Impacts of Driving Patterns on the Life Cycle Performance of Plug-in Hybrid

Electric Vehicles

Leon Raykin

SupervisorsHeather L. MacLean Matthew J. Roordahttp://myecoproject

.org/tag/electric-cars

MASc PresentationSeptember 7, 2011

Light-Duty Vehicle (LDV) Fleet

2Sources: Ribeiro et al. (2007); IEA (2010)

95%

5%Petroleum Other

27%

73%

LDV Fleet Other

10%

90%

LDV Fleet Other

Transportation fuelsused by LDV Fleet

Global petroleumenergy use

Energy-related greenhouse gas (GHG) emissions

Alternative Vehicle Options

ICE

Wheels

Fuel Tank

ICE: Internal Combustion Engine

Internal Combustion Engine Vehicle (ICEV)

Electric MotorICE

Wheels

BatteryFuel Tank

Parallel HybridConfiguration

Hybrid ElectricVehicle (HEV)

Plug-In Hybrid ElectricVehicle (PHEV)

Series Hybrid Configuration

Electric Motor

Battery

Wheels

ICE

Fuel Tank

3

Driving Patterns

Driving Conditions Driving Distance Between Recharging

http://www.pikeresearch.com/research/electric-vehicle-charging-equipment 4

2

3

4

5

6

7

8

9

10

11

Driving Distance

Aver

age

Petr

oleu

m E

nerg

y U

se (L

/100

km)

Driving Distance (Between Recharging)

5

ICEV

HEV

PHEV

ElectricMode

HEVMode

Adapted from Shiau et al. (2009)

Driving Cycles

• Used to represent driving patternsCertification driving

cycles represent fleet-average driving

• Exhibit significant regional variationSpecific regional driving

cycles can be estimated using travel demand models

0 300 600 900 12000

10

20

30

40

50

60

70

80

90

100

Time (s)

Spee

d (k

m/h

)

6

Life Cycle Assessment

• Technique for evaluating the environmental performance of a product or process over all stages of its life

• Life cycle assessment of transportation fuels/vehicles is known as a Well-To-Wheel (WTW) analysis

7

WTW Analysis

Well-To-Tank (WTT)

Tank-To-Wheel (TTW)

Objectives

• Paper 1 Apply a travel demand modeling approach to

estimate driving cycles for regional driving patterns Examine impacts of driving patterns on TTW

energy use of PHEVs• Paper 2

Evaluate implications of driving patterns on the WTW environmental performance of PHEVs:

Total, fossil, and petroleum energy use (MJ/km) GHG emissions (g CO2-eq/km)

9

Paper 1 – Travel Demand Modeling Approach

Travel Demand Modeling Approach - Methods

Lake Ontario

Max speed, length and congestion

Origin-destination demands

1. Travel Demand

Data

2. Traffic Assign-ment

Driving Cycles

3. Vehicle Motion

Modeling

PHEVsHEVICEV

4. Vehicle Selection

TTW results

4. Vehicle Simulation

Petroleum and electricity

use

1. Transportation Tomorrow Survey2. Emme 33. CALMOB64. Autonomie

Tools Used

11

1 & 2. Travel Demand Modeling

• Range of driving patternsDifferent commute

orientationsConstant commute

duration

12

Lake Ontario

Downtown Toronto

Increasing Distance and Speed

Increasing Congestion

Suburban HighwayCity

3. Vehicle Motion Modeling

Congestion

Complete Stop

Partial Stop

Reduced Cruise

City Driving Cycle

HighwayDriving Cycle

Free Flow

13

4. Vehicle Selection & Simulation

14www.dealertrend.com; www.funcar.cc

• Two PHEV designs• One HEV• One ICEV

• Vehicles normalized:Body and tire specificationsAcceleration performance

TTW Petroleum Energy Use Results

http://green.autoblog.com/2008/10/27/autoexpress-looks-at-mpg-myths-little-impact-from-tire-pressure/ 15

Average TTW Petroleum Energy Use

16

0

2

4

6

8

10

12

PHEVHEVICEV

TTW

Pet

role

um E

nerg

y U

se

(L/1

00 k

m)

