulting from the life cycle stages of poplar production, ethanol, bio-electricity, gasoline

and grid-electricity production, as well as gasoline and E85 consumption (see Appendix A for

detail).

To evaluate potential GHG mitigation, ethanol displacing gasoline and bio-electricity displacing

grid-electricity are stated by Lemoine et al.126 as being most relevant. Figure 4-2a presents GHG

mitigation values for pathways whereby gasoline is displaced by lignocellulosic ethanol and

grid-electricity is displaced by bio-electricity. The GHG mitigation values represent the

difference in GHG emissions between the bioenergy and reference pathways (assuming a

common vehicle powertrain) per unit of biomass input. This comparison removes the vehicle

cycle (production/disposal) and pump-to-wheel impacts.

The reductions in GHG emissions and fossil energy are similar for ethanol and bio-electricity

production. GHG mitigation for both alternatives is ~1 t CO2eq./dry t biomass (Figure 4-2a),

while fossil energy mitigation is ~10 GJ/dry t. In terms of net GHG emissions and fossil energy

use, neither the bio-electricity nor the ethanol pathways have a clear advantage.

59

Figure 4-2: GHG emissions mitigation resulting from displacing reference fuels with

bioenergy alternatives: a) comparing mitigation potential of ethanol with that of bio-

electricity, and b) sensitivity of mitigation potential of ethanol to ethanol yield

Note: Base Case refers to the U.S. average electricity GHG intensity (605 gCO2eq./kWh) and ethanol yield (313

L/dry t) assumptions used throughout our study. The Coal-based Grid-e and Renewables-based Grid-e are based

on the Western Electricity Coordinating Council Rockies and Northwest Power Pool areas, respectively,

forecasted for the year 2015 by the U.S Energy Information Administration. The GHG intensity of the Coal-based

and Renewables-based grids are 1030 gCO2eq./kWh and 350 gCO2eq./kWh, respectively.42

0.0

0.5

1.0

1.5

Ethanol Bio-electricity

GH

G M

itig

atio

n(t

CO

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/dry

t b

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ass)

U.S. Average Grid-e (Base Case) Renewables-based Grid-e Coal-based Grid-e

a)

Gasoline Displacement

}Co-product

Credit

0.0

0.5

1.0

1.5

300 325 350 375 400

GH

G M

itig

atio

n(t

CO

2eq

/dry

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Ethanol Yield (L/dry t)

Bas

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ase

b)

60

4.2.3 Life cycle energy use and GHG emissions results of our study contrast with findings in literature, primarily because vehicles with similar characteristics are evaluated in the current study.

Our results suggest total energy use and GHG emissions can be similar for ethanol and bio-

electricity pathways. The E85 HEV, Bio-e/E85 PHEV and Bio-e BEV have similar total energy

use, while all bioenergy pathways, including the E85 CV, have similar GHG emissions. We find

that the bio-electricity pathways outperform the E85 CV for total energy use, which is in line

with results in literature,23 however, our overall findings differ from prior literature, which

concluded that in general, bio-electricity pathways were superior across the feedstock,

conversion technologies and vehicle classes examined. The different conclusions result primarily

from our study analyzing comparable vehicles (i.e., size, shape and performance characteristics)

to estimate pump-to-wheel performance, whereas dissimilar vehicles were used in those prior

studies. Our findings are consistent with results from the GREET fuel-cycle model.7

Conclusions reported in Campbell et al.23 and Clarens et al.85 result, in part, from the studies not

examining comparable vehicles. For example, Campbell et al.23 claim that the favorable outcome

for the bio-electricity pathway in the small sport utility vehicle class can be attributed to the

electric motor being 3.1 times more efficient than the internal combustion engine. However, the

electric vehicles selected were up to 5.7 times more efficient than the gasoline vehicles they were

compared with, because of other factors that impact relative energy efficiency. For example,

within the “small car” category, the study compared a 2001 Ford Th!nk City BEV with a 90

km/h (55 mph) maximum speed, a 65-80 km (40-50 mile) range and two seats,127 to a highway-

capable, gasoline Suzuki Swift CV with four seats. The study’s “midsize car” comparison

included a 62 kW (83 hp) Nissan Altra BEV128 and a more powerful 112 kW (150 hp) Nissan

Altima CV.129 The hypothetical HEVs examined by Campbell et al.23 are based on the CVs, and

thus are also implicitly larger and/or more powerful than the BEV. Campbell et al.23 and Clarens

et al.85 assumed gasoline vehicles could operate on E100 or biodiesel, respectively, on an energy

equivalent basis. Although a spark ignition gasoline vehicle can be modified to operate on

ethanol blends, we note, however, that operation is limited to E85 because of starting issues, and

that ethanol blends would achieve a higher combustion efficiency than gasoline on an energy

basis.7 Diesel vehicles, which have compression ignition engines, would have been more

appropriate for examining biodiesel fuels. These vehicles are designed to take advantage of the

61

diesel fuels’ properties, and compression ignition engines are more efficient than the spark

ignition engines used in conventional gasoline vehicles.42

To demonstrate the impact of assumptions regarding vehicle characteristics, we substituted the

HEV and BEV “midsize car” fuel consumption data from Campbell et al.23 into our pump-to-

wheel models. With this substitution, the E85 HEV pathway would now have 75% higher total

energy use than the Bio-e BEV. In contrast, substituting GREET fuel-cycle model7 fuel

consumption data into our models does not impact overall conclusions, with the E85 HEV

continuing to have total energy use within ~20% of that of the Bio-e BEV (see Scenario Analysis

in Appendix A for details). This comparison illustrates the importance of analyzing comparable

vehicles in life cycle studies; we have chosen comparable vehicles, and found no clear advantage

for ethanol versus bio-electricity, whereas Campbell et al.23 and Clarens et al.85 used vehicles

with dissimilar characteristics, which contributed to their conclusion regarding the superiority of

bio-electricity.

The fuel production (well-to-pump) efficiencies of the bioenergy pathways are also investigated

to determine whether they account for the different results in the current study versus those in the

literature. Substituting the higher bio-electricity production efficiency (32% versus 27%) and

higher ethanol production yield from Campbell et al.23 (382 versus 313 L/dry t) into our well-to-

pump models did not impact relative results. Additionally, substituting bio-electricity and

ethanol production data from the GREET fuel-cycle model13 into our models also leads to similar

total energy use among the E85 HEV, Bio-e/E85 PHEV and Bio-e BEV pathways and similar

net GHG emissions among all bioenergy pathways. See Scenario Analysis in Appendix A.

4.2.4 Regional characteristics may create conditions under which either ethanol or bio-electricity may be a more attractive option.

The regional electricity grid mix, the presence of industries with complementary resource

requirements, and existing land use in a region may lead to either ethanol or bio-electricity being

a more attractive option from energy use and GHG emissions perspectives in particular regions.

This study used U.S. average characteristics; however, regional grid-electricity characteristics

can affect GHG emissions of bio-electricity pathways and those of ethanol pathways (the latter

through excess electricity being produced as an ethanol co-product) (see Figure 4-2a). In regions

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that have a GHG-intensive electricity grid (e.g., Coal-based Grid-e in Figure 4-2a), the

production of bio-electricity to directly displace grid-electricity could lead to a greater GHG

emissions reduction than displacing gasoline with ethanol. Conversely, in jurisdictions with

Renewable Portfolio Standards,108 grid-electricity GHG reductions could be achieved through the

use of water, wind, solar or biomass energy sources (e.g., Renewables-based Grid-e in Figure

4-2a). In such cases, where an array of options are available, there may be greater overall benefit

in displacing gasoline with ethanol, and generating excess (co-product) electricity to supplement

other renewable power sources.

Besides displacing grid-electricity, there may be other regional opportunities to consider when

developing co-products and co-product credits related to lignocellulosic ethanol production.

Alternatives to excess electricity production include the production of fuel pellets,112 sweeteners

(e.g., xylitol)130 and process heat. Different co-product scenarios lead to different energy

requirements and GHG reductions; data for additional scenarios are presented in Appendix A. A

pessimistic scenario corresponds to a case wherein no ethanol co-products are produced. A more

favorable scenario arises from co-location of a lignocellulosic ethanol plant with an existing

facility that can utilize excess heat from the ethanol plant. In this scenario the ethanol plant can

make greater use of lignin-derived renewable co-products (heat and electricity),112 and increase

the GHG emissions reduction possible from lignocellulosic ethanol production. In either of these

scenarios, conclusions regarding similar biomass, fossil and total energy use among the E85

HEV, Bio-e/E85 PHEV and Bio-e BEV pathways in the current study would not change. In

contrast, the net GHG emissions from the ethanol pathways are sensitive to facility site and co-

product options, and can thus be higher or lower than net GHG emissions from the bio-electricity

pathways. The GHG emissions are more sensitive to co-product assumptions than is energy use

because of the high net GHG intensity (energy basis) of U.S. average grid-electricity and heat

generated from natural gas, as compared to the bioenergy alternatives. Regardless, the net GHG

emissions for the ethanol pathways are consistently below those of reference pathways.

Direct LUC is another factor that is based on local conditions and impacts GHG emissions.

Depending on the type of land converted to dedicated poplar plantations, management practices,

etc., net GHG emissions or sequestration may occur.48, 114 The high degree of uncertainty in

available estimates and the lack of analysis specific to poplar prevent their inclusion in the

current study.112 Under conditions whereby the ethanol facility and the bio-electricity facility

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source the same feedstock from the same land area, LUC would impact both sets of pathways

equally, on a per unit biomass basis. On a per 100 VKT basis, similar net GHG emissions would

still result from the E85 HEV, Bio-e/E85 PHEV and Bio-e BEV pathways, but LUC would affect

the GHG reductions relative to the reference fuels (gasoline or grid electricity).

Particular regions may be interested in other metrics not discussed above. Heavily populated

areas may be concerned with air pollutants, such as particulate matter and ozone precursors, due

to their detrimental health impacts. The impact of these pollutants may be substantial, but are

extremely sensitive to local conditions, population and exposure aspects. These issues are

beyond the scope of this study. Jurisdictions that import petroleum may favor pathways that

displace petroleum for reasons of energy security. Petroleum use is further discussed in the

following section.

4.2.5 Ethanol displacement of gasoline or vehicle electrification can reduce petroleum use, while bio-electricity may displace non-petroleum energy sources.

Vehicle gasoline use, including the gasoline portion of E85, is the dominant contributor to

petroleum use in the bioenergy pathways. Most energy use within the Gasoline CV pathway is

petroleum based, while in the Grid-e and Bio-e BEV pathways almost no petroleum is used.

Thus, a greater reduction in petroleum use can be achieved via vehicle electrification than by fuel

switching to ethanol.

In line with our analysis of GHG emission mitigation and fossil energy use mitigation in Figure

4-2, and analysis by Lemoine et al.,126 we assume that mitigation of petroleum use through the

use of bio-electricity is based on the displacement of grid-electricity. Bio-electricity may not

mitigate petroleum use because very little U.S. grid-electricity is generated from petroleum

products.127 Therefore, while bio-electricity production is able to displace coal and natural gas, it

is expected to have a lesser impact on petroleum consumption, as compared to ethanol

production. However, over time, plug-in electric vehicles and charging infrastructure could

reduce petroleum consumption by displacing conventional gasoline fuelled vehicles.

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4.2.6 Future Developments

Although the total life cycle energy use and GHG emissions can be similar among bioenergy

pathways, there are distinct differences at each life cycle stage. There is limited opportunity to

improve well-to-pump impacts of ethanol production, because an increase in ethanol yield leads

to a corresponding reduction in co-product credits (Figure 4-2-b). This trade-off is more

significant for higher value co-products (as illustrated by greater sensitivity of co-product credits

to ethanol yields if Coal-based Grid-e is displaced, as opposed to Renewables-based Grid-e in

Figure 4-2b). Conversely, there are greater opportunities to improve the well-to-pump

efficiencies of electricity pathways, such as with gasification technologies. In a scenario analysis

discussed in Appendix A, future bioenergy production technologies reduce total energy use in

the Bio-e BEV by 40%, while the E85 HEV is reduced by only 10%.

There are greater opportunities to improve the pump-to-wheel efficiencies of lignocellulosic

ethanol and gasoline pathways, because electric powertrain efficiency is already high. However,

all pathways can benefit from powertrain “agnostic” pump-to-wheel development (e.g.,

aerodynamic drag and rolling resistance), which also has the compounded benefit of reducing the

engine and battery mass. This benefit is expected to be particularly important for electric vehicle

pathways, because of the high battery mass required to meet distance/range objectives. For all

pathways, the reduction of pump-to-wheel energy use also has the compounded benefit of

reducing well-to-pump energy use, on a per VKT basis. In a scenario analysis discussed in

Appendix A, future vehicle technologies reduce life cycle total energy use in both the Bio-e BEV

and E85 HEV by 10%. Vehicle cycle (production/disposal) energy use and GHG emissions are

relatively minor for all pathways.

4.2.7 Policy Considerations

Several existing policies are aimed at improved environmental performance of bioenergy and

transportation technologies. Insights from our study may inform refinements of these, and

development of upcoming policies. Cohesive energy policy should recognize the potentially

complementary nature of ethanol and bio-electricity production, the similar life cycle impacts of

hybrids and fully electric vehicles, and sensitivity of environmental performance to unique

regional characteristics.

65

The production of ethanol or bio-electricity independently can be less efficient than if they are

co-produced. Optimization of the yields of both products, from financial and environmental

perspectives, can improve overall efficiency. This co-production is currently not supported by

U.S. Renewable Fuel Standards11 based on liquid biofuel volumes. Fuel producers should be

encouraged to take advantage of potential co-product opportunities, as is the case with Low

Carbon Fuel Standards47 that take into account co-product credits.

Hybrid vehicle (both HEV and PHEV) pathways achieve many of the life cycle energy use and

GHG emissions benefits of fully electric BEVs. The emphasis on miles per gallon gasoline

equivalent energy use on U.S. vehicle labels123 and electric vehicle tax credits based on battery

size,16 both focus on pump-to-wheel, rather than life cycle, performance. Vehicle labels and

incentives based on life cycle impacts would empower consumers to make more informed

decisions about vehicle environmental performance.

The method employed in our study maintains constant vehicle size and performance

characteristics, unlike some previous studies. This is an effort to isolate differences in powertrain

technologies and to avoid favoring particular pathways through the use of smaller vehicles and/or

those having lower performance standards. ‘Comparable’ vehicles should be examined to ensure

that conclusions are valid.

The reported GHG emissions and energy use benefits of bio-electricity pathways compared to

ethanol pathways presented in existing literature are highly sensitive to assumptions. Regional

characteristics may create conditions under which either ethanol or bio-electricity may be the

preferred option; however, this analysis shows that neither has a clear advantage in terms of

GHG emissions, biomass, fossil, or total energy use. This conclusion remained robust under the

scenarios investigated. Policies should not focus on minor differences among alternatives, if

overall, those alternatives have favorable environmental performance compared with the status

quo (reference pathways).

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Chapter 5 Life Cycle Air Emissions Impacts and Ownership Costs of Light-Duty Vehicles Using Natural Gas As A Primary Energy Source

*Adapted with permission from Luk, J., Saville, B., MacLean, H. Life cycle air emissions impacts and ownership

costs of light-duty vehicles using natural gas as a primary energy source, Environmental Science & Technology,

2015, 49 (8) 5151-5160. http://pubs.acs.org/articlesonrequest/AOR-3bqzfeAZcSBvFxjutSWh. Copyright 2015

American Chemical Society.

The US transportation sector is dependent on petroleum fuels for the vast majority of its energy

use.131 There is increasing interest in plug-in electric vehicles as a means of mitigating the use of

petroleum, which is not typically used to generate electricity. However, plug-in vehicles have

had limited success competing against non-plug-in vehicles2 in part because of high purchase

prices.41-43

The environmental impacts of plug-in vehicles, including those resulting from life cycle air

emissions, depend in large part on the source of electricity.71, 75, 76, 91, 132, 133 Replacing

conventional gasoline vehicles with plug-in vehicles can result in similar greenhouse gas (GHG)

emissions if the source of electricity is coal,76, 91 or lower emissions if natural gas is utilized.71, 76,

91, 93, 133 Additionally, replacing conventional gasoline vehicles with plug-in vehicles can increase

or decrease detrimental health impacts from criteria air contaminant (CAC) emissions if coal or

natural gas is used, respectively, to generate electricity.75 However, natural gas can also be used

in non-plug-in vehicles, in the form of compressed natural gas (CNG), and reduce both GHG and

CAC emissions when displacing gasoline.55, 75, 91, 93

The literature does not comprehensively distinguish between the merits of alternative energy

sources and those of plug-in vehicles themselves. Not all of the benefits associated with plug-in

vehicles are unique. This distinction can have important policy implications for regions that rely

on non-petroleum sources of electricity, which is increasingly natural gas in much of the US.2

The natural gas available in a region could be utilized by the transportation sector in different

ways: as CNG for conventional vehicles (CV) to provide the benefits of fuel switching from

petroleum use; as CNG for hybrid electric vehicles (HEV) that also reduce life cycle energy use;

as a source of electricity for plug-in battery electric vehicles (BEV) that can also shift CAC

emissions from vehicles in urban areas to power plants in rural areas.

http://pubs.acs.org/articlesonrequest/AOR-3bqzfeAZcSBvFxjutSWh

67

This study evaluates the incremental life cycle air emissions (GHG and CAC) impact benefits

and life cycle ownership costs of non-plug-in (CV and HEV) and plug-in (BEV) vehicles using

natural gas a common primary energy source. A gasoline CV is used as a reference pathway. US

energy use and emissions from well-to-pump (fuel production), pump-to-wheel (vehicle

operation) and vehicle cycle (vehicle production, maintenance and disposal) stages are

comprehensively analyzed to consider temporal and geographical distributions. Ownership costs

consist of both vehicle purchase and operating costs. Air emissions impacts consist of climate

change and human health costs from GHG and CAC emissions, respectively. These metrics and

pathways are used to investigate the merits of natural gas to produce electricity for use in plug-in

electric vehicles compared to CNG use in non-plug-in vehicles.

5.1 Methods

5.1.1 Individual Pathway Analysis

This study determines the life cycle air emissions impacts and life cycle ownership costs of a set

of light-duty passenger vehicle pathways. The focus of the work is on the four pathways listed

below, whose key assumptions are listed in Table 5-1. Details of the incremental benefit-cost,

uncertainty and sensitivity analyses are discussed in the following subsections.

Gasoline CV: Vehicle fuelled by gasoline with a conventional powertrain

CNG CV: Vehicle fuelled by compressed natural gas, with a conventional powertrain

CNG HEV: Vehicle fuelled by compressed natural gas, with a hybrid electric powertrain

NG-e BEV: Vehicle powered by natural-gas-derived electricity, with a battery electric

powertrain

Pathways are constructed in an Excel spreadsheet using publically available models and data

sources. The functional unit of one Model Year 2020 vehicle lifetime facilitates analysis of

potential near-term technologies. For example, there are no consumer CNG HEVs currently

available, but this powertrain type has been developed for a concept vehicle.134 Results for both

air emissions impacts and ownership costs are presented on a net present value (NPV) basis in

2010 USD. The use of a recent currency base year is typical135 to enable the use of historical

68

gross domestic product as an economic index,136 and has precedence in analyses of future vehicle

models.8, 42

5.1.2 Life Cycle Energy Use and Emissions Inventory

Life cycle inventories of energy use and emissions are developed for each of the four pathways.

GREET 1 fuel-cycle and GREET 2 vehicle-cycle models7 are used to determine GHG (CO2, CH4

and N2O) and CAC (PM2.5, VOC, NOx and SOx) emissions. These emissions are disaggregated

by life cycle stage.

GREET default fuel economy performances are based on a gasoline CV with a spark ignition

internal combustion engine.7 GREET assumes that a dedicated CNG CV and CNG HEV can

achieve fuel economy ratings that are 5% and 40% higher on an energy equivalent basis,

respectively, than the reference vehicle.7 Dedicated CNG engines (as opposed to bi-fuel engines

more common in Europe and in aftermarket retrofits) can have higher compression ratios than

gasoline engines and thus higher thermal efficiencies; however, CNG is stored in heavy fuel

tanks that can offset potential fuel economy improvements.137 This study assumes that GREET

CNG fuel economy performances above can be achieved, but design factors may result in lower

fuel economy than equivalent gasoline vehicles, and are captured in the Low Fuel Economy

CNG Vehicle Scenario in Appendix B.

GREET vehicle operation default emission factors are based on gasoline CV results from the

MOVES model.7 The MOVES model was developed by the EPA to allow jurisdictions to

simulate the combustion, evaporative, and tire and brake wear emissions from vehicles that are

designed to meet air emissions standards.138 GREET and its documentation do not state specific

details regarding particular emissions control technologies assumed for vehicles in the model, but

rather, GREET includes emissions factors that reflect vehicles that meet current Tier 2

standards.138 The Model Year 2020 vehicles in this study are assumed to be produced during a

transition period (2017-2025), where only a portion of vehicles produced will meet stricter Tier 3

standards (the potential impact of which is discussed in the Policy Implications section).139

GREET default alternative vehicle emissions factors are calculated relative to emissions factors

for a gasoline CV, as estimated by Argonne National Laboratory researchers.7 In particular,

methane emissions from a CNG CV are assumed to be 10 times higher than those of the gasoline

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CV because of methane slip. The absolute value of 0.07 g/mile is similar to Argonne National

Laboratory test results of a 2012 Honda Civic Natural Gas vehicle on the EPA’s Urban

Dynamometer Driving Schedule.28 These relatively high emissions are permitted because

methane is not regulated by Tier 2 (nor future Tier 3) emissions standards, unlike CACs and non-

methane hydrocarbon emissions.139 For comparison, a High Methane Emission CNG Vehicle

Scenario is developed and presented in Appendix B. Note that speciation of the CAC emissions

is not included in GREET and is beyond the scope of this study.

5.1.3 Air Emissions Impacts and Ownership Costs

The NPVs of climate change and human health impacts are estimated from the GHG and CAC

emissions, respectively. Air emissions impacts are determined from the product of life cycle

emissions quantities and specific (per unit mass) impact costs (Equation 5-1). GHG climate

change impact costs are from the Interagency Working Group on Social Cost of Carbon, which

was convened by the US Government.140

This study uses the APEEP (Air Pollutant Emissions Experiment and Policy analysis) model to

estimate the marginal health impact costs from a ton of PM2.5, NOx, SOx and VOC emissions.97

APEEP was developed by Muller and Mendelsohn to quantify damage caused by air pollution in

the US.102 The model takes into account factors such as background emission levels and

dispersion patterns when estimating the impacts from emissions occurring in different US

counties and at different elevations (e.g., vehicle tailpipe emissions are ground sources). The

weighted averages of impacts from emissions individual counties are used and calculated

(Equation 5-2) based on the geographic distributions of each life cycle stage activity listed in

Table 5-1 and further explained in Appendix B.

There is precedence for using the APEEP model to examine emissions impacts of the

transportation sector. The approach we used to estimate CAC health impact costs is based on

National Research Council’s (NRC) Hidden Costs of Energy study,6 which includes analysis of

the transportation sector based on emissions factors from GREET and emissions impact costs

from APEEP. NRC noted that other models were considered, but that the GREET and APEEP

models, “were clearly appropriate for the task” of analyzing life cycle air emissions impacts of

alternative (including gasoline, CNG and plug-in electric) vehicles and “had received sufficient

prior use and performance evaluation.”6 Michalek et al.8 also used GREET and APEEP to

70

analyze transportation sector air emissions impacts, while Mashayekh et al.103 (2011) used

APEEP and MOBILE6 (the predecessor of MOVES, the model used by GREET to obtain its

vehicle emissions factors).141 Tessum et al.75 used GREET emission factors combined with

WRF-Chem Eulerian meteorology and a chemical transport model to analyze the air emissions

impacts from alternative light-duty vehicles.

As in the NRC study6, Michalek et al.8 and Mashayekh et al.103, we use marginal emissions

impacts. This assumption of linear air emissions impacts was used to facilitate our Monte Carlo

analysis and is valid for relatively small changes in air emissions (consistent with a Taylor Series

expansion). Tessum et al.75 analyzed the air emissions impacts resulting from 10% market

penetration of alternative light-duty vehicles by 2020 (including CNG and plug-in electric with

GREET emission factors), and found that the air quality impacts scaled in an approximately

linear fashion with changes in the size of the functional unit. In comparison, CNG and plug-in

vehicles are projected to have a combined 1% market share in the US by model year 2020.2

Therefore, our assumption of linear air emissions impacts is supported both mathematically and

by literature results, and can be expected to be a reasonable approximation for near-term changes

in the light-duty vehicle market.

Ownership costs include both vehicle retail purchase price and operating expenses. Vehicle

purchase prices are based on the Vehicle Attribute Model,3 which was developed by General

Motors. Operating costs are based on fuel prices from the Annual Energy Outlook2 and

maintenance costs are from Oak Ridge National Laboratory.142 Ownership costs are further

discussed in Appendix B, including major vehicle price components and maintenance expenses

itemized.

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Table 5-1: Key assumptions used to develop fuel cycle and vehicle models

Life Cycle Inventory Variable Assumption Justification

Gasoline CV powertrain fuel economy 13 km/L (29.5 MPG)

Based on GREET model year 2020 passenger car7, 132

CNG CV powertrain fuel economy 14 km/L (31.0 MPG)

CNG HEV powertrain fuel economy 18 km/L (41.3 MPG)

BEV powertrain fuel economy 52 km/L (118 MPG)

BEV battery size 28 kWh

Battery replacement 0%

Lifetime vehicle travel 260,000 km

Vehicle emissions standards Tier 2

CNG fuel tank material Carbon fibre To enable the efficiency gain over gasoline vehicles

Lifetime vehicle age 12 years Michalek et al.8

Petroleum resource mix 14% oil sands/86% conventional

GREET projections for 20207

Natural gas resource mix 42% shale gas/58% conventional

NG-e generation technology 88% combined cycle

/12% gas or steam turbine

CNG compression efficiency 96% efficiency

Ownership Cost Variable Assumption Justification

Gasoline CV purchase price $24,100 Model Year 2020 retail price based on Vehicle Attribute

Model, vehicle fuel economy, CNG fuel tank material and BEV battery capacity characteristics above3

CNG CV purchase price $28,800

CNG HEV purchase price $30,000

BEV price (excl. battery) $23,800

BEV battery price $430/kWh Average of Vehicle Attribute Model 2020 price range3

CNG engine modification price $1400

CNG CV/HEV fuel tank price $3300/$2600 Vehicle Attribute Model 2020 carbon fibre tank3

Brent spot crude oil price $17/GJ ($98/bbl)

Annual Energy Outlook reference scenario for 20202

US Gasoline price $0.75/L

Henry Hub natural gas price $4.50/GJ

US CNG price $14/GJ ($0.44/Lge)

US NG-e price $97/MWh ($0.88/Lge)

Ownership discount rate 8% Vehicle Attribute Model default value3

Air Emissions Impact Variable Assumption Justification

Social discount rate 3% APEEP model97 value and NRC median value140

GHG impact specific cost $43/t CO2eq. NRC median value140

CAC Impact Specific Cost /t PM2.5 /t NOx /t SOx /t VOC Geographic Distribution Weighting

Vehicle operation $28,100 $1,300 $10,000 $2,600 Household travel 131 and population143

Oil and gas extraction $11,600 $1,300 $4,300 $1,100 Petroleum and NG extraction*143

Gasoline fuel production $23,100 $1,000 $10,100 $2,100 Petroleum refining*143

CNG fuel production $27,800 $1,200 $10,700 $2,500 Natural gas distribution*143

NG-e fuel production $8,300 $600 $3,500 $800 Natural gas electricity production25

Vehicle parts $16,700 $1000 $6,600 $1,500 Motor vehicle part manufacturing*143

Vehicle battery $19,200 $1000 $7,700 $1,700 Battery manufacturing*143

Vehicle fluids $25,100 $1,300 $8,500 $2,300 Petro. lubricating oil manufacturing*143

Vehicle assembly $17,100 $800 $5,700 $1,600 Automotive manufacturing*143

Notes: CV = conventional vehicle, HEV = hybrid electric vehicle, BEV = battery electric vehicle, CNG =

compressed natural gas, NG-e = natural gas derived electricity, Lge = liters gasoline equivalent, Costs are presented

in 2010 USD. *These activities are weighted according to employment.6, 8

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Equation 5-1: CAC emissions human health impact equation

I County = e × iCounty

Where:

I= NPV of CAC emissions human health impacts ($)

e= CAC emissions (t)

i= NPV of specific CAC emissions human health impacts ($/t)

County = US County where emissions/activity occurs

Equation 5-2: Weighted average of CAC emissions human health impact equation

I =∑ I County × ACounty

∑ ACounty

Where:

A= Life cycle stage activity

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5.1.4 Incremental Benefit-Cost Analysis

Incremental benefit-cost analysis is used to directly compare the individual pathways. Benefits

are defined here as a reduction in the NPV of life cycle air emissions impacts, whereas cost

refers to an increase in the NPV of life cycle ownership costs. The incremental benefits and costs

between one pathway (defender) and the next (challenger) are calculated with Equations 5-3 and

5-4, respectively, as opposed to comparing each natural gas pathway with the Gasoline CV. The

four pathways are analyzed in the following three incremental comparisons:

1. Fuel switching: CNG CV challenger replacing reference Gasoline CV defender

2. Energy efficiency: CNG HEV challenger replacing CNG CV defender

3. Emissions shifting: NG-e BEV challenger replacing CNG HEV defender

Equation 5-3: Incremental benefit equation

B = Idefender − Ichallenger

Where:

B = NPV of incremental benefit ($)

I = NPV of life cycle air emissions impacts ($)

Defender = defending pathway

Challenger = challenging pathway

Equation 5-4: Incremental cost equation

C = Ochallenger − Odefender

Where:

C = NPV of incremental cost ($)

O = NPV of life cycle ownership costs ($)

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5.1.5 Uncertainty and Sensitivity Analyses

Uncertainty and sensitivity analyses are completed to examine the robustness of the natural gas

pathway results. Crystal Ball software98 is used to perform a Monte Carlo uncertainty analysis

with 10,000 trials. The Monte Carlo analysis includes not only random error but also variability,

due to the nature of aggregate data available to consumers and used by policy makers.106 An

example of variability is different vehicle fuel economy performance based on local climate and

proportions of city versus highway driving characteristics, but fuel economy testing results

available to both consumers and policy makers are uniform across the US.15 The variables, which

are collectively investigated in the uncertainty analysis, are examined individually in the

sensitivity analysis. The incremental benefit-cost analysis captures correlations between

pathways that are a result of these variables (e.g., the $/t life cycle GHG impact is simultaneously

changed for all pathways). These variables and their probability distributions are detailed in

Appendix B. For example, the probability distribution of the specific impact cost ($/t) of PM2.5

emissions during vehicle operation is a discrete distribution based on the fraction of national

vehicle kilometers travelled in each county and the impact specific cost of ground source PM2.5

emissions occurring in each county.


