PHEV Energy Storage 

Performance/Life/Cost Trade-off Analysis

8th Advanced Automotive Battery Conference

Tampa, Florida 

May 15th, 2008

Tony Markel, Kandler Smith,

and Ahmad Pesaran

(Tony_Markel@nrel.gov)

National Renewable Energy Laboratory

Supported by 

Energy Storage R&D

Vehicle Technologies Program

Office of Energy Efficiency and Renewable Energy 

U.S. Department of Energy 

NREL/PR-540-43159
2

Acknowledgements

David Howell and Tien Duong,

US Department of Energy (DOE) 

Vehicle Technologies Program 

Jeff Belt and John Christophersen, 

Idaho National Laboratory (INL)

Vince Battaglia,

Lawrence Berkeley National Laboratory (LBNL)

Loic Gaillac, 

Southern California Edison (SCE)

Todd Rhodes and Steve Lasher,

TIAX

Aaron Brooker and Gi-Heon Kim,

National Renewable Energy Laboratory (NREL)
3

Outline

• Purpose and Goal

• Approach

• Basis for Performance, Life, and Cost Models

• Preliminary Results

• Alternative Approach

• Conclusion

• Next Steps
4

Purpose

Linking Battery Performance/Life/Cost Models

• Reduce risk of

— Premature battery failure

— Falling short of consumer expectations

• Reduce incremental cost

— Use data to minimize necessary energy/power margin

• Accelerate market penetration to achieve 

significant fuel savings

Goal: Develop linked parametric modeling tools to 

mathematically evaluate battery designs to satisfy challenging 

operational requirements for a PHEV.
5

PHEV Battery Requirement Analysis

USABC’s Two Sets of Battery Requirements

• The battery requirements were selected 

based on two sets of electric range and 

time frame:

— A 10-mile all-electric-range (over UDDS) for 

a crossover vehicle in the mid-term (2012)

» Supporting potential early market experience

— A 40-mile all-electric-range (over UDDS) for 

a midsize car in the long-term (2015-2016)

» Supporting the President’s Initiative

High Energy 

to Power 

Ratio (E/P)

Battery

High Power

to Energy 

Ratio (P/E)

Battery
6

USABC PHEV Battery Targets

Characteristics at EOL (End of Life) High Power/Energy Ratio 

Battery

 High Energy/Power Ratio 

Battery

Reference Equivalent Electric Range miles 10 40

Peak Pulse Discharge Power - 2 Sec / 10 Sec kW 50 / 45 46 / 38

Peak Regen Pulse Power (10 sec) kW 30 25

Available Energy for CD (Charge Depleting) Mode, 10 kW Rate kWh 3.4 11.6

Available Energy for CS (Charge Sustaining) Mode kWh 0.5 0.3

Minimum Round-trip Energy Efficiency (USABC HEV Cycle) % 90 90

Cold cranking power at -30°C, 2 sec - 3 Pulses kW 7 7

CD Life / Discharge Throughput Cycles/MWh 5,000 / 17 5,000 / 58

CS HEV Cycle Life, 50 Wh Profile Cycles 300,000 300,000

Calendar Life, 35°C year 15 15

Maximum System Weight kg 60 120

Maximum System Volume Liter 40 80

Maximum Operating Voltage Vdc 400 400

Minimum Operating Voltage Vdc >0.55 x Vmax >0.55 x Vmax

Maximum Self-discharge Wh/day 50 50

System Recharge Rate at 30°C kW 1.4 (120V/15A) 1.4 (120V/15A)

