Do More Batteries Make A Plugin Hybrid Better

Do More Batteries Make A Plugin Hybrid Better?Implications from Optimal Vehicle Design and Allocation

Lawrence Berkeley Laboratory SeminarJune 18, 2010

ChingShin Norman ShiauPostdoctoral Research FellowMechanical EngineeringCarnegie Mellon University

CS Norman Shiau – Implications of Optimal PHEV Design and Allocation

15

20

25

30

35

40

1978 1988 1998 2008 2018

EISA35 mpg

Obama 35.5 mpg

32.5

24.5

CA

FE s

tand

ards

(mpg

)

Year

CAFE Policy

Research Overview

2

Product Design

Public Policy

Market Systems

Vehicle Design Decisions for Market and Policy

Design for Market Systems

Implications of VehicleElectrification

Plug-in Hybrid

Market Competitionand Structure

w1a

M1

Consumer

CRa CRb

M2w1bw2a w2b

p1a p1bp2a p2b

M1

Consumer

CS1w1 = p1

M2CS2

w2 = p2

Company store

Franchised RetailerM1

FR1 FR2

w1

p1

w2

p2

M2

Consumer

Multiple Common Retailers


Plugin Hybrid Technology

3

What are the economic and environmental implications of plug-in hybrid vehicles?

• Public concerns: global warming on GHG emissions and foreign oil dependency in the US transportation sector

• Plug-in hybrid electric vehicle (PHEV) is considered as a potential technology to address these issues

• Net effects of PHEVs depend critically on vehicle design and battery technology


Vehicle Technologies

4

CV HEV PHEV BEV

X X

X X

XGasoline

Electricity

Power Convertor Engine MotorEngine & Motor Engine & Motor

Battery Pack None Small Medium Large


PHEV Powertrain System

5

Control motor

Traction motor

Planetary gear set

Power to

wheels

Electrical Energy Flow

Gasoline Energy Flow

Engine

Fuel tank

Power inverter

100%Max SOC

Target SOCMin SOC

DOD(s)

Phys

ical

cap

acity

lim

it

Rate

d ca

paci

ty

Swin

g

Battery pack

0%Driving distance s

Stat

e of

Cha

rge

(SO

C)

Charge-Deplete

(CD)

Charge-Sustain (CS)

AER


PHEV Literature Review Prior PHEV Studies◦ EPRI Report (2001)◦ Simpson [NREL] (2006)◦ EPRI-NRDC Report (2007)◦ Lemoine, Kammen, and Farrell [UC Berkeley] (2008)◦ Samaras and Meisterling [CMU-EPP] (2008)◦ Kromer and Heywood [MIT] (2009)◦ Sioshansi and Denholm [NREL] (2009)◦ NRC PHEV analysis report (2009)◦ Plotkin and Singh [ANL] (2009)

This study focuses on the implications of optimal PHEV designs and driver allocations on life cycle performance

6

What are the best vehicle choices to reducing life cycle GHGs, cost and fuel consumption?


PHEV Optimization Framework

7

Benevolent dictator to determine optimal PHEV designs and driver allocations for social objectives

Optimization ModelMinimize total fuel/cost/GHGs

with respect to design and allocationsubject to vehicle design constraints

SolutionsOptimal vehicle types, drivervehicle allocations and design decisions

PHEV PerformanceMetamodel for simulation data

National Driving DataDistribution for driving distance

Battery DegradationModel for battery life


Vehicle Simulation

8

Use the Powertrain Systems Analysis Toolkit (PSAT) vehicle simulator developed by Argonne National Laboratory (ANL)

Start with a model of a Toyota Prius Use EPA UDDS cycles to test PHEV performance













Battery DegradationModel for battery lifeDaily travel distribution

9Federal Highway Administration, 2010, "National Household Travel Survey 2009," Department of Transportation, Washington, DC.