This study examines the life cycle air emissions impact benefits and life cycle ownership costs of

a range of vehicles using natural gas as a common primary energy source in comparison to a

reference Gasoline CV. The merits of natural gas use and those of the alternative vehicle

powertrain technologies are distinguished through an incremental benefit-cost analysis. This

section is divided into three sub-sections: individual pathway results; incremental cost-benefit

analysis; and policy considerations.

5.2.1 Individual Pathway Results

Life cycle inventory analysis results for select metrics are shown in Figure 5-1, disaggregated by

life cycle stage, per vehicle lifetime (260,000 km). Life cycle GHG climate change impacts,

CAC health impacts and ownership costs for each pathway are illustrated in Figure 5-2 on an

NPV basis per vehicle lifetime. Only the base case results are compared in these figures, while

the uncertainties are presented in Appendix B.

75

5.2.1.1 Life Cycle Energy Use and Emissions Inventory

Life cycle energy use (Figure 5-1a) is closely related to life cycle CO2 emissions (Figure 5-1b),

which are mainly a result of primary energy source combustion. The Gasoline CV has both the

highest base case energy use (900 GJ per vehicle lifetime) and CO2 emissions (60 t), while the

CNG CV results are 10% and 20% lower, respectively. This is because the CNG CV is a more

fuel efficient vehicle (on an energy equivalent basis for the base case estimate – this assumption

is examined in the Low Fuel Economy CNG Vehicle Scenario in Appendix B), and CNG is also

a less carbon intensive fuel. The CNG HEV energy use and CO2 emissions are both 30% lower

than those of the CNG CV, because of fuel economy differences. These metrics are similar for

both the CNG HEV and NG-e BEV because differences in vehicle operation and fuel production

stages largely offset each other.

CAC emissions from these pathways are also primarily a consequence of energy use. However,

unlike uncontrolled CO2 emissions, the relative contributions of each life cycle stage to total NOx

(Figure 5-1c), PM2.5, VOC and SOx (Figure 5-1d) emissions are dissimilar to those of energy use.

Compared to CO2 emissions, vehicle operation is a smaller contributor to the CAC emissions

because gasoline and CNG both have low sulfur contents, and Tier 2 emissions standards require

the use of emissions control equipment to reduce vehicle tailpipe and evaporative NOx, PM2.5 and

VOC emissions. However, the vehicle operation stage is still a major contributor of VOC

emissions because of windshield washer fluid (which contains methanol)144,145 use and PM2.5

emissions, in part because of tire and brake wear. Vehicle production is the largest contributor to

plug-in vehicle PM2.5 emissions and SOx emissions for all vehicles, because activity is

concentrated in the US Midwest, which relies, in large part, on electricity from coal-fired power

plants that emit substantial quantities of these pollutants.

76

Mainly from primary energy source (petroleum or NG) use HEV and BEV similar due to trade-off in fuel production and vehicle efficiencies

Mainly from primary energy source combustion NG has lower carbon intensity than gasoline CO2 is dominant source of GHG emissions, over CH4 and N2O (as shown in Appendix B), even for CNG vehicles

Mainly from primary energy source combustion Vehicle emissions control equipment reduces contribution of gasoline and CNG tailpipe emissions

Mainly from coal combustion (from vehicle production electricity use) Primary energy sources have relatively low sulfur contents

Mainly from vehicle fuel and coal combustion (latter from electricity use during vehicle production) Vehicle emissions control equipment reduces gasoline and CNG tailpipe emissions All vehicles have operating emissions from tire and brake wear

Vehicle emissions control equipment reduces gasoline and CNG tailpipe and evaporative emissions All vehicles have operating emissions from windshield washer fluid use

Figure 5-1: Base case life cycle (a) energy use, (b) CO2, (c) NOx, (d) SOx, (d) PM2.5 and (d)

VOC emissions inventory results

Notes: Results are presented per 300,000 km vehicle lifetime. Vehicle disposal is a small contributor included in

vehicle production. Resource extraction is a small contributor included in fuel production.

0

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5.2.1.2 Life Cycle Air Emissions Impacts and Ownership Costs

Air emissions impacts are the product of life cycle emissions quantities and specific impact costs.

The Gasoline CV has both the highest base case GHG climate change ($3000) and CAC health

($700) impacts (note that both the absolute and relative costs of these two impact categories are

highly uncertain, because of their sensitivity to the variables discussed subsequently in the Policy

Considerations section, among others). The natural gas pathway results relative to the Gasoline

CV are similar for both climate change impacts (Figure 5-2a) and CO2 emissions (Figure 5-1b),

because CO2 is the dominant GHG emission and the contribution to climate change is the same

regardless of where the emissions occur. The results for the High Methane Emission CNG

Vehicle Scenario are presented in Appendix B. The results for this Scenario and the base case are

equivalent, considering the magnitude of life cycle emissions and the numerous sources of

uncertainty discussed in the following subsection.

CAC health impacts (Figure 5-2b) depend on exposure to NOx, SOx, PM2.5 and VOC emissions,

but are dominated by upstream fuel and vehicle production processes (which is consistent with

findings from Michalek et al.8 and NRC6) because of vehicle emissions control equipment

required by non-plug-in vehicles to meet strict Tier 2 emissions standards. Therefore, the NG-e

BEV has the lowest vehicle operation CAC health impacts (which is consistent with results from

Tessum et al.75 comparing a gasoline CV, CNG CV and NG-e BEV), because of the lack of

tailpipe and fuel tank evaporative emissions, which often occur in populated areas, but life cycle

CAC health impacts are approximately the same ($600) for all natural gas pathways.

Speciation of the CAC emissions is not included in GREET and is beyond the scope of this

study, although it is a relevant issue for future study. For example, the formaldehyde and

benzene fractions of VOC emissions from CNG vehicles are higher and lower, respectively, than

those from gasoline vehicles146 and these differences can potentially result in different health

impacts.

The base case ownership costs (Figure 5-2c) for the three non-plug-in vehicle pathways are

approximately $40,000. This similarity is because higher priced vehicles in this study have lower

operating (fuel and maintenance) costs and is consistent with life cycle ownership costs of non-

plug-in vehicle in Michalek et al.8. The NG-e BEV pathway has the highest cost of ownership,

30% higher than those of non-plug-in vehicle pathways despite having the lowest operating

78

expenses. This high cost is largely due to the $13,000 battery that provides a 125 km (80 mi)

driving range.

▪ Vehicle Operation: mainly from combustion CO2 emissions ▪ Fuel Production: mainly from combustion CO2 emissions, some CH4 from natural gas leakage ▪ Vehicle Production: mainly from combustion CO2 emissions

▪ Vehicle Operation: mainly from tailpipe NOx, PM2.5 and VOC emissions, also tire/brake wear PM2.5 and fuel/windshield washer fluid evaporative VOC ▪ Fuel Production: mainly from NOx and SOx emissions, also gasoline refining PM2.5 and VOC ▪ Vehicle Production: mainly from coal SOx emissions

▪ Fuel: based on $0.75/L gasoline, $0.44/Lge CNG and $0.88/Lge NG-e prices in 2020 ▪ Maintenance: mainly from expenses related to powertrain type ▪ Vehicle Purchase: highest for BEV because of plug-in battery with 125 km driving range

Figure 5-2: Base case life cycle (a) GHG climate change impacts, (b) CAC health impacts

and (c) ownership costs

Notes: GHG climate change (from the quantities of CO2, CH4 and N2O emissions) and CAC health impacts per

vehicle lifetime (from the quantities and geographic distributions of NOx, SOx, PM2.5 and VOC emissions) are based

on results in Figure 5-1.

0

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5.2.2 Incremental Benefit-Cost Analysis

The incremental benefit-cost planes in Figure 5-3a present Monte Carlo analysis results for fuel

switching (the CNG CV replacing the Gasoline CV), energy efficiency (the CNG HEV replacing

the CNG CV), and emissions shifting (the NG-e BEV replacing the CNG HEV). Incremental

benefits and costs are reductions in life cycle air emissions impacts and increases in life cycle

ownership costs, respectively. Tornado plots are presented in Figure 5-3b and 3c to highlight the

sensitivity of the results to deviations in key variables used in the Monte Carlo analysis. All

uncertainty and sensitivity analysis results discussed in the following sections refer to 90%

confidence intervals.

a) Fuel Switching CNG CV replacing

Gasoline CV

Energy Efficiency CNG HEV replacing

CNG CV

Emissions Shifting NG-e BEV replacing

CNG HEV

Life

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$1

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Life Cycle Incremental Air Emissions Impact Benefit ($1000)

b) Life Cycle Incremental Air Emissions Impact Benefit ($1000)

Gasol. Prod. CAC Impact ($/t):

Life Cycle GHG Impact ($/t):

Lifetime VKT (km/vhcl):

Vhcl Oper Impact ($/t):

CNG Fuel Tank (Material):

Plug-in Battery Size (kWh):

NG Power Plant Efficiency(%):

c) Life Cycle Incremental Ownership Cost ($1000)

Plug-in Battery Size (kWh):

Plug-in Battery Price ($/kWh):

Battery Replacement (%):

Life Cycle VKT (km/vehicle):

CNG Fuel Tank (Material):

CV Fuel Economy (L/km)):

Gasoline Price ($/L):

Figure 5-3: Life cycle incremental (a) benefit-cost Monte Carlo analysis, (b) benefit

sensitivity analysis, and (c) cost sensitivity analysis results

Note: Benefit refers to reduction in air emissions impact and cost refers to increase in ownership costs.

-50

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5.2.2.1 Fuel switching in conventional vehicles can provide life cycle air emissions impact benefits without significantly changing life cycle ownership costs

The incremental benefit-cost results for fuel switching overlay the positive x-axis in Figure 5-3a.

This indicates that, compared with a Gasoline CV, a CNG CV can be expected to provide life

cycle air emissions impact benefits (90% confidence interval: $0 to $4000 benefit per vehicle

lifetime), but it is uncertain if there are incremental life cycle ownership costs (-$4000 to $3000).

The magnitude of the air emissions impact benefit from fuel switching is sensitive to both the

quantity of emissions and the specific cost assumed for emissions impacts as shown in Figure

5-3b. Despite CAC emissions having lower base case estimates of air emissions impacts than

GHG emissions (Figure 5-2) the specific cost of gasoline production health impacts ($300 to

$1,300/t PM2.5, $400 to $10,100/t SOx, $1,300 to $69,700/t NOx, $700 to $39,500/t VOC) is a

larger source of uncertainty than the specific cost of climate change impacts ($30 to $110/t

CO2eq.), as shown in the Figure 5-3b tornado plot for fuel switching. Gasoline production can

occur in both rural or urban areas, including Los Angeles County (which includes 6% of US oil

refining capacity147). Compared with other US metropolitan centers, Los Angeles has a large

population exposed to major sources of CAC emissions, combined with geographic and climate

conditions that exacerbate their health impacts.148

It is uncertain if there are incremental life cycle ownership costs of fuel switching from the

Gasoline CV to the CNG CV (range encompasses zero, from -$4000 to $3000). Ownership costs

can increase if the reduction in fuel expenses is less than the additional purchase price of the

CNG fuel system. However, the magnitude of the incremental cost is insignificant relative to the

sensitivity of ownership costs to the uncertainty in real world CV powertrain fuel economy

(Gasoline CV:10 to 16 km/L gasoline), life cycle VKT (150,000 to 460,000 km), and gasoline

price ($0.62 to $1.02/L in 2020) variables shown in Figure 5-3c.

The Monte Carlo analysis results presented here analyze both the base case CNG CV and a lower

priced CNG CV that achieve, respectively, a higher and lower energy equivalent fuel economy

than the Gasoline CV. The difference in price and fuel economy is calculated based on the use

carbon fibre versus stainless steel for CNG fuel tanks. A Low Fuel Economy CNG Vehicle

Scenario is also developed by conducting a Monte Carlo analysis that conservatively assumes

that the lower price/efficiency vehicle is the only option. The results presented in Appendix B

81

show that the incremental air emissions impacts and ownership costs are essentially unchanged

from those of the base case, implying that price and fuel economy (within this range) have a

small impact relative to uncertainties in the other sources of emissions and cost drivers, including

those discussed above.

5.2.2.2 Improving energy efficiency with hybrid electric vehicles can provide life cycle air emissions impact benefits and reduce life cycle ownership costs

Energy efficiency results are below the positive x-axis of the incremental benefit-cost plane in

Figure 5-3a. This indicates that a CNG HEV can be expected to have air emissions impact

benefits ($0 to $3000) over a CNG CV and lower ownership costs (-$5000 to $0), though the

degree of savings is uncertain. In addition to the sources of uncertainty discussed in the previous

subsection, the relative fuel economy between different powertrain technologies affect the ability

for HEV fuel savings to offset the vehicle purchase price premium for an HEV over a CV. For

example, an HEV utilizes regenerative braking to provide a fuel economy advantage over a CV

in stop-and-go city driving conditions, but the technology has little use in steady highway driving

conditions.77 Note that this study uses the fuel economy probability distribution function from

GREET7, which is not disaggregated by driving conditions; however, both Raykin et al.76 and

Karabasoglu et al.77 have found that changes in driving patterns affect the fuel economy of CV

and HEV powertrains differently, and thus the relative performance of an HEV versus a CV.

5.2.2.3 Shifting emissions with plug-in vehicles can increase life cycle ownership costs without providing life cycle air emissions impact benefits

In the incremental cost-benefit plane, results for emissions shifting from tailpipes to power plants

overlay the positive y-axis in Figure 5-3a. This indicates that ownership costs of the NG-e BEV

can be expected to be higher ($1000 to $28,000) than those of the CNG HEV. The uncertainty in

the degree to which ownership costs increase is not primarily due to random error, but variability

in battery size and thus driving range (80-250 km). The plug-in vehicles could have ownership

costs similar to those of non-plug-in vehicles, but would require substantial sacrifices to the

battery size.

It is unclear if there are incremental life cycle air emissions benefits for the NG-e BEV over the

CNG HEV (-$1000 to $2000). The uncertainty in BEV driving range and natural gas power plant

82

efficiency (36% to 60%) overshadows the advantage of mitigating tailpipe emissions in urban

areas. Laser et al.27 determined that BEV driving range (and thus vehicle mass and fuel

economy) can determine whether plug-in vehicles have higher or lower life cycle energy use

than non-plug-in vehicles using the same common primary energy source (based on their

analysis of bioenergy use). Curran et al.28 concluded that the production of electricity in low

efficiency (gas or steam turbine) or high efficiency (combined cycle) power plants can determine

whether natural gas use in a plug-in vehicle results in higher or lower life cycle GHG emissions

than natural gas use in a non-plug-in vehicle.

The results presented in this study are based on comparisons of vehicles using natural gas as a

common primary energy source. However, the concerns over plug-in vehicles raised here (as

compared to non-plug-in vehicles) are more broadly applicable. The NRC,6 Michalek et al.8 and

Tessum et al.75 all found that electricity use in a BEV can result in higher detrimental CAC

emissions impacts than a Gasoline CV. Michalek et al.8 also showed that only plug-in vehicles

with small battery capacities could have life cycle ownership costs comparable to non-plug-in

vehicles.

Although this study uses a BEV to represent plug-in vehicles, the emissions and ownership cost

findings in this section likely also apply to plug-in hybrid electric vehicles (PHEV). PHEVs can

operate in a manner similar to a BEV (charge depleting mode), an HEV (charge sustaining

mode) or a combination of the two (blended mode). Therefore, if the CNG HEV and NG-e BEV

air emissions impacts are similar, those of a CNG/NG-e PHEV can be expected to be similar as

well. The purchase cost premium of a model year 2020 PHEV over an HEV is also unlikely to be

offset by fuel cost savings.3 Unlike a BEV, reducing the size of a PHEV plug-in battery may not

remove ownership cost impediments, because of the additional upfront and operating expenses of

the internal combustion engine.

5.2.3 Policy Considerations

5.2.3.1 Plug-in vehicles should be evaluated as a niche market product for the foreseeable future

Consumers are generally reluctant to pay more for alternative vehicles.149 While both purchase

and life cycle ownership costs of BEVs can be similar to those of non-plug-in vehicles, this

would come with a substantial trade-off in battery size, which limits functionality. A small

83

battery may not impede the functionality of a PHEV, but this could result in a reliance on the

internal combustion engine system, which limits the ability for fuel savings to offset the purchase

cost premium over an HEV (e.g., $800 charger costs, regardless of battery size). Therefore, the

trade-off between ownership costs and electric driving range likely relegates plug-in vehicles to

niche markets for the foreseeable future.150

Previous studies have used US average characteristics (e.g., grid-electricity mix) to evaluate

vehicle air emissions impacts.6, 8 However, this assumes plug-in vehicles operate throughout the

country, which does not accurately reflect their niche market. Approximately 50% of US plug-in

vehicles (BEVs and PHEVs) are sold in San Francisco, Los Angeles, New York City, Seattle and

Atlanta.24 These sales are disproportionately high compared to the 14% and 12% of total US

drivers and vehicle travel, respectively, collectively in these five Metropolitan Statistical

Areas.131 Assumptions based on the characteristics of select regions, instead of national averages,

may be more representative of the air emissions impacts of plug-in vehicles.

5.2.3.2 Plug-in vehicle policies should target urban areas with poor air quality because they can provide local air emissions impact benefits even if they may not provide life cycle air emissions impact benefits

Plug-in vehicle sales forecasts2 suggest that federal policies will likely fail to achieve deployment

targets.151 The sales of plug-in vehicles have largely been limited to nonattainment areas, which

exceed air emissions limits established by National Ambient Air Quality Standards.152 The Clean

Air Act requires the states governing nonattainment areas to develop policies that improve air

quality.153 In the five aforementioned cities where plug-in vehicle sales are concentrated, state-

level incentives are provided for plug-in vehicles to supplement federal tax credits.45 Michalek et

al.8 found that using US average grid electricity in a BEV can lead to higher or lower life cycle

air emissions impacts than gasoline use in a CV or HEV, depending on the counties where the

vehicle emissions occur. While geographic location alone may not determine whether plug-in

vehicles provide incremental life cycle air emissions benefits when natural gas is used as a

common primary energy source (as shown in Figure 5-3b, the Vehicle Operation CAC Impact

tornado plot) plug-in vehicles in these areas can still provide valuable local air quality benefits.

Targeting incentives at regions with poor air quality can limit unintended negative consequences

of plug-in vehicles, which would be exacerbated if these vehicles become a mass market

84

alternative across all geographical areas. This includes an increase in upstream emissions

because of fuel and vehicle production. Plug-in vehicles could also increase vehicle travel

because of the rebound effect caused by substantially reduced marginal (fuel and operating) costs

and reduce the need for vehicle manufactures to improve the fuel economy of non-plug in

vehicles under Corporate Average Fuel Economy regulations.149 Therefore, policies should

encourage the targeted adoption of plug-in vehicles in niche markets, particularly urban areas

with poor air quality; because alternative fuel use in non-plug-in vehicles is likely more cost-

effective at providing life cycle air emissions impact benefits.

Tier 3 vehicle emissions standards have recently been approved and will require automakers to

fully comply by model year 2025 by reducing vehicle operation emissions.139 This will have little

effect on life cycle air emission impacts because CAC vehicle tailpipe and evaporative emissions

are a relatively minor contributor (as illustrated by the results of the Zero CAC Emission Non-

Plug-in Vehicle Scenario presented in Appendix B). Nonetheless, the legislation will have

important local level implications. As non-plug-in vehicle emissions are reduced, so too is the

incremental benefit of using plug-in vehicles. This further emphasizes the importance of

strategically using plug-in vehicles in areas with particularly poor air quality.

5.2.3.3 Climate change regulation may not be sufficient to reduce overall air emissions impacts

Improved air quality can be a co-benefit of reducing GHG emissions. This would be expected

with a simple reduction in fossil fuel consumption.154 However, complexities are introduced

when fuels are substituted for others and/or consumed in substantially different manners. For

example, Tessum et al.75 found that the use of US average-grid or biomass-derived electricity to

replace gasoline as a transportation fuel can result in lower GHG emissions, but higher CAC

emissions. Conversely, emissions control equipment required to meet Tier 2 vehicle CAC

emissions standards slightly reduces fuel economy, which increases GHG emissions.155

The GHG climate change and CAC health impacts do not correlate across the pathways in this

study, which is consistent with the findings of Tessum et al.75 The quantity of GHG emissions is

highly sensitive to changes in the fuel cycle, while the majority of CAC emissions are from

vehicle production. This results in vehicle fuel economy improvements that reduce climate

change impacts without decreasing life cycle health impacts (e.g., CNG CV vs CNG HEV).

85

Unlike GHG emissions, the impact of CAC emissions depends on the geographic location where

they occur, which provides a local advantage for plug-in vehicles that is not captured when using

climate change impacts (or GHG emissions) as metrics.

The relative contribution of GHG emissions to total air emissions impacts is highly uncertain.

The Gasoline CV GHG climate change impacts ($2000 to $10,000) can overshadow or be similar

to those of CAC health impacts ($400 to $4000). There are also other non-health impacts of CAC

emissions,97 such as agricultural crop damages that have not been included in this study.

Therefore, climate change impacts may not only be an incomplete measure, but also a poor proxy

of environmental or social merits of alternative vehicles.

Climate change regulations and CAC emission policies should aim for synergies in reducing

negative impacts. There can be trade-offs as illustrated by emissions control systems in non-

plug-in vehicles; excess air (above stoichiometric air-fuel ratio) reduces GHG emissions by

improving fuel economy but at the expense of higher NOx emissions.156 Consequently, policies

such as Tier 3 tailpipe CAC emissions standards139 are important to have alongside legislation

designed to reduce GHG emissions149 to avoid unintentional increases in either health or climate

change impacts.

5.2.3.4 Carbon pricing or internalizing costs of overall air emissions impacts may not be enough to change consumer behavior

Ownership costs are an order of magnitude greater than the costs of air emissions impacts

evaluated in this study. This is a significant margin even when uncertainties discussed in the

previous subsections are considered. Therefore, internalizing carbon costs or even the overall

costs of air emissions impacts may have little influence on the total ownership costs of driving.

A carbon price can be effective at influencing the economics of electricity generation and

encouraging coal to natural gas fuel switching in power plants.157 Increased natural gas

electricity production would make the pathway comparisons in this study relevant to even more

regions. This change, particularly in regions that produce vehicle components, would have the

co-benefit of reducing life cycle CAC emissions.

86

Chapter 6 Vehicle Design Options To Meet 2025 Corporate Average Fuel

Economy Standards

Corporate Average Fuel Economy (CAFE) standards aim to reduce light-duty vehicle petroleum

use, GHG emissions, and fuel costs by requiring automakers to produce more fuel efficient

vehicles.149 Recent amendments to the legislation for US vehicle model years 2012 to 2025 are

scaled by vehicle footprint (product of wheelbase and track width) to distribute the burden across

the light-duty vehicle market and encourage automakers to maintain or expand the variety of

vehicles consumers can currently choose from. However, legislated fuel economy improvements

can be expected to affect other vehicle attributes, such as size and acceleration performance,

which change over time.33, 158

Studies of the potential impact of future CAFE standards on vehicle attributes have reached

different conclusions. The Regulatory Impact Analysis conducted by the National Highway

Transportation Safety Administration29 (NHTSA) concluded that vehicle prices will increase as

automakers add fuel efficiency technologies to vehicles. Knittel30 concluded that a reduction in

acceleration performance alone can theoretically meet fuel economy targets, but that a reduction

in size is likely required. Conversely, Cheah and Heywood35 found that scenarios in which the

plug-in electric vehicle market share is increased could preserve vehicle size and acceleration

performance.

Knittel30 and Cheah and Heywood35 did not analyze the price of fuel efficiency technologies,

whereas the NHTSA29 excluded elasticity in vehicle size and acceleration demand in response to

changes in price. While these studies provide important insights, the fluctuations in historical

average vehicle price, size, and acceleration suggest that each of these variables should be

analyzed and compared to examine implications of CAFE standards.13 Evolving consumer

interests are expressed through sales of vehicles from different size classes and with options that

may prioritize low price, rapid acceleration or high fuel economy; for example, the current

(model year 2014 to present) Honda Accord is available with a (comparatively) affordable 2.4 L

engine, powerful 3.5 L engine or efficient 2.0 L engine within a hybrid powertrain.63 Automakers

respond to consumer demands by modifying vehicles and their options each generation; for

example, the current (model year 2010 to present) entry level Chevy Equinox has higher fuel

87

economy and lower price, but reduced acceleration and interior volume compared to the previous

generation – a redesign that has resulted in higher annual sales.63, 159, 160 Therefore, a more

complete analysis of fuel economy targets should consider potential changes in vehicle price,

size, and acceleration performance.

Research on CAFE standards by Shiau et al.37 and Whitefoot and Skerlos38 combined economic

and engineering modelling. Shiau et al.37 highlighted the need to balance fuel economy targets

with penalties for violations, while Whitefoot and Skerlos38 advised of the potential for footprint-

based targets to be a moral hazard that encourages the production of larger vehicles. Both studies

arrived at their conclusions by modelling the elasticity of consumer demand for vehicle size and

power, to vehicle price and fuel economy. Neither study considered technology changes nor fuel

economy targets over time; therefore, the studies do not provide insight into how the stringent

future year CAFE standards can be met.

The objective of this case study is to systematically compare vehicle attributes that can be

modified to improve fuel economy and meet model year 2012 to 2025 CAFE standards. Vehicle

design options are developed that modify only one of four attributes on a reference 2012 vehicle:

a crossover sport utility vehicle (the fastest growing vehicle segment in the US) with an average

light-duty vehicle footprint (fuel economy targets are scaled by footprint). This method illustrates

how aggregate changes in an automaker’s fleet (which CAFE standards regulate) could manifest

in a typical vehicle, but not the design limitations of individual vehicles, because the sale of

vehicles that exceed fuel economy targets can facilitate the sale of vehicles that do not meet

targets. Changes in vehicle price, size and acceleration, as well as driving range from the use of

emerging plug-in electric vehicles, are examined. These vehicle design options incorporate

estimates of technological development, based on expected component cost reductions and

introduction of fuel efficiency technologies over time.

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6.1 Methods

This study systematically compares vehicle attributes that can be modified to improve fuel

economy and meet model year 2012 to 2025 CAFE standards. This comparison is based on four

vehicle design options, which are defined here as series of vehicle models that improve fuel

economy over time by changing a particular vehicle attribute. Together, they outline some of the

flexibility automakers have in redesigning vehicles to meet CAFE standards. The designations

and brief descriptions of the options are as follows:

1. Vehicle price option, which solely utilizes added fuel efficiency technologies (e.g.,

lightweight materials and hybrid electric powertrains)

2. Vehicle acceleration option, which modifies the engine power rating and utilizes added

fuel efficiency technologies

3. Vehicle size option, which modifies the vehicle body and utilizes added fuel efficiency

technologies

4. Driving range option, which replaces the conventional gasoline vehicle powertrain with a

battery electric vehicle powertrain and utilizes added fuel efficiency technologies

The vehicle design options meet the increasing CAFE standards, illustrated in Figure 6-1a, by

modifying a Chevy Equinox-like model year 2012 reference vehicle. A crossover SUV is

selected because they are the fastest growing market segment and can have typical vehicle

specifications. For example, the current entry level Chevy Equinox has the same 4.5 m2 (48 ft2)

footprint and 9.3 s 0-96 km/h (0-60 mph) acceleration time as the US model year 2012 light-duty

vehicle average, while its 3700 L (130 ft3) interior volume and 34 mpg laboratory fuel economy

rating are higher and lower, respectively.13, 63 Note that fuel economy values (in mpg) are

presented here to be consistent with CAFE standards but fuel consumption values (in L/100 km)

are also reported in the Appendix C.

The series of vehicle models that comprise the vehicle design options in this study are developed

in two main components; base vehicle models and added fuel efficiency technologies. The latter

is modelled as a continuous range (as opposed to discrete set) of technologies, based on the

individual technologies presented in Figure 6-11b (among others), varying degrees of their use

and different combinations of the technologies. Figure 6-1c shows the price of a base vehicle

model, using the 2012 reference vehicle as an example, and the relationship between vehicle

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price (all prices in constant 2010 US dollars (USD)) and fuel economy when increasing the

utilization of added fuel efficiency technologies. Note that for simplicity Figure 6-1c shows only

one of the three price scenarios analyzed in this study; these scenarios are described at the end of

the Methods section.

The development of the 2012 reference vehicle and the vehicle design options is discussed

below. This is followed by a description of Autonomie and the Vehicle Attribute Model, which

are used to develop the base vehicle models and added fuel efficiency technologies components,

respectively. Additional details on the methods, including vehicle specifications, are provided in

Appendix C.

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Figure 6-1: a) CAFE standards29 for different model years and a 4.5 m2 Chevy Equinox-

like vehicle footprint63, b) potential incremental fuel economy improvements and vehicle

price increases2 from example added fuel efficiency technologies, and c) illustration of the

vehicle price model used to develop the 2012 reference vehicle

Notes: Part a) Vehicle footprint is the product of wheelbase and track width. Part b) the Vehicle Attribute Model

cites the Energy Information Administration2 for the technologies presented here, among others, and applies these

technologies to different degrees (e.g., point estimate data is not provided for the use of lightweight materials, which

consist of a broad range of potential material/component substitutions) and/or combines them (e.g., hybrid electric

vehicle utilizing both regenerative braking and engine start-stop technologies). The potential incremental fuel

economy improvement presented for each technology is for model year 2025 and may not be applicable in prior

years (e.g., lean burn direct injection is forecasted to be commercially available starting in 2020).

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6.1.1 2012 Reference Vehicle

The 2012 reference vehicle is a Chevy Equinox-like vehicle that meets model year 2012 CAFE

standards63 and whose attributes are changed to develop vehicle design options that meet future

year CAFE standards shown in Figure 6-1a. The base vehicle model is developed with a glider

(vehicle without powertrain) based on a first generation (2005-2009) Chevy Equinox and a

conventional gasoline powertrain that provides average model year 2012 0-96 km/h acceleration

time of 9.3 s. Added fuel efficiency technologies, which introduce more recent technological

developments, are applied to the base vehicle model (shown in Figure 6-1c) to meet the model

year 2012 CAFE standard of 31.2 MPG (Figure 6-1a). Note that comparisons of future vehicles

to the 2012 reference vehicle are used to examine the magnitude of potential changes over time;

this study does not presume 2012 vehicles will be produced in model year 2025.

