NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

26th International Battery 

Seminar & Exhibit

Fort Lauderdale, FL

March 16-19, 2009

Kandler Smith

Kandler.Smith@nrel.gov

Tony Markel

Tony.Markel@nrel.gov

Ahmad Pesaran

Ahmad.Pesaran@nrel.gov

NREL/PR-540-45048

PHEV Battery Trade-Off Study

and Standby Thermal Control
National Renewable Energy Laboratory 2 Innovation for Our Energy Future

Overview

• Motivation for PHEV battery trade-off analysis

• Battery life model

– Calendar life vs. cycle life: Complex interdependency

– An empirical model capturing major degradation 

factors (temperature, time, # cycles, ΔDOD*, voltage)

• Battery life/cost interactions

– Lowest cost system that meets technical requirements

– Life sensitivity to different temperatures

• Battery standby thermal management

– Concept

– Vehicle-battery thermal interactions

– Benefits of temperature management

• Conclusions

– Future test needs

* DOD = depth of discharge
3

USABC PHEV Battery Goals

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

National Renewable Energy Laboratory Innovation for Our Energy Future
4

USABC PHEV Battery Goals

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

National Renewable Energy Laboratory Innovation for Our Energy Future

Available Energy = 11.6 kWh 

SOC Swing = 70%

Total EOL Energy = 16.6 kWh

Fade over Life = 20%

Total BOL Energy = 20.7 kWh

Calendar Life at 35°C = 15 Years

System Price = $3,400

Max Weight = 120 kg

Max Volume = 80 L
5

Optimization 

with vehicle 

simulations under 

realistic driving 

cycles and 

environments

PHEV Battery Design Optimization

Design/size PHEV batteries to meet USABC technical 

goals/requirements at minimum cost.

Source: INL, LBNL

Battery Life

Source: VARTA

Battery Performance Battery Cost 

Life prediction 

represents greatest 

uncertainty.

Complex dependency 

on t1/2, t, # cycles, T, 

V, ΔDOD

National Renewable Energy Laboratory Innovation for Our Energy Future
National Renewable Energy Laboratory 6 Innovation for Our Energy Future

Motivation: Minimize Battery Cost, Maximize Life

How?

0) Select a high-quality, low-cost cell.

1) Size battery appropriately so as not to

overstress/over-cycle, but with minimum 

cost and mass

1) Accelerated calendar & cycle life testing

2) Accurate life and DOD predictive models

2) Minimize time spent at high temperatures

1) Standby thermal management (vehicle parked!)

2) Active thermal management (vehicle driving)

3) Proper electrical management, control design

:

System 

design

Component 

design/ 

selection
National Renewable Energy Laboratory 7 Innovation for Our Energy Future

How Can We Predict Battery Life?

Cycling Fade

• Poorly understood

• Typical t or N dependency

• Often correlated log(# cycles) with ΔDOD or log(ΔDOD)

0 0.2 0.4 0.6 1

1.05

1.1

1.15

1.2

1.25

1.3

1.35

Time (years)

Relative Resistance

30

40

47.5

55

Calendar Life Study at various T (°C)

Calendar (Storage) Fade

• Relatively well established & understood

• Typical t1/2 time dependency

• Arrhenius relation describes T dependency

Source: V. Battaglia (LBNL), 2008

Life (# cycles)

Life (# cycles)

Source: John C. Hall (Boeing), IECEC, 2006. Source: Christian Rosenkranz (JCS/Varta) EVS-20

ΔDOD

ΔDOD
National Renewable Energy Laboratory 8 Innovation for Our Energy Future

Accelerated Cycle Life Tests Are Not Always 

Conservative! 

• Li-ion – high-voltage, nonaqueous chemistry – calendar life effect important

• Real-time tests necessary for proper extrapolation of accelerated results

Source: John C. Hall, IECEC, 

2006. (Boeing, Li batteries for 

a GEO Satellite) 

Real Time Test

(1 cyc./day)

Accelerated Test

(4 cyc./day)

ΔDOD fraction

Life (# cycles)
National Renewable Energy Laboratory 9 Innovation for Our Energy Future

Our Objectives for Battery Life Modeling

Develop a power and energy degradation model 

that —

1. Uses both accelerated and real-time calendar 

and cycle life data as inputs.

2. Is mathematically consistent with all calendar 

and cycle life empirical data.