City Suburban Highway

Increasing Distance and Speed

Increasing Congestion

0

1

2

3

4

5

6

7

8

9

PHEVHEV

Redu

ction

in T

TW P

etro

leum

Ene

rgy

Use

Rel

ative

to IC

EV (L

/100

km)

TTW Petroleum Savings Relative to ICEV

17

Increasing Distance and Speed

Increasing Congestion

City Suburban Highway

Paper 1 Summary

• Applied a travel demand modeling approach to estimate driving cycles for specific regional driving patterns

• Examined TTW energy use of vehicles for a wide range of driving patternsTrends in TTW energy use were generally as expected

Both driving distance and driving conditions affect TTW petroleum energy use of PHEVs

Driving patterns have opposite effects on TTW petroleum energy use of PHEVs/HEVs and ICEVs

Paper 2 – WTW Analysis

Gasoline

WTW Analysis Methods

20

TTW petroleum &

electricity use

HydroelectricNatural

GasCoalOntario

Mix

Electricity Scenario Selection

Energy use and GHG emissions

Life Cycle Inventory Analysis

WTT Inventory

Liquid Fuel Selection

WTW Results

GREET & Misc.Tools Used

www.opg.com

WTW Energy Use Results

21

WTW Total Energy Use

22

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

PHEV - Hydroelectric PHEV - Ontario Mix PHEV - Natural Gas PHEV - Coal HEV ICEV

WTW

Tot

al E

nerg

y U

se (M

J/km

)

Increasing Distance and Speed

City Suburban Highway

Increasing Congestion

-Electric Propulsion-Gasoline Propulsion

WTW Fossil Energy Use

23

0

0.5

1

1.5

2

2.5

3

3.5

4

PHEV - Hydroelectric PHEV - Ontario Mix PHEV - Natural Gas PHEV - Coal HEV ICEV

WTW

Fos

sil E

nerg

y U

se (M

J/km

)

Increasing Distance and Speed

City Suburban Highway

Increasing Congestion

WTW Petroleum Energy Use

Increasing Distance and Speed

City Suburban Highway

Increasing Congestion24

0

0.5

1

1.5

2

2.5

3

3.5

4

PHEV - Hydroelectric PHEV - Ontario Mix PHEV - Natural Gas PHEV - Coal HEV ICEV

WTW

Pet

role

um E

nerg

y U

se (M

J/km

)

WTW GHG Emissions Results

25

WTW GHG Emissions

26

0

50

100

150

200

250

300

WTW

GH

G E

mis

sion

s (g

CO

2-eq

/km

)

Increasing Distance and Speed

City Suburban Highway

Increasing Congestion

WTT vs TTW

Total Energy Use

Fossil Energy Use

Petroleum Energy Use

GHG Emissions

0%10%20%30%40%50%60%70%80%90%

100%

Gasoline Propulsion

#REF!TTW Stage

Cont

ribu

tion

to W

TW R

esul

ts

27

Paper 2 Summary

• Driving patterns substantially affect the WTW performance of PHEVs

• City favorable over highway for WTW performance of PHEVs

• Extent to which driving patterns affect WTW performance depends on electricity supply

• When charging from coal, PHEVs only result in WTW (non-petroleum) energy use and GHG emissions reductions relative to ICEVs due to differences in vehicle fuel efficiency

28

Future Research Directions

• Further calibration and evaluation of the vehicle motion model

• Application to other trips and jurisdictions• Evaluation of additional metrics and lifecycle

activities• PHEV scenario analyses using Transportation

Tomorrow Survey microdata

29

Conclusions

• Paper 1Applied a travel demand modeling approach for estimating

driving cycles for regional driving patternsEvaluated impacts of regional driving patterns on TTW

energy use of PHEVs General trends in TTW energy use were as expected

• Paper 2Demonstrated that driving patterns and the electricity

generation supply interact to substantially affect the WTW energy use and GHG emissions of PHEVs Jurisdictions characterized as having favorable electricity generation

supply and frequent traffic congestion should be most willing to support PHEVs on basis of energy use and GHG emissions benefits

30

Acknowledgements

• Heather L. MacLean and Matthew J. Roorda• UofT Graduate Students• David Checkel and Dan Handford (University of

Alberta)• Matthew Stevens (CrossChasm)

Questions?

32