6.1.2 Vehicle Design Options

Vehicle design options are series of vehicle models (illustrated in Error! Reference source not

found.) that modify the 2012 reference vehicle to meet model year 2015, 2020 and 2025 CAFE

standards in a manner in which only one of four vehicle attributes changes; either price,

acceleration, size or driving range. All vehicle design options require changes to both the base

vehicle model and utilization of added fuel efficiency technologies. The price of the base vehicle

model is a function of production costs, which change over time because the cost of producing a

particular component is reduced in subsequent model years. Physical modifications to the base

vehicle model are also required to analyze the different vehicle design options; a base vehicle

model with a different engine power rating, body, or powertrain-type is required to model

changes to vehicle acceleration, size and driving range, respectively. Added fuel efficiency

technologies are then applied to each base vehicle model to meet CAFE standards (in the case of

the vehicle price option) or to take advantage of base vehicle model price reductions over time

and to maintain the total price of the 2012 reference vehicle (in the cases of all other vehicle

design options). The particular vehicle design attributes of each vehicle design option are

calculated either iteratively or via interpolation among the large set of vehicle models produced.

A conceptual overview of the development of each vehicle design option is provided in Error!

Reference source not found.. Although the tools in this study are able to model vehicles with

multiple attribute changes, this is beyond the scope of this study. A more detailed explanation of

the development of the vehicle design options is provided in Appendix C.

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6.1.3 Autonomie and the Vehicle Attribute Model

This study is primarily based on two vehicle modeling tools, both of which have had industry

input from (Chevy Equinox automaker) General Motors during their development. One is

Autonomie vehicle simulation software,96 which estimates vehicle manufacturing costs and

simulates fuel economy and acceleration performance tests based on detailed component

assumptions, including aerodynamic drag coefficient and engine power rating. The software

includes complete vehicle templates with components that can be manually adjusted or replaced

with components from numerous real world vehicles, such as the aforementioned Chevy

Equinox. Autonomie was developed by Argonne National Laboratory, in partnership with

General Motors, as a successor to PSAT, which has been used to support automotive research

and development by companies including General Motors, Ford, Chrysler, Hyundai and

Toyota.96

The second tool is the Vehicle Attribute Model,3 which General Motors developed for the

National Petroleum Council report, Advancing Technology for America’s Transportation.3 The

model estimates vehicle price based on higher level vehicle characteristics, such as size class,

fuel economy and model year. The Vehicle Attribute Model3 uses equations that relate

incremental price to fuel economy improvements over time, applied to vehicles in different

classes with average model year 2008 characteristics, by aggregating added fuel efficiency

technologies. The Vehicle Attribute Model does not detail these added fuel efficiency

technologies, but does highlight the use of lightweight materials, and distinguishes between

hybrid and non-hybrid powertrain technologies. The examples provided in Figure 6-1b are from

the Energy Information Administration2, which the Vehicle Attribute Model cites.

These two tools are used to develop the base vehicle models and added fuel efficiency

technologies components. Base vehicle models are first developed in Autonomie96 based on a

vehicle glider (vehicle without powertrain) and powertrain that approximately represent model

year 2008 components (as noted above, model year 2008 is the basis for the Vehicle Attribute

Model). The manufacturing costs and fuel economy of base vehicle models are then estimated

within Autonomie96 and compiled within a spreadsheet. The prices of base vehicle models are

calculated by adding a 30% markup (from the Vehicle Attribute Model3) to manufacturing costs.

The incremental prices of added fuel efficiency technologies, which introduce post-2008

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developments, are based on Vehicle Attribute Model equations that are presented in Appendix C

and are illustrated in Figure 6-1c and Error! Reference source not found.. We assume the use

of added fuel efficiency technologies changes base vehicle model price and fuel economy, but not

interior volume or acceleration performance. This assumption is further discussed in Appendix

C. Unfortunately, we are unable to verify this assumption because the Vehicle Attribute Model3

does not detail how specific added fuel efficiency technologies are aggregated within its

incremental price vs. fuel economy improvement curves.

The uncertainty in prices of vehicles with known physical specifications is estimated in this

study, which is consistent with the approaches used in Autonomie96 and the Vehicle Attribute

Model.3 Autonomie96 provides manufacturing costs that correspond to the level of risk in

achieving that cost (e.g., lower cost estimates are associated with higher risk of not achieving

them); the high risk case is “aligned with aggressive technology advancement based on the U.S.

DOE [Department of Energy] Vehicle Technologies program,” while the low risk case is

“aligned with original-equipment-manufacturer improvements based on regulations.”105 The

Vehicle Attribute Model3 provides upper and lower bound incremental prices of added fuel

efficiency technologies. The average risk estimates provided in Autonomie96 and the average of

the two bounds from the Vehicle Attribute Model3 are used to produce a mid-price scenario.

High risk prices from Autonomie96 are combined with lower bound prices from the Vehicle

Attribute Model3 to examine a low price scenario, and vice versa.

6.1.4 Comparison with the literature

As discussed in the Introduction, the NHTSA 29, Knittel 30 and Cheah and Heywood 35 have

investigated the ability for changes in vehicle attributes to meet future CAFE standards. Shiau et

al. 37 and Whitefoot and Skerlos 38 examined trade-offs at a point in time, as opposed to changes

over a period of time. However, the methods used in the individual studies do not facilitate a

systematic comparison of the range of vehicle design options in this study. Thus, a novel

approach is developed for the purposes of this study. Although Autonomie 96 and the Vehicle

Attribute Model 3 are each individually developed with/by the automotive industry and

established in the scientific literature, results from the combined use of these models (explained

above) should be evaluated by comparing with past approaches.

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The NHTSA 29 assessment of CAFE standards includes an estimate of the average additional

vehicle price that would result from fuel economy increasing between model years 2012-2025,

while other vehicle attributes remained constant. The estimate was based on an analysis of the

forecasted price of fuel efficiency technologies conducted by Volpe, which is a part of the US

Department of Transportation. This result is compared with those from the vehicle price option

in the Results and Discussion section.

Knittel 30 and Cheah and Heywood 35 analyzed the effect of changes to vehicle size and

acceleration performance on future vehicle fuel economy. The relationships among the variables

are based on a projection of future technological capabilities based on an extrapolation from

historical vehicle characteristics. This method was proposed by An and DeCicco 33, who found

that the product of average annual US vehicle fuel economy (in mpg), interior volume (in ft3)

and the ratio of engine power rating over vehicle mass (in hp/lb, which is an indicator of

acceleration performance) was approximately linear between model years 1977 and 2005, which

suggests steady technological improvements over time. The work by Cheah and Heywood 35 and

Knittel 30 was limited to examining 2016 and 2020 CAFE standards, respectively. Thus, we use

the approach proposed by An and DeCicco 33 (further explained in Appendix C) to estimate how

changes to vehicle size and acceleration performance can be used to meet 2025 CAFE standards

and compare the results with those from the vehicle acceleration option and vehicle size option

in the Results and Discussion section.

The NHTSA 29, Knittel 30 and Cheah and Heywood 35 did not analyze the driving range in which

future battery electric vehicles would be the same price as current gasoline vehicles. Nor do their

methods used facilitate this analysis. Therefore, the vehicle driving range option is a novel aspect

of this study for which there are no comparison data available in the literature.


This case study systematically compares vehicle attributes that can be modified to improve fuel

economy and meet model year 2012 to 2025 CAFE standards, by developing vehicle design

options that modify only one of four attributes on an example reference 2012 vehicle. The results

in Figure 6-2 show that the 66% increase in fuel economy targets from 2012 to 2025 could be

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met with a 10% vehicle price increase (corresponding to a lightweight hybrid electric vehicle), a

31% increase in acceleration time (smaller engine), a 17% decrease in vehicle size (smaller

body), or a 94% decrease in driving range (battery electric vehicle powertrain) relative to the

2012 reference vehicle. Some combinations of these changes would also be feasible, which could

make changes in vehicle price, acceleration, size and/or driving range less perceptible to

consumers. However, the development of combinations that would be expected to be attractive

to consumers is beyond the scope of this study. Although there is uncertainty in future

technology prices, a set of price scenarios agree that expected component cost reductions over

time are insufficient to offset the costs of additional fuel efficiency technologies to meet 2025

fuel economy targets while preserving other 2012 reference vehicle attributes. These findings are

discussed in the following sections, while vehicle specifications are presented in Appendix C.

6.2.1 Meeting 2025 CAFE standards will require changes to vehicle attributes beyond fuel economy

The vehicle price option is shown as the black dashed price curve in Figure 6-2a. CAFE

standards are met in this pathway by increasingly utilizing added fuel efficiency technologies,

such as lightweight materials and gasoline hybrid electric vehicle powertrains. Constant fuel

economy price curves are included in Figure 6-2a to illustrate how vehicle price can decrease

over time, assuming other vehicle attributes remain constant, due to decreasing manufacturing

costs. The constant fuel economy price curves intersect the vehicle price option price curve to

indicate vehicle price and fuel economy, as determined by CAFE standards, over time.

The vehicle price option price curve intersects the 53 mpg 2025 CAFE standard price curve at

$22,970; this indicates a $2,070 (10%) increase in vehicle price compared to the 2012 reference

vehicle may be sufficient to meet 2025 CAFE standards while maintaining other 2012 reference

vehicle attributes. This price increase is consistent with the $1,870-$2,120 range estimated by the

NHTSA.29 In the nearer term, prices remain similar to that of the 2012 reference vehicle because

the cost of the relatively minor fuel economy improvements can be offset by component cost

reductions over that same time period. The price of a vehicle meeting CAFE standards in 2015

and 2020 is $140 (1%) lower and $460 (2%) higher, respectively, than the 2012 reference

vehicle. The reason vehicle prices increase more in later years is because of the accelerating rate

at which CAFE standards increase over time and because the marginal cost of improving fuel

economy increases as the most affordable added fuel efficiency technologies are utilized first.149

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The 2025 vehicle price option model utilizes lightweight materials and a hybrid electric

powertrain, but note that the $2070 price difference discussed above is compared to the $20,900

price of the 2012 reference vehicle and not compared to the $18,400 price of a hypothetical

model year 2025 conventional vehicle with the same 32 MPG fuel economy rating as the 2012

reference vehicle, as shown in in Figure 6-2a.

Although there is uncertainty in future technology prices, different technology price scenarios

agree that expected component cost reductions over time are insufficient to offset the costs of

utilizing added fuel efficiency technologies to meet 2025 CAFE standards. The error bars in

Figure 6-2a show price scenarios that capture 100% of the price estimate ranges from Autonomie

and the Vehicle Attribute Model. The high price scenario (upper bound of error bars) shows that

vehicle prices can remain similar to the 2012 reference vehicle until about 2015 before

increasing by $3520 (16%) to meet 2025 CAFE standards. The low price scenario (lower bound

of error bars) shows that vehicle prices can remain similar for a longer period of time, until about

2020, before increasing by a lesser amount, $570 (3%), to meet 2025 CAFE standards.

Whether or not automakers will produce the vehicles discussed here will depend on the

willingness of consumers to pay for these vehicles. This will likely depend on factors beyond

vehicle attributes. Average US car prices increased rapidly in the 1980’s and 1990’s, but real

prices (based on constant currency) have since fallen.136 This trend is negatively correlated with

US gasoline prices,1 which suggests consumers may adapt to high fuel prices by purchasing

more affordable (e.g., smaller) vehicles, rather than paying higher prices for vehicles with

advanced fuel efficiency technologies. Therefore, future gasoline prices (among other factors)

may play an important role in determining if consumers are willing to pay more for vehicles that

meet CAFE standards. The following sections discuss the vehicle design options for meeting

CAFE standards that do not involve increasing vehicle prices.

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Figure 6-2: New vehicle price curves representing, a) the Vehicle Price Option and vehicles

with constant fuel economy, b) the Vehicle Acceleration Option and vehicles with constant

0-96 km/h acceleration times, c) the Vehicle Size Option and vehicles with constant interior

volumes, and d) the Vehicle Driving Range Option and vehicles with different driving

ranges.

Note: Price curves are based on the mid-price scenario and error bars in a) capture the high and low price scenarios.

The dashed black line shows the vehicle price option and represents the same data in each subfigure. Grey, yellow,

blue and orange reference lines are shown to identify changes in vehicle design option characteristics over time. The

green lines represent vehicle design options that do not require increasing vehicle prices.

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6.2.2 Modifying vehicle acceleration performance or size could preserve other vehicle attributes while meeting CAFE standards

The vehicle acceleration option is shown as the horizontal green (constant) price curve in Figure

6-2b. Constant 0-96 km/h acceleration time price curves generally trend upwards over time

because added fuel efficiency technologies are increasingly utilized to meet CAFE standards, as

opposed to reducing engine power rating (and thus acceleration performance). These constant

acceleration price curves intersect the vehicle acceleration option price curve to indicate the

acceleration performance required to meet future CAFE standards without changing vehicle

price. The black dashed price curve illustrates the vehicle price option (from Figure 6-2a), which

represents the 9.3 s 0-96 km/h acceleration performance of the 2012 reference vehicle. This

constant acceleration price curve falls below the vehicle acceleration option price curve between

model years 2012 and 2017 because the cost of increasingly utilizing added fuel efficiency

technologies is offset by component cost reductions over that same time period, as discussed in

the previous section. Altogether, the price curves in Figure 6-2b map the trade-off between

vehicle price and acceleration over time.

The vehicle acceleration option price curve intersects the 12.2 s 0-96 km/h acceleration price

curve in model year 2025; this indicates a 2.9 s (31%) increase in 0-96 km/h acceleration time

may be required to meet 2025 CAFE standards while maintaining the 2012 reference vehicle

price. A similar increase of 2.5 s (26%) is estimated based on the simplified method proposed by

An and DeCicco 33 and described in the Methods section. A 12.2 s acceleration time is on par

with average US light-duty vehicle performance in 199013 and the current entry level Chevy

Spark,63 which is an example of a vehicle with a design that prioritizes high fuel economy and

low price. Other modern vehicles are even slower, such as the entry level Smart ForTwo (14.1

s).63 Although acceleration performance improvements through much of the history of CAFE

standards suggest that a widespread reversal is not realistic, decreases can be seen in some

vehicle model lines. For example, the current (model year 2010 to present) entry level model of

the Chevy Equinox has slower acceleration than the previous generation.63 However, the

magnitude of the change in 0-96 km/h acceleration time modelled in the vehicle acceleration

option is much more severe, with an increase of 2.9 s versus 0.6 s. Analysis based on the low and

high technology price scenarios also results in relatively severe changes to vehicle acceleration,

with increases of 1.7 s and 7.7 s, respectively.

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The vehicle size option is shown as the dashed green price curve in Figure 6-2c. The figure is

similar to Figure 6-2b, in that constant size price curves are included that generally slope upward

as CAFE standards increase. These include the black dashed price curve, which represents the

vehicle price option and the 4000 L size of the 2012 reference vehicle. The vehicle size option

price curve intersects the 3300 L price curve in model year 2025; this indicates a 700 L (17%)

decrease in interior volume may be required to meet 2025 CAFE standards while maintaining the

2012 reference vehicle price. To put the 3300 L size into perspective, the EPA defines midsize

cars as being between 3100-3400 L.15 A 700 L decrease in size may not be feasible for a

particular vehicle model line, but size reductions are not unprecedented. The interior volume of

the current Chevy Equinox is 300 L (8%) smaller than the previous generation, manifested in the

form of both reduced cargo and second row passenger volumes.160 This real world change is

actually more than the 200 L (5%) change required based on the low price scenario, but much

less than the 900 L (23%) change based on the high price scenario. The vehicle size option

results could also be interpreted as suggesting that a shift in market share from SUVs to cars can

be used to meet 2025 CAFE standards. As with changes to average vehicle price, the likelihood

of a shift in vehicle size may depend on fuel prices. The market share of cars relative to trucks

and SUVs has generally increased with higher gasoline prices and vice versa.1

The vehicle size option results (5% to 23% reduction) are more moderate than those based on the

simplified method (26% reduction) proposed by An and DeCicco 33 and described in the

Methods section This may be because An and DeCicco 33 used interior volume specifications

from the EPA 13, which tracks the metric for cars only. An and DeCicco 33 acknowledged that

their size metric does not adequately capture the emergence of SUVs. The fuel economy of

SUVs may be more sensitive than the fuel economy of cars to relative changes in interior

volume. For example, downsizing from an SUV to a car (as modelled in this study)

simultaneously reduces both vehicle mass and aerodynamic drag 96. However, downsizing from

one car to another may not reduce aerodynamic drag, because of the challenges involved in

designing aerodynamic bodies with small car dimensions 36.

6.2.3 Meeting 2025 CAFE standards by modifying driving range alone would require a drastic compromise in functionality

The driving range option is shown as the dotted green price curve in Figure 6-2d. Constant

driving range price curves that represent vehicles meeting each model year’s CAFE standard, are

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also shown. These include the gasoline-fuelled vehicle price option, which is based on a 600 km

range, and two battery electric vehicles that provide ranges of 25 and 35 km. Constant driving

range price curves representing these different powertrains trend in opposite directions over time

because the vehicle price option requires increasing levels of added fuel efficiency technologies

to meet CAFE standards, while battery electric vehicles exceed CAFE standards and do not

require those technologies (but do utilize them to a lesser degree to improve driving range). The

manufacturing cost of the batteries is also expected to decrease more rapidly than the costs of

other vehicle components.3 High battery costs prevent the design of a model year 2015 battery

electric vehicle for the driving range option because a battery capacity large enough to maintain

the 2012 reference vehicle acceleration and size would increase vehicle price.

The driving range option price curve intersects the 35 km price curve in model year 2025; this

indicates that a 565 km (94%) decrease in driving range may be required to meet 2025 CAFE

standards while maintaining 2012 reference vehicle price, acceleration and size. Analysis based

on the high and low technology price scenarios results in similar findings, with driving ranges of

25 and 45 km, respectively. These driving ranges all represent a substantial compromise in

functionality compared to gasoline vehicles and even battery electric vehicles currently on the

market. For example, the Scion iQ EV has a driving range of 60 km, which is the shortest of any

highway-capable, light-duty vehicle in the US.15 (Coincidentally, the price of a Chevy Equinox-

like battery electric vehicle with a 60 km range would be approximately the same as the model

year 2025 vehicle price option.) However, 35 km is still sufficient for the daily driving needs of

45% of US drivers131 and thus, there may be a niche market opportunity for short range vehicles.

For example, the US army utilizes 4000 neighborhood electric vehicles (which are not highway

capable) that replaced conventional gasoline vehicles and reduced the fuel costs of “campus-type

operations” without increasing vehicle prices.161 Additionally, convenient charging infrastructure

(e.g., at home and workplace) could result in battery electric vehicle driving ranges not needing

to be comparable to those of gasoline vehicles to provide similar functionality. Therefore, future

highway capable electric vehicles that have the same price as current gasoline vehicles may be

attractive to fleet operators or individual consumers who do not require long driving ranges.

It should be emphasized that future battery electric vehicles are not restricted to the driving

ranges discussed here. The 35 km driving range could be higher if vehicles were more expensive,

slower, and/or smaller. Federal tax credits for plug-in electric vehicles are also scaled to battery

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size, which offsets some of the consumer cost of purchasing plug-in vehicles with long driving

ranges.162 Other factors are discussed in the following subsection.

Plug-in hybrid electric vehicle powertrains are a means to extend the driving range of electric

vehicles without increasing the size of costly batteries. However, this type of powertrain requires

an internal combustion engine system (including associated cooling and emissions control

systems) in addition to the plug-in battery system (including charger and associated electronics).

Therefore, it is unlikely that a plug-in hybrid electric vehicle could be priced the same as the

2012 reference vehicle price (before model year 2025 and without subsidies).3 Although, the

analysis of plug-in hybrid electric vehicles is beyond the scope of this study, in the longer term

should component costs decrease sufficiently, they may be an example of a technology which

may one day eliminate the need to change vehicle price, acceleration, size or driving range to

meet fuel economy targets.

6.2.4 Impact of CAFE Standards Depend on How Vehicles Are Designed

CAFE standards are scaled to vehicle footprint to encourage automakers to continue to provide

the range of products consumers can currently choose from. However, this study finds that 2025

CAFE standards are unlikely to be met without modifying some vehicle attributes. The four

vehicle design options developed in this study illustrate not only the flexibility that automakers

have to meet CAFE standards, but also uncertainty policymakers have in predicting the policy’s

societal impact.

The NHTSA Regulatory Impact Analysis29 assumes vehicle prices will increase to improve fuel

economy while maintaining other vehicle attributes. The impacts of the legislation will differ

from those reported by NHTSA should vehicle prices not increase. The financial benefits to

consumers would increase, as fuel savings could be had without increasing upfront expenses.

The life cycle negative environmental impacts could decrease if less energy intensive vehicle

production is required (e.g., cars instead of SUVs with extensive use of lightweight materials), a

less GHG intensive fuel can be utilized (e.g., electricity from low carbon sources instead of

gasoline), or if lower prices facilitate more new vehicle sales (replacing less fuel efficient older

vehicles).7 Negative environmental impacts could also increase if low vehicle prices increase

overall vehicle ownership and exacerbate the rebound effect (increased vehicle travel as fuel

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efficiency improvements lower the marginal cost of driving).163 Public/driver safety impacts

would also depend on the vehicle design changes used to improve fuel economy, in comparison

to other vehicles on the road.164

6.2.5 External Factors Influence How Vehicles Will Be Designed to Meet CAFE Standards

Other policies will influence how automakers respond to CAFE standards. If tax credits persist,

the manufacturing cost of battery electric vehicles may not need to be comparable to gasoline

fuelled vehicles to be financially competitive.162 California’s Zero Emission Vehicle program

requires major automakers to sell battery electric vehicles (or fuel cell vehicles, although these

are not yet commercially available for sale15) in California or partner states.10 Some of these

“compliance cars”64 are sold at a loss, thus making a limited number of battery electric vehicles

more attractive to consumers than what was suggested in the previous section, which assumed a

fixed price markup over manufacturing costs. For example, Fiat-Chrysler has claimed losses of

$14,000 on every Fiat 500e sold,44 and GM has described Chevy Spark EV sales as necessary for

the sales of other vehicles, as opposed to being a financially feasible product on its own.165 The

sale of battery electric vehicles (or any others that exceed CAFE standards) also reduces the fuel

economy improvement required for other vehicles, because the legislation is based on an

automaker’s sales weighted average fuel economy, not the fuel economy of individual vehicles.

Socio-economic factors will also influence how consumers respond to CAFE standards. As

discussed above, high fuel prices are correlated with consumers purchasing smaller and more

affordable vehicles. There are also demographic changes because US drivers are aging and

young adults are less likely to get a driver’s license than in the past.166 Changing priorities may

favor larger vehicles (that provide accessibility and visibility)167 rather than acceleration

performance (because of reduced reaction time).168 Therefore, policies should be developed and

analyzed with an understanding of the historical precedence of changing vehicle attributes and

the continuing influence of external factors.

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Chapter 7 Potential Impact of Corporate Average Fuel Economy Standards

On The Ability For Non-Petroleum Vehicle To Mitigate Greenhouse Gas Emissions

The US transportation sector is highly reliant on petroleum fuels such as gasoline.2 CNG

(compressed natural gas), E85 (85% ethanol, 15% gasoline by nominal volume), and electricity

are among the few alternatives that can be used in consumer light-duty vehicles.15 Non-

petroleum vehicles can help mitigate petroleum use by displacing gasoline vehicles, but it is

important to consider the impact on other environmental metrics, such as GHG (greenhouse gas)

emissions.

The life cycle GHG emissions of alternative vehicle fuels depend on the fuel economy ratings of

the vehicles in which they are used. For example, Campbell et al.23 compared the use of

lignocellulosic biomass-derived ethanol and electricity and concluded the latter was favorable in

terms of life cycle GHG emissions because of the higher efficiency of battery electric vehicles

(BEVs) as compared to internal combustion vehicles (ICEVs). Luk et al.132 and Laser and Lynd27

subsequently conducted similar analyses but did not reach the same conclusion as Campbell et

al.23 and both attributed the discrepancies to differences between the vehicles being compared.

Among other differences, Luk et al.132 increased the fuel economy of ICEVs by assuming they

were designed for dedicated ethanol (instead of gasoline) use, while Laser and Lynd27 reduced

the fuel economy of BEVs by analyzing batteries large enough (in terms of both energy capacity

and mass) to provide driving ranges comparable to ICEVs. Although these assumptions were

made systematically to produce fair comparisons, in practice, when financial and policy

considerations (among others), including high battery prices and non-petroleum fuel vehicle

incentives, can affect vehicle choices.

Corporate Average Fuel Economy (CAFE) standards, which are increasingly stringent until

model year 2025.9CAFE standards also incentivize the production of non-petroleum vehicles by

only accounting for 15% of the energy used when determining compliance with fuel economy

targets. Furthermore, the exclusion of fuel production emissions from complementary GHG

targets particularly benefits BEVs. This incentive can be used by automakers to meet CAFE

standards in lieu of implementing potentially costly fuel efficiency technologies to improve

vehicle fuel economy. Anderson and Sallee32 found that automakers previously added E85 flex

105

fuel (gasoline with 0-85% ethanol by nominal volume) capability to relatively inefficient

vehicles in their fleet to reduce the cost of meeting CAFE standards. Although credits for E85

flex fuel vehicles are being phased out, they will remain for dedicated non-petroleum vehicles.9

The model year 2015 Honda Civic Natural Gas is a dedicated CNG ICEV, which is less fuel

efficient than its gasoline counterpart.15 The CNG vehicle continues to use a less inefficient 5-

speed automatic transmission, while the gasoline versions have been upgraded to a higher priced

(and more efficient) continuously variable transmission.2, 15 These efficiency differences

illustrate how exemptions from increasingly stringent fuel economy targets and credits for

dedicated non-petroleum fuel vehicles within CAFE standards could affect the relative GHG

emissions of vehicles using different fuels.

The literature does not examine the impact of dedicated non-petroleum fuel credits within CAFE

standards on model year 2025 vehicle life cycle GHG emissions. Cheah and Heywood35, and

Knittel30 investigated how different vehicles can be designed to meet 2016 and 2020 CAFE

standards, respectively, but excluded analysis of GHG emissions and dedicated non-petroleum

vehicles. Bandivadikar et al.34 investigated the GHG emissions from vehicles complying with

2016 CAFE standards and also excluded dedicated non-petroleum vehicles. Luk et al.,169 Curran

et al.,28 Burnham et al.91 and Venkatesh et al.,55 each compared GHG emissions from BEVs and

dedicated CNG ICEVs to gasoline vehicles but do not account for the effect of future CAFE

standards.

This study compares the well-to-wheel GHG emissions and ownership costs of a set of

hypothetical model year 2025 vehicles. It explores the implications of future non-petroleum fuel

use, as a result of different vehicle designs. A gasoline ICEV with a fuel economy rating that

meets 2025 CAFE standards is used as a reference vehicle. It is compared to a set of dedicated

non-petroleum fuel vehicles with different fuel economy ratings, with all vehicles exceeding

CAFE standards because of dedicated non-petroleum fuel vehicle incentives within the

standards. Ownership costs and BEV driving ranges are estimated to provide context as these can

influence the vehicles automakers choose to produce and consumers choose to purchase. The

results of this study aim to inform researchers and other stakeholders about the potential impact

of CAFE standards on the ability for non-petroleum vehicles to mitigate GHG emissions by

displacing increasingly fuel efficient petroleum vehicles.

106

7.1 Methods

This study is based on the set of hypothetical model year 2025 petroleum and non-petroleum fuel

vehicles that meet or exceed CAFE standards and are illustrated in Figure 7-1. The development

of these vehicles is described below, with additional details in Table 7-1 and in Appendix D.

The non-petroleum fuels used in this study are CNG and electricity, which are the only two fuels

used by dedicated non-petroleum fuel vehicles currently available in the US for consumer

purchase.15 The fuel cycle GHG emissions and ownership costs (consisting of vehicle price and

net present value of lifetime fuel costs) of the vehicles are compared. Base case estimates are

provided using the assumptions described in the subsections below, and are supported by

sensitivity, uncertainty and scenario analyses. All prices are presented in 2010 USD.