3. Is extendable to arbitrary usage scenarios (i.e., it 

is predictive).
National Renewable Energy Laboratory 10 Innovation for Our Energy Future

Source: J.P.Christopersen, J. Electrochem. Society, 2006.

Cycling has been shown to suppress impedance growth.

Effect of an unintended full 

discharge and charge

Commercial LiCoO2 cells 

stored at 25oC; DC resistance 

measured with 1 pulse/day

Impedance Growth Mechanisms: Complex 

Calendar and Cycling Dependency
National Renewable Energy Laboratory 11 Innovation for Our Energy Future

Impedance Growth Mechanisms: Complex 

Calendar and Cycling Dependency

SEM Images: John C. Hall, IECEC, 2006.

Cell stored

at 0oC

NCA chemistry: Different types of electrode surface film layers can grow.

(1) “Electrolyte film” (2) “Solid film”

Cell cycled

1 cycle/day 

at 80% DOD

Electrolyte film*

• grows during storage α t

1/2

• suppressed by cycling

Solid film

• grows only with cycling α t or N

*Often called Solid-Electrolyte Interphase (SEI) layer
12

Curve-fit at 51% ΔDOD:

a1 = 1.00001e-4 Ω/day1/2

a2 = 5.70972e-7 Ω/cyc

R2 = 0.9684

National Renewable Energy Laboratory 12 Innovation for Our Energy Future

Impedance (R): Cycling at Various ΔDODs

Fitting t1/2 and N Components

(Note: For 1 cycle/day, N = t)

4.0 EoCV Data: John C. Hall, IECEC, 2006.

• Simple model fit to cycling test data: Boeing GEO satellite application, 

NCA chemistry

• Model includes t1/2 (~storage) and N (~cycling) component.

R = a1 t

1/2 + a2 N
13

Curve-fit at 51% ΔDOD:

a1 = 1.00001e-4 Ω/day1/2

a2 = 5.70972e-7 Ω/cyc

R2 = 0.9684

National Renewable Energy Laboratory 13 Innovation for Our Energy Future

Impedance (R): Cycling at Various ΔDODs

Fitting t1/2 and N Components

(Note: For 1 cycle/day, N = t)

ΔDOD a1 (Ω/day1/2) a2 (Ω/cyc) R2

68% 0.98245e-4 9.54812e-7 0.9667

51% 1.00001e-4 5.70972e-7 0.9684

34% 1.02414e-4 0.988878e-7 0.94928

17% 1.26352e-4 -7.53354e-7 0.9174

4.0 EoCV Data: John C. Hall, IECEC, 2006.

• Simple model fit to cycling test data: Boeing GEO satellite application, 

NCA chemistry

• Model includes t1/2 (~storage) and N (~cycling) component.

R = a1 t

1/2 + a2 N
National Renewable Energy Laboratory 14 Innovation for Our Energy Future

R2 = 0`.9943

R2 = 0.9836

High t1/2 resistance growth 

on storage is suppressed 

by cycling.

High DOD cycling grows 

resistance α N.

Low DOD cycling reduces 

resistance α N.

Additional models are fit to describe a1

and a2 dependence on ΔDOD.

a2 < 0 not physically realistic. An equally statistically 

significant fit can be obtained enforcing constraint a2 > 0.

x

a2 / a1 = c0 + c1 (ΔDOD)

a1 = b0 + b1 (1 – ΔDOD)b2

Impedance (R): Cycling at Various ΔDODs

Capturing Parameter Dependencies on ΔDOD

R = a1 t

1/2 + a2 N
National Renewable Energy Laboratory 15 Innovation for Our Energy Future

0% ΔDOD

(storage)

68% ΔDOD

51% ΔDOD

34% ΔDOD

17% ΔDOD

Fit to data

Extrapolated

using model

R = a1 t

1/2 + a2 N

a1 = b0 + b1 (1 – ΔDOD)b2

a2 / a1 = max[0, c0 + c1 (ΔDOD)]

Distinctly different 

trajectories result from 

storage, severe cycling 

and mild cycling. 4.0 EoCV Data: John C. Hall, IECEC, 2006.