•Use the 2009 NHTS data to estimate the national average distance driven per day over the population of drivers•Size of the 2009 survey: 136,410 households

0 50 100 150 2000

0.01

0.02

0.03

Daily driving miles

Prob

abilit

y de

nsity

func

tion

S ; 0sf s e s λ = 0.0296 (maximum likelihood)

•We fit the weighted driving data using the exponential distribution


0 0.2 0.4 0.6 0.8 1Ba

ttery

lfie

cha

rge

cycl

eDepth of discharge per cycle

(%)

103

104

105

106

Battery degradation

10

0.88

0.92

0.96

1

0 1 2 3 4

Rel

ativ

e en

ergy

capa

city

Normalized energy processed(Wh processed / Wh capacity)

1×103 2×103 3×103 4×103

Peterson energy-based model

Peterson, S.B., Whitacre, J.F., and Apt, J. (2010) Lithium-Ion Battery Cell Degradation Resulting from Realistic Vehicle and Vehicle-to-Grid Utilization. Journal of Power Sources. 195(8) 2385–2392.

Rosenkranz, K. (2003) Deep-Cycle Batteries for Plug-in Hybrid Application. Presentation in EVS-20 Plug-In Hybrid Vehicle Workshop, November 16-19, Long Beach, CA

RosenkranzDOD-based model

Markel and Simpson [NREL] (2006)Simpson [NREL] (2006)Kromer and Heywood [MIT] (2009)

LiFePO4 cell








Estimated battery life using two models• Rosenkranz model • Not account for the battery

degradation in CS mode because of no DOD variation

• Can be too optimistic for short distance driving

11Plots using a PHEV40 with battery

9.5kWh and swing at 80%

Battery energy status

swin

g

CD mode CS mode

Driving distance

Sta

te o

f ene

rgy

Charging

AER

Fully charged

Target SOC

• Peterson model• Tested under variable C-rate• Account for energy processed

in CS mode and charging

0

100

200

300

400

0 50 100 150 200

Batte

ry li

fe in

VM

T (m

iles)

Daily driving distance (miles)

Battery life in VMT








BAT NN VEH NC OP

BAT NR

Buy: ,, , 1,Lease: ,,

c CRF r TCRF r T c CRF r Tf s c

c CRF r BCRF r T D D

x

Objective Functions

12

Fuel consumption per day:

GG

G

, sf s

x = efficiency (miles/kWh or miles/gal)

Equivalent annualized cost (EAC) per day:

operating vehicle battery

Average lifecycle GHG emissions per day:

BATVEHV OP

BAT

Buy:,

Lease:v T

f sv BTD

x

operatingvehicle battery

Design variables:x = vehicle design variable (engine, motor, battery and swing)si = vehicle allocation range

Constraints:060mph acceleration time, minimum SOC after multiple US06 cycles

T: vehicle life yearB: battery life yearD: driving days per year

rN: nominal (market) discount raterR: real (inflation-free) discount rate








Core concept of the optimization model

13

1O S, 1, , 1

minimize ,i

ii i

n s

iss i n i

f s f s ds

x

x

Objective function per day {$, GHG, gal}

pdf of distance driven per day

sis

< si per daydrive #1

> si per daydrive #2

Vehicle #1Vehicle #2 ,i s x

,i s x Population-weighted objective


Assumptions

Fuel and electric efficiency estimated by UDDS cycles $400 per kWh Li-ion battery pack cost (PHEV) $600 per kWh NiMH battery pack cost (HEV) $3.30 per gal gasoline (2008 average) $0.11 per kWh electricity (2008 average) 0.69 kg-CO2-eq/kWh grid emission (US average mix) $0 per ton-CO2-eq allowance price 5% discount rate EPRI powertrain cost model Buy-lease battery replacement Peterson battery degradation model

14


Optimal PHEV Designs and Allocations

15

Base case settings: Buy-lease battery replacement, Peterson battery degradation model, $400/kWh Li-ion battery cost, $600/kWh NiMH cost, $3.30/gal gasoline, $0.11/kWh electricity, grid emission 0.69 kg-CO2-eq/kWh, $0/ton CO2-eq allowance price, and 5% discount rate

Pop

ulat

ion-

wei

ghte

d ob

ject

ive

per v

ehic

le p

er d

ay

Minimum Fuel Consumption

Minimum Lifecycle GHGs

Minimum Lifecycle Costs

PHEV87(21.6kWh*80%)

