National Renewable Energy Laboratory 1 Innovation for Our Energy Future

Impact of the 3Cs of Batteries on PHEV Value 

Proposition: Cost, Calendar Life, and Cycle Life

The 9th Advanced Automotive Battery Conference 

Long Beach, California

June 10-12, 2009

Ahmad Pesaran,

Kandler Smith, and Tony Markel

National Renewable Energy Laboratory 

ahmad.pesaran@nrel.gov

NREL/PR-540-45887

Funded by Energy Storage R&D (David Howell)

Vehicle Technologies Program

U.S. Department of Energy

Operated by the Alliance for Sustainable Energy, LLC
National Renewable Energy Laboratory 2 Innovation for Our Energy Future

Overview

• Introduction and background

• Motivation for PHEV battery trade-off analysis

• Battery calendar and cycle life models

• Battery cost model

• Battery life/cost trade-off results

• Impact of temperature on battery life and cost

• Summary 
National Renewable Energy Laboratory 3 Innovation for Our Energy Future

Introduction

• PHEVs have the potential to significantly reduce (imported) 

petroleum consumption (and GHG emissions) by improving 

efficiency and use of electricity

• Capacity, c-rate, cost, cycle life, and calendar life are all 

critical in making batteries for PHEVs commercially viable

• Incremental cost of the long-lasting batteries could be offset

with government incentives and high petroleum prices 
National Renewable Energy Laboratory 4 Innovation for Our Energy Future

Introduction

• Cost, calendar life, and cycle life are the least known and 

have the biggest impact on PHEV value proposition 

• Cost, fuel savings, and battery degradation characteristics at 

beginning of life vs. end of life must be evaluated 

• The spectrum of battery degradation rates due to both cycle 

life and calendar life in various climates and operating states 

of charge (SOCs) are needed

• NREL has been studying trade-offs between the performance, 

life, and cost of batteries

• PHEVs have the potential to significantly reduce (imported) 

petroleum consumption (and GHG emissions) by improving 

efficiency and use of electricity

• Capacity, c-rate, cost, cycle life, and calendar life are all 

critical in making batteries for PHEVs commercially viable

• Incremental cost of the long-lasting batteries could be offset

with government incentives and high petroleum prices 
National Renewable Energy Laboratory 5 Innovation for Our Energy Future

Major Battery Requirements (5Cs)

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 6 Innovation for Our Energy Future

Major Battery Requirements (5Cs)

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

Available Energy = 11.6 kWh (ΔSOC = 70%)

Capacity (EOL) = 16.6 kWh 

Calendar Life at 35°C = 15 Years

Cost (system) = $3,400

Cycle Life (depleting) = 3K-5K cycles

Cycle Life (sustaining) =200K-300K cycles

Peak Power Discharge (2S/10S) = 46/38 kW 

C-rate ~ 10-15 kW
National Renewable Energy Laboratory 7 Innovation for Our Energy Future

The Three Important Cs of Batteries

• Cost

• Cycle Life

• Calendar Life

These three attributes vary significantly from 

supplier to supplier, are not consistently 

reported, and dramatically affect the market 

potential of PHEVs and EVs.
National Renewable Energy Laboratory 8 Innovation for Our Energy Future

C3 Data Is Critical to Many Analysis Efforts

Cost

Cycle

Life

Calendar

Life

Vehicle

Systems

Simulation

Economic Analysis

& Value Proposition

Linkage

with

Renewables

Performance

Modeling
National Renewable Energy Laboratory 9 Innovation for Our Energy Future

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: ANL, INL, LBNL, SNL

Cycle Life,

Calendar Life

Source: VARTA

Cost Capacity and C-rate

Performance

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 National Renewable Energy Laboratory 10 Innovation for Our Energy Future 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/overcycle, but with minimum 

cost and mass

1) Accelerated calendar and 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 being driven)

3) Use proper electrical management, control 

design

System 

design

Component 

design/ 

selection
National Renewable Energy Laboratory 11 Innovation for Our Energy Future

Overview

• Introduction and background

• Motivation for PHEV battery trade-off analysis

• Battery calendar and cycle life models

• Battery cost model

• Battery life/cost trade-off results

• Impact of temperature on battery life and cost

• Summary 
National Renewable Energy Laboratory 12 Innovation for Our Energy Future

Modeling to 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 13 Innovation for Our Energy Future

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 14 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” or SEI layer (2) “Solid film”

Cell cycled

1 cycle/day 

at 80% DOD 

and 0°C

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 Inter-phase (SEI) layer
National Renewable Energy Laboratory 15 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)
National Renewable Energy Laboratory 16 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 17 Innovation for Our Energy Future

Details of Calendar and Cycle Life Models 

Are Presented by Kandler Smith in the 

Poster Session forAABC-09
National Renewable Energy Laboratory 18 Innovation for Our Energy Future

Life Model Summary

• Model structure set by Boeing satellite battery dataset1,2

• Difficult to decouple ΔDOD and voltage degradation effects from cell-level dataset

• Model adjusted to reflect more recent experience with 

NCA-graphite cells from various Labs 3-6

• 4.5 years storage at 40oC, 50% SOC  10% capacity fade4

• 13.7 years storage at 35oC  110% resistance growth5

• 2700 PHEV charge depletion cycles at 25oC  8% capacity fade, 50% resistance growth6

• The following analysis illustrates trade-offs for a cell with 

low capacity fade but high resistance growth over life.