Impedance: Cycling at Various ΔDODs

Example Model Projections

100% ΔDOD
16

R = a1 t

1/2 + a2,t t + a2,N N

a2,t = a2 (1 - αN) 

a2,N = a2 αN

αN = 0.285, R2 = 0.9488

National Renewable Energy Laboratory 16 Innovation for Our Energy Future

Impedance: Multiple Cycles per Day

[1] Corrected data from J.C. Hall et al., 208th ECS Mtg., Oct. 16-21, Los Angeles, CA. [2] Data from J.C. Hall et. al., 4th IECEC, June 26-29, San Diego, CA.

Dependence on t1/2, N model

overpredicts effect of

accelerated cycling.

(not used)

R = a1 t

1/2 + a2 N

Dependence on 

t

1/2

, t, N model

predicts

accelerated and

real-time cycling 

much better.
National Renewable Energy Laboratory 17 Innovation for Our Energy Future

Impedance: Voltage and Temperature Acceleration

• Increased impedance growth due to 

elevated voltage & temperature fit using 

Tafel & Arrhenius-type equations.

• Dedicated lab experiments required to 

fully decouple voltage-ΔDOD relationship.

a1 = a1,ref k1 exp(α1F/RT x V)

a2 = a2,ref k2 exp(α2F/RT x V)

• This work assumes values for k1 & α1.

• Activation energies, Ea1 and Ea2, are taken from similar chemistry.

k1 = k1,ref exp(-Ea1 x (T-1 - Tref

-1) /R)

k2 = k2,ref exp(-Ea2 x (T-1 - Tref

-1) / R)
National Renewable Energy Laboratory 18 Innovation for Our Energy Future

Capacity Fade: Calendar (storage) and Cycling 

Effects

Qsites = e0 + e1

x (a2,t t + a2,N N)

Q = min( QLi, Qsites )

Capacity Loss During Storage

Loss of cyclable Li

Capacity Loss During Cycling

Isolation of active sites

a1 and a2 capture 

temperature, ΔDOD, and 

voltage dependencies

(previous slides).

• 4 cycle/day - cycling stress significant.

• 1 cycle/day - voltage stress significant.

3.7V, 20oC storage data: John C. Hall, IECEC, 2006. 1 cyc/day, 20oC data: John C. Hall, IECEC, 2006.

Q = Relative Capacity

QLi = d0 + d1 

x (a1 t

1/2)
National Renewable Energy Laboratory 19 Innovation for Our Energy Future

Life Model Summary (equations & coefficients)

Impedance Growth Model

• Temperature

• Voltage

• ΔDOD

• Calendar Storage (t1/2 term)

• Cycling (t & N terms)

Capacity Fade Model

• Temperature

• Voltage

• ΔDOD

• Calendar Storage (Li loss)

• Cycling (Site loss)

From impedance 

growth model

Reasonably fits available data 

Actual interactions of degradation mechanisms may be more complex.

QLi = d0 + d1 x (a1 t

1/2)

Qsites = e0 + e1 x (a2,t t + a2,N N)

a1 = a1,ref k1 exp(α1F/RT x V)

a2 = a2,ref k2 exp(α2F/RT x V)

k1 = k1,ref exp(-Ea1 x (T-1 - Tref

-1) /R)

k2 = k2,ref exp(-Ea2 x (T-1 - Tref

-1) / R)

a2,t = a2 (1 - αN) 

a2,N = a2 αN

a1 = b0 + b1 (1 – ΔDOD)b2

a2 / a1 = max[0, c0 + c1 (ΔDOD)]

Q = min( QLi, Qsites )

R = a1 t

1/2 + a2,t t + a2,N N
National Renewable Energy Laboratory 20 Innovation for Our Energy Future

Life/Cost Trade-Offs: Approach

• Life model adjusted slightly to reflect experience with present-day 

PHEV battery technology (NCA chemistry).

• Cost model from previous work:

• Manufacturing cost of a complete pack at high volume production

• Requirements from USABC/DOE

• Energy: 3.4 kWh PHEV10; 11.6 kWh PHEV40

• CD Cycle Life: 5000 CD cycles

• Calendar Life: 15 years at 35oC 

• Too aggressive for present-day technology 

• Instead used 10 years at 30oC for analysis (next two slides)

• Questions:

• What ΔDOD & P/E meet life at minimum cost?

• Which controls life? Calendar or cycle life?

• What environmental parameters cause greatest life sensitivity?