0.000

0.004

0.008

0.012

0 100 200

f G(x

* ,s)f S

(s)

CV

HEV

PHEV870.00

0.04

0.08

0.12

0.16

0 100 200

HEV

PHEV40

PHEV25

CV

f V(x

* ,s)f S

(s)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200

f C(x

* ,s)f S

(s)

CV

PHEV20

HEV

Daily travel milesPHEV25(7.0kWh*68%)PHEV40(9.6kWh*80%)

PHEV20(5.4kWH*67%)

31 50


Breakdown analysis

16

Lifecycle GHGs Lifecycle EAC

0

2

4

6

8

10

12

14

CV HEV PHEV25 PHEV40

Lifecycle

GHG

s pe

r day (k

g‐CO

2‐eq

)

GHGs on operationGHGs on batteryGHGs on vehicle

2% 4%

87%

76%73% 71%

13% 23%

1%

25% 25% 0

2

4

6

8

10

CV HEV PHEV20

EAC pe

r day ($

)

EAC on operationEAC on batteryEAC on vehicle

50% 69% 70%

3% 10%

50%28% 20%

Assume travel distance 30 miles a day


Sensitivity analysis for min. GHGs

17

• US average grid mix: 0.69 kg-CO2e/kWh (Weber et al., 2010)• Nuclear: 0.07 kg-CO2e/kWh (Sovacool, 2008)• IGCC w/ CCS: 0.25 kg-CO2e/kWh (Jaramillo et al., 2009)• Natural gas: 0.47 kg-CO2e/kWh (Weisser, 2007)• Coal: 0.90 kg-CO2e/kWh (Weisser, 2007)

0% 20% 40% 60% 80% 100%

Coal 8.19 kgCO2e/vehicleday

Natural gas 6.20 kgCO2e/vehicleday

IGCCCCS4.60 kgCO2e/vehicleday

Nuclear3.22 kgCO2e/vehicleday

Min. GHGs base case7.73 kgCO2e/vehicleday

Population (%)

PHEV3637 miles

34 miles

62 miles

Daily distance (miles) 0 7.5 17.3 31.0 54.5 200

HEV

PHEV60PHEV87

PHEV27 PHEV87PHEV4783 miles

VMT (%) 0% 2% 10% 24% 49% 100%

PHEV3637 miles 62 miles

PHEV59 PHEV87

PHEV3437 miles 65 miles

PHEV57 PHEV87


Sensitivity analysis for min. cost

18

• Consider 3-vehicle segment as base case

• High battery cost, low gas price and high elec. price make PHEV not cost competitive

• To make PHEVs part of the least-cost solution:Li-ion < $590/kWh at 5%Li-ion < $410/kWh at 10%

0% 20% 40% 60% 80% 100%

Carbon price $100/ton$8.05/verhicleday

Nm. discount rate 10%$9.31/vehicleday

Elec. price $0.30/kWh$7.39/vehicleday

Elec. price $0.06/kWh$7.02/vehicleday

Gas price $6.00/gal$7.89/vehicleday

Gas price $1.50/gal$6.18/vehicleday

Liion battery $1000/kWh$7.39/vehicleday

Liion battery $250/kWh$6.99/vehicleday

Min. cost base case$7.23/vehicleday

Population (%)

PHEV1620 miles

31 miles 65 miles

Daily distance (miles) 0 7.5 17.3 31.0 54.5 200

51 milesPHEV25 HEV

PHEV19 PHEV25PHEV35

HEV

HEV

HEV

PHEV17 PHEV49PHEV3220 miles 38 miles

PHEV17 PHEV32 HEV23 miles 69 miles

VMT (%) 0% 2% 10% 24% 49% 100%

PHEV16 PHEV20 PHEV2620 miles 50 miles

CV HEVPHEV188 miles 32 miles


ARRA tax credit vs. carbon tax

19

Tax credit in the ARRA bill (Page 212-219)

Light-duty vehicles: Vehicle base amount is $2,500 for a PHEV can draw propulsion energy from a battery with not less than 5 kWh of capacity (base $417), plus $417 for each kWh of capacity in excess of 5 kilowatt hours. Total amount for battery shall not exceed $5,000. Thus the tax credit can be calculated by min($7500, $2500 + $417 + $417*(kWh − 5))