Figure 7-1: illustrative comparison of how petroleum and non-petroleum vehicle fuel

economy can evolve to meet or exceed CAFE standards

Notes: Additional vehicle descriptions are provided in Table 1, added fuel efficiency technologies = lightweight

materials and other technologies that can improve vehicle fuel economy, ICEV = internal combustion engine

vehicle, BEV = battery electric vehicle, CNG = compressed natural gas, NGCCe = electricity from a natural gas

combined cycle facility, MPGe = miles per gallon of gasoline energy equivalent

NGCCe Short-Distance BEV

NGCCe Mid-Distance BEV

NGCCe Long-Distance BEV

CNG High-Efficiency ICEV

Gasoline High-Efficiency ICEVCNG Mid-Efficiency ICEV

CNG Low-Efficiency ICEV

0

20

40

60

80

100

2015 2020 2025

Veh

icle

Fu

el E

con

om

y (M

PG

e)

Vehicle Model Year

Set of BEVs that exceed CAFE standards and whose fuel economies can improve over time with the use of added fuel efficiency technologies, or decrease if battery size (and thus vehicle mass) is increased to improve driving range

Set of CNG ICEVs that exceed CAFE standards because of non-petroleum fuel credits (and exceed complementary tailpipe GHG targets because of low CNG carbon intensity) thus may or may not use added fuel efficiency technologies to improve fuel economies over time

Gasoline ICEV fuel economy improves over time to meet 2025 CAFE standards, which can be achieved with added fuel efficiency technologies

107

Table 7-1: Overview of base case assumptions used in this study

Variable Value Notes

Vehicle Fuel Economy Developed with Autonomie96 and Vehicle Attribute Model7 for model year 2025

Gasoline High-Efficiency ICEV

41 MPGea

(5.7 L/100 km) Gasoline vehicle with fuel economy ratingb that meets 2025 CAFE standards


46 MPGea

(0.16 GJ/100 km) Modifiedc (for CNG use) version of gasoline vehicle with fuel economy ratingb that

meets 2025 CAFE standards

CNG Mid-Efficiency ICEV

36 MPGea




29 MPGea




85 MPGea

(23 kWh/100 km) BEV with weight of battery that provides 100 km driving range,15 which is

comparable to bestselling Model Year 2014 BEV (130 km Nissan Leaf)60 NGCCe Mid-Distance BEV

78 MPGea

(26 kWh/100 km)

BEV with weight of battery that provides 300 km driving range, which is comparable

to near future BEVs planned by major automakers (e.g., 320 km Chevy Bolt)86


70 MPGea

(30 kWh/100 km)

BEV with weight of battery that provides 500 km driving range, which is comparable

to gasoline ICEVs (560-820 km)27

Vehicle Price Developed with Autonomie and Vehicle Attribute Model for model year 2025


$23,000 ICEV with upgraded (lightweight) glider and (hybrid electric) powertrain


$26,000 ICEV with upgraded (lightweight) glider and (hybrid electric) powertrain, and

modificationsc for CNG use

CNG Mid-Efficiency ICEV

$23,000 ICEV with upgraded (lightweight) glider, and modificationsc for CNG use


$22,000 ICEV with modificationsc for CNG use


$49,000 BEV with upgraded (lightweight) glider and 32 kWh battery

NGCCe Mid-Distance BEV




Fuel Production GHGs Obtained from GREET 1 2014 default parameters for the year 2025

Gasoline 20 kg CO2eq/GJ Based on 90% gasoline (16% oil sands/84% conventional crude) and 10% corn ethanol (9% wet mill/91% dry mill, includes both indirect land use change and

biogenic carbon sequestration) by nominal volume

CNG 19 kg CO2eq/GJ Based on US natural gas feedstock (58% conventional/42% shale gas, includes

methane leakage with 100 year global warming potential of 34)

NGCCe 15 kg CO2eq/GJ Based on US natural gas feedstock and a natural gas combined cycle facility, which

is the fastest growing source of electrical generating capacity in the US.2

Fuel Prices Obtained from Annual Energy Outlook 2014 Reference Scenario for the year 2025

Gasoline $25/GJ ($3.00 gged) Based on West Texas Intermediate crude oil spot price of $110 per barrel

CNG $15/GJ Based on Henry Hub natural gas spot price of $5.30 per million Btu

NGCCe $28/GJ Based on US delivered electricity price of $0.10/kWh

Vehicle Lifetime Obtained from various sources

Kilometers 290,000 km Based on GREET default value for SUVs

Years 17 Years Based on median consumer vehicle age from Transportation Energy Data Book

Fuel Discount Rate 8% Based on Vehicle Attribute Model default value aestimate of real world (5-cycle) fuel economy presented on a miles per gallon of gasoline energy equivalent

(MPGe) basis bCAFE standard for vehicle with a Chevy Equinox-like footprint (4.5 m2)63 in model year 2025 is 53 MPG but is

based on unadjusted laboratory (2-cycle) tests, which produce higher ratings than adjusted real world (5-cycle)

estimates9 cCNG modifications facilitate higher engine compression ratios and thus thermal efficiencies137

Notes: CNG = compressed natural gas, NGCCe = natural gas combined cycle-derived electricity, ICEV = internal

combustion engine vehicle, BEV = battery electric vehicle, gge = gallon gasoline equivalent (lower heating value),

all prices in 2010 USD

108

7.1.1 Vehicle Modelling

All vehicles were modelled using software tools developed with industry input, namely

Autonomie96 and the Vehicle Attribute Model.3 For comparability, each vehicle is based on a

base vehicle model with a common (Chevy Equinox-like) glider (vehicle without powertrain)

and powertrain scaled to provide a 0-96 km/h acceleration time of 9.3 s (US Model Year 2013

light-duty vehicle average).13 Base vehicle models are upgraded with added fuel efficiency

technologies, as shown in Figure 7-2. The term added fuel efficiency technologies is used here to

describe the use of lightweight materials and other technologies analyzed in the Vehicle

Attribute Model3 that can be used to improve the fuel economy of the base vehicle model. This

study is concerned with the price of fuel economy improvements provided by added fuel

efficiency technologies but not the specific technologies themselves, which are not fully detailed

by the Vehicle Attribute Model.3 An overview of the vehicle models is provided in the following

subsections, with detailed specifications provided Table 7-1 and in Appendix D.

7.1.1.1 Reference Petroleum Vehicle

The petroleum vehicle in this study is referred to as the gasoline high-efficiency ICEV.

Autonomie96 was used to estimate the fuel economy and price of a base vehicle model with a

conventional gasoline powertrain. The Vehicle Attribute Model3 was used to estimate the prices

of added fuel efficiency technologies required to improve the fuel economy rating to meet 2025

CAFE standards for a Chevy Equinox-sized vehicle footprint (vehicle wheelbase multiplied by

track width). The high-efficiency ICEV uses both lightweight materials and a hybrid electric

powertrain. Note that ICEV is used here to broadly describe vehicles that are propelled by

internal combustion engines, as a means to distinguish them from BEVs, which have the unique

design considerations discussed in the following subsection.

109

Figure 7-2: Relationship between vehicle price and fuel economy for a) internal combustion

engine vehicles and b) battery electric vehicles

0

5000

10000

15000

20000

25000

30000

27 33 38 44 49 55

Veh

icle

Pri

ce (

20

10

USD

)

Fuel Economy (MPG)

Internal Combustion Engine Vehicles

Added Fuel Efficiency Technologies

Gasoline ICEV Base Vehicle Model

a)

ICEV Added Fuel Efficiency Technologies can increase vehicle fuel economy, at the expense of higher vehicle price

0

5000

10000

15000

20000

25000

30000

71 74 77 80 82 85

Veh

icle

Pri

ce (

20

10

USD

)

Fuel Economy (MPGe)

Battery Electric Vehicles


100 km Driving Range Battery

BEV Base Vehicle Model withoutbattery

BEV Added Fuel Efficiency Technologies can increase vehicle fuel economy while decreasing vehicle price, because costly

b)

The potential fuel economy improvement from Added Fuel Efficiency Technologies is greater for ICEVs than BEVs, because ICEVs can use technologies already found in BEVs, such as

110

7.1.1.2 Dedicated Non-Petroleum Vehicles

The non-petroleum vehicles used in this study are similar to the gasoline high-efficiency ICEV.

Differences include powertrain modifications that are required to account for the use of different

fuels. Additionally, the level of added fuel efficiency technologies (based on the Vehicle

Attribute Model3) varies as these vehicles do not require fuel efficiency improvements to meet or

exceed CAFE standards. These differences are further discussed below.

The CNG-fuelled vehicles modelled capture the ability for dedicated CNG vehicles to have

different fuel economy ratings while still exceeding CAFE standards. The CNG ICEVs are

versions of the gasoline high-efficiency ICEV with engine and fuel tank modifications based on

assumptions from the Vehicle Attribute Model and different levels of added fuel efficiency

technologies.3 The low- and mid-efficiency ICEVs are used to illustrate the effect of automakers

adopting different upgrade timelines for petroleum and non-petroleum fuel vehicles , such as in

the aforementioned case of Honda adding a continuously variable transmission to the gasoline

version but not the CNG version of their Civic.15 Beyond highlighting the use of lightweight

materials and distinguishing between hybrid and non-hybrid electric powertrains, the Vehicle

Attribute Model3 does not provide detail on how other specific technologies, such as

transmissions, are factored into its relationship between incremental vehicle price and fuel

economy improvement.

The electricity-fuelled vehicles modelled encompass BEVs with different driving ranges, while

still exceeding CAFE standards. These vehicles consist of base vehicle models with BEV

powertrains developed within Autonomie.96 Each BEV has a different battery size (in terms of

both energy capacity and mass) to provide the different driving ranges. The particular battery

size and level of added fuel efficiency technologies used for each vehicle are determined

iteratively to minimize the vehicle price that achieves the target driving range, as shown in

Figure 7-2.

7.1.2 Fuel Modelling

Fuel production GHG emissions detailed in Table 7-1 are default GREET7 values for 2025

gasoline, CNG and electricity from a natural gas combined cycle facility (NGCCe). The latter is

the fastest growing source of electricity generating capacity in the US.2 Electricity produced

111

from higher and lower carbon intensity energy sources is also examined because the location

where the BEVs are charged and the underlying source of electricity used affects GHG

emissions. Scenario analyses are conducted to illustrate the importance of this source of

variability, as opposed to aggregating a variety of sources based on US grid-average

characteristics.

Fuel use GHG emissions are a function of fuel carbon intensity, vehicle fuel economy and

vehicle methane emissions. Gasoline and CNG contain 72 and 56 g of carbon per MJ,

respectively, based on GREET data.7 GREET assumes gasoline vehicles emit 0.006 g CH4/km,

while methane emissions from CNG vehicles are ten times higher.7 Nonetheless, vehicle

methane emissions are negligible when compared to well-to-wheel GHG emissions.7

7.1.3 Sensitivity, Uncertainty and Scenario Analyses

Variables examined in the sensitivity, uncertainty and scenario analyses are selected based on

results of studies evaluating the uncertainty of GHG emissions from vehicles using natural gas-

derived fuels/electricity (Luk et al.,169Curran et al.,28 Burnham et al.91 and Venkatesh et al.55).

Probability distribution functions for each of the examined variables (in Table 7-1) are detailed

in Appendix D. The variables are examined individually in the sensitivity analysis and

collectively in the uncertainty analysis. The uncertainty analysis is conducted by using Crystal

Ball software and simulating 10,000 trials. These results are presented in the form of 90%

confidence intervals (i.e., 5th to 95th percentile results). A scenario analysis is also conducted to

analyze the use of other non-petroleum energy sources (coal, biomass and landfill gas). This is

done to distinguish this major (in terms of GHG emissions7) source of variability among the

many other sources of uncertainty (e.g., real world fuel economy169).

112


This study compares the ownership costs and well-to-wheel GHG emissions of a set of

hypothetical model year 2025 vehicles that meet or exceed (in the case of non-petroleum

vehicles) CAFE standards. The results show that CAFE standards present an opportunity for

automakers to produce dedicated non-petroleum vehicles that have lower ownership costs than

petroleum vehicles. The results also show that this flexibility may lead to non-petroleum vehicles

that could have higher well-to-wheel GHG emissions than petroleum vehicles on a per km basis,

even if the non-petroleum energy source is less carbon intensive on an energy equivalent basis.

The results are illustrated in Figure 7-3. The ownership costs and well-to-wheel GHG emissions

are presented in Figure 7-3a and Figure 7-3b, respectively. The disaggregated base case

estimates for each vehicle are presented (left column) and the Monte Carlo analysis of

incremental results (center column), which are the differences between each of the non-

petroleum fuel vehicles and the gasoline vehicle (a negative value indicates the non-petroleum

vehicle has lower ownership costs or GHG emissions than the gasoline high-efficiency ICEV).

The column on the right presents sensitivity analysis variables that have the greatest impact upon

the results; two variables are shown for each incremental comparison.

7.2.1 Model year 2025 CNG vehicles can have lower vehicle price and ownership costs than gasoline vehicles that meet CAFE standards

Figure 7-3a shows the base case ownership costs are approximately $31,000 for each of the three

CNG vehicles, which are less the $33,600 costs of the gasoline vehicle. The similarity in CNG

vehicle ownership costs are due to a trade-off between CNG vehicle price ($22,100-$25,700) and

fuel cost ($5,800-$9,100) because added fuel efficiency technologies increase vehicle price while

reducing fuel costs. The difference between the CNG and gasoline vehicle ownership costs is

highly uncertain. The Monte Carlo analysis of the incremental ownership costs shows that

among the CNG vehicles, only the low- and mid-efficiency models are likely (within a 90%

confidence interval) to have lower ownership costs than the gasoline vehicle. The higher vehicle

price of the CNG high-efficiency ICEV may not be offset by reduced fuel costs. The sensitivity

analysis shows that the ownership costs of the CNG high-efficiency ICEV can be higher than

those of the gasoline vehicle, depending on fuel price. Therefore, although automakers could

produce (and consumers could subsequently purchase) CNG vehicles that are more fuel efficient

113

than gasoline vehicles that meet 2025 CAFE standards, there is no clear financial incentive to do

so because less fuel efficient CNG vehicles can have lower vehicle prices (CNG low-efficiency

ICEV) and ownership costs (CNG low- and mid-efficiency ICEVs) while still exceeding CAFE

standards.

The above findings provide insights into the design decisions regarding real world CNG

vehicles. As noted previously, the CNG version of the model year 2015 Honda Civic is less

efficient than the gasoline models.15 This dedicated CNG vehicle does not require fuel economy

improvements to meet CAFE standards and continuing to use older, less efficient technologies

can help avoid increases in vehicle price and potentially total ownership costs. Thus, there is a

financial incentive for automakers to produce (or for consumers to purchase) CNG vehicles that

are less fuel efficient than gasoline vehicles.

114

a)

Base case results

Monte Carlo analysis of incremental results relative to Gasoline High-Efficiency ICEV

Sensitivity analysis of incremental results relative to Gasoline High-Efficiency ICEV

Ow

ne

rsh

ip C

ost

s

b)

Base case results

Monte Carlo analysis of incremental results relative to Gasoline High-Efficiency ICEV

Sensitivity analysis of incremental results relative to Gasoline High-Efficiency ICEV

We

ll-to

-Wh

ee

l GH

G E

mis

sio

ns

Figure 7-3: Base case results and Monte Carlo and sensitivity analyses of the incremental

results relative to the Gasoline High-Efficiency ICEV for a) ownership costs and, b) well-to-

wheel GHG emissions

Notes: List on the right-hand side label indicates sensitivity analysis variables (see Appendix D), ICEV = internal

combustion engine vehicle, BEV = battery electric vehicle, CNG = compressed natural gas, NGCCe = electricity

from a natural gas combined cycle facility, η = efficiency, other sources of electricity are examined with scenario

analyses presented in Appendix D

$0 $20 $40 $60



CNG Mid-Efficinecy ICEV


NGCCe Long-Distance ICEV

NGCCe Mid-Distance ICEV

NGCCe Short-Distance ICEV

(Thousand 2010 USD)

Vehicle Price Fuel Costs (NPV)

-25% 0% 25% 50%

(90% Confidence Interval)

-25% 0% 25% 50%

Fuel Price

CNG Fuel Tank

Fuel Price

CNG Fuel Tank

Fuel Price

CNG Fuel Tank

Fuel Price

Battery Price

Fuel Price

Battery Price

Fuel Price

Battery Price


0 50 100 150 200



CNG Mid-Efficinecy ICEV


NGCCe Long-Distance ICEV

NGCCe Mid-Distance ICEV

NGCCe Short-Distance ICEV

(g CO2eq/km)

Vehicle Use Fuel Production

-60% -30% 0% 30%


-60% -30% 0% 30%

CNG ICEV Mod.

CNG Comp. η

CNG ICEV Mod.

CNG Comp. η

CNG ICEV Mod.

CNG Comp. η

Fuel Economy

NGCCe Gen. η

Fuel Economy

NGCCe Gen. η

Fuel Economy

NGCCe Gen. η


115

7.2.2 Model year 2025 battery electric vehicles can have similar ownership costs as gasoline vehicles that meet CAFE standards

Figure 7-3a shows that the base case ownership cost for the NGCCe short-distance BEV

($33,600) is approximately the same as that of the gasoline high-efficiency ICEV because the

differences in vehicle price and fuel costs can offset each other. This is not the case with the

other two BEVs, which each have higher vehicle prices ($27,400-$50,200), fuel costs ($6,200-

$7,600) and, therefore, ownership costs ($42,000 and $57,000). Vehicle prices are higher for the

BEVs with longer driving ranges, because they have batteries with a larger energy capacity,

increasing vehicle mass and lowering fuel economy. These similarities and differences in

ownership costs, which take into account uncertainties in both battery and fuel prices, are

significant at a 90% confidence level.

The above findings provide insights into design decisions for real world plug-in electric vehicles.

Automakers offer consumers the option of plug-in vehicles with extended driving ranges, at the

expense of lower fuel economy and higher vehicle price. The model year 2015 Tesla Model S is

a BEV available with 330 km ($69,900 and 95 MPGe) and 420 km ($79,900 and 89 MPGe)

driving ranges.15 Consumers also have the option of gasoline plug-in hybrid electric powertrains

as a means of extending plug-in electric vehicle driving ranges in lieu of larger batteries, though

the internal combustion engine system that provides the additional functionality also adds to

vehicle mass and reduces fuel economy. The model year 2015 BMW i3 is available as a BEV

with an all-electric range of 130 km ($42,400 and 124 MPGe) and as a gasoline plug-in hybrid

electric vehicle with a combined gasoline and electric range of 240 km ($46,250 and 117 MPGe

when operating on electricity).15 Thus, the financial attractiveness of plug-in electric vehicles

compared with gasoline vehicles depends in large part on driving range.

Note that the results in Figure 7-3 capture the uncertainty in price for a battery with fixed

physical characteristics, based on forecasted improvements in usable energy density. It is

possible that higher priced batteries with higher energy densities could be used, which would

reduce the battery mass required for a given driving range. However, this may not be desirable

for consumers because of the already high price of BEVs and relatively low price of electricity.

On the other hand, a breakthrough in both battery energy density and price could reduce the

sensitivity of BEV fuel economy to driving range.

116

7.2.3 CAFE standards could lead to the use of non-petroleum energy sources that are less carbon intensive than petroleum use on an energy equivalent basis but result in higher GHG emissions on a per km basis

The use of CNG in model year 2025 vehicles can result in higher or lower well-to-wheel GHG

emissions than the gasoline high-efficiency ICEV (Figure 7-3b). This is despite the fact that

CNG is a less carbon intensive fuel than gasoline on an energy equivalent basis. The base case

GHG emissions from the CNG high- and mid-efficiency ICEVs are lower (119 and 149 g/km,

respectively), while those of the CNG low-efficiency ICEV are higher (185 g/km), than those of

the gasoline high-efficiency ICEV (161 g/km). These differences are significant at a 90%

confidence llevel. The results are relatively insensitive to Vehicle Attribute Model3 assumptions

regarding internal combustion engine and fuel tank modifications for CNG use, which change

the relative fuel efficiencies of the CNG and gasoline ICEVs.

The base case estimates of GHG emissions from the NGCCe short-, mid- and long-distance

BEVs (101, 110 and 122 g/km, respectively) are all lower than those of the gasoline high-

efficiency ICEV. However, only the NGCCe short-distance BEV has lower emissions at a 90%

confidence level, despite the fact that natural gas is a less carbon intensive energy source than

petroleum, and despite the superior fuel economy ratings of the three BEVs compared to the

gasoline high-efficiency ICEV. The BEV GHG emissions are particularly sensitive to NGCCe

production efficiency and fuel economy, based on GREET probability distribution functions.7

The uncertainties in ICEV and BEV fuel economy are assumed to be uncorrelated due to their

different responses to variables such as weather78 and driving patterns.77

Natural gas is the only non-petroleum energy source included in Figure 7-3 but others are shown

in Appendix D. The use of carbon intensive energy sources (coal for electricity production) can

result in all of the BEVs analyzed having higher GHG emissions than the gasoline high-

efficiency ICEV, regardless of driving range. Conversely, the renewable energy sources (landfill

gas and biomass for CNG and electricity production, respectively) can result in all of the non-

petroleum vehicles analyzed having lower GHG emissions than the gasoline high-efficiency

ICEV. These findings are important for BEVs because the carbon intensity of grid-electricity can

vary greatly across the US and the incremental cost of many forms of low carbon electricity is

relatively minor.2 Therefore, the well-to-wheel GHG emissions of non-petroleum fuel use can be

117

higher or lower than that of vehicles using petroleum, depending on the vehicles they are used in

and how the fuels are produced.

7.2.4 The relative fuel economy ratings of vehicles using different fuels will likely change over time

The well-to-wheel GHG emissions of alternative fuels depend on how vehicle designs respond to

increasingly stringent CAFE standards. The results in Figure 7-3a provide context in the form of

vehicle prices and fuel costs, which influence the design decisions of automakers and purchase

decisions of consumers. The results suggest that, for CNG vehicles, there is a financial incentive

to limit the incorporation of fuel efficiency technologies. This includes technologies that may

otherwise be added to gasoline vehicles, to meet increasingly stringent CAFE standards.

Therefore, the assumption made in Luk et al.169 and Curran et al.28 that future CNG vehicles

could be equally or more fuel efficient than future gasoline vehicles may be optimistic. The

results in those studies may be overstating some of the potential environmental benefits of future

CNG vehicles.

There are other factors that will influence the relative fuel economy ratings of BEVs and

gasoline vehicles. Figure 7-3 shows the detrimental impact of increasing driving range on fuel

economy.3 Additionally, unlike with CNG ICEVs, many powertrain (as opposed to glider)

technologies that can improve future gasoline ICEV fuel economy may not be transferable to

BEVs; for example, most current gasoline ICEVs could benefit from the addition of regenerative

braking, which BEVs already have.2 This results in the CAFE standard fuel economy targets

between 2015 and 2025 increasing at a rate that exceeds the maximum potential for improvement

in BEV fuel economy, as estimated by the Vehicle Attribute Model.3 During this time period, the

Energy Information Administration forecasts that the fuel economy of conventional gasoline

vehicles will improve by 44%, while BEVs (with a 160 km driving range) will increase by only

4%.2 Therefore, the fuel economy ratings of BEVs and gasoline vehicles will likely approach

each other over time.

7.2.5 Effectiveness of low carbon fuel standards depends on their ability to capture differences in vehicle fuel economy

The California Low Carbon Fuel Standard (LCFS) aims to capture differences in fuel economy

ratings of vehicles using petroleum and non-petroleum fuels with energy economy ratios.12

118

These ratios are calculated by dividing the fuel economy of new non-petroleum vehicles by the

fuel economy of otherwise similar petroleum vehicles and are periodically revised.12

The California LCFS currently uses an energy economy ratio of 1.0 and 3.4 for CNG and

electricity, respectively.12 The energy economy ratios in this study, using the base case fuel

economy, ranges from 0.7 to 1.1 for CNG and 1.7 to 2.1 for electricity. Should the energy

economy ratio in California’s LCFS be reduced in the future to 0.7 for CNG or 1.7 for electricity,

the results in Figure 7-3b suggest that CNG and NGCCe may no longer be considered a low

carbon fuel (despite having relatively low carbon intensities on an energy equivalent basis),

because their use could result in higher GHG emissions on a per km basis than the gasoline high-

efficiency ICEV.

7.2.6 Stakeholders should be aware of the potential impacts of CAFE standards on the relative performance of alternative vehicles and fuels

Stakeholders examining alternative vehicles and fuels should to be aware of the impact of

increasingly stringent CAFE standards. This may be a particular issue for the evaluation of plug-

in electric vehicles, whose fuel economy advantage (on an energy equivalent basis) over gasoline

ICEVs will likely decrease over time. Nordelöf et al.79 conducted a review of electric vehicle life

cycle assessments and found that temporal assumptions were often not stated. Thus, for example,

when Kennedy170 reviewed the scientific literature and proposed that countries should aim to

reduce electricity generation emissions to 600 t CO2e/GWh or less, so that plug-in electric

vehicles could be used to mitigate GHG emissions by displacing gasoline vehicles, there was a

lack of temporal context. The vehicle fuel economy forecasts by the Energy Information

Administration2 suggest that the breakeven point for electricity generation emissions may be a

moving target. Our results show that even the use of electricity with a carbon intensity of less

than the threshold proposed by Kennedy170 (NGCCe base case GHG emissions of 450 t

CO2e/GWh), could result in higher GHG emissions on a per km basis than a gasoline vehicle

designed to meet 2025 CAFE standards. Findings based on historical or currently available

vehicles could quickly become outdated as increasingly fuel efficient gasoline vehicles are

produced. The results of this study aim to inform researchers and other stakeholders about how

CAFE standards may influence the ability of non-petroleum vehicles to mitigate GHG emissions

by displacing increasingly fuel efficient petroleum vehicles.

119

Chapter 8 Conclusion


emissions and costs of alternative light-duty vehicles in a more robust manner than is done in the

literature. In particular, there is an emphasis on distinguishing among the technological and

policy limitations and opportunities. The findings in this thesis are aimed at contributing to the

scientific literature as well as informing public policy.

8.1 Chapter Conclusions

This thesis consists of four primary research chapters. Each of these chapters is motivated by a

question introduced in Section 1.1. The findings are discussed in the subsections below.

8.1.1 Should light-duty vehicles use ethanol or bio-electricity?

Renewable fuel standards promote the use of ethanol, which has become the dominant non-

petroleum fuel used in US light-duty vehicles. However, ethanol production from biomass (and

thus ability to mitigate greenhouse gas emissions (GHG) and petroleum use) is limited by

feedstock and land availability. The development of plug-in electric vehicles provides another

potentially more efficient means of utilizing biomass as a transportation energy source. Several

studies concluded that the use of bio-electricity as a transportation fuel is favorable because of

the efficiency of plug-in electric vehicles. Chapter 4 is based on a life cycle energy use and GHG

emission inventory analysis of hybrid poplar biomass use in model year 2015 conventional and

emerging vehicle powertrains. All of the E85 (85% ethanol) and bio-electricity pathways

developed have similar life cycle GHG emissions (~5 kg CO2eq./100 vehicle kilometers

travelled), considerably lower (65-85%) than those of reference gasoline and U.S. grid-electricity

pathways. E85 use in a hybrid electric vehicle (HEV) and bio-electricity use in a battery electric

vehicle (BEV) also have similar life cycle biomass and total energy use (~350 and ~450 MJ/100

vehicle kilometers travelled, respectively); differences in well-to-pump and pump-to-wheel

efficiencies can largely offset each other. These energy use and net GHG emissions results

contrast with some of the findings in literature, which report better performance on these metrics

for bio-electricity compared to ethanol. The primary source of differences in the studies is related

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to our development of pathways with comparable vehicle characteristics. Regional

characteristics may create conditions under which either ethanol or bio-electricity may be the

superior option; however, neither has a clear advantage in terms of GHG emissions or energy

use.

8.1.2 Do plug-in electric vehicles provide life cycle air emission impact benefits over internal combustion engine vehicles using the same primary energy source?

Unlike GHG emissions, the effects of criteria air contaminant (CAC) emissions depend on the

location they are released. The Zero Emission Vehicle program promotes the use of BEVs,

which lack tailpipe emissions, and use non-petroleum fuels, which is increasingly natural gas-

derived grid electricity. The ability for automakers to meet regulations by producing low

emission compressed natural gas (CNG) vehicles is being phased out. However, CNG vehicles

may have lower upstream emissions and ownership costs than BEVs. Chapter 5 compares CNG

use directly in a conventional vehicle (CV) and HEV, and natural gas-derived electricity use in a

plug-in BEV. The incremental life cycle air emissions (climate change and human health)

impacts and life cycle ownership costs of model year 2020 non-plug-in (CV and HEV) and plug-

in (BEV) light-duty vehicles are evaluated. Replacing a gasoline CV with a CNG CV, or a CNG

CV with a CNG HEV can provide life cycle air emissions impact benefits without increasing

ownership costs; however, the BEV using natural-gas-derived electricity will likely increase

costs (90% confidence interval: $1000 to $31,000 incremental cost per vehicle lifetime).

Furthermore, eliminating HEV tailpipe emissions via plug-in vehicles has an insignificant

incremental benefit, due to high uncertainties (90% confidence interval: -$1000 and $2000).

Vehicle CACs are a relatively minor contributor to life cycle air emissions impacts because of

strict vehicle emissions standards. Therefore, policies should focus on adoption of plug-in

vehicles in non-attainment regions, because CNG vehicles are likely more cost-effective at

providing overall life cycle air emissions impact benefits.

8.1.3 How can vehicles be designed to meet future Corporate Average Fuel Economy standards?

The costs and benefits of non-petroleum fuelled vehicles are typically quantified in comparison

to petroleum fuelled vehicles. However, recently amended Corporate Average Fuel Economy

(CAFE) standards will require substantial design changes to US light-duty vehicles. Chapter 6 is

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a case study that systematically examines four vehicle attributes that can be modified to improve

fuel economy and meet future year CAFE standards, by developing vehicle design options that

modify only one of the four attributes on a reference model year 2012 Chevy Equinox-like CV: a

crossover sport utility vehicle (the fastest growing vehicle segment) that has an average light-

duty vehicle footprint (fuel economy targets are scaled by footprint). This method illustrates how

aggregate changes in an automaker’s fleet (which CAFE standards regulate) could manifest in a

typical vehicle, but not the design limitations of individual vehicles, because the sale of vehicles

that exceed fuel economy targets can facilitate the sale of vehicles that do not meet targets. The

results show that the reference vehicle can meet the 66% increase in fuel economy targets

between model years 2012 to 2025 with (i) a 10% vehicle price increase (lightweight HEV

powertrain), (ii) a 31% increase in 0-96 km/h acceleration time (smaller engine), (iii) a 17%

interior volume decrease (smaller body), or (iv) a 94% driving range decrease (BEV powertrain),

while other attributes are maintained. Although there is uncertainty in future technology prices, a

set of price scenarios agree that expected component cost reductions over time are insufficient to

offset the costs of additional fuel efficiency technologies needed to meet model year 2025 fuel

economy targets while preserving other vehicle attributes. The research highlights the flexibility

that automakers have to meet CAFE standards, and also the uncertainty in predicting the policy’s

societal impact.

8.1.4 How might CAFE Standards effect the ability for alternative fuel vehicles to mitigate GHG emissions by displacing petroleum fuel vehicles?

The life cycle greenhouse gas (GHG) emissions of alternative fuels dependent upon to the fuel

economy ratings of the vehicles they are used in. Chapter 7 compares well-to-wheel GHG

emissions and ownership costs of a set of hypothetical model year 2025 vehicles. A reference

gasoline vehicle has a fuel economy rating that meets CAFE standards. It is compared to a set of

dedicated CNG vehicles and BEVs with different fuel economy ratings, but all vehicles exceed

CAFE standards because of dedicated non-petroleum fuel vehicle incentives. Ownership costs

and BEV driving ranges are estimated to provide context as these can influence the automaker

and consumer decisions. The results show that CAFE standards present an opportunity for

automakers to produce CNG vehicles that have lower ownership costs than gasoline vehicles.

However, this flexibility may lead to lower efficiency CNG vehicles and BEVs with long driving

ranges that could have higher well-to-wheel GHG emissions than gasoline vehicles on a per km

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basis, even if the non-petroleum energy source is less carbon intensive on an energy equivalent

basis. These results aim to inform stakeholders about how CAFE standards may influence the

ability for non-petroleum vehicles to mitigate GHG emissions, by displacing increasingly fuel

efficient petroleum vehicles.

8.2 Thesis Conclusions

The research in four primary research chapters produced findings beyond insights into the

specific questions introduced in Section 1.1. Two overarching thesis conclusions are discussed

here.

8.2.1 Plug-in electric vehicles do not have a clear technological advantage over non-plug-in vehicles in terms of life cycle energy use, GHG emissions and life cycle air emissions impacts.