Unassisted Operating & Charging Temperature Range °C -30 to +52 -30 to +52

Survival Temperature Range °C -46 to +66 -46 to +66

Maximum System Production Price @ 100k units/yr $ $1,700 $3,400

Requirements of End of Life Energy Storage Systems for PHEVs

www.uscar.org/commands/files_download.php?files_id=118

Supporting simulations assumed degradation in Power (~30%) and 

Energy (~20%) from Beginning of Life (BOL) to End of Life (EOL)
7

Objective

• Develop a process to optimize PHEV battery designs for 

performance, life, and cost from vehicle system 

perspective

2. Energy Storage Simulation and Analysis

Battery

Cost 

Model

Battery 

Design, 

Performance, 

Sizing 

Models

Battery

Calendar Life, 

Cycle Life

Models

Vehicle 

Simulation 

Model 

Battery 

Design

Parameters

Economic 

Factors Economic 

Model

Driving 

Patterns and 

Environment

Tradeoff

Evaluation

Vehicle 

Attributes & 

Performance 

Input

Input

Input

Input

Battery

Cost 

Model

Battery 

Design, 

Performance, 

Sizing 

Models

Battery

Calendar Life, 

Cycle Life

Models

Vehicle 

Simulation 

Model 

Battery 

Design

Parameters

Economic 

Factors Economic 

Model

Driving 

Patterns and 

Environment

Tradeoff

Evaluation

Vehicle 

Attributes & 

Performance 

Input

Input

Input

Input

Performance,

Life, Cost
8

PHEV Battery Tradeoff Study: Approach

• Use physics-based battery models to:

— Improve understanding of battery design/performance/life 

tradeoffs

— Develop capability to predict battery life under any usage 

scenario

— Reduce the number of iterations in the prototype battery design 

& testing process

— Reduce the experimental burden of technology life verification

• Use credible battery cost models developed by others

• Use vehicle simulation tools

• Run optimization routine to come up with designs that 

have best combination of performance, life, and cost
9

Optimization

PHEV Battery Design Optimization

Designing PHEV batteries to meet requirements, such as DOE/USABC, at minimum cost.

Source: INL, LBNL

Life Model

Source: VARTA

Performance Model Cost Model
10

Constant current discharge:

• Saft data sheet (C/3, 1C, 2C, 150A)

• INL data (1C)

Saft data sheet

Electrochemical

Performance Model

Model

Saft VL41M: 

Graphite negative/ 

NCA positive

Electrochemical Performance Model

• Used Newman-type model – coded in Matlab

• Chose electrochemical input parameters representative of current technology

• Tuned to constant current data (below) & INL HPPC data (not shown)
11

Objective: Quantify degradation for any given 

usage profile

• Time at T

• Time at SOC

• # cycles at ΔDODi

• rate dependency

Mechanical (cycling stress, expansion/contraction)

Thermal (chemical reactions at T, SOC)

+ Electrochemical (side reactions in use)

= Total Stress Factor (TSF)

Method: Include various stress factors

1

0

Stress Factor

Years

End of Life

MSF

TSF

ESF

TSF

Time

Usage Profile

Temperature

Current

SOC

Stress Factors

Approach for Developing Semi-Empirical Life Model
12

Example: EPRI/SCE PHEV Cycling profile decomposed 

into Ncycles @ ΔDOD and Time @ T, OCV 

SCE Accelerated Testing Experimental Data for Sprinter Li-Ion Module

End of microcycle

Start of microcycle

ΔSOC

= 0.025
13

Continued Example: Extracting Cycle Statistics

for use with Life Model

Time at high SOC

Ncycles @ ΔDOD

Time @ T, SOC

Source: INL, LBNL

Life Model

Source: VARTA

Operation Attributes

One large SOC swing

(84% contribution to loss of capacity!)

Many small SOC swings

(minor contribution to loss of capacity)
14

Model Forecasts Capacity Loss and Impedance Growth 

From Operational Data

{ } 5.0

10 Z exp Z )ln( t sei cell = ββ ⋅++

Source: INL, LBNL

Thermal Stress

(chemical reactions at various T, SOC)

Mechanical Stress

(cycling stress, expansion/contraction)

(at fixed temperature)

Life Model

Ncycles @ ΔDOD Time @ T, SOC

Capacity Loss, Impedance Growth

Christian Rosenkranz (JCS/Varta) EVS-20 Vince Battaglia (DOE/TLVT)
15

Fitting the Life Model to DataImpedance Growth Model Using SCE Data

Thermal Stress (impedance growth ~t1⁄2) and Mechanical Stress 

(capacity loss) models simultaneously fit to accelerated cycling data.

Calendar life and Cycle 

life testing being done on 

these cells in parallel 

would allow accurate 

separation of Mechanical 

and Thermal Stress 

contributions.

* INL Data: 10s resistance scaled to 18s.

Accelerated cycling at Temp of 25C

SCE Data presented at EVS23 by Loic Gaillac
16

Capacity Loss Prediction Using SCE Data

This Mechanical Stress 

(capacity loss) model fails 

to capture apparent 

accelerating trend.