$/pack = 11.1*kW + 224.1*kWh + 

4.53*BSF + 340

$3500

$2860

$2600

Detailed 

Model: 3

NCA

11.46 3.5 $4290 $3680

8.46 4.7 $3510 $3020

6.88 5.8 $3120 $2660

Simple Model: 1.2

$=11*kW+224

*kWh+680

Detailed 

Model: 3

NCM

Nominal P/E

Energy

(kWh)

$3500

$2860

$2600

Detailed 

Model: 3

NCA

11.46 3.5 $4290 $3680

8.46 4.7 $3510 $3020

6.88 5.8 $3120 $2660

Simple Model: 1.2

$=11*kW+224

*kWh+680

Detailed 

Model: 3

NCM

Nominal P/E

Energy

(kWh)

NCA - Nickel Cobalt Alumina; NCM- Nickel Cobalt Manganese

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. 

BSF = Battery Size Factor
National Renewable Energy Laboratory 21 Innovation for Our Energy Future

Life/Cost Trade-Offs: Usable ΔDOD

PHEV10 battery sized for 

10 years at 30oC, 1 cycle/day*

Largest 

mass

Pay for

energy

Smallest 

mass

Pay for

power Lowest

cost

• Optimal P/E ratio

(~15 hr-1) yields lowest 

cost battery

• Too much P/E is 

preferred to too little P/E

- small increase in cost

- reduces mass

• Expanding ΔDOD 

window

- reduces total battery 

energy & mass

- requires higher P/E to 

meet power 

requirements at low 

SOC

* using 3.9 EoCV (90% SOCmax)
National Renewable Energy Laboratory 22 Innovation for Our Energy Future

Life/Cost Trade-Offs: Temperature Sensitivity

PHEV10 battery sized for 

10 years at 25oC, 30oC, & 35oC* • Temperature 

exposure drastically 

impacts system size 

necessary to meet 

goals at end of life.

25oC: 70% to 80% 

ΔDOD is usable

35oC: 50% to 65% 

ΔDOD is usable

• Modifying life 

requirements from 

10 years at 25oC to 

10 years at 35oC

increases battery cost 

by > $500.

* 1 cycle/day, 3.9 EoCV (90% SOCmax)
Current Li-ion 

technology can require 

30% to 70% excess 

power to last 15 years.

Advanced Li-Ion technologies 

still could require 15% to 30% 

excess power to last 15 years.

National Renewable Energy Laboratory 23 Innovation for Our Energy Future

• Assume vehicle is always parked (storage calendar life effect only).

• Typical Meteorological Year (TMY) hour-by-hour geographic dataset used to provide ambient conditions.

• Assume Tbattery = Tambient (Realistic? No. Solar loading on vehicle cabin & battery will cause even more power loss.)

Phoenix

44oC max, 24oC avg

0oC

The Case for Thermal Control of PHEV Batteries

Storage at elevated temperatures responsible for significant impedance 

growth; most passenger vehicles are parked >90% of the time.

Houston

39oC max, 20oC avg

Minneapolis

37oC max, 8oC avg 10oC

20oC

30oC

Thermal control of 

PHEV batteries is 

needed even during 

standby.

40oC
National Renewable Energy Laboratory 24 Innovation for Our Energy Future

Study of PHEV Battery Standby Thermal Control

• Investigate the technical and economic merits of 

various thermal control strategies during standby 

– Experiments using NREL PHEV Test Bed and other vehicles

– Vehicle thermal modeling with various solar and ambient 

loads

Qsolar

Tamb

T1

T2 T3

T4

Battery

Environment

Cabin

Image: Volvo
National Renewable Energy Laboratory 25 Innovation for Our Energy Future

On Sunny Days, Solar Warming of Cabin Can Cause Battery 

Temperature to Be Much Hotter than Ambient

Qsolar

Tamb

T1

T2 T3

T4

Battery

Environment

Cabin

Rooftop 

pyranometer 

& RTD

NREL PHEV Test Bed instrumented to 

correlate solar radiation & ambient 

temperature with battery temperature Battery Pack RTD

Vehicle thermal model extracted for 

geographic scenario analysis

0 20 40 60 80 100 120

-20

0

20

40

60

Time (hr)

Temperature (°C)

Ambient

Cabin

Battery

0 20 40 60 80 100 120

0

200

400

600

800

Time (hr)

Solar Radiation (W/m2)

0 20 40 60 80 100 120

-20

0

20

40

Time (hr)

Temperature (°C)

Cabin above Ambient

Battery above Ambient

Image: Volvo
National Renewable Energy Laboratory 26 Innovation for Our Energy Future

Model: Solar Warming of Cabin (and its effect on battery temperature)

Is Important for Predicting Battery Degradation

PHEV10 – Power loss after 15 years

Degradation predictions with and without effects of vehicle solar loading.