When comparing plug-in and non-plug-in vehicles using a common primary energy source

(Chapters 4 and 5), neither has a clear advantage in terms of life cycle energy use, GHG

emissions, or life cycle air emissions impacts. This similarity in the results is despite the superior

efficiency of electric motors over internal combustion engines. Among other reasons, this is

because HEVs are non-plug-in vehicles that benefit from the use of electric motors. Non-plug-in

vehicles are also required to have CAC emissions control systems, which reduces some of the

advantage that plug-in vehicles have regarding tailpipe emissions. Beyond vehicle design, there

is substantial uncertainty in real world vehicle fuel economy and some driving conditions favor

certain vehicle powertrains over others. Additionally, there are upstream variables including

electricity generation efficiency, which can be a major source of uncertainty. Differences in

upstream (fuel production) and downstream (vehicle fuel consumption) efficiencies and

emissions among plug-in and non-plug-in vehicles can offset each other. Therefore, broad

generalizations based on point estimates should be avoided and alternative vehicles/fuels should

be evaluated for the particular conditions under which they will be used.

8.2.2 The environmental and financial competitiveness of alternative fuels will change as the efficiency of petroleum use improves

The environmental and financial competitiveness of alternative fuels depend on the vehicles they

are used in. Although the cost of alternative vehicle technologies will be gradually reduced over

time, so will petroleum vehicle fuel consumption as vehicle fuel economy improves to meet

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CAFE standards. This could reduce the potential GHG emission and fuel cost reductions from

using non-petroleum fuels. Thus, for example, plug-in vehicles will continue to require short

driving ranges to be financially competitive with gasoline vehicles in model years 2020 (Chapter

5) and 2025 (Chapters 6 and 7), despite battery cost reductions.

8.3 Limitations

This thesis is based on assumptions discussed in Chapters 4-7. The thesis conclusions should be

understood within the context of these assumptions. The main limitations of these assumptions

are discussed here.

8.3.1 Research Scope

The range of potential light-duty vehicles is diverse and changes over time. This thesis draws

conclusions from analyzing particular sets of vehicles at particular points in time. Two important

factors to consider include consumer availability and vehicle class.

8.3.1.1 Consumer Availability

The results of Chapter 4 are based on Autonomie vehicle models with comparable

characteristics. A common vehicle glider (vehicle without powertrain) was used to maintain size,

aerodynamic drag and rolling resistance. The different powertrains were scaled to provide a

consistent 0-96 km/h acceleration performance. This approach is in contrast to the methods

commonly used in the literature, which involved selecting real world vehicles often with

dissimilar characteristics. The use of similar vehicles is more appropriate for comparing the

technological potential of alternative fuels and powertrains because they are less likely to

conflate other vehicle design considerations that are shaped by market conditions at a particular

point in time. However, this type of comparison may be irrelevant to consumers who are limited

by the choices available to them. For example, there are no model year E85 HEVs available, let

alone one with comparable characteristics to a BEV.15 There is also relatively little cellulosic

ethanol and bio-electricity being produced.2 The findings in Chapter 4 are important for

policymakers who help shape the future availability of alternative fuels and vehicles but are not

applicable to near-term consumer decisions.

124

Consumer availability is also a limitation of the findings in Chapter 5, although to a lesser extent.

CNG HEVs have been developed,134 but none are currently available.15 However, these vehicles

may be available by the model year 2020 examined. Additionally, natural gas is already an

abundantly used energy source.2

8.3.1.2 Vehicle Class

All of the results in this thesis are based on US light-duty vehicles. Chapters 4 and 5 are based on

a midsize car (typically used in the literature), while Chapters 6 and 7 are based on a small

crossover SUV (to facilitate the analysis of downsizing to a car). Both the absolute and relative

performance of the alternative fuels and technologies will differ for other vehicle classes. For

example, the energy that can be potentially captured by regenerative braking depends on vehicle

mass. Beyond physical differences, there are also differences in applicable policies. For example,

trucks are subject to different fuel economy standards than cars and heavy-duty vehicles are not

regulated by the same emissions standards as light-duty vehicles.9, 139 The location and the

manner in which these different vehicles operate will affect life cycle environmental and health

impacts.

Results may also differ when examining vehicles in other markets, for similar reasons. Local

consumer preferences can result in different characteristics being required to model

representative vehicles. Policy differences include aforementioned vehicle fuel economy and

emissions policies, but also fuel and/or carbon taxes, which can affect the cost-effectiveness of

achieving environmental aims with alternative fuels and technologies. The marginal health

impacts of air emissions are also dependent on background air emission levels and dispersion

patterns.

8.3.2 Research Tools

The complexities of alternative light-duty vehicles in this thesis were modelled with the tools

introduced in Chapter 3. In particular, Autonomie, the Vehicle Attribute Model, GREET and

APEEP are also further discussed in Chapters 4-7. There are limitations to these tools and the

manners in which they are used.

125

8.3.2.1 Autonomie and Vehicle Attribute Model

Chapters 4-7 rely on vehicle models developed within Autonomie96 and/or using assumptions

from the Vehicle Attribute Model.3 Both of the models are based on recent, but not current

technologies and prices. For example, Chapter 4 is based on the midsize car template within

Autonomie,96 which has a seventh generation (model year 2003-2007) Honda Accord sedan

glider. Component specifications for forecasted near-term component mass reductions are

adjusted in Chapter 4, but other technological developments may not be captured in the modelled

relationships between vehicle fuel economy and acceleration. Likewise, the Vehicle Attribute

Model3 is used in Chapter 5 to estimate vehicle prices based on technology price/performance

forecasts that are extrapolated by General Motors from model year 2008 vehicle characteristics.

Longer-term forecasts may not capture the real world development, particularly for less mature

technologies that have greater potential for technological breakthroughs, such as plug-in electric

vehicle batteries.

Autonomie96 and the Vehicle Attribute Model3 are used together in Chapters 6 and 7.

Autonomie96 is used to produce base vehicle models, whose prices and fuel economy ratings are

adjusted based on the prices of fuel efficiency improvements from the Vehicle Attribute Model.3

Other vehicle attributes are assumed to be constant. However, acceleration performance is

dependent on vehicle mass. Therefore, lightweight materials can improve acceleration

performance by reducing the load on the powertrain. Conversely, the addition of components to

improve powertrain efficiencies (e.g., HEV components) can increase mass, which would reduce

acceleration performance. The level of detail in the Vehicle Attribute Model3 makes it unclear if

these opposing factors offset each other or if one is dominant in each of the particular vehicle

models analyzed.

8.3.2.2 GREET and APEEP

Chapter 4 describes the GHG emissions from biomass (poplar tree) production modelled with

GREET.7 Although GREET7 includes the emissions from land use change for more common

ethanol feedstock, such as corn, no value is provided for tree farming. Life cycle GHG emissions

could increase if agricultural production is displaced or decrease if marginal land is used (and

soil carbon is increased). Thus, biomass production emissions will depend on particular

circumstances.

126

Chapter 5 models life cycle CAC emissions with GREET.7 GREET7 assumes vehicle cycle

processes largely occur within US boundaries. US grid-average electricity is assumed to be used

for vehicle production, which results in the vehicle cycle accounting for a substantial portion of

life cycle CAC emissions. However, the automotive sector is concentrated in parts of the US (in

particular the Midwest), which relies more heavily on coal than the US average. Additionally,

even vehicles assembled within the US use components produced elsewhere, including Ontario,

which relies on nuclear and hydro sources for electricity production. The underlying vehicle

component assumptions within GREET7 may also not match the components required to achieve

the performances modelled with Autonomie96 and the Vehicle Attribute Model.3 Therefore,

vehicle production emissions will depend on particular circumstances.

Chapter 5 uses the APEEP97 model to estimate the human health impacts of CAC emissions. The

model uses recent background emissions levels, which may not reflect future levels. Only

domestic health impacts are estimated, while the US air pollutants can affect the health of those

beyond US borders. APEEP97 models emissions dispersion, transformation and its effect on

human heath, but simplifies these processes. The use of more resource intensive models could

produce more robust results (e.g., Tessum et al.75 uses the WRF-Chem model,171 which does not

operate on personal computers). Additionally, the Monte Carlo analysis in Chapter 5 accounts

for uncertainty of CAC emissions impacts by varying the geographic location in which emissions

are released but does not factor the numerous other sources of uncertainty (e.g., the

aforementioned dispersion modelling).

8.4 Future Research

The broad scope of this thesis lends itself to further examination in future research. Some

opportunities are based on the limitations discussed in Section 8.3. Others are arise from the

dynamic nature of the automotive industry and policies it is shaped by.

8.4.1 Future Vehicle Cycle Impacts

Chapters 6 and 7 examined vehicle design options for meeting model year 2025 CAFE standards

but did not examine vehicle (production, maintenance and disposal) cycle impacts. This is

because the energy use and GHG emissions contribution of this life cycle stage is minor

127

compared to gasoline use in current and near-future vehicles. However, the relative contribution

may increase as fuel consumption is reduced beyond model year 2025 requirements.

The use of lightweight materials is highlighted as an example means of improving vehicle fuel

economy by the Vehicle Attribute Model.3 However, the literature does not always agree on the

effect of vehicle weight reduction on life cycle energy use. One source of variability can be

attributed to different ways vehicle mass can be reduced. Different materials are not completely

interchangeable. Individual vehicle components have unique requirements that determine what

alternative materials are or are not suitable and the degree to which mass can be reduced.

Additionally, the effect of mass reductions on vehicle fuel economy depends on the location

within the vehicle; rotational mass reductions (e.g., in wheels) yield greater improvements than

equivalent mass reductions in components that move linearly with the vehicle. The life cycle

implications of some lightweight components have been examined independent of each other

based on unique assumptions.

The potential life cycle merits of lightweight materials also depend on the type of vehicle in

which they are utilized. More aerodynamic vehicles can attribute a greater fraction of their

energy use to mass-related loads. HEVs have regenerative braking to capture some of the kinetic

energy in the vehicle mass that would otherwise be lost during braking, and the recovered energy

assists with the acceleration of the vehicle mass. BEVs have heavy batteries, which provide the

potential for substantial secondary mass reduction because the battery size can be reduced while

still providing the same driving range if vehicle mass is reduced elsewhere to improve fuel

economy. These life cycle financial and environmental impacts of these variables have been

partially examined in the literature, but have not been systematically compared.172,26

8.4.2 Additional Opportunities

Economic modelling of the technologies investigated in this thesis could be conducted. This

thesis discusses discrepancies between technological possibilities and real world consumer

options, and the resulting life cycle environmental impacts. An integrated assessment model that

includes general equilibrium modelling of economic conditions would provide insights into

decisions automakers and consumers will make in response to different policies. This could

inform not only potential demand for particular product options, but changes in vehicle usage,

which are important to understand overall environmental implications.

128

More detailed air emissions modelling would provide further insights into the potential benefits

of plug-in electric vehicles. However, models such as WRF-Chem171 are resource intensive.

Therefore, the scope could be limited to vehicles in California, which has the Zero Emission

Vehicle program10 and where air quality is of particular concern.

Life cycle environmental impacts beyond those from air emissions could also be examined. For

example, GREET7 has recently been updated to include water use. Non-renewable resource use

beyond fossil fuels would also be of interest as alternative vehicles are produced using different

materials.

The scope of the research in this thesis could be extended to other regions and vehicle classes.

China is a particularly fast growing vehicle market. Heavy- and medium-duty vehicles have

particularly poor fuel economy and thus have greater potential for improvement, as compared to

light-duty vehicles. Differences in what, where and how vehicle technologies and fuels are used

provide intriguing opportunities to be explored.

The analysis in this thesis could also be revised in the future. This can be in response to policy

changes or technological developments. For example, the research conducted in this study could

also be extended to hydrogen fuel cell vehicles. This thesis focused on plug-in electric vehicles,

which have become commercially available in recent years. However, automakers are

announcing the release of hydrogen fuel cell vehicles for sale (as opposed to for lease in limited

quantities to meet minimum Zero Emission Vehicle program requirements) in the near future,

such as the Toyota Mirai.173 As the environmental concerns over petroleum use and climate

change grow over time, so too does the importance of understanding the life cycle implications

of alternative light-duty vehicle technologies.

129

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Appendix A: Chapter 4 Supporting Information

Methods Section Details

Research Scope

Figure A-1 illustrates the system boundaries and resources used to develop the pathways outlined

in Table 4-1. They consist of Well-to-Pump, Pump-to-Wheel and Vehicle Cycle processes

introduced within the Methods section of Chapter 4 and discussed here. Not all processes shown

are applicable for each pathway. Infrastructure for these pathways (e.g., bioenergy production

and fueling facilities) is excluded, as the embedded energy is expected to be minor in comparison

to the accounted for energy flows through the facilities throughout its life time. Land use change

impacts are excluded from the main results, for reasons discussed within the Methods section of

Chapter 4.

Life cycle energy use and GHG emissions are examined for the pathways. Biomass energy use

accounts for the lignocellulosic feedstock used directly in the bioenergy pathways considered in

our study, and excludes the minor use of biomass energy currently used for grid-electricity or

gasoline production. Petroleum energy use includes gasoline and diesel use, and associated

upstream losses. Fossil energy use includes petroleum, natural gas and coal energy. Total energy

use is the sum of all energy use, including nuclear and non-biomass sources of renewable energy.

Higher heating values are presented in this study to more accurately reflect the total energy use

in each process. Results are presented based on a functional unit of 100 vehicle kilometer

travelled (VKT), which is commonly used to compare vehicle fuel consumption.

147

Figure A-1: Pathway System Boundaries

Process 1: Biomass Production

Our study uses the latest release of the GREET Fuel-Cycle model7 to obtain data for tree

farming, which is used as a proxy for hybrid poplar short rotation forestry. This information

consists of impacts as a result of onsite energy use and feedstock transportation as well as from

fertilizer (nitrogen, phosphorus, potassium and calcium) and pesticide (herbicide and insecticide)

use. The relevant quantity, GHG emission and energy use data are presented in Table A-1. As

discussed within the Methods section of Chapter 4, both our study and the latest release of the

GREET Fuel-Cycle model7 do not consider the effects of land use change related to tree farming.

The assumed proximate analysis, ultimate analysis and biochemical components of the delivered

whole tree hybrid poplar are detailed in Table A-2, obtained from the NREL databank of

biomass components.174

Legend

Biomass Production (GREET 1 2012)

Chemical Production (MacLean & Spatari 2009)

Reference Fuel Production (GREET 1 2012 rev 2)

Bioenergy Production (AspenPlus)

Vehicle Fuel Consumption (Autonomie)

Direct & Indirect Land Use Change

Vehicle Disposal (GREET 2 2012 rev 1)

Vehicle Production (GREET 2 2012 rev 1)

Infrastructure Construction

Bioenergy Delivery (GREET 1 2012 rev 2)

Well-to-Pump

Pump-to-Wheel

Vehicle Cycle

Out of Scope

148

Table A-1: Biomass production data from the GREET Fuel-Cycle model7

Tree Farming

Fertilizer Production Pesticide

Production

Wood Delivery Total N P2O5 K2O CaCO3

Herb-icide

Insect-icide

Quantity 1000 kg 3.0 kg 1.0 kg 2.0 kg 2.4 kg 0.2 kg 0.0 kg 50 km n/a

CH4 (g) 34 62 1 3 0 8 0 21 131

N2O (g) 0 5 0 0 0 0 0 0 6

CO2 (kg) 23 8 1 1 0 3 0 14 50

GHG (kg CO2eq.) 24 12 1 1 0 3 0.0 15 55

Petroleum Use (MJ) 273 5 4 5 0 19 0 171 478

Fossil Energy Use (MJ) 308 146 8 15 0 39 0 193 709

Total Energy Use (MJ) 309 148 8 17 0 41 0 193 717

Note: All values are per dry t biomass

Table A-2: Physical characteristics for hybrid poplar

Proximate Analysis

Moisture Content 50%

Volatile Matter 84%

Fixed Carbon 15%

Ash 0.87%

Ultimate Analysis

Carbon 51%

Hydrogen 6.0%

Oxygen 42%

Nitrogen 0.17%

Sulfur 0.09%

Ash 0.92%

Higher Heating Value 20 MJ/kg

Biochemical Components

Cellulose 47.3%

Hemicellulose 20.9%

Lignin 24.8%

149

Process 2: Chemical Production

A variety of chemical inputs are required for ethanol production. Data in Table A-3 from

MacLean and Spatari175 are used as an estimate of the GHG and energy use impacts of the

production of these chemicals. Although MacLean and Spatari175 discuss the uncertainty of these

values, they are assumed to have a negligible effect on the final results of our study, due to the

limited quantities of the chemicals used. The quantity of these chemicals required to produce

ethanol is calculated within AspenPlus115 models, as discussed in the following section.

Table A-3: Chemical production data from MacLean and Spatari175

Sul-phuric Acid

Hy-drated Lime

Am-monia

Lime Sodium Hydr-oxide

Per-oxide

Diam-monium

Phosphate

Cellulase

H2SO4 Ca(OH)2 NH3 CaO NaOH H2O2

GHG Emissions (g CO2eq.) 130 930 1740 1240 1450 3900 640 2300

Petroleum Use (MJ) 3 1 1 1 1 1 0 2

Fossil Energy Use (MJ) 4 4 27 6 16 4 9 25

Total Energy Use (MJ) 6 5 28 7 17 5 9 27

Note: All values are per kg chemical

150

Process 3: Bioenergy Production

The bioenergy production models explained in the Methods section of Chapter 4 are further

described here. These include base case and future models for both ethanol and bio-electricity

production, developed using Aspen Plus115 to obtain mass and energy balances. Key assumptions

for the models are compiled in Table A-4, while Table A-5 summarizes subroutines used in

AspenPlus4 to simulate process operations in different configurations.

Table A-4: Bioenergy production data

Base case Future

Ethanol Parameters

Cellulose conversion 87% 95%

Hemicellulose conversion 95% 96%

Glucose fermentation yield 95% 97%

Xylose fermentation yield 85% 97%

Enzymatic loading 8 mg/g cellulose 8 mg/g cellulose

Ethanol recovery 99.9% 99.9%

Steam Electricity Generation Parameters

Boiler efficiency 85% 85%

Steam turbine/Electrical generator efficiency 37.5% 37.5%

Syngas Electricity Generation Parameters

Gasification efficiencies n/a 77.5%

Gas turbine/Electrical generator efficiency n/a 36.6%

Table A-5: Aspen115 subroutines used to develop production models

Process Equipment AspenPlus115 Subroutine

Pretreatment, Enzymatic hydrolysis and Fermenter Rstoic

Solid-Liquid Seperation SEP

Distillation Columns Radfrac

Evaporators Flash2 + heatx

Pumps, Compressor, Valve Pump, Compressor, Valve

Burner RGibbs

Gasification Unit RYield, RGibbs, Rstoic

Gas Turbine RGibbs, Turbine

Steam Turbine Turbine

151

Process Descriptions

Base Case Ethanol Production

Mascoma has developed a process for production of bioenergy from hybrid poplar.116 In the

modeling of the ethanol process, we used Mascoma’s pilot data to define pretreatment,

hydrolysis and fermentation reaction yields. In this process, the biomass is steam exploded at a

temperature about 200° C for 8 min without adding any chemicals, and then the slurry is sent to

enzymatic hydrolysis reactors where the carbohydrate polymers are broken down in to their

corresponding sugar monomers. The temperature of the hydrolysis reactors is kept at 50° C for

48 h. The conversion of cellulose and hemicellulose to their monomer sugars is 87% and 95%,

respectively. The slurry leaving the hydrolysis reactor is filtered and the solid residues (lignin)

are separated. The fermentable sugars (glucose and xylose) content of hydrolyzate is converted

to ethanol in fermenters which operate at 30°C. The conversion of glucose and xylose to ethanol

is 95 and 85%, respectively. The fermenter products are distilled and ethanol is purified. The

ethanol yield is 313 L of ethanol/ dry t biomass. The stillage leaving distillation columns is

concentrated in multistage evaporators. The separated lignin is combusted to generate electricity

and steam in a combined heat and power plant. The heat and power plant consists of a burner,

boiler and turbogenerator subsystem. The generated steam in the heat and power plant is utilized

in pretreatment, distillation and evaporation stages. The electricity generated is sufficient to

satisfy the electricity requirement of the bioethanol process, and excess electricity is exported.

Process flows are detailed in Figure A-2 and Table A-6.

152

PretreatmentEnzymatic

Hydrolysis

Filteration/

Washing

Heat and Power

Plant

Fermentation

Distillation

Evaporation

Hybrid

Poplar

Ethanol

Solid Fuel To Heat

and Power Plant

1 3 4 5

6

7

8

9

10

11

2

Steam

Figure A-2: Flow diagram of the ethanol production process

Note: Stream numbers refer to those itemized in Table A-6 and Table A-7

Table A-6: Base case ethanol production model material flow balance

Stream Number

1 2 3 4 5 6 7 8 9 10 11

Cellulose (t∕h) 9.4 0 9.2 0.8 0.8 0 0 0 0 0 0

Hemicellulose (t∕h) 4.2 0 2.2 1.0 1.0 0 0 0 0 0 0

C5 sugars (t∕h) 0 0 1.6 3.0 0.2 2.8 0.3 0 0.3 0.3 0

C6 sugars (t∕h) 0 0 0 9.3 1.0 8.3 0 0 0 0 0

Ethanol (t∕h) 0 0 0 0 0 0 5.3 5.3 0 0 0

Lignin (t∕h) 4.9 0 4.9 4.9 4.9 0 0 0 0 0 0

Water (t∕h) 19.9 11.5 22.0 61.7 3.2 58.5 60.7 0.0 60.1 3.4 6.9

CO2 (t/h) 0 0 0 0 0 0 0 0 0 0 12.7

N2O (t/h) 0 0 0 0 0 0 0 0 0 0 0.0006

CH4(t/h) 0 0 0 0 0 0 0 0 0 0 0

N2(t/h) 0 0 0 0 0 0 0 0 0 0 52.8

O2(t/h) 0 0 0 0 0 0 0 0 0 0 3.9

Others (t∕h) 2.8 0 2.2 4.5 1.1 3.2 3.7 0 3.7 3.4 1.7

Total (t∕h) 41.2 11.5 42.1 85.2 12.2 72.8 70 5.3 64.1 7.1 78

Note: Stream numbers refer to those illustrated in Figure A-2. Stream 11 represents the flue gas.

153

Future Ethanol Production

In the future ethanol production scenario, the cellulose conversion is expected to increase to 95%

and the lignin leaving the filtration stage is washed with the water coming to enzymatic

hydrolysis reactor. High sugar recovery (97%) is achieved by washing the lignin.176 The sugar

recovery and higher conversion of cellulose increase the ethanol yield of the process to 355

L/dry t biomass. Process flows are detailed in Figure A-2 and Table A-7.

Table A-7: Future ethanol production model material flow balance

Stream Number

1 2 3 4 5 6 7 8 9 10 11

Cellulose (t∕h) 9.4 0 9.2 0.5 0.5 0 0 0 0 0 0

Hemicellulose (t∕h) 4.2 0 2.2 1 1 0 0 0 0 0 0

C5 sugars (t∕h) 0 0 1.6 3.0 0.1 2.9 0.4 0 0.4 0.4 0

C6 sugars (t∕h) 0 0 0 9.3 0.0 9.3 0 0 0 0 0

Ethanol (t∕h) 0 0 0 0 0 0 6.0 6.0 0 0 0

Lignin (t∕h) 4.9 0 4.9 4.9 4.9 0 0 0 0 0 0

Water (t∕h) 19.9 11.5 22.0 61.7 3.2 58.5 60.7 0.0 60.1 3.4 6.2

CO2 (t/h) 0 0 0 0 0 0 0 0 0 0 11.3

N2O (t/h) 0 0 0 0 0 0 0 0 0 0 0.0005

CH4(t/h) 0 0 0 0 0 0 0 0 0 0 0

N2(t/h) 0 0 0 0 0 0 0 0 0 0 47

O2(t/h) 0 0 0 0 0 0 0 0 0 0 3.5

Others (t∕h) 2.8 0 2.2 4.5 1.1 3.2 3.7 0 3.7 3.4 1.5

Total (t∕h) 41.2 11.5 42.1 84.9 10.8 73.9 70.8 6.0 64.2 7.2 69.5


154

Base Case Bio-electricity Production

In this configuration, the biomass is sent to the heat and power plant, and is combusted to

produce steam and electricity (Figure A-3 and Table A-8). The heat and power plant was

described in the near term ethanol production scenario. A small part of the generated electricity

(3%) is utilized internally.122 The excess electricity is assumed to displace U.S average grid

electricity. All calculations for the near-term bioelectricity production were performed based on

the model reported by NREL.122

Burner Boiler Turbine/Generator

Hybrid

Poplar1 2

Steam

Electricity

Figure A-3: Flow diagram of the base case bio-electricity production process

Table A-8: Base case bio-electricity production model material flow balance

Stream Number

1 2

Cellulose (t∕h) 9.4 0

Hemicellulose (t∕h) 4.2 0

Lignin (t∕h) 4.9 0

Water (t∕h) 19.9 50

CO2 (t/h) 0 61.4

N2O (t/h) 0 0.003

CH4(t/h) 0 0

N2(t/h) 0 198

O2(t/h) 0 9.2

Others (t∕h) 2.8 5.0

Total (t∕h) 41.2 323.6


155

Future Bio-electricity Production

For the gasification process, the model of Jin et al.177 was considered. However, due to the high

initial moisture content of hybrid poplar, a dryer is added before the gasifier. In this configuration,

the biomass is dried and its moisture content is reduced to 20% to be utilized in the gasifier; the

dried biomass is then sent to an oxygen-blown gasifier. The biomass is converted to syngas, and

is used to generate electricity and steam. The pressure of gasifier is 30 bar177 and the temperature

of the product syngas is 800°C. The oxygen and nitrogen required in gasification are provided by

employing an air separation unit. The generated syngas is cooled to 350°C and is cleaned before

it is sent to a gas turbine. The electricity generated by steam and gas turbines is 29 and 71% of

total generated electricity, respectively. About 5% of the generated electricity is used internally.

The drying process utilizes 26 % of the total generated steam. The excess electricity is exported to

the US grid. Process flows are detailed in Figure A-4 and Table A-9.

Drying

Hybrid

Poplar Gasification Syngas Cooling Gas Turbine

Electricity

Steam TurbineAir Separation Unit

Electricity

Steam

1 2 6

3

4 5

7

8

Figure A-4: Flow diagram of the high efficiency bio-electricity production process

156

Table A-9: Future bio-electricity production model material flow balance

Stream Number

1 2 3 4 5 6 7 8

CO (t∕h) 0 0 0 0 0 68.0 68.0 0

CO2 (t∕h) 0 0 0 0 0 180.5 180.5 333

CH4 (t∕h) 0 0 0 0 0 21.8 21.8 0

N2O (t/h) 0 0 0 0 0 0 0 0

H2 (t∕h) 0 0 0 0 0 7.0 7.0 0

N2 (t∕h) 0 0 0 18.5 2.0 20.8 20.8 1424

O2 (t∕h) 0 0 0 0.2 79.9 0 0 251.7

C2H4 (t∕h) 0 0 0 0 0 0.7 0.7 0

C2H6 (t∕h) 0 0 0 0 0 0.8 0.8 0

H2S (t∕h) 0 0 0 0 0 0.2 0.2 0

Tar (t∕h) 0 0 0 0 0 0.2 0.2 0

Cellulose (t∕h) 83.4 83.4 0 0 0 0 0 0

Hemicellulose (t∕h) 37.3 37.3 0 0 0 0 0 0

C5 sugars (t∕h) 0 0 0 0 0 0 0 0

C6 sugars (t∕h) 0 0 0 0 0 0 0 0

Ethanol (t∕h) 0 0 0 0 0 0 0 0

Lignin (t∕h) 43.5 43.5 0 0 0 0 0 0

Water (t∕h) 188.9 47.2 49.8 0 0 84.8 84.8 199.1

Others (t∕h) 24.8 24.8 0 0 0 2.4 2.4 26

Total (t∕h) 377.8 236.2 49.8 18.7 81.9 387.2 387.2 2233.8

Temperature (oC) 15 100 236 205 192 764 350 90

Pressure (bar) 1 1 31.6 34.3 31.4 28.8 26.8 1


157

Process 4: Bioenergy Delivery

Ethanol delivery data are obtained from the GREET Fuel-Cycle model.7 The data are an estimate

for ethanol produced within the U.S. and used domestically as a transportation fuel. In Table A-

10, “Ethanol Transportation” assumes that 40% of the fuel leaves the ethanol plant by barge for a

distance of 520 miles (837 km), 40% by rail for a distance of 800 miles (1287 km) and 20% by

truck for a distance of 80 miles (129 km). “Ethanol Distribution” consists of a 30 mile (48 km)

truck transport from a bulk terminal to fueling stations.

Bio-electricity losses are assumed to be delivered to the end user via the U.S. electricity grid.

Therefore, transmission and distribution losses are assumed to be equal to those of grid-

electricity losses. The value of 8% is obtained from the GREET Fuel-Cycle model.7

Table A-10: Ethanol delivery data from the GREET Fuel-Cycle model7

Ethanol Transportation Ethanol Distribution Total

Average Distance (km) 875 48 923

CH4 (g/ 1,000,000 L) 40 9 49

N20 (g/ 1,000,000 L) 1 0 1

CO2 (g/ 1000 L) 28 6 34

GHG Emissions (g CO2eq./1000 L) 29 6 35

Petroleum Use (MJ/1000 L) 344 73 417

Fossil Energy Use (MJ/1000 L) 314 66 379

Total Energy Use (MJ/1000 L) 345 74 418

158

Process 5: Reference Fuel Production

Gasoline and grid-electricity characteristics are assumed to be representative of the U.S. average.

The data in Table A-11 are obtained from the GREET Fuel-Cycle model.7 Data for the year 2015

and 2020 are used for the near term and future scenarios, respectively. The different values for

the two time frames are due to expected changes in legislation, resource mix and process

efficiencies.

The GHG emissions and energy inputs for gasoline increase over time. The proportion of oil

sands derived crude increases as conventional resources deplete and technology improves. Other

changes include improved oil sands recovery efficiencies.

The GHG emissions and energy inputs for grid-electricity marginally decrease over time.

Average electricity generation efficiencies improve, with increased prevalence of combined

cycle (CC) technologies in both coal and natural gas fuelled facilities. The resource mix, shown

in Table A-12, changes moderately, with fossil energy use declining from 66% to 65% of total

energy use and petroleum remaining at 1%.

This data are a primary source in the development of the reference pathways, but are also used in

bioenergy pathways. Gasoline production is also present within the ethanol pathways, to model

the use of E85. Grid-electricity production is also introduced to the ethanol pathways, in the form

of the co-product credit.