Impedance contributions 

to apparent capacity 

(underdischarging & 

undercharging) 

investigated as 

accelerating trend but 

effects found negligible.

Small accelerating 

influence predicted by 

model due to increase of 

ΔSOC cycling severity 

with capacity loss.
17

Capacity loss (cycling) has appreciable impact on 

measured discharge resistance growth 

* INL Data: 10s resistance scaled to 18s.

Static Impedance

Dynamic Impedance

Capacity Loss
18

Developing Simplified Cost Model

Estimating Manufacturer Pack Cost

• Battery cost estimates from EPRIled HEV study as original source1

• EPRI HEV Cost model used for 

NREL’s EVS-22 paper on PHEV 

Cost Benefit Analysis2

• DOE-sponsored TIAX study 

reviewed cost details of two li-ion 

cathodes (NCA and NCM) 

manufacturing3

• Modified fixed costs to include a 

per cell component based on TIAX 

estimates this study

Simplified Pack Cost Model

$/pack = 11.1*kW + 224.1*kWh + 4.53*BSF + 340

Nominal 

Energy

(kWh)

P/E Detailed 

Model: 3

NCM

Detailed 

Model: 3

NCA

Simple Model: 1.2

$=11*kW+224

*kWh+680

6.88 5.8 $3120 $2600 $2660

8.46 4.7 $3510 $2860 $3020

11.46 3.5 $4290 $3500 $3680

1. Graham, R. et al. “Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options,” Electric Power Research Institute (EPRI), 2001.

2. Simpson, A., “Cost Benefit Analysis of Plug-In Hybrid Electric Vehicle Technology,” 22nd International Electric Vehicle Symposium, Yokohama, Japan, Oct. 2006.

3. “Cost Assessment for Plug-In Hybrid Vehicles,” TIAX LLC, Oct. 2007. 

EPRI-led HEV Study

NCA - Nickel Cobalt Alumina; NCM- Nickel Cobalt Manganese
19

Summary of Components for 

Performance/Life/Cost Modeling Effort

• Developed performance model representative of Saft VL41M 

data

• Employed simplified cost model based on kWh, P/E ratio 

and cell number connected in series (BSF) representative for 

NCA chemistry 

• Life model representative of hypothetical design :

— Mechanical Stress

» fit with SCE capacity loss

— Thermal Stress

» using TLVT impedance growth method

Source: INL, LBNL

Life Model

Source: VARTA

Performance Model Cost Model

TLVT – Technology Life Verification Test
20

Preliminary Trade-off Study – Approach

• Parametric study on number of cells connected in 

series (BSF), cell capacity, and electrode thickness

— Calculated BOL and EOL (15 years @ 35C; 5,000 CD 

cycles; 300,000 CS cycles)† characteristics

†USABC PHEV Battery Test Manual

CD: Charge Depleting; CS: Charge Sustaining

As electrode thickness 

varies, cell dimensions vary 

to provide equivalent total 

energy for each constant 

energy scenario

Relative Electrode Thickness

Relative 

Total Energy

δ

A

Electrode impedance ∞ δ/A

Electrode capacity ∞ δ*A
21

Power and Energy Margin With Respect to 

USABC Hybrid Pulse Power Characterization Testing

Power 

Margin

Energy

Margin
22

Beginning of Life: Energy and Power Margin

52.8 Ah

17.6 Ah

• USABC energy & power margin both increased with:

— Increased total energy (# cells or cell capacity)

— Decreased electrode thickness (more power)

• Cell capacity has negligible influence on energy & power margin

52.8 Ah

17.6 Ah

Rel Total Energy=1 and Rel Electrode Thickness=1 is baseline VL41M design with BSF = 44
23

Beginning of Life: Cost

• Cell capacity, electrode thickness, number of cells in 

series all have strong influence on cost

52.8 Ah

17.6 Ah  Using the largest 

capacity cell results in 

pack ~$310 cheaper 

than the smallest 

capacity cell.

 Use largest capacity 

cell possible that still 

meets pack voltage 

constraints.

Observations
24

Combined Cycling + Calendar Scenario

Energy Margin

• End of Life energy margin calculated at 

— 5000 CD cycles; 300,000 CS cycles; 15 years at 35 ̊C

Energy

Margin

(for 41 Ah cell)

BOL

EOL

These thick electrode 

designs with smaller 

plate area have too 

small an energy 

window (power-limited) 

at EOL and cannot 

meet life goal.