ARRA: http://fdsys.gpo.gov/fdsys/pkg/BILLS-111hr1ENR/pdf/BILLS-111hr1ENR.pdf

• Tax credit in the American Recovery and Reinvestment Act (ARRA) for battery size in PHEV/EV

•ARRA incentivizes PHEVs with larger batteries (AER 33-49), which results in 6% higher social costs than under a $100/ton CO2 tax scenario

0

1000

2000

3000

4000

5000

6000

7000

8000

0 5 10 15 20Tax c

redit ($)

Battery size (kWh)

GM Volt

Priusplug-in


More sensitivity tests

20

•Alternative vehicle base costs and powertrain cost models The base case optimal solution is robust

•Battery life must outlast vehicle life Optimal battery swing reduced 2-5%

•Battery leasing (prorated: paid per portion)Larger battery packs with reduced swings

•Battery end of life (EOL) at 20% capacity fade Makes PHEVs less cost competitive

•Rosenkranz DOD-base degradation modelOptimal PHEV11 with a 5.2 kWh battery at 39% swing

(a battery size equivalent to a PHEV23 at 80% swing)


Take away 1

• Large PHEVs with AER > 50 miles can reduce petroleum consumption

• PHEVs with AER 25-50 miles can reduce GHGs

• PHEVs with AER 15-25 miles for short distance travel and HEVs for longer distance travel can save cost

• Li-ion battery pack cost below $590/kWh at a 5% discount rate (or below $410/kWh at 10%) to make PHEV part of least-cost choice

21


Take away 2

Battery swing in excess of 60% with LiFePO4technology should be utilized to achieve minimum life cycle cost, GHGs, and petroleum consumption

Carbon allowance prices have marginal impact on optimal design or allocation of PHEVs

Battery tax credit in the ARRA bill may result in higher net costs than under a $100/ton CO2 tax scenario

22


Future work

Consumer and market behaviors PHEV performance on real-world driving cycles Uncertainty in GHG emissions◦ Regional effects, charge timing, and marginal dispatch

Battery technology◦ Thin-electrode for high-power and thick-electrode for high-energy

batteries◦ Calendar (storage) degradation◦ Blended-mode PHEVs

Gasoline prices and grid characteristics can become endogenous after significant PHEV market penetration

23


Publications

24

Shiau, C.-S.N. and J.J. Michalek (2009) Optimal product design under price competition. ASME Journal of Mechanical Design, 131(7), 071003.

Shiau, C.-S.N. and J. J. Michalek (2009) Should designers worry about market systems? ASME Journal of Mechanical Design, 131(1), 011011.

Shiau, C.-S.N., C. Samaras, R. Hauffe and J. J. Michalek (2009) Impact of battery weight and charging patterns on the economic and environmental benefits of plug-in hybrid vehicles. Energy Policy, 37(7), 2653-2663.

Shiau, C.-S.N., J.J. Michalek and C.T. Hendrickson (2009) A structural analysis of vehicle design responses to Corporate Average Fuel Economy policy. Transportation Research Part A: Policy and Practice, 43(9-10), 814-828.

Shiau, C.-S.N., J.J. Michalek, N. Kaushal, C.T. Hendrickson, S.B. Peterson and J.F. Whitacre(2010) Optimal plug-in hybrid electric vehicle design and allocation for minimum life cycle cost, petroleum consumption and greenhouse gas emissions. ASME Journal of Mechanical Design: Special Issue on Sustainable Design, In review.

And 9 peer-reviewed conference papers.


Acknowledgements

25

I would like to thank:Professor Jeremy MichalekProfessor Chris HendricksonProfessor Jay WhitacreDr. Paulina JaramilloNikhil KaushalScott PetersonCamilo ResendeDr. Constantine SamarasElizabeth Traut

This research was supported in part by a grant from the NSF MUSES program: Award #0628084, the NSF CAREER Award #0747911, CMU CIT Liang Ji-Dian Graduate Fellowship, and grants from Ford Motor Company and Toyota Motor Corporation.

Documents

Do More Batteries Make A Plugin Hybrid Better