Table A-11: Reference fuel production data from the GREET Fuel-Cycle model7

Gasoline (/L) Grid-Electricity (/kWh)

CH4 (g) 5 2

N20 (g) 0 0

CO2 (g) 541 569

GHG Emissions (g CO2eq.) 655 605

Petroleum Use (MJ) 2 0

Fossil Energy Use (MJ) 7 7

Total Energy Use (MJ) 7 8

Table A-12: Grid-electricity resource mix from the GREET Fuel-Cycle model7

Residual oil 1%

Natural gas 26%

Coal 40%

Nuclear power 21%

Biomass 0%

Others 12%

159

Process 6: Vehicle Fuel Consumption

Vehicle fuel consumption data are modeled within Autonomie96 software. Template vehicles are

selected with each of the powertrain technologies sought for our study; specifically the Conv

Midsize Auto 2wd Default (CV), Split Midsize SingleMode HEV 2wd Default (HEV), Series

Engine FixedGear PHEV 2wd Default (PHEV) and Elec Midsize FixedGear 2wd Default (BEV)

models. These pre-built models are modified, as described within the Methods section of Chapter

4, in an attempt to isolate fuel consumption differences to those inherent to the powertrain

technologies. Input vehicle characteristics are detailed in Table A-13 and calculated vehicle

characteristics are itemized in Table A-14.

PHEV and BEV battery capacities are adjusted to achieve the desired driving ranges. Battery

capacity is increased while maintaining the minimum voltage of the electric motor. Additional

characteristics not outputted by Autonomie9 are calculated based on individual Johnson Controls

Saft VL41M cell properties, assumed by the software. Voltage is calculated as the number of

cells in series multiplied by the individual cell nominal voltage of 3.6 V. Total energy capacity is

calculated as the number of cells in total multiplied by the individual cell energy capacity of 41

Ah. The usable energy capacity for charge depleting operation is the difference between the

default target state-of-charge (SOC) of 30% and the initial SOC of 90% (default) for the PHEV

and 100% (assumed) for the BEV models. [The different SOC swings are due to battery

degradation from charge cycling, which would otherwise be more severe in PHEVs that are

designed to be depleted regularly. Unlike a PHEV, a depleted BEV battery results in a stranded

vehicle.] Battery mass is calculated based on an individual cell mass of 1.07 kg and 25%

additional mass in the form of packaging, as described within Autonomie.96

Table A-13: Vehicle design and performance characteristics

Base Case Vehicle Future Vehicle

Acceleration (0-100 km/h) 9 sec +/- 0.3 sec 9 sec +/- 0.3 sec

Glider mass 891 kg 792 kg

Frontal area 2.2 m2 2.2 m2

Drag coefficient 0.26 0.25

Rolling resistance 0.0075 0.0070

Internal Combustion Engine specific power 880 W/kg 990 W/kg

Electric motor specific power 1250 W/kg 1600 W/kg

Electric battery charging efficiency 85% 85%

E85/gasoline ICE energy efficiency 107% 107%

City/highway driving conditions 55%/45% by VKT 55%/45% by VKT

160

Table A-14: Mass and battery characteristics of vehicle models created in Autonomie96

Base Case Vehicles Future Vehicles

CV HEV PHEV BEV CV HEV PHEV BEV

Internal Combustion Engine Size (kW) 115 90 70 n/a 105 85 60 n/a

Electric Motor Size (kW) n/a 75 100 110 n/a 60 90 95

Total Vehicle Mass Input (kg) 1698 1810 1911 1980 1570 1628 1709 1765

Li-ion Battery Size (# cells) 0 75 108 396 0 75 84 312

Li-ion Battery Mass (kg) 0 35 144 530 0 35 112 417

Li-ion Battery Capacity (kWh) 0 2 16 58 0 2 12 46

State of Charge Swing (%) n/a n/a 60 70 n/a n/a 60 70

Charge Depleting Capacity (kWh) n/a n/a 10 41 n/a n/a 9 37

Note: n/a = not applicable to vehicle model

The vehicle models are simulated on the FTP (Federal Test Procedure) and HWFET (Highway

Fuel Economy Driving Schedule) driving cycles, created by the U.S. Environmental Production

Agency (EPA) to represent performance under ideal city and highway driving conditions,

respectively.178 These results are detailed in Table A-15. Driving cycle fuel efficiencies are then

adjusted using Equations A-1 and A-2, as prescribed by the U.S. EPA178 5-cycle methodology to

closer represent actual vehicle performance, to a maximum adjustment of 30%.179

Equation A-1: City driving fuel economy adjustment factor

5 − cycle City =1

0.003259 +1.1805

FTP

Equation A-2: Highway driving fuel economy adjustment factor

5 − cycle Highway =1

0.001376 +1.3466HWFET

Table A-15: Un-weighted fuel consumption results of vehicle models created in

Autonomie96

Drive Cycle

Drive Mode Units

Base Case Vehicles Future Vehicles

CV HEV PHEV BEV CV HEV PHEV BEV

City (FTP) CS Efficiency (MPG) 29.9 55.8 45.5 n/a 32.9 63.7 51.4 n/a

CD Efficiency (Wh/mi) n/a n/a 217.6 220.4 n/a n/a 193.8 194.3

Highway (HWFET)

CS Efficiency (MPG) 45.7 53.4 45.4 n/a 50.0 59.6 51.0 n/a

CD Efficiency (Wh/mi) n/a n/a 223.9 223.0 n/a n/a 202.1 199.7

Note: CS = battery charge sustaining, CD = battery charge depleting

Driving ranges discussed in the Methods section of Chapter 4 are calculated based solely on the

Urban Dynamometer Driving Schedule (UDDS) driving cycle used in other studies.8

161

Fuel consumption ratings presented in the Methods section of Chapter 4 reflect the different

energy densities and efficiencies of use of each fuel in the vehicle. Energy densities are as

follows; gasoline 34.7 MJ/L, pure ethanol 23.6 MJ/L, and E85 25.7 MJ/L.7 The breakdown of

vehicle emissions and consumption of each fuel is shown in Table A-16.

Table A-16: Vehicle emissions and fuel consumption

Reference Vehicles (/100 VKT) Bioenergy Vehicles (/100 VKT)

Gasoline CV

Gasoline HEV

Grid-e/ Gasoline

PHEV Grid-e

BEV E85 CV E85 HEV Bio-e/

E85 PHEV Bio-e BEV

CH4 (g) 1 0 0 0 3 2 1 0

N2O (g) 1 0 0 0 3 2 1 0

CO2 (kg) 21 14 6 0 15 10 4 0

GHG (kg CO2eq.) 21 14 6 0 16 11 5 0

Gasoline (MJ) 313 213 88 0 75 51 21 0

Ethanol (MJ) 0 0 0 0 214 145 60 0

Electricity (MJ) 0 0 54 83 0 0 54 83

Total Energy (MJ) 313 213 142 83 289 195 135 83

Process 7: Vehicle Cycle

The GREET Vehicle-Cycle model7 is used to estimate environmental impacts attributed to

vehicle production and disposal. Vehicle and battery masses are obtained directly from the

aforementioned Autonomie models, detailed in Table A-14. Vehicles are based on Conventional

Material characteristics and default assumptions are used. Major material inputs include steel,

wrought aluminum, cast aluminum, lead and nickel, which are comprised of 26%, 11%, 85%,

73%, and 44% recycled material, respectively. Vehicles are assumed to operate for 250,000

lifetime VKT and lithium ion batteries are assumed to last the life of the vehicle. Energy use and

GHG emissions for vehicle disposal are small in comparison to energy use and GHG emissions

for the total vehicle cycle, and are assumed to be the same for each vehicle; therefore no specific

considerations were given to lithium ion battery disposal, which can include recycling to reduce

virgin material inputs or re-purposing for non-vehicle uses. The GHG emissions and energy use

for vehicle cycle stages are compared in Table A-17.

162

Table A-17: Vehicle cycle results for vehicle models based on GREET Vehicle-Cycle model7

CV HEV PHEV BEV

Components Production (w/o battery)

CH4 (kg) 23 25 24 21

N20 (kg) 0 0 0 0

CO2 (t) 6 7 7 5

GHG Emissions (t CO2eq.) 7 7 7 6

Petroleum (mmBtu) 8 8 7 7

Fossil Energy (mmBtu) 81 85 84 70

Total Energy (mmBtu) 87 91 90 75

Assembly, Disposal and Recycling

CH4 (kg) 5 5 5 5

N20 (kg) 0 0 0 0

CO2 (t) 1 1 1 1





Battery Production

CH4 (kg) 0 0 2 8

N20 (kg) 0 0 0 0

CO2 (t) 0 0 1 2





Fluids Production

CH4 (kg) 2 2 2 1

N20 (kg) 0 0 0 0

CO2 (t) 1 1 1 0





Total

CH4 (kg) 31 32 34 36

N20 (kg) 0 0 0 0

CO2 (t) 8 9 9 9





Note: All values are per vehicle.

163

Results

Fuel Production Energy Balance

Fuel production energy balance results are shown in Figure A-5. Energy inputs include the

energy content of each fuel, to provide an energy input/output ratio. In the well-to-wheel

comparisons, the energy content of the fuel is allocated to the pump-to-wheel stage.

Figure A-5: Fuel production energy balance

Note: Co-product credits result in negative energy inputs

-1

0

1

2

3

4

5

Gasoline Grid-e Ethanol Bio-e

Ener

gy B

alan

ce (

MJ/

MJ

fuel

at

pu

mp

or

plu

g)

Other

Biomass

Coal and Natural Gas

Petroleum

164

Life Cycle Results

Detailed pathway results are shown in Table A-18. An aggregated set of these data is presented

in Figure 4-1.

Table A-18: Life cycle pathway results


Gasoline CV

Gasoline HEV

Grid-e/ Gasoline

PHEV Grid-e

BEV E85 CV

E85 HEV

Bio-e/ E85 PHEV

Bio-e BEV

GHG Emissions (kg CO2eq. /100 VKT)

Carbon Absorption 0 0 0 0 -54 -37 -35 -31

WTP Emissions 6 4 11 14 45 30 33 32

Co-product 0 0 0 0 -4 -3 -1 0

PTW 21 14 6 0 16 11 5 0

VC Battery 0 0 0 1 0 0 0 1

Non-Battery 3 4 4 3 3 4 4 3

Total 31 22 21 18 6 5 5 5

Biomass (MJ / 100 VKT)

WTP 0 0 0 0 366 247 266 252

PTW 0 0 0 0 214 145 114 83

Total 0 0 0 0 580 392 380 335

Petroleum (MJ / 100 VKT)

WTP Energy Use 22 15 8 3 24 16 11 7

Co-product 0 0 0 0 -1 -1 0 0

PTW 313 213 89 1 75 51 21 0

VC Battery 0 0 1 3 0 0 1 3

Non-Battery 7 6 6 3 7 6 6 3

Total 342 235 104 10 104 73 39 13

Fossil Energy (MJ / 100 VKT)

WTP Energy Use 64 43 122 161 53 36 22 11

Co-product 0 0 0 0 -47 -32 -13 0

PTW 313 213 133 70 75 51 21 0

VC Battery 0 1 4 13 0 1 4 13

Non-Battery 44 45 45 36 44 45 45 36

Total 421 303 304 280 124 100 78 60

Total Energy (MJ / 100 VKT)

WTP Energy Use 65 44 143 193 420 284 288 263

Co-product 0 0 0 0 -56 -38 -16 0

PTW 313 213 142 83 289 195 135 83

VC Battery 0 1 4 15 0 1 4 15

Non-Battery 47 48 48 38 47 48 48 38

Total 425 306 337 329 699 490 459 400

Note: Grid-e = grid-electricity, Bio-e = bio-electricity, CV = conventional vehicle, HEV = hybrid electric vehicle,

PHEV = plug-in hybrid electric vehicle, BEV = battery electric vehicle, VKT = vehicle kilometers traveled, WTP =

well-to-pump, PTW = pump-to-wheel, VC = vehicle cycle, Carbon absorption = CO2 offset during feedstock

growth, Co-product = credit from grid-electricity offset

165

Mitigation Results

GHG, fossil energy and petroleum mitigation results associated with bioenergy displacing

reference fuels are compiled in Table A-19. The GHG mitigation results are illustrated in Figure

4-2.

Table A-19: GHG emissions, fossil energy and petroleum mitigation results

Ethanol (in the form of E85) Bio-electricity

U.S. Average

Grid-e

Renewables-based Grid-e

Coal-based Grid-e

U.S. Average Grid-e

Renewables-based Grid-e

Coal-based Grid-e

GHG Mitigation (t CO2eq. /dry t biomass)

0.84 0.78 0.94 0.78 0.43 1.37

Fossil Energy Mitigation (GJ /dry t biomass)

10.2 9.6 10.8 13.1 7.4 18.3

Petroleum Energy Mitigation (GJ /dry t biomass)

8.2 8.2 8.2 -0.2 -0.4 -0.4

166

Scenario Analysis

Co-product

Figure A-6 and Figure A-7 compare the impact of different co-product scenarios on life cycle

total energy use and net GHG emissions, respectively. Without any co-product credit, total

energy use and GHG emissions of pathways utilizing ethanol are increased, but the relative

performance of the bioenergy pathways remains similar. A more favorable scenario assumes

excess heat from bioenergy production can be utilized in an adjacent facility. This scenario is

modeled based on co-product assumptions from literature.112 For both ethanol and bio-electricity

production it is assumed that the excess process heat is utilized to generate steam (at 63%

efficiency), which offsets natural gas use in an adjacent facility requiring thermal energy.112

Utilizing this process heat reduces the total energy use and GHG emissions for all bioenergy

pathways. GHG emissions become negative for the latter scenario for all of the bioenergy

pathways. This indicates the GHG emissions offset by the co-product exceed those emitted by

the bioenergy pathway. The GHG emissions are much more sensitive than total energy use to

these co-product assumptions, because of the GHG intensity (energy basis) of the displaced grid-

electricity and natural gas, as compared to the bioenergy alternatives. Ethanol pathway GHG

emissions can become higher or lower than those of the bio-electricity pathways depending upon

the co-product assumptions. Regardless, bioenergy pathways continue to have GHG emissions

lower than those of reference pathways.

Figure A-6: Life cycle sensitivity of total

energy use to co-product scenarios

Figure A-7: Life cycle sensitivity of net

GHG emissions to co-product

0

500

1000

Gas

olin

eC

V

E85

CV

E85

HEV

Bio

-e/E

85

PH

EV

Bio

-e B

EV

Tota

l En

ergy

(M

J/1

00

VK

T)

Base Case

No Co-Product

Combined Heat and Power

-20

0

20

40

Gas

olin

eC

V

E85

CV

E85

HEV

Bio

-e/E

85

PH

EV

Bio

-e B

EV

Net

GH

G E

mis

sio

ns

(kg

CO

2eq

./1

00

VK

T)

Base Case

No Co-Product

Combined Heat and Power

167

Bioenergy Production Efficiency

Figure A-8 and Figure A-9 compare the impact on life cycle total energy use and net GHG

emissions, respectively, of using different bioenergy production efficiencies. Compared to the

base case assumptions in our study, Campbell et al.23 assumes a higher ethanol yield and bio-

electricity generation efficiency. Substituting bioenergy production efficiencies from Campbell

et al.23 into our study’s models, does not significantly affect the relative results among the

pathways. The GREET fuel-cycle7 model assumes a higher ethanol yield but a lower bio-

electricity generation efficiency than our study. Substituting GREET fuel-cycle7 assumptions into

our study’s model results in the E85 HEV pathway having the lowest total energy use, but the

result still being similar to the Bio-e BEV pathway.

The future “high efficiency” bioenergy production models are developed for our study in

AspenPlus.115 Substituting these assumptions into the pathways suggests a potential for bio-

electricity production efficiency to improve greatly, by utilizing integrated gasification combined

cycle technology. GHG emissions are less sensitive than total energy use to bioenergy

production efficiency assumptions, because changes in biomass feedstock use are largely offset

with changes in carbon absorption during feedstock growth.

Figure A-8: Life cycle total energy use

results for bioenergy production efficiency

scenarios

Figure A-9: Life cycle net GHG emissions

results for bioenergy production efficiency

scenarios

0

500

1000

Gas

olin

e C

V

E85

CV

E85

HEV

Bio

-e/E

85

PH

EV

Bio

-e B

EV

Tota

l En

ergy

(M

J/1

00

VK

T)

Base Case

Campbell et al. Bioenergy Production

GREET fuel-cycle model Bioenergy Production

High Efficiency Bioenergy Production

23

7

0

20

40

Gas

olin

e C

V

E85

CV

E85

HEV

Bio

-e/E

85

PH

EV

Bio

-e B

EV

Net

GH

G E

mis

sio

ns

(kg

CO

2eq

./1

00

VK

T)

Base Case

Campbell et al. Bioenergy Production

GREET fuel-cycle model Bioenergy Production

High Efficiency Bioenergy Production

23

7

168

Vehicle Models

Figure A-10 and Figure A-11 examine the impact of vehicle fuel efficiency assumptions on life

cycle total energy use and net GHG emissions, respectively. Fuel cycle results (both well-to-

pump and pump-to-wheel stages) of our study are modified through substituting alternative

vehicle fuel consumption data from other studies into our models, while vehicle cycle

(production/disposal) impacts remain unchanged. Substituting the lower efficiency ethanol

fuelled vehicles, and higher efficiency bio-electricity fuelled vehicles used by Campbell et al.23

into our models results in a substantial difference in energy use. As described in the Results and

Discussion section of Chapter 4, this illustrates that the bio-electricity pathways are “favored” by

the vehicle fuel consumption models used by Campbell et al..23 Similarly, fuel consumption

models from the GREET fuel-cycle model7 and the future high efficiency vehicles modelled in

our study with Autonomie are examined. Substituting GREET fuel-cycle model7 fuel economy

assumptions into our models results in total energy use that is higher or similar to the base case

values shown, whereas the future high efficiency vehicles result in reduced total energy use for

all pathways, but relative results remain similar for these assumptions. GHG emissions are less

sensitive than total energy use to vehicle fuel consumption assumptions, because changes in

biomass feedstock use are largely offset by changes in carbon absorption during feedstock

growth.

Figure A-10: Life Cycle total energy use

results for vehicle efficiency scenarios

Figure A-11: Life Cycle net GHG emissions

results for vehicle efficiency scenarios

0

500

1000

Gas

olin

e C

V

E85

CV

E85

HEV

Bio

-e/E

85

PH

EV

Bio

-e B

EV

Tota

l En

ergy

(MJ/

10

0 V

KT)

Base Case

Campbell et al. Vehicles

GREET fuel-cycle model Vehicles

High Efficiency Vehicles

23

7

0

20

40

Gas

olin

e C

V

E85

CV

E85

HEV

Bio

-e/E

85

PH

EV

Bio

-e B

EV

Net

GH

G E

mis

sio

ns

(kg

CO

2eq

./1

00

VK

T)

Base Case

Campbell et al. Vehicles

GREET fuel-cycle model Vehicles

High Efficiency Vehicles

7

23

169

Appendix B: Chapter 5 Supporting Information

Supplemental Methods

The approach summarized in the Methods section in Chapter 5 is elaborated upon here.

Air Emissions Impacts

Air emissions impacts are determined with a life cycle assessment. A life cycle inventory

analysis is first conducted for both fuel and vehicle cycle activities. This is followed by an

estimate of the NPV of life cycle impact of greenhouse gas (GHG) and criteria air contaminant

(CAC) emissions.

Fuel Cycle Inventory Analysis

The fuel cycle consists of fuel production (gasoline, CNG, and NG-e, including feedstock

production) and consumption (during vehicle operation) activities. All pathways are created

within GREET 17 for the year 2020. Natural gas and petroleum feedstock are assumed to be from

the default forecasted mix of conventional and unconventional sources. Average tailpipe

emissions from GREET 17 are scaled to increase as vehicles age according to MOVES (Motor

Vehicle Emission Simulator).141 Electricity generation emissions are based on GREET-

calculated emissions factors, as opposed to emissions factors based on EPA (Environmental

Protection Agency) and EIA (Energy Information Administration) databases. This results in

emissions representing forecasted technology mixes (e.g., proportion of combined cycle

facilities) and not historical performance.

Vehicle Cycle Inventory Analysis

The vehicle cycle consists of vehicle production (parts production and assembly), maintenance

(tire and fluids replacement) and end-of-life processes (disposal and recycling). Gasoline and

plug-in vehicle models were created within GREET 27 based on conventional materials. Vehicle

mass is adjusted for CNG vehicles based on fuel tank assumptions in Table B-1. Plug-in lithium

ion batteries are expected to last the life of each vehicle under base case the assumptions.7

170

Life Cycle Impact Assessment

The NPVs of climate change and health impacts are calculated based on GHG and CAC

emissions, respectively. Air emissions impacts are the product of life cycle emissions quantities

and specific impact costs (calculated with Equation 5-1). To represent the high level of inherent

uncertainty in these models, a wide range of specific impact cost estimates are used in the Monte

Carlo and sensitivity analyses.

Climate Change Impacts of GHG Emissions

Climate change can have impacts on agricultural yields, property damage, and ecosystems,

among others. Climate change specific impact costs ($/t CO2eq.) are from the Interagency

Working Group on Social Cost of Carbon,140 which is based on three integrated assessment

models: Dynamic Integrated Climate and Economy, Policy Analysis of the Greenhouse Effect

and Climate Framework for Uncertainty, Negotiation and Distribution.140 These models

represent a range of socio-economic forecasts, climate sensitivity probability distributions,

approaches to estimate potential damages, and discount rates to relate future costs to present day

emissions. Global costs are accounted for due to the international nature of the impacts, and are

higher than estimates based solely on domestic US implications. The base case value of $43/t

CO2eq. (2010 USD) used in this study is based on the average social cost estimate from the three

models with the median discount rate of 3% for the emissions in the year 2020.

Health Impacts of CAC Emissions

Exposure to CAC emissions can have human health impacts including chronic morbidity and

mortality from bronchitis and asthma. Health impacts from CAC emissions in individual US

counties are from the Air Pollution Emission Experiments and Policy analysis model.97 The

model estimates marginal impact costs of increased CAC emissions, and allocates these costs to

the US County in which they are released. Weighted averages of these specific costs (shown in

Table 5-1) are used to represent the geographic distributions of each life cycle stage (calculated

with Equation 5-2). Impacts from vehicle operation emissions (from tailpipe, tire and brake wear

and windshield washer fluid use) are estimated with the distribution of vehicle miles travelled

across the US according the National Household Travel Survey.131 The distributions of natural

gas electricity generation emissions are based on production patterns from the eGRID database.25

Other emissions are allocated according to US Census county business patterns for petroleum

171

and natural gas extraction, petroleum refining, natural gas distribution, motor vehicle parts

manufacturing, battery manufacturing, petroleum lubricating oil and grease manufacturing, and

automobile manufacturing.143 This methodology is similar, but more detailed, compared to what

has been utilized in previous studies.6, 8, 80, 103, 140

Ownership Costs

Ownership costs include vehicle retail price and lifetime operating expenses, which include both

fuel and maintenance.

Vehicle Price

The Vehicle Attribute Model3 estimates vehicle retail price equivalents. The model evaluates the

trade-off between vehicle price and fuel economy to minimize the cost of the vehicle and three

years of fuel. In contrast, this study requires a methodology to estimate the price of vehicles with

specific fuel economy, CNG fuel tank and BEV battery capacity characteristics. As such, the

Vehicle Attribute Model3 is not used directly, but underlying assumptions and calculations are

utilized in this study as explained in the following subsections.

Gasoline CV

The Vehicle Attribute Model3 is based on historical baseline Model Year 2008 vehicles and

accounts for changes in time, fuel economy and fuel type. The price of all of the components in

CVs are considered mature technologies that decrease 1% per year from baseline data to estimate

future prices (model year 2020 in this study). Two cost curves, both described by Equation B-1

and shown in Figure B-1, are applied to represent upper and lower bound estimates for the

additional costs of fuel efficiency technologies required to account for incremental differences

between the Gasoline CV model used in this study and the Vehicle Attribute Model3 baseline

vehicle fuel economy. The average of the upper and lower bound estimates is used for the base

case results in this study.

172

Figure B-1: Incremental costs of changes in relative fuel economy

Equation B-1: Incremental cost of changes in relative fuel economy calculation

C =b

k(e

kFE

FEo − ek)

Where:

C = incremental cost

blower = 1108 for CV and HEV, 0.00001 for BEV

bupper = 2758 for CV and HEV, 0.00134 for BEV

klower = 0.9 for CV and HEV, 18.0 for BEV

klower = 0.7 for CV and HEV, 15.0 for BEV

FE = Fuel economy

FEo = Reference fuel economy

0

1

2

100% 110% 120% 130%

Incr

emen

tal C

ost

($

10

00

)

Relative Fuel Economy

CV andHEV

BEV

173

CNG CV

The Gasoline CV model is modified to estimate the price of the CNG CV. Two separate CNG

CV models are developed to capture the wide range in possible costs, the higher of which is used

as the base case estimate: the low cost model assumes a stainless steel CNG fuel tank; the high

cost model assumes a carbon fibre CNG fuel tank. CNG fuel tank cost and mass parameters are

detailed in Table B-1. Structural modifications are required to accommodate the fuel tanks,

which result in an additional change in mass (50% of powertrain changes) and cost ($8/kg). Fuel

economy is then reduced by 6% per 10% increase in total mass. The cost of additional fuel

economy adjustments are estimated with Equation B-1, to achieve the base case assumption of

an overall 5%7 energy equivalent fuel economy improvement for vehicles operating on CNG

instead of gasoline. Additionally, engine modification cost estimates range from $500-$2300, the

average of which is used as the base case estimate.

Table B-1: CNG fuel tank and BEV battery cost and mass parameters

Storage system CNG Fuel Tank BEV Battery

Cost Stainless Steel: $260 + $20/Lge Carbon Fibre:$390 + $60/Lge

Low: $760 + $240/kWh High: $760 + $410/kWh

Mass Stainless Steel: 4 kg/Lge Carbon Fibre: 1 kg/Lge

Low: 8 kg/kWh High: 10 kg/kWh

Driving range 500 km, similar to 2013 Honda Civic NG180 125 km, similar to 2013 Nissan Leaf15

Fuel characteristics 32 MJ/Lge 89 kWh/Lge

Note: A 30% markup is added to the costs above to estimate retail price3. Lge – liter gasoline equivalent

CNG HEV

The cost premium of the CNG HEV over the Gasoline CV, are estimated with Equation B-1 to

achieve the 40% base case fuel economy improvement obtained from GREET. CNG fuel tank

capacity is estimated using the assumptions outlined in Table B-1. Structural modifications are

required to accommodate the fuel tanks, which result in an additional change in mass (50% of

powertrain changes) and cost ($8/kg). Fuel economy is then reduced by 4% (as opposed to 6%

used for the CNG CV, which does not have regenerative braking) per 10% increase in total mass.

CNG HEV134 passenger vehicles have been developed by major automakers, but are not

commercially available options.

174

NG-e BEV

Unlike the other powertrains in this study, BEVs do not have internal combustion engines. In a

BEV, an internal combustion engine based powertrain is replaced with an electric motor

equivalent. The cost, mass and efficiency specifications of both powertrain systems are listed in

Table B-2. The ranges of cost and mass estimates for BEV batteries are detailed in Table B-1,

the average of which is used as the base case assumption. Structural modifications are required to

accommodate the new powertrain, which result in an additional change in mass (50% of

powertrain changes) and cost ($8/kg). Fuel economy is then reduced by 4% per 10% increase in

total mass. The cost of additional fuel economy adjustments to reach BEV fuel economy used in

this study is calculated with Equation B-1. Finally, electric vehicle supply equipment (charger)

costs are added to the vehicle for $760.

Table B-2: CV and BEV powertrain cost, mass and efficiency parameters

Powertrain CV BEV

Cost (excl. energy storage) $2650 + $20/kW $20/kW

Mass (excl. energy storage) 3 kg/kW 1 kg/kW

Efficiency 20%

85% battery charging 95% battery discharging

90% electric motor 73% overall

Regenerative Braking n/a 11% useful energy recaptured

Operating Costs

Operating costs are calculated as the sum of lifetime fuel and maintenance expenses.

Fuel

Gasoline, E85, CNG and electricity prices are based on the Annual Energy Outlook.2. Base case

assumptions are from transportation sector prices from the 2014 reference case, which are based

on Brent Spot prices for crude oil of $98/bbl and Henry Hub natural gas prices of $4.30/GJ.

Gasoline, E85 and CNG prices include fuel taxes and dispensing costs (storage, transmission and

distribution, retail markup). The electricity prices here are based on a mix of resources; however,

due to the small contribution (7%) of electricity costs to the life cycle ownership costs of the

BEV pathways, errors caused by this simplification will have negligible impact on the

conclusions of this study. Doubling or completely removing the cost of electricity will still result

in life cycle ownership costs that are lower for non-plug-in vehicles than plug-in vehicles, with

the exception of those with particularly short driving ranges.

175

Maintenance

Vehicle maintenance costs and frequencies are itemized in Table B-3 and based on data from

Oak Ridge National Laboratory.142 E85 and CNG vehicle maintenance costs are assumed to be

identical to gasoline fuelled vehicles with equivalent powertrain (e.g., gasoline CV or HEV).

BEV maintenance costs are not estimated by Oak Ridge National Laboratory,142 but these

vehicles do not require oil change, air filter, spark plug, or timing chain replacements costs.8

Brake replacements are assumed to be equivalent to those of HEVs and PHEVs, due to the use of

regenerative braking reducing the frequency of replacements.8 Other scheduled maintenance

costs are assumed to be similar across powertrains.8 Unscheduled maintenance only considers

the potential for BEV replacement battery because the focus of this study is on the relative costs

between pathways – the cost of other unscheduled maintenance (e.g., windshield repair) are

assumed to be similar for all vehicles.

Table B-3: Vehicle maintenance cost and frequency parameters

Parts and Labor Cost CV Frequency HEV Frequency BEV Frequency

Oil Changes142 $80 8,000 km 12,000 km Not applicable

Air Filter Replacements142 $50 50,000 km 50,000 km Not applicable

Spark Plug Replacements142 $220 100,000 km 100,000 km Not applicable

Timing Chain Adjustments142 $350 160,000 km 160,000 km Not applicable

Front Brake Replacements142 $460 80,000 km 160,000 km 160,000 km

Additional Maintenance* $7900 80,000 km 80,000 km 80,000 km

Battery Replacement** 0-100% of initial battery

cost Not applicable Not applicable 160,000 km

*Costs from Oak Ridge National Laboratory,142 frequency assumed to coincide with typical year 5 peak in

maintenance costs 181 **Assumed to not be required in reference scenario, but could occur after warranty period182 in the uncertainty

analysis

176

Uncertainty and Sensitivity Analysis

The assumptions used to develop Monte Carlo and sensitivity analyses are presented below in

Table B-4, Table B-5 and Table B-6. These complement Table 5-1, which lists the assumptions

used to develop the base case results in this study.