Energy Margin: Amount of Power left above the EOL requirements
25

Combined Cycling + Calendar Scenario

Power Margin

BOL

EOL

• All designs have excess power margin at beginning of life.

• The ideal (least expensive) design will have zero power & energy margin 

at end of life.

Power

Margin

(for 41 Ah cell)

• End of Life power margin calculated at 

— 5000 CD cycles; 300,000 CS cycles; 15 years at 35 ̊C
26

Combined Cycling + Calendar Scenario

Zero Energy/Power Margin Designs at End of Life

• Designs on this line have zero margin at end of life

Energy Margin

Power Margin
27

Combined Cycling + Calendar Scenario

Impact of Design Options on Pack Cost

$3890

$4130 • Amongst these “zero margin” 

designs, the highest P/E design is 

cheapest.

Observations:

• Battery packs should be designed 

with minimal energy content that 

satisfies life goals.

• Increasing P/E is effective in 

increasing useable energy.

Energy

Margin

Power

Margin

Pack

Cost
28

Some Thoughts on Analysis toward USABC 

Requirements

• Modifications to the cell design attributes can be used to 

reduce cost and satisfy USABC requirements

• Is “design for degradation” the best approach?

• Why 20%-30% degradation?

• Linked performance/life/cost model tied to vehicle 

simulation could be used to evaluate the tradeoff between 

upfront cost of battery with margin vs. degraded long-term 

fuel savings for a “just enough” battery design
29

Lower upfront costs may lead to greater market share

Greater market share with slightly lower fuel savings may translate 

to more fleet fuel savings and volume cost reductions sooner.

Margin or No Margin?

Time (Years)

Annual Fuel Savings ($)

Option 1: Battery with 20% BOL Margin

Option 2: Battery with 0% BOL Margin

Upfront 

Cost

Lifetime 

Savings

Option 1 $$$ $$$

Option 2 $$ $$

Typical End of Life

When BOL Margin 

is Depleted
30

Beginning of Life and End of Life Vehicle Simulations

• Vehicle simulation with 20% degradation in both Energy and Power, 

— CD range decreases from 22 to 17 miles

— Over 30 miles, EOL fuel consumption is double BOL consumption 

however EOL consumption still only a fraction of HEV consumption

0 5 10 15 20 25 30

0

0.5

1

Distance (mi)

Gasoline (gal)

0 5 10 15 20 25 30

0.2

0.4

0.6

0.8

1

Distance (mi)

ESS SOC (--)

PHEV20_BOL

PHEV20_EOL

HEV

BOL cost savings likely 

more valuable than 

EOL fuel savings:

$1000 in year 1 ≠ 

$1000 over 15 yrs

Battery life model linked 

with vehicle simulation will 

provide better estimate of 

change in operation and 

savings over vehicle 

lifetime
31

Summary and Conclusion

• NREL is developing tools, algorithms, and a framework 

for battery investigators to identify battery design options 

for PHEVs with trade-offs in mind

• It is possible to decrease initial battery cost with better 

understanding of life and performance impacts of design 

and usage pattern

• First principals performance model clarifies degradation 

contributions due to cycling and calendar aspects

• Alternative designs may accelerate market growth
32

Next Steps

• Collaborate with battery providers and 

OEMs to refine the cost/life/performance 

models

• Develop performance models for other 

chemistries

• Incorporate climatic variation effects

— Cold  performance reduction

— Hot  calendar life 

• Link vehicle simulation, performance, and 

life models to evaluate options

— Designed for end of life – no change in 

performance

— Designed for beginning of life with 

change in performance

• Employ optimization and robust design 

tools to identify key design attributes

Work with others to demonstrate usefulness of 

this trade-off analysis framework
33

Vision: How This Battery Trade-off Framework May be used 

by Companies with Confidential Models/Data? 

Firewall

Trade-off Analysis 

Tool Company A

Battery Design/Performance Model

Company B

Battery Cost Model/Information

- Exchange of model parameters and results through secure Internet firewalls.

- Confidential data/models maintained internally, key results shared to formulate 

optimum solutions

Vehicle Simulation

Generic Performance Model

Generic Life Model

Generic Cost Model

Company A

Battery Design/Performance Model