Current Li-ion technology

Phoenix Houston Minneapolis
National Renewable Energy Laboratory 27 Innovation for Our Energy Future

Eliminating Peak Battery Temperatures (e.g., battery insulation, active 

cooling, reducing solar load) Can Greatly Improve Battery Life

PHEV10 – Power loss after 15 years

Ambient temperature & solar radiation climate data input to vehicle/battery thermal model. 

Assume peak battery temperatures can be eliminated.

Current Li-ion technology

Phoenix Houston Minneapolis
National Renewable Energy Laboratory 28 Innovation for Our Energy Future

Conclusions

• Useful life of a given cell design is dictated by the complex 

interaction of parameters (t1/2, t, N, T, V, DOD).

• Successful introduction of PHEV Li-ion technology requires 

careful consideration of 

– Design factors: battery DOD, end of charge voltage, ...

– Real-world use: # cycles/day, temperature exposure.

• Battery life is extremely sensitive to temperature exposure; 

solar loading on cabin can cause severe battery heating.

• PHEV battery standby thermal control can reduce power loss 

by >15% over baseline 15-year Phoenix, AZ, scenario.

• Accurate degradation prediction requires large parametric 

dataset at the cell level (next slide) plus dedicated laboratory 

tests at the material level.
National Renewable Energy Laboratory 29 Innovation for Our Energy Future

Need for Parametric Data on Li-ion Technologies:

Battery life model is only as good as the dataset populating it.

4 Cycles per Day at Average CD Current

 - Size CD region

30 34 38 42

3.9 4 4.1 4.25

90 20 ̊C -10 ̊C, 0 ̊C, 20 ̊C, 40 ̊C 20 ̊C 20 ̊C, 40 ̊C

70 20 ̊C 20 ̊C 20 ̊C

30 20 ̊C -10 ̊C, 0 ̊C, 20 ̊C, 40 ̊C 20 ̊C 20 ̊C, 40 ̊C

10 20 ̊C 20 ̊C 20 ̊C

1 Cycle per Day at Average CD Current

 - Allow proper extrapolation of 4 cycle/day accelerated tests

30 34 38 42

3.9 4 4.1 4.25

90 0 ̊C, 20 ̊C, 40 ̊C 20 ̊C, 40 ̊C

80 20 ̊C

65 0 ̊C, 20 ̊C, 40 ̊C 20 ̊C, 40 ̊C

30 20 ̊C

Open Circuit Storage

 - Choose OCV to match CD & CS cycling scenarios

3.4 4 4.1 4.25

40 ̊C 20 ̊C, 40 ̊C, 60 ̊C 40 ̊C 20 ̊C, 40 ̊C, 60 ̊C

Continuous Cycling in Various CS Windows

 - Find optimum CS region and window

30 34 38 42

5% 10% 20% 50%

20 ̊C

20 ̊C

20 ̊C

20 ̊C

0 ̊C, 20 ̊C

0 ̊C, 20 ̊C

0 ̊C, 20 ̊C

0 ̊C, 20 ̊C

SOC (%) ΔDOD (%)

2

5

Capacity (Ah)

EoCV (V) ΔDOD (%)

Capacity (Ah)

EoCV (V) ΔDOD (%)

OCV (V)

Capacity (Ah)

Special Considerations:

•Expose possible 

accelerating mechanisms:

- high T material degradation

- low T mechanical stress.

•Low ΔDOD cycling is 

important for decoupling 

t

1/2 and t dependencies.

•Test to >Vmax to improve 

model fit at elevated 

voltages.

•When cells die:

- destructive physical analysis, or

- continue cycling at less severe 

rate (enable 2nd use/resale).
National Renewable Energy Laboratory 30 Innovation for Our Energy Future

Acknowledgements

• Battery life modeling and trade-off study supported by 

DOE’s Office of Vehicle Technologies 

- Dave Howell, Energy Storage Program

• Thermal standby analysis supported by NREL’s 

Strategic Initiative

- Funding: Dale Gardner, Director, NREL Renewable Fuels and 

Vehicle Systems

- Technical support: Larry Chaney and Anhvu Le