Table B-4: Key life cycle inventory assumptions used to develop Monte Carlo and

sensitivity analyses

Life Cycle Inventory Variable 5th/95th Percentile Probability Distribution

Vehicle Fuel Economy 83%/125%

Weibull dist. With location of 18.3, scale of 11.4 and shape of 3.2 for Gasoline CV (distribution multiplied by 100% for

stainless steel CNG, 105% for carbon fibre CNG, 140% for HEV, and 400% for BEV)

CH4 tailpipe emissions 78%/139% Weibull dist. With location of 0.00871, scale of 0.00397 and shape of 1.5805 for Gasoline CV (distribution multiplied by

1000% for CNG CV, 500% for CNG HEV and 0% for BEV)

N2O tailpipe emissions 95%/129% Gamma dist. With location of 0.03946, scale of 0.000246 and

shape of 3.1159 (distribution multiplied by 0% for BEV)

PM2.5 tailpipe emissions 58%/301% Weibull dist. With location of 0.00241, scale of 0.00523 and

shape of 1.2447 (distribution multiplied by 0% for BEV)

VOC tailpipe emissions 41$/241% Weibull dist. With location of 0.03946, scale of 0.07566 and

shape of 1.0347 for Gasoline CV and CNG CV (distribution multiplied by 54% for HEV and 0% for BEV)

VOC evaporative emissions 100%/399% Weibull dist. With location of 0.059, scale of 0.01239 and

shape of 0.41316 for Gasoline CV (distribution multiplied by 50% for CNG and 0% for BEV)

NOx tailpipe emissions 45%/215% Gamma dist. With location of 0.04772, scale of 0.06234 and

shape of 1.2009 for Gasoline CV and CNG CV (distribution multiplied by 84% for HEV and 0% for BEV)

BEV driving range 80/250 km Normal dist. of minimum acceptable range of new BEV drivers,

with 145 km mean and 90 km std dev183

Battery replacement 0%/68% Triangular dist. with base case as most likely, and limits

assuming entire battery pack replacement least likely184

CNG fuel tank material Stainless steel/carbon fibre Discrete, equally weighted binary dist., used to change cost,

mass and fuel economy (scaled to mass)3

Lifetime vehicle travel 150,000/460,000 km Discrete dist. based on shares of US vehicle annual miles of

travel and vehicle age1

Lifetime vehicle age 8/27 years Discrete 6-30 year dist. weighted according to US car

scrappage rates1

Petroleum resource mix 9%/80% oil sands Triangular dist. with base case representing US average as most likely, and limits of 0% and 100% acknowledging

individual unit of fuel can be entirely from a particular source of petroleum/natural gas/power plant technology7

Natural gas resource mix 14%/83% shale gas

NG-e generation technology mix

21%/92% combined cycle

CNG compression efficiency 94%/98% Triangular dist. with base case as most likely, and 94% - 98%

limits from the literature55


compressed natural gas

177

Table B-5: Key ownership cost and emissions impact assumptions used to develop Monte

Carlo and sensitivity analyses

Ownership Cost Variable 5th/95th Percentile Probability Distribution

Gasoline CV price $23,900/$24,400 Uniform dist. +/- 200 (gasoline CV and CNG CV with stainless steel tank), 800 (CNG HEV with stainless steel tank), 1500 (BEV

with 28 kWh battery), based on Vehicle Attribute Model forecasted fuel efficiency technology and CNG engine

modification price range3

CNG CV price $26,300/$28,400

CNG HEV price $27,500/$30,800

BEV price (excl. battery) $22,300/$25,400

BEV battery price $330/$530 per kWh Uniform dist. +/- $110/kWh, based on Vehicle Attribute Model

battery forecasted price range3

2020 Brent spot crude oil $73/$123 per bbl Triangular dist. with base case as most likely limits representing high and low oil price scenarios2 US Gasoline price $0.64/$0.98 per L

2020 Henry Hub natural gas $4.50/$7.00 per GJ Triangular dist. with base case as most likely, and limits representing Annual Energy Outlook 2 low and National Energy

Board 185 high gas price scenarios US CNG price $13/$17 per GJ

US Electricity price $93/$116 per MWh

Ownership cost discount rate 6%/17% Triangular dist. with base case most likely and limits based on

the perspective of social or individual consumer interests42

Air Emission Impact Variable 5th/95th Percentile Probability Distribution

GHG impact specific cost $25/$115 per CO2eq. Triangular dist. with base case most likely and limits based on

National Research Council illustrative range140

CAC impact specific cost See Table B-6 Discrete dist. based on quantity of life cycle stage activity


compressed natural gas, Costs are in 2010 USD. *These activities are weighted according to employment.6, 8, 80

Table B-6: Specific costs of CAC emissions impacts used to develop Monte Carlo and

sensitivity analyses

Life Cycle Stage Percentile /t PM2.5 /t NOx /t SOx /t VOC County

Vehicle operation

5th $3,600 $400 $3,500 $300 Tehama County, California

95th $91,900 $4,100 $20,500 $8,400 Union County, New Jersey

Oil and gas extraction

5th $700 $1,300 $200 $700 Eddy County, New Mexico

95th $1,200 $7,900 $1,700 $5,400 Guilford County, North Carolina

Gasoline fuel prod.

5th $1,300 $1,300 $400 $700 Kay County, Oklahoma

95th $300 $69,700 $10,100 $39,500 Los Angeles County, California

CNG fuel production

5th $800 $1,500 $200 $700 Dawson County, Montana

95th $300 $28,500 $3,800 $14,300 San Diego County, California

NG-e fuel production

5th $1,200 $200 $400 $1,200 Yoakum County, Texas

95th $9,000 $1,200 $37,500 $33,300 San Diego County, California

Vehicle parts prod.

5th $1,900 $2,100 $800 $900 Wyandotte County, Kansas

95th $400 $10,600 $3,100 $23,800 Wayne County, Michigan

Vehicle battery prod.

5th $1,500 $1,400 $600 $700 Buchanan County, Missouri

95th $500 $12,100 $3,200 $19,100 San Mateo County, California

Vehicle fluids prod.

5th $1,800 $2,200 $900 $700 Rockwall County, Texas

95th $3,200 $16,800 $6,700 $12,400 Union County, New Jersey

Vehicle assembly

5th $2,400 $1,900 $1,200 $800 Wyandotte County, Kansas

95th $500 $8,800 $3,300 $28,600 Wayne County, Michigan

178

Supplemental Results

The focus in Chapter 5 is on discussing aggregate life cycle results, while the results for

individual emissions are presented in greater detail here. Figure B-2 shows CH4 and N2O

emissions, which are not included in Figure 5-1, in addition to GHG and CAC impacts

disaggregated by emission, as opposed to by life cycle stage in Figure 5-2. Figure B-3, Figure B-

4 and Figure B-5 show the life cycle energy use and emissions inventory, and air emissions

impacts and ownership cost Monte Carlo analysis results for each vehicle pathway. Note that

overlapping 90% confidence intervals in Figure 5-2. Figure B-3, Figure B-4 and Figure B-5 do

not necessarily indicate that there is no significant difference between pathway results because

some uncertainty is correlated. For example, the specific impact costs per tonne CO2 emissions

have high uncertainty but the values should be identical for all vehicles in any direct comparison.

Similarly, lifetime vehicle kilometers travelled is a variable that contributes to the uncertainty in

all metrics but is assumed to be identical for each vehicle pathway. Figure B-5 shows that the

90% confidence intervals representing life cycle air emissions impacts of the gasoline CV and

CNG CV overlap; however, the incremental analysis in Figure 5-3 shows that when common

variables (e.g., life time VKT and $/t GHG) are the same, the CNG CV results in consistently

lower life cycle air emissions impacts. On the other hand, Figure B-5 shows that the 90%

confidence intervals representing the life cycle air emissions impacts of the CNG HEV and NG-e

BEV also overlap, and the incremental analysis results in Figure 5-3 agree that the life cycle air

emissions impacts are similar. This is why we present incremental differences, to capture these

correlations when we introduce the discussion of uncertainty in Chapter 5.

179

Figure B-2: Life cycle CH4 and N2O emissions disaggregated by life cycle stage and life

cycle GHG and CAC impacts disaggregated by emission

0

1

2

3


N2O

Em

issi

on

s(k

g)

Vehicle Operation

Fuel Production

Vehicle Production

a)

0

100

200

300


CH

4Em

issi

on

s(k

g)

Vehicle Operation

Fuel Production

Vehicle Production

b)

0

1

2

3


GH

G C

limat

e C

han

ge Im

pac

ts

($1

00

0)

N2O

CH4

CO2

c)

0.0

0.3

0.6

0.9


CA

C H

ealt

h

Imp

acts

($

10

00

)

NOx

PM2.5

VOC

SOx

d)

180

Figure B-3: Life cycle energy use, CO2, CH4 and N2O emission Monte Carlo analysis

results, including 90% confidence intervals in the legend.

0%

25%

50%

75%

100%

Life Cycle Energy Use (TJ)

Gasoline CV (90% CI: 1.2-2.2)

CNG CV (90% CI: 1.0-1.9)

CNG HEV (90% CI: 0.8-1.4)

NG-e BEV (90% CI: 0.7-1.3)

0%

25%

50%

75%

100%

Life Cycle CO2 Emissions (t)

Gasoline CV (90% CI: 82-147)

CNG CV (90% CI: 60-107)

CNG HEV (90% CI: 47-80)

NG-e BEV (90% CI: 43-81)

0%

25%

50%

75%

100%

Life Cycle CH4 Emissions (kg)


CNG CV (90% CI: 264-468)

CNG HEV (90% CI: 194-339)

NG-e BEV (90% CI: 107-205)

0%

25%

50%

75%

100%

Life Cycle N2O Emissions (kg)


CNG CV (90% CI: 2.5-4.1)

CNG HEV (90% CI: 2.1-3.5)

NG-e BEV (90% CI: 0.7-1.2)

181

Figure B-4: : Life cycle NOx, SOx, VOC and PM2.5 emission Monte Carlo analysis results,

including 90% confidence intervals in the legend.

0%

25%

50%

75%

100%

Life Cycle NOx Emissions (kg)


CNG CV (90% CI: 62-134)

CNG HEV (90% CI: 51-110)

NG-e BEV (90% CI: 41-75)

0%

25%

50%

75%

100%

Life Cycle SOx Emissions (kg)


CNG CV (90% CI: 40-49)

CNG HEV (90% CI: 47-54)

NG-e BEV (90% CI: 50-72)

0%

25%

50%

75%

100%

Life Cycle VOC Emissions (kg)


CNG CV (90% CI: 65-142)

CNG HEV (90% CI: 57-107)

NG-e BEV (90% CI: 41-48)

0%

25%

50%

75%

100%

Life Cycle PM2.5 Emissions (kg)


CNG CV (90% CI: 5-11)

CNG HEV (90% CI: 5-10)

NG-e BEV (90% CI: 5-7)

182

Figure B-5: : Life cycle air emissions impacts and ownership costs Monte Carlo analysis

results, including 90% confidence intervals in the legend.

0%

25%

50%

75%

100%

Life Cycle Air Emissions Impacts ($1000)


CNG CV (90% CI: 1.9-8.7)

CNG HEV (90% CI: 1.6-6.8)

NG-e BEV (90% CI: 1.4-6.0)

0%

25%

50%

75%

100%

<30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 >70

Life Cycle Air Emissions Impacts ($1000)


CNG CV (90% CI: 34-53)

CNG HEV (90% CI: 33-50)

NG-e BEV (90% CI: 39-70)

183

Supplemental Scenarios

Four supplemental scenarios are developed to examine quantitative effects of:

1. assuming no non-CO2 vehicle tailpipe or evaporative emissions (Zero CAC Emission

Non-Plug-in Vehicle Scenario),

2. assuming no fuel economy advantage for CNG use over gasoline use (Low Fuel

Economy CNG Vehicle Scenario),

3. assuming no uncertainty in BEV fuel economy (independent of battery capacity changes)

(Constant Fuel Economy Plug-in Vehicle Scenario), and

4. assuming high (95th percentile) methane emissions from CNG vehicles (High Methane

Emission CNG Vehicle Scenario) are minor due the numerous other sources of

uncertainty analyzed in this study.

These four scenarios are presented in Figure B-6 and Table B-7. The qualitative conclusions of

incremental life cycle ownership and air emissions impact costs in the Chapter 5 remain

applicable in these scenarios.

Table B-7: Incremental life cycle ownership and emissions impact cost 90% confidence

intervals for supplementary scenarios

Fuel Switching CNG CV replacing

Gasoline CV

Energy Efficiency CNG HEV replacing

CNG CV

Emissions Shifting NG-e BEV replacing

CNG HEV Incremental

Life Cycle Ownership Costs

Incremental Life Cycle

Air Emissions Impact Benefit


Ownership Costs




Ownership Costs



Results from Chapter 5

90% CI: -$3000 to $4000

90% CI: $0 to $4000

90% CI: -$5000 to $0

90% CI: $0 to $2000

90% CI: $1000 to $28,000

90% CI: -$1000 to $2000

Zero CAC Emission Non-Plug-in Vehicle Scenario

90% CI: -$4000 to $3000

90% CI: $0 to $4000

90% CI: -$5000 to $0

90% CI: $0 to $2000

90% CI: $0 to $27,000

90% CI: -$1000 to $1000

Low Fuel Economy CNG Vehicle Scenario

90% CI: -$2000 to $4000

90% CI: $0 to $4000

90% CI: -$4000 to $1000

90% CI: $0 to $3000

90% CI: $1000 to $28,000

90% CI: -$1000 to $2000

Constant Fuel Economy Plug-in Vehicle Scenario

90% CI: -$3000 to $3000

90% CI: $0 to $4000

90% CI: -$5000 to $0

90% CI: $0 to $2000

90% CI: $1000 to $28,000

90% CI: -$1000 to $1000

High Methane Emission CNG Vehicle Scenario

90% CI: -$3,000 to $3,000

90% CI: $0 to $4000

90% CI: -$5000 to $0

90% CI: $0 to $2000

90% CI: $1000 to $28,000

90% CI: -$1000 to $2000

184

a) Zero CAC Emission Non-Plug-in Vehicle Scenario

Fuel Switching

Energy Efficiency

Emissions Shifting

Life

Cyc

le In

crem

enta

l

Ow

ner

ship

Co

st (

$1

00

0)


b) Low Fuel Economy CNG Vehicle Scenario

Fuel Switching

Energy Efficiency

Emissions Shifting

Life

Cyc

le In

crem

enta

l

Ow

ner

ship

Co

st (

$1

00

0)


c) Constant Fuel Economy Plug-in Vehicle Scenario

Fuel Switching

Energy Efficiency

Emissions Shifting

Life

Cyc

le In

crem

enta

l

Ow

ner

ship

Co

st (

$1

00

0)


d) High Methane Emission CNG Vehicle Scenario

Fuel Switching

Energy Efficiency

Emissions Shifting

Life

Cyc

le In

crem

enta

l

Ow

ner

ship

Co

st (

$1

00

0)


Figure B-6: Incremental life cycle ownership and emissions impact cost results for

supplementary scenarios

-50

0

50

-5 5

Trade-Off

Lose-Lose

Win-Win

Trade-Off

-50

0

50

-5 0 5

Lose-Lose

Trade-Off

Trade-Off

Win-Win

-50

0

50

-5 0 5

Lose-Lose

Trade-Off

Trade-Off

Win-Win

-50

0

50

-5 5

Trade-Off

Lose-Lose

Win-Win

Trade-Off

-50

0

50

-5 0 5

Lose-Lose

Trade-Off

Trade-Off

Win-Win

-50

0

50

-5 0 5

Lose-Lose

Trade-Off

Trade-Off

Win-Win

-50

0

50

-5 5

Trade-Off

Lose-Lose

Win-Win

Trade-Off

-50

0

50

-5 0 5

Lose-Lose

Trade-Off

Trade-Off

Win-Win

-50

0

50

-5 0 5

Lose-Lose

Trade-Off

Trade-Off

Win-Win

-50

0

50

-5 5

Trade-Off

Lose-Lose

Win-Win

Trade-Off

-50

0

50

-5 0 5

Lose-Lose

Trade-Off

Trade-Off

Win-Win

-50

0

50

-5 0 5

Lose-Lose

Trade-Off

Trade-Off

Win-Win

185

Appendix C: Chapter 6 Supporting Information

Methods Details

The vehicles in this study are developed in two main components; base vehicle models and

added fuel efficiency technologies. The Methods section of Chapter 6 provides a conceptual

overview of the development of these components and how these two components are combined

to produce the vehicle models. Additional details are provided in the following sections.

Base Vehicle Models

The 2012 reference vehicle and the series of vehicles that comprise of each of the vehicle design

options are all developed with different base vehicle models. The diversity in base vehicle

models is required to capture physical and temporal (model year vehicle is manufactured)

differences among the vehicles, as explained in the Methods section of Chapter 6. Physical

differences are discussed first, followed by temporal differences (see Manufacturing Cost

subsection for the latter).

Physical Specifications

Physical differences in base vehicle models are used to quantify the ability for changes in vehicle

acceleration, size and driving range to improve fuel economy. These vehicle attributes are

analyzed by developing base vehicle models with different engine power ratings (100, 125, and

150 kW), body-type (Chevy Equinox-like and Honda Accord-like), and powertrain-type

(conventional and battery electric with 6 and 16 kWh battery capacities), respectively. A flow

chart depicting how these variables are modelled within Autonomie96 vehicle simulation

software is shown in Figure C-1.

186

Figure C-1: Flow chart depicting base vehicle model development with Autonomie96

All base vehicle models are developed by modifying Autonomie templates because there are no

templates within the software that meet the exact specifications required for this study.

Templates are based on different vehicle powertrains. The gasoline vehicles in this study are all

based on a conventional vehicle template, while the plug-in electric vehicles used to develop the

driving range option are based on a battery electric vehicle template. Both of these powertrains

are further discussed in the following two subsections.

Both the conventional vehicle and battery electric vehicle template are based on a midsize car

glider (vehicle without powertrain). Autonomie also includes vehicle components from specific

vehicles detailed in Table C-1. The SUVs in this study are all based on a first generation (model

year 2005-2009)63 Chevy Equinox SUV, while the smaller vehicles used to develop the vehicle

size option are based on a seventh generation (model year 2003-2007)63 Honda Accord sedan,

which has nearly the same footprint as the Chevy Equinox-like SUV,63 and thus same fuel

economy targets under CAFE standards.

Engine or Motor Power

Rating

Battery Energy

CapacityGliderPowertrain

Autonomie Base Vehicle Model

Conventional Gasoline

Chevy Equinox-like

n/a

100 kW

125 kW

150 kW

Honda Accord-like

n/a

100 kW

125 kW

150 kW

Battery ElectricChevy Equinox-

like

6 kWh

100 kW

125 kW

150 kW

16 kWh

100 kW

125 kW

150 kW

187

Table C-1: Chevy Equinox-like and Honda Accord-like components

Chassis Chevy Equinox Honda Accord

Massa (kg) 1180 990

Drag Area (m2) 0.977 0.675

Drag Coefficienta 0.37 0.30

Frontal Areaa (m2) 2.64 2.25

Footprint (m2) 4.49 4.26

Wheelbaseb (m) 2.86 2.74

Trackb (m) 1.57 1.55

Interior Volume (L) 4024 3305

Passenger Volumeb,c (L) 3013 2908

Cargo Volumeb,c (L) 1011 396

Final Drive Namea 406_au_VUT 444_accord

Final Drive Ratioa 4.06 4.44

Wheels Namea 0357_P235_60_R17 0326_P205_60_R16

Radiusa (m) 0.357 0.326

Widtha (m) 0.235 0.205

Aspect Ratioa (%) 60 60

Rim Diametera (m/inch) 0.432/17 0.406/16 aObtained from Autonomie96 bObtained from Edmunds63 cObtained from The Car Connection160

The performance of the vehicle powertrain, glider, battery energy capacity and engine/motor

power rating combinations outlined in Figure C-1 are simulated in Autonomie. Acceleration

performance is quantified by simulating 0-96 km/h acceleration time. Fuel economy is quantified

by simulating the vehicle in both city and highway driving cycles. For the purposes of meeting

CAFE standards, combined fuel economy is calculated by weighting the unadjusted city and

highway fuel economy results by 55% and 45%, respectively.29

188

Gasoline Vehicles

Gasoline base vehicle models are detailed in Table C-2 and use the Conv AutoTrans 2wd Midsize

Autonomie template. This is a midsize sedan with a conventional powertrain that has a two-

wheel drive 5-speed automatic transmission. This template is modified with either first

generation (model year 2005-2009) Chevy Equinox-like or seventh generation (model year

2003-2007) Honda Accord-like components detailed in Table C-1. The entry level version of

both of these real world vehicles came with two-wheel drive 5-speed automatic transmissions,63

which is consistent with the template. Fuel economy and acceleration performance is simulated

within Autonomie96 for internal combustion engine power ratings of 100, 125 and 150 kW (134,

168, 201 hp).

Table C-2: Gasoline Chevy Equinox-like and Honda Accord-like vehicle specifications for a

range of engine power ratings

Chevy Equinox-like Honda Accord-like

Engine Power Rating (kW) 100 125 150 100 125 150

Vehicle Mass (kg) 1892 1925 1959 1787 1820 1854

0-60 mph Acceleration (s) 13.7 10.9 9.2 12.5 10.1 8.6

Fuel Economy/Consumption (mpg/km L per L100 km)

32/7.4 30/7.8 27/8.7 35/6.7 31/7.6 29/8.1

City (mpg/kmL per L100 km) 28/8.4 26/9.0 24/9.8 30/7.8 27/8.7 25/9.4

Highway (mpg/kmL per L100 km) 37/6.4 35/6.7 32/7.4 43/5.5 39/6.0 37/6.4

Gasoline fuel tank size is not a parameter within Autonomie96 so gasoline vehicle driving range

is assumed to be a constant 600 km. This is approximately the same capability of the entry level

first generation (model year 2003-2007) Chevy Equinox.15 A gasoline fuel tank is a minor

contributor to total vehicle mass and price, unlike with plug-in vehicle batteries.3 Therefore,

adjustments in fuel tank size required to maintain a constant driving range would have negligible

impact on fuel economy and price.

189

Plug-in Electric Vehicles

Plug-in electric base vehicle models are detailed in Table C-3 and are based on the BEV

FixedGear 2wd Midsize Autonomie template. This is a midsize sedan with a battery electric

powertrain that has a two-wheel drive fixed-gear transmission. This template is modified with a

Chevy Equinox-like glider previously detailed in Table C-1 and GM Voltec battery cell

specifications provided within Autonomie (detailed in Table C-3).96 GM Voltec battery cells,

which are used in the Chevy Volt,186 specifications are selected to provide higher power densities

than the default battery, which has higher energy density. Power density is prioritized because

the driving range option consists of vehicles with sufficiently low energy capacities to maintain

the price of the 2012 reference vehicle, while also maintaining acceleration performance. Fuel

economy and acceleration performance is simulated within Autonomie for vehicles with total

battery energy capacities of 6 (smallest that could maintain 9.3 s 0-96 km/h acceleration

performance) and 16 kWh (Chevy Volt capacity) and electric motor power ratings of 100, 125

and 150 kW. Usable battery energy capacity is assumed to be 77.5% in 2015 and increase by 2.5

percentage points every five vehicle model years.3 Driving ranges are calculated within a

spreadsheet based on vehicle fuel economy, total battery energy capacity and percentage of

usable battery energy capacity.

Table C-3: Plug-in electric Chevy Equinox-like vehicle specifications for range of motor

power ratings and battery capacities

Battery Energy Capacity (kWh) 6 16

Cells (#) 108 288

Energy Capacity96 (Ah/cell) 15 15

Power Capacity96 (kW/cell) 1.041 1.041

Motor Power Rating (kW) 100 125 150 100 125 150

Vehicle Mass (kg) 1783 1811 1839 2028 2056 2084

0-60 mph Acceleration (s) 11.1 10.1 09.3 12.6 10.0 08.4

Fuel Economy/Consumption (mpg/km L per L100 km)

73/3.2 72/3.3 69/3.4 67/3.5 67/3.5 64/3.7

City (mpg/km L per L100 km) 77/3.1 76/3.1 72/3.3 73/3.2 72/3 68/3.5

Highway (mpg/km L per L100 km) 69/3.4 69/3.4 67/3.5 63/3.7 63/3.7 61/3.9

Driving Range

Model Year 2015 (km) 16.8 16.4 16.0 41.6 40.6 39.6

Model Year 2020 (km) 17.4 17.0 16.5 42.9 41.9 40.9

Model Year 2025 (km) 17.9 17.5 17.1 44.3 43.2 42.1

190

Manufacturing Costs

Autonomie96 provides low-, average- and high-risk estimates of component manufacturing costs

over time. The set of costs are presented in Table C-4 to C-7 in 2010 USD for model years 2012,

2015, 2020 and 2025, respectively. The earliest model year included with Autonomie is 2013, so

2012 costs are extrapolated from 2013 and 2015 estimates. The three cost estimates capture

uncertainty and are based on the risk of achieving cost reduction projections, as discussed in the

Methods section. The average risk cost estimates are used to develop the mid-price scenarios.

The high risk cost estimates are used to develop the low price scenarios, and vice versa.

Autonomie only provides point estimate costs for the wheels and the torque converter; although

there is likely some uncertainty in real world costs, there would be negligible impact on the

results of this study because these components comprise less than 4% of total base vehicle model

costs and an even smaller fraction of total vehicle price (which includes added fuel efficiency

technologies).

The Vehicle Attribute Model3 was utilized for certain manufacturing cost assumptions.

Autonomie chassis costs increase in the future, which reflects the increasing use of lightweight

materials.105 Unfortunately, this overlaps with the added fuel efficiency technologies, from the

Vehicle Attribute Model, that are applied. Further, the actual weight reduction from the use of

lightweight materials is not specified within Autonomie.96 Therefore, future base vehicle model

chassis costs are estimated based on a 1% annual reduction based on the Vehicle Attribute

Model.3 The cost of electric vehicle supply equipment (i.e., charger), which is necessary for

plug-in electric vehicles, is also utilized from the Vehicle Attribute Model3 because it is not

included in Autonomie.96

The vehicle price is estimated by adding a 30% markup over manufacturing costs.3

191

Table C-4: Model Year 2012 vehicle manufacturing costs

High Price Mid-Price Low Price

Power Rating (kW) 100 125 150 100 125 150 100 125 150

Manufacturing Cost ($) 15368 15668 16234 14778 15035 15517 14364 14593 15025

Chassis ($) 10483 10483 10483 10483 10483 10483 10483 10483 10483

Engine ($) 2351 2539 2993 1992 2152 2536 1813 1958 2308

Gearbox ($) 1610 1722 1834 1400 1498 1595 1190 1273 1356

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Starter ($) 45 45 45 33 33 33 27 27 27

Generator ($) 16 16 16 15 15 15 11 11 11

Accessories ($) 251 251 251 251 251 251 251 251 251

Torque Converter ($) 109 109 109 109 109 109 109 109 109

Wheels ($) 443 443 443 443 443 443 443 443 443

Note: all costs are presented in 2010 USD. Costs are primarily estimated with Autonomie and exceptions are

explained in the text.

192



Power Rating (kW) 100 125 150 100 125 150 100 125 150

Gasoline Chevy Equinox-like Vehicle


Chassis ($) 10171 10171 10171 10171 10171 10171 10171 10171 10171

Engine ($) 2281 2464 2904 1933 2088 2461 1759 1900 2240

Gearbox ($) 1562 1671 1780 1359 1453 1548 1155 1235 1316

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Starter ($) 45 45 45 33 33 33 26 26 26

Generator ($) 15 15 15 15 15 15 11 11 11

Accessories ($) 240 243 246 240 243 246 240 243 246


Wheels ($) 443 443 443 443 443 443 443 443 443

Gasoline Honda Accord-like Vehicle


Chassis ($) 9757 9757 9757 9757 9757 9757 9757 9757 9757

Engine ($) 2281 2464 2904 1933 2088 2461 1759 1900 2240

Gearbox ($) 1562 1671 1780 1359 1453 1548 1155 1235 1316

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Starter ($) 45 45 45 33 33 33 26 26 26

Generator ($) 15 15 15 15 15 15 11 11 11

Accessories ($) 240 243 246 240 243 246 240 243 246


Wheels ($) 335 335 335 335 335 335 335 335 335

Plug-in Electric Chevy Equinox-like 16 kWh Vehicle


Plug-in Battery ($) 5095 5095 5095 233 4233 4233 3919 3919 3919

Chassis ($) 10171 10171 10171 10171 10171 10171 10171 10171 10171

Engine ($) 1900 2375 2850 1700 2125 2550 1100 1375 1650

Gearbox ($) 2685 2685 2685 1790 1790 1790 1492 1492 1492

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Charger ($) 842 842 842 842 842 842 842 842 842

Starter ($) 62 62 62 52 52 52 39 39 39

Generator ($) 970 970 970 970 970 970 970 970 970

Accessories ($) 300 300 300 300 300 300 300 300 300

Wheels ($) 443 443 443 443 443 443 443 443 443



Plug-in Battery ($) 1911 1911 1911 1587 1587 1587 1470 1470 1470

All other costs equal to those of 16 kWh Vehicle



193



Power Rating (kW) 100 125 150 100 125 150 100 125 150



Chassis ($) 9673 9673 9673 9673 9673 9673 9673 9673 9673

Engine ($) 2169 2343 2762 1838 1986 2340 1673 1807 2130

Gearbox ($) 1486 1589 1693 1292 1382 1472 1098 1175 1251

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Starter ($) 45 45 45 33 33 33 25 25 25

Generator ($) 15 15 15 15 15 15 10 10 10

Accessories ($) 228 231 234 228 231 234 228 231 234


Wheels ($) 443 443 443 443 443 443 443 443 443



Chassis ($) 9279 9279 9279 9279 9279 9279 9279 9279 9279

Engine ($) 2169 2343 2762 1838 1986 2340 1673 1807 2130

Gearbox ($) 1486 1589 1693 1292 1382 1472 1098 1175 1251

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Starter ($) 45 45 45 33 33 33 25 25 25

Generator ($) 15 15 15 15 15 15 10 10 10

Accessories ($) 228 231 234 228 231 234 228 231 234


Wheels ($) 335 335 335 335 335 335 335 335 335



Plug-in Battery ($) 4233 4233 4233 3331 3331 3331 2498 2498 2498

Chassis ($) 9673 9673 9673 9673 9673 9673 9673 9673 9673

Engine ($) 1300 1625 1950 1000 1250 1500 700 875 1050

Gearbox ($) 2386 2386 2386 1611 1611 1611 1313 1313 1313

Charger ($) 761 761 761 761 761 761 761 761 761

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Starter ($) 62 62 62 52 52 52 39 39 39

Generator ($) 922 922 922 922 922 922 922 922 922

Accessories ($) 300 300 300 300 300 300 300 300 300

Wheels ($) 443 443 443 443 443 443 443 443 443



Plug-in Battery ($) 1587 1587 1587 1249 1249 1249 937 937 937




194



Power Rating (kW) 100 125 150 100 125 150 100 125 150



Chassis ($) 9199 9199 9199 9199 9199 9199 9199 9199 9199

Engine ($) 2063 2228 2626 1748 1888 2226 1591 1718 2025

Gearbox ($) 1413 1511 1610 1229 1314 1400 1044 1117 1190

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Starter ($) 45 45 45 33 33 33 24 24 24

Generator ($) 15 15 15 14 14 14 10 10 10

Accessories ($) 217 220 223 217 220 223 217 220 223


Wheels ($) 443 443 443 443 443 443 443 443 443



Chassis ($) 8824 8824 8824 8824 8824 8824 8824 8824 8824

Engine ($) 2063 2228 2626 1748 1888 2226 1591 1718 2025

Gearbox ($) 1413 1511 1610 1229 1314 1400 1044 1117 1190

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Starter ($) 45 45 45 33 33 33 24 24 24

Generator ($) 15 15 15 14 14 14 10 10 10

Accessories ($) 217 220 223 217 220 223 217 220 223


Wheels ($) 335 335 335 335 335 335 335 335 335



Plug-in Battery ($) 4233 4233 4233 3135 3135 3135 2351 2351 2351

Chassis ($) 9199 9199 9199 9199 9199 9199 9199 9199 9199

Engine ($) 1260 1575 1890 965 1206 1448 625 781 938

Gearbox ($) 1671 1671 1671 1492 1492 1492 1193 1193 1193

12 V Battery ($) 62 62 62 52 52 52 39 39 39

Charger ($) 723 723 723 723 723 723 723 723 723

Starter ($) 62 62 62 52 52 52 39 39 39

Generator ($) 877 877 877 877 877 877 877 877 877

Accessories ($) 300 300 300 300 300 300 300 300 300

Wheels ($) 443 443 443 443 443 443 443 443 443



Plug-in Battery ($) 1587 1587 15887 1176 1176 1176 882 882 882




195


Added fuel efficiency technologies are based on Equation C-1 from the Vehicle Attribute Model3

that estimates the price (manufacturing cost and markup) of fuel economy improvements. The

Vehicle Attribute Model3 provides upper and lower bound price equation parameters for

different vehicle classes, fuel type and model years (see Table C-8). The two bounds provide two

price estimates that capture uncertainty, the average of which is used to develop the mid-price

scenario. The lower bound estimates are used to calculate the low price scenario, and vice versa.

The small SUV and large car class sizes are used for the Chevy Equinox-like and Honda Accord-

like vehicles, respectively. The Vehicle Attribute Model3 only provides equation parameters for

2015 onwards, so values for 2012 are extrapolated from 2015 and 2020 parameters.

Equation C-1: Price of added fuel efficiency technologies

P =b

k(e

kFE

FEo − ek)

Where:

P = incremental price [$]

b = cost parameter shown in Table S8 [$]

k = cost scaling parameter shown in Table S8 [dimensionless]

FE = improved fuel economy [MPG]

FEo = base vehicle model fuel economy [MPG]

196

Table C-8: Parameters for calculating price of added fuel efficiency technologies from

Vehicle Attribute Model3

2012 2015 2020 2025


Low Price b 1289 1220 1048 947

k 0.900 0.900 0.900 0.900

High Price b 2957 2900 2758 2623

k 0.700 0.700 0.700 0.700


Low Price b n/a 1290 1180 1001

k n/a 0.900 0.900 0.900

High Price b n/a 2900 2758 2623

k n/a 0.700 0.700 0.700

Plug-in Electric Chevy Equinox-like Vehicle

Low Price b n/a 1.33x10-5 1.14x10-5 1.03x10-5

k n/a 18.00 18 18

High Price b n/a 1.41x10-3 1.34x10-3 1.27x10-3

k n/a 15 15 15

We assume the use of added fuel efficiency technologies from the Vehicle Attribute Model

changes base vehicle model price and fuel economy, but not interior volume or acceleration

performance. Although individual added fuel efficiency technologies can have an impact on

acceleration, the data 2 cited by the Vehicle Attribute Model 3 does take into account internal

combustion engine downsizing when estimating the incremental cost and fuel economy

improvement of adding a turbocharger and regenerative braking/launch assist, which reduces the

impact on vehicle horsepower, and thus acceleration. The use of lightweight materials can reduce

vehicle mass and thus improve acceleration performance, but this is assumed to be offset by the

cumulative impact of other technologies, such as added mass of components that facilitate direct

fuel injection, variable compression ratios, engine start-stop and cylinder deactivation (among

others). Unfortunately, we are unable to verify this assumption because the Vehicle Attribute

Model 3 does not detail how specific added fuel efficiency technologies are aggregated within its

incremental price vs. fuel economy improvement curves.

Vehicle Design Options

The data presented in Table C-2 through Table C-7 are used to quantify relationships between

different vehicle component sizing specifications (vehicle interior volume, engine/motor power

rating and battery engine capacity) and base vehicle model physical specifications (price, fuel

economy and acceleration), over time (model year). The parameters in Table C-8 are used to

quantify the relationship between vehicle price increase and fuel economy improvement

provided by utilizing added fuel efficiency technologies, over time. Collectively, these

197

relationships map the trade-offs among overall vehicle fuel economy, price, acceleration, size

and driving range, over time.

Quadratic lines of best fit are developed within Excel for engine/motor power ratings versus base

vehicle model price, fuel economy and acceleration performance. The engine/motor power rating

to achieve the target 0-96 km/h acceleration time of 9.3 s is calculated iteratively for each vehicle

model. The exception being vehicle models that comprise the vehicle acceleration pathway, in

which case the engine/motor power rating that maintained 2012 reference vehicle price and met

future CAFE standards, after added fuel efficiency technologies are applied, is iteratively

determined. In the case of the vehicle price option, future CAFE standards are met by applying

added fuel efficiency technologies only, to base vehicle models with 0-96 km/h acceleration

times of 9.3 s.

A linear relationship is assumed for vehicle size versus base vehicle model price, fuel economy

and acceleration performance, over time. This simplified relationship is due to the lack of vehicle

body types beyond a sedan and SUV within Autonomie to quantify the characteristics of vehicles

with different interior volumes by similar footprint (e.g., no wagons or range of SUVs with same

footprint). Linearly interpolating between car and SUV characteristics is consistent with the

method used in the Vehicle Attribute Model to estimate the relationship between vehicle price

and mass.3 The vehicle size option is developed by linearly interpolating between the Honda

Accord-like and Chevy Equinox-like vehicles, all of which have engine power ratings that

provide 9.3 s 0-96 km/h acceleration time and added fuel efficiency technologies that meet CAFE

standards. Vehicle model specifications are calculated for a vehicle with an interior volume that

would maintain the 2012 reference vehicle price.

A linear relationship is assumed for battery energy capacity versus base vehicle model price, fuel

economy and acceleration performance. This relationship is assumed to be applicable across the

narrow range of battery energy capacities examined (even upper end of range is less than

currently real world battery electric vehicle capacities),15 which are limited by vehicle price and

acceleration performance constraints as described above. Additionally, batteries are modular

(compiled of individual cells) with approximately linear impacts on vehicle price96 and fuel

economy.3 The vehicle driving range option is developed by linearly interpolating between 6

kWh and 16 kWh battery electric vehicles, all of which have motor power ratings that provide

198

9.3 s 0-96 km/h acceleration time. Added fuel efficiency technologies are then applied to

maintaining the 2012 reference vehicle price. Vehicle model specifications are iteratively

determined based on the combination of battery energy capacity and utilization of added fuel

efficiency technologies that maximize vehicle driving range.

Evaluation of Study Results through Comparison with Literature

This study uses a novel means of analyzing CAFE standards. As such, the results are compared

and contrasted with those derived from methods found in the literature. However, there are no

estimates of the degree to which vehicle size or acceleration could increase from 2012 levels to

meet 2025 CAFE standards. Thus, the approach proposed by An and DeCicco 33 is used to

supplement the results of this study.

An and DeCicco33 developed the performance, size and fuel economy index (PSFI). This is

calculated as the product of average annual US engine power rating to vehicle mass ratio (hp/lb),

size (ft3), and fuel economy (mpg). PSFI increased approximately linearly from 1977 to 2005. If

this trend continues, PSFI could increase by 24% from 2012 to 2025, which is less than the 66%

increase in CAFE standard fuel economy targets during the same time frame. Therefore, a

reduction in engine power rating to vehicle mass ratio (hp/lb) or size (ft3) by 26% could facilitate

this fuel economy increase, according to this approach [(100%-

26%)*(100%+66%)=(100%+24%)].

Figure C-2: PSFI projected to 2025 based on 1977-2005 data33 (adapted from An and

DeCicco)33

Note: PSFI is the product of average annual US engine power rating to vehicle mass ratio (hp/lb), size (ft3), and fuel

economy (mpg)

0

100

200

300

1975 1985 1995 2005 2015 2025

PSF

I (h

p/l

b*f

t3 *m

pg)

Model Year

24% increase in PSFI from 2012 to 2025

199

The relationship between vehicle acceleration and the engine power rating to vehicle mass ratio

is not linear. Therefore, Equation C-2 from the EPA 13 is used to estimate 0 to 60 mph (96 km/h)

acceleration time. A 26% decrease in the performance ratio results in a 27% increase in 0 to 60

mph (96 km/h) acceleration time.

Equation C-2: Acceleration performance as a function of engine power rating to vehicle

mass 13

Z60 = 0.892P−0.805

Where:

Z60 = 0 to 60 mph (96 km/h) acceleration time [s]

P = performance ratio of engine power rating to vehicle mass [hp/lb]

Results Details

The results used to produce Figure 6-2 are presented in this section. 2012 reference vehicle

specifications are detailed in Table C-9, while 2015, 2020 and 2025 vehicle models that

comprise of each vehicle design options are detailed in Table C-10. The price curves in Figure

6-2 are based on the mid-price scenario, while the high and low price scenarios form the ends of

the error bars in Figure 6-2a.

Table C-9: 2012 Reference vehicle model specifications

Engine Power Rating (kW) 147

0-96 km/h Acceleration (s) 9.3

Interior Volume (L) 4020

Driving Range (km) 600

Footprint (m2) 4.5

2012 CAFE standard –fuel economy/consumption target for 4.5 m2 footprint (mpg/km L per L100 km)

32/137.4

Base Vehicle Model (mpg/km L per L100 km) 27/128.7

Level of added fuel efficiency tech (%) 16


Vehicle Retail Price ($) 22100 20900 20000

Base Vehicle Model ($) 21000 20100 19500

Added fuel efficiency technologies ($) 1000 800 600

200

Table C-10: Vehicle design option model specifications


2015 2020 2025 2015 2020 2025 2015 2020 2025

Vehicle Price Option

Vehicle Retail Price (2010 USD) 22100 23200 25600 20800 21400 23000 19800 19800 20600

Base vehicle model 20400 19500 18900 19400 18600 18000 18500 17700 17200

Added fuel efficiency technologies 1600 3700 7000 1300 1800 5200 900 1800 3400

Fuel economy /consumption (mpg/kmL per L100 km)

34/ 6.9

42/ 5.6

53/ 4.4

34/ 6.9

42/ 5.6

53/ 4.4

34/ 6.9

42/ 5.6

53/ 4.4

Base vehicle model (mpg/kmL per L100 km)

27/ 8.7

27/ 8.7

27/ 8.7

27/ 8.7

27/ 8.7

27/ 8.7

27/ 8.7

27/ 8.7

27/ 8.7

Level of added fuel efficiency tech (%) 25 54 93 25 54 93 25 54 93

Vehicle Acceleration Option

0-60 mph Acceleration (s) 9.1 9.9 12.2 9.1 9.9 12.2 9.1 9.9 12.2

Engine Power Rating (kW) 150 138 112 150 138 112 150 138 112





34/ 6.9

42/ 5.6

53/ 4.4

34/ 6.9

42/ 5.6

53/ 4.4

34/ 6.9

42/ 5.6

53/ 4.4


27/ 8.7

28/ 8.4

30/ 7.8

27/ 8.7

28/ 8.4

30/ 7.8

27/ 8.7

28/ 8.4

30/ 7.8


Vehicle Size Option

Interior Volume (L) 4081 3836 3305 4081 3836 3305 4081 3836 3305

Engine Power Rating (kW) 148 144 138 148 144 138 148 144 138





34/ 6.9

41/ 5.7

52/ 4.5

34/ 6.9

41/ 5.7

52/ 4.5

34/ 6.9

41/ 5.7

52/ 4.5


27/ 8.7

28/ 8.4

30/ 7.8

27/ 8.7

28/ 8.4

30/ 7.8

27/ 8.7

28/ 8.4

30/ 7.8


Driving Range Option

Driving Range (km) n/a 25 35 n/a 25 35 n/a 25 35

Battery Capacity (kWh) n/a 10 13 n/a 10 13 n/a 10 13

Motor Power Rating (kW) n/a 145 140 n/a 145 140 n/a 145 140

Vehicle Retail Price (2010 USD) n/a 23200 22000 n/a 20900 20900 n/a 19700 19400

Base vehicle model n/a 23100 21900 n/a 20900 20800 n/a 19700 19300

Added fuel efficiency technologies n/a 0 200 n/a 0 100 n/a 0 0


n/a 72/ 3.3

69/ 3.4

n/a 72/ 3.3

69/ 3.4

n/a 72/ 3.3

69/ 3.4


n/a 68/ 3.5

67/ 3.5

n/a 68/ 3.5

67/ 3.5

n/a 68/ 3.5

67/ 3.5

Level of added fuel efficiency tech (%) n/a 1 3 n/a 1 3 n/a 1 3

201

Appendix D: Chapter 7 Supporting Information

Supplemental Methods

All vehicles in this study are based on a method discussed in Chapter 7 and first developed in

Chapter 6. Each vehicle includes a base vehicle model developed within Autonomie. All base

vehicle models are modified using assumptions from the Vehicle Attribute Model to improve

fuel economy and, in the case of compressed natural gas (CNG) vehicles, for CNG use. An

overview of this process is illustrated in Figure D-1 and discussed below.

Figure D-1: Overview of vehicle models

Notes: CAFE = Corporate Average Fuel Economy standards, CNG = compressed natural gas, ICEV = internal

combustion engine vehicle, BEV = battery electric vehicle

Complete Vehicle Models

CNG Use Modifications

(Vehicle Attribute Model)


(Vehicle Attribute Model)

Base Vehicle Model Plug-in Battery Capacity (Autonomie)

Base Vehicle Model Plug-in Battery Cell Type

(Autonomie)

Base Vehicle Model Powertrain

(Autonomie)

Base Vehicle Model Glider

(Autonomie)

Chevy Equinox-

Like

Gasoline Conventional Vehicle

Not Applicable

Not Applicable

93% fuel economy increase

(2025 CAFE)

Not Applicable

Gasoline High-

Efficiency ICEV

Engine and tank mods for CNG use

CNG High-

Efficiency ICEV


(2020 CAFE)


CNG Mid-Efficiency

ICEV


(2015 CAFE)


CNG Low-

Efficiency ICEV

Battery Electric Vehicle

High Power

32 kWh


(Max)

Not Applicable

Short-Distance

BEV

High Energy

98 kWh


(Max)

Not Applicable

Mid-Distance

BEV

169 kWh


(Max)

Not Applicable

Long-Distance

BEV

202

Base Vehicle Models

Autonomie vehicle simulation software is used to develop the base vehicle models.96 This tool

simulates vehicle performance (e.g., fuel economy) and estimates manufacturing costs based on

detailed component level assumptions (e.g., aerodynamic drag).96 The base vehicle models for

internal combustion engine vehicles (ICEV) and battery electric vehicles (BEV) are based on

Autonomie vehicle templates with gasoline conventional and battery electric powertrains,

respectively. Both templates are modified to have a Chevy Equinox-like glider (vehicle without

powertrain).63 A common glider is selected for comparability and a crossover SUV is chosen to

better represent the light-duty vehicle market than a car or truck-based SUV. Different

powertrain components (internal combustion engine and electric motor) power ratings are tested

for acceleration and fuel economy performance. The component specifications that provide

average light-duty vehicle 0-98 km/h acceleration time of 9.3 s13 are interpolated, along with

associated fuel economy ratings presented in Table D-1.

Plug-in batteries are a unique aspect of the BEV base vehicle models. Batteries are required to

provide sufficient energy and power capacities. Batteries energy capacities are sized

(interpolated iteratively alongside the level of added fuel efficiency technologies, as discussed in

the following subsection) to provide the targeted 100 km, 300 km and 500 km driving ranges

using both high power cells (1041 W and 148 Wh per cell) and high energy cells (800 W and

324 Wh per cell) defined within Autonomie.96 The lowest price option that can still provide the

sufficient power to achieve the 0-98 km/h acceleration time of 9.3 s is selected. The short-

distance BEV uses a 32 kWh battery comprised of high power cells. The mid- and long-distance

BEVs use 98 kWh and 169 kWh batteries, respectively, comprised of high energy cells.

The price of the base vehicle models are detailed in Table D-1. Prices are based on Autonomie96

component manufacturing costs. The exception is the price of the charger, which is not included

in Autonomie96 and thus from the Vehicle Attribute Model.3 A 30% retail price markup to be

consistent with the added fuel efficiency technologies, which are discussed in the following

subsection.3 The average of the price ranges are used for the base case results in Chapter 7.

203

Table D-1: Fuel economy and price of base vehicle models

ICEV Short-Distance BEV

Mid-Distance BEV

Long-Distance BEV

Fuel Economy (2-cyclea/5-cycleb MPGec) 21/27 70/100 63/90 56/80

Price (Low/High Estimate)

Glider $12000/$12000 $12000/$12000 $12000/$12000 $12000/$12000

Engine/Motor $2600/$3400 $1000/$2000 $1100/$2300 $1300/$2700

Gearbox $1500/$2100 0/0 0/0 0/0

Plug-in Battery 0/0 $8200/$11500 $14200/$24100 $24600/$41800

Charger 0/0 $900/$900 $900/$900 $900/$900

Other $1100/$1200 $2200/$2300 $2200/$2300 $2200/$2300 Aunadjusted laboratory rating used for CAFE standard compliance badjusted rating used for real world fuel consumption and driving range estimate cMiles per gallon gasoline on an energy equivalent basis


The Vehicle Attribute Model3 estimates the incremental price of fuel economy improvements

based on the aggregation of different fuel efficiency technologies (e.g., lightweight materials and

hybrid electric powertrain components) forecasted to be commercially available in future model

years. This tool is used to model added fuel efficiency technologies. Added fuel efficiency

technologies are modelled with the Vehicle Attribute Model,3 which is structured to model

incremental changes to increase fuel economy, unlike Autonomie.96 The price of these

incremental fuel economy improvements are added to the base vehicle models. The fuel

economy of ICEVs are improved to meet 2015, 2020 and 2025 CAFE standards,9 respectively,

for a 4.5 m2 Chevy Equinox-like footprint,63 as discussed in Chapter 7. The fuel economy of

BEVs are improved to the maximum forecasted to be feasible within the Vehicle Attribute

Model,3 which was determined iteratively to be a more cost-effective means to provide the

driving ranges targeted in this study than further increasing battery capacity. The price of fuel

economy improvements are estimated with Equation D-1 from the Vehicle Attribute Model,3

which is based on an aggregation of individual technologies from the Energy Information

Administration.2 The incremental fuel economy and vehicle price of added fuel efficiency

technologies are shown in Table D-2. The average of the price ranges are used for the base case

results in Chapter 7.

204

Equation D-1: Price of added fuel efficiency technologies

𝑃 =𝑏

𝑘(𝑒

𝑘𝐹𝐸

𝐹𝐸𝑜 − 𝑒𝑘)

Where:

𝑃=incremental price [$]

𝑏=price parameter [$], 947 to 2623 for ICEVs and 1.03x10-5 to 1.27x10-3 for battery electric vehicles

𝑘=price scaling factor [dimensionless], 0.7 to 0.9 for ICEVs and 15 to 18 for battery electric vehicles

𝐹𝐸𝑜=Initial fuel economy [MPG]

𝐹𝐸=Improved fuel economy [MPG], limited by technological options to a maximum of 121% and 20% greater

than the initial fuel economy for internal combustion engine vehicles and battery electric vehicles, respectively

Table D-2: Incremental fuel economy and price from added fuel efficiency technologies

Low-Efficiency

ICEV

Mid-Efficiency

ICEV

High-Efficiency

ICEV Short-

Distance BEV Mid-Distance

BEV Long-

Distance BEV

Fuel economy improvement over base vehicle model

25% 54% 93% 20% 20% 20%

Price (Low/High Estimate) $700/$1500 $1700/$3500 $3400/$7000 $500/$1200 $500/$1200 $500/$1200

Notes: ICEV = internal combustion engine vehicle, BEV = battery electric vehicle

205

CNG Modifications

The Vehicle Attribute Model3 is used to model modifications of gasoline ICEVs modifications

for CNG use, which is not possible within Autonomie.96 Engine modification costs range from

$500 to $2000 and result in a thermal efficiency improvement of 14%, but additional fuel tank

mass offsets some of this benefit.3 A stainless steel fuel tank has a fixed cost of $290, a variable

cost of $40 per Lge (liter gasoline energy equivalent) and a mass of 4 kg per Lge.3 A carbon fibre

fuel tank has a fixed cost of $320, a variable cost of $40 per Lge (liter gasoline energy

equivalent) and a mass of 1 kg per Lge.3 Fuel economy is reduced by 6% per 10% increase in

vehicle mass.3 Fuel tank size is scaled to provide an average light-duty vehicle driving range of

600 km.3 The range in price and fuel economy from the modifications are shown in Table D-3,

with higher fuel economy associated with higher vehicle price. The average of these ranges are

used for the base case results in Chapter 7.

Table D-3: Incremental fuel economy and price from CNG modifications

Low-Efficiency ICEV

Mid-Efficiency ICEV

High-Efficiency ICEV

Fuel economy improvement over base vehicle model (Low/High) 6%/12% 8%/12% 9%/12%

Price (Low/High) $2400/$4000 $2100/$3700 $1800/$3400

Notes: ICEV = internal combustion engine vehicle

206

Crystal Ball

The Monte Carlo analyses were conducted with Crystal Ball software.98 The input parameters are

provided in Table D-4. Results are based on simulations of 10,000 trials.

Table D-4: Monte Carlo and sensitivity analyses assumptions

5th/50th/95th Percentile Assumption Distribution

Operation

ICEV Fuel Economy 80%/100%/120% of base case Weibull distribution based on fuel

economy from GREET7

BEV Fuel Economy 80%/100%/120% of base case Weibull distribution based on fuel

economy from GREET7

Lifetime Years 7/17/27 years Discrete distribution based on US statistics

from the Transportation Energy Data Book1

Lifetime vehicle miles travelled 90,000/180,000/240,000 miles Discrete distribution based on US statistics

from Transportation Energy Data Book1

Discount Rate 4%/8%/20% Discrete distribution based on discount

rates from Argonne National Laboratory42

Fuel Price Low Oil Price/Reference/High Oil Price

Scenario Discrete distribution based on Annual

Energy Outlook price forecasts2

Vehicle Design

Base Vehicle Model Price 16%/50%/84% of difference between

high and low price estimates Triangular distribution on prices from

Autonomie96

Fuel Efficiency Improvement Costs 16%/50%/84% of difference between

high and low price estimates Triangular distribution based on prices

from Vehicle Attribute Model3

CNG Modification Costs 16%/50%/84% of difference between

high and low price estimates Triangular distribution on price range from

Vehicle Attribute Model3

Battery Costs 16%/50%/84% of difference between

high and low price estimates Triangular distribution on prices from

Autonomie96

Fuel Production

Gasoline Production GHGs 97%/100%/103% of base case Normal distribution based on gasoline

refining efficiency from GREET7

CNG Production GHGs 98%/100%/102% of base case Triangular distribution based on CNG compression efficiency from GREET7

Electricity Production GHGs 80%/100%/108% of base case Weibull distribution based on combined

cycle electricity generation efficiency from GREET7

Notes: ICEV = internal combustion engine vehicle, BEV = battery electric vehicle

207

Supplemental Results

The Monte Carlo analysis results presented in Figure 7-3 and discussed in Chapter 7 are

presented in greater detail here in Figure D-2. The ownership costs and well-to-wheel GHG

emissions from using CNG or natural gas-derived electricity (NGCCe) can be higher or lower

than those of the gasoline high-efficiency ICEVs.

Figure D-2: Histogram and 90% confidence intervals (CI) of incremental ownership costs

and well-to-wheel GHG emissions relative to gasoline use

Notes: ICEV = internal combustion engine vehicle, BEV = battery electric vehicle, CNG = compressed natural gas,

NGCCe = natural gas combined cycle derived electricity

0

4000

8000

<-1

2%

-12

% t

o -

10

%

-10

% t

o -

8%

-8%

to

-6

%

-6%

to

-4

%

-4%

to

-2

%

-2%

to

0%

0%

to

2%

2%

to

4%

4%

to

6%

>6%

Freq

uen

cy o

f R

esu

lts

Incremental GHG Emissions (Relative to Gasoline Vehicle)

CNG ICEV Incremental Ownership Costs

High-Efficiency (90% CI: -11% to 5%)

Mid-Efficiency (90% CI: -13% to 0%)

Low-Efficiency (90% CI: -11% to 0%)

0

4000

8000

<-3

0%

-30

% t

o -

20

%

-20

% t

o -

10

%

-10

% t

o 0

%

0%

to

10%

10%

to

20

%

20%

to

30

%

30%

to

40

%

40%

to

50

%

50%

to

60

%

>60%

Freq

uen

cy o

f R

esu

lts


NGCCe BEV Incremental Ownership Costs

Short-Distance (90% CI: -11% to 13%)

Mid-Distance (90% CI: 12% to 35%)

Long-Distance (90% CI: 34% to 53%)

0

4000

8000

<-2

5%

-25

% t

o -

20

%

-20

% t

o -

15

%

-15

% t

o -

10

%

-10

% t

o -

5%

-5%

to

0%

0%

to

5%

5%

to

10%

10%

to

15

%

15%

to

20

%

>20%

Freq

uen

cy o

f R

esu

lts


CNG ICEV Incremental GHG Emissions

High-Efficiency (90% CI: -28% to -23%)

Mid-Efficiency (90% CI: -9% to -3%)

Low-Efficiency (90% CI: 13% to 21%)

0

4000

8000

<-4

5%

-45

% t

o -

40

%

-40

% t

o -

35

%

-35

% t

o -

30

%

-30

% t

o -

25

%

-25

% t

o -

20

%

-20

% t

o -

15

%

-15

% t

o -

10

%

-10

% t

o -

5%

-5%

to

0%

>0%

Freq

uen

cy o

f R

esu

lts


NGCCe BEV Incremental GHG emissions

Short-Distance (90% CI: -52% to -4%)

Mid-Distance (90% CI: -47% to 6%)

Long-Distance (90% CI: -40% to -19%)

208

The Monte Carlo analysis results for the alternative scenarios discussed in Chapter 7 are

presented in Figure D-3. The GHG emissions from each of the renewable CNG ICEVs and

biomass-derived electricity BEVs are lower than those of the gasoline high-efficiency ICEVs.

The GHG emissions from each of the coal-derived electricity BEVs are higher than those of the

gasoline high-efficiency ICEVs.

Figure D-3: Histogram and 90% confidence intervals (CI) of incremental well-to-wheel

GHG emissions relative to gasoline use for vehicles using renewable compressed natural

gas, biomass-derived electricity or coal-derived electricity

Notes: ICEV = internal combustion engine vehicle, BEV = battery electric vehicle, CNG = compressed natural gas,

NGCCe = natural gas combined cycle derived electricity

0

4000

8000

<-8

4%

-84

% t

o -

83

%

-83

% t

o -

82

%

-82

% t

o -

81

%

-81

% t

o -

80

%

-80

% t

o -

79

%

-79

% t

o -

78

%

-78

% t

o -

77

%

-77

% t

o -

76

%

-76

% t

o -

75

%

>-7

5%

Freq

uen

cy o

f R

esu

lts


Renewable CNG ICEV Incremental GHG Emissions

High-Efficiency (90% CI: -84% to -83%)

Mid-Efficiency (90% CI: -81% to -79%)

Low-Efficiency (90% CI: -76% to -74%)

0

4000

8000

<-9

5%

-95

% t

o -

94

%

-94

% t

o -

93

%

-93

% t

o -

92

%

-92

% t

o -

91

%

-91

% t

o -

90

%

-90

% t

o -

89

%

-89

% t

o -

88

%

-88

% t

o -

87

%

-87

% t

o -

86

%

>-8

6%

Freq

uen

cy o

f R

esu

lts


Biomass-Derived Electricity BEV Incremental GHG Emissions

Short-Distance (90% CI: -95% to -90%)

Mid-Distance (90% CI: -95% to -89%)

Long-Distance (90% CI: -94% to -88%)

0

4000

8000

<0%

0%

to

25

%

25

% t

o 5

0%

50

% t

o 7

5%

75

% t

o 1

00

%

10

0%

to

12

5%

12

5%

to

15

0%

15

0%

to

17

5%

17

5%

to

20

0%

20

0%

to

22

5%

>22

5%

Freq

uen

cy o

f R

esu

lts


Coal-Derived Electricity BEV Incremental GHG Emissions

Short-Distance (90% CI: 14% to 129%)

Mid-Distance (90% CI: 25% to 152%)

Long-Distance (90% CI: 41% to 182%)

209

Copyright Acknowledgements

Chapter 5 and Appendix A is adapted with permission from Luk, J., Pourbafrani, M., Saville, B.,

MacLean, H. Ethanol or Bio-electricity? Life cycle assessment of bioenergy use in light-duty-

vehicles, Environmental Science & Technology, 2013, 47 (18) 10676-10684.

http://pubs.acs.org/articlesonrequest/AOR-JzIibBUXwnhzN6Exqysg. Copyright 2015 American

Chemical Society.

Chapter 6 and Appendix A is adapted with permission from Luk, J., Saville, B., MacLean, H.

Life cycle air emissions impacts and ownership costs of light-duty vehicles using natural gas as a

primary energy source, Environmental Science & Technology, 2015, 49 (8) 5151-5160.

http://pubs.acs.org/articlesonrequest/AOR-3bqzfeAZcSBvFxjutSWh. Copyright 2015 American

Chemical Society.

http://pubs.acs.org/articlesonrequest/AOR-JzIibBUXwnhzN6Exqysg

http://pubs.acs.org/articlesonrequest/AOR-3bqzfeAZcSBvFxjutSWh

Documents

Advancing Life Cycle Comparisons of Future Alternative Light-duty Vehicles