18

References:

1. J. Hall, T. Lin, G. Brown, “Decay Processes and Life Predictions for Lithium Ion Satellite Cells,” 4th International Energy Conversion Engineering Conference & Exhibit, 

San Diego, CA, June 26-29, 2006.

2. J. Hall, A. Schoen, A. Powers, P. Liu, K. Kirby, “Resistance Growth in Lithium Ion Satellite Cells. I. Non Destructive Data Analyses,” 208th Electrochem. Soc. Mtg., Los 

Angeles, CA, October 16-21, 2005.

3. J.P. Christophersen, I. Bloom, E.V. Thomas, K.L. Gering, G.L. Henriksen, V.S. Battaglia, D. Howell, “Advanced Technology Development Program for Lithium-Ion 

Batteries: DOE Gen 2 Performance Evaluation Final Report,” Idaho National Laboratory, INL/EXT-05-00913, July, 2006.

4. M.C. Smart, K.B. Chin, L.D. Whitcanack, B.V. Ratnakumar, “Storage Characteristics of Li-Ion Batteries,” NASA Aerospace Battery Workshop, Huntsville, AL, 

November 14-16, 2006.

5. P. Biensan, Y. Borthomieu, “Saft Li-Ion Space Batteries Roadmap,” NASA Aerospace Battery Workshop, Huntsville, AL, November 27-29, 2007.

6. L. Gaillac, “Accelerated Testing of Advanced Battery Technologies in PHEV Applications,” 23rd Electric Vehicle Symposium, Anaheim, CA, December 2-5, 2007.
National Renewable Energy Laboratory 19 Innovation for Our Energy Future

Life Analysis Conducted Using Simplified Cycling 

Profiles

Major input parameters that are varied.

Constant 

current 

profile 

generator

Li-ion 

Cell

Life 

Model

ΔDOD

Ncycles/day

EoCV = 3.9V

(SOCmax = 90%)

Age (Yrs)

T (oC)

V (t) • Capacity (t)

• Resistance (t) 

:

• BOL Energy & 

Power needed 

to meet EOL 

requirements

:

• Cost
National Renewable Energy Laboratory 20 Innovation for Our Energy Future

Results: Which Dominates — Calendar or Cycling?

Capacity Fade − Energy

• Predominantly cycling controlled

(calendar fade just 30% to 40% of cycling fade)

Moderate Climate

Calendar

controlled

Calendar

controlled

Cycling

controlled

Hot Climate

• Cycling controlled for High ΔDOD

• Calendar controlled for Low ΔDOD

• 35 • 20oC, 1 cycle/day, SOCmax = 90% oC, 1 cycle/day, SOCmax = 90%

Generally cycling controlled, though it depends on temperature

NCA chemistry
National Renewable Energy Laboratory 21 Innovation for Our Energy Future

Results: Which Dominates — Calendar or Cycling?

Resistance Growth − Power

• Calendar degradation:

> 60% of total resistance growth

Moderate Climate Hot Climate • 35 • 20oC, 1 cycle/day, SOCmax = 90% oC, 1 cycle/day, SOCmax = 90%

Calendar effect dominates, though both are important.

• Calendar degradation:

> 70% of total resistance growth

NCA chemistry
National Renewable Energy Laboratory 22 Innovation for Our Energy Future

Overview

• Introduction and background

• Motivation for PHEV battery trade-off analysis

• Battery calendar and cycle life models

• Battery cost model

• Battery life/cost trade-off results

• Impact of temperature on battery life and cost

• Summary 
National Renewable Energy Laboratory 23 Innovation for Our Energy Future

Developing a 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)

• Cost at volume manufacturing at 

2007 materials’ prices

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

6.9 5.8 $3120 $2600 $2660

8.5 4.7 $3510 $2860 $3020

11.6 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

BSF = Battery Size Factor
National Renewable Energy Laboratory 24 Innovation for Our Energy Future

Life-Cost Trade-Off Study: Approach

• Choose a cycle life model and a calendar life model

• We picked curve fits from slide 13 for NCA chemistry

• Choose a cost model

• Manufacturing cost of a complete pack at high-volume production

• We picked the equation on slide 18 for NCA chemistry

• Select the required battery energy and power 

• Energy: 3.4 kWh PHEV10; 11.6 kWh PHEV40 (USABC requirements)

• Select the required battery life

• Cycles (charge depleting): 5000 CD cycles (USABC requirements)

• Calendar life: 10 years at 30oC (less aggressive than 15-year USABC) 

• Perform analysis to answer the following questions:

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

• Which controls life? Calendar or cycle life?

• What environmental parameters cause greatest life sensitivity?
National Renewable Energy Laboratory 25 Innovation for Our Energy Future

Life-Cost Trade-Off: Energy and Power Margin to 

Meet EOL Performance Requirements

BOL

Energy 

Margin

BOL Total Energy

EOL Available Energy 

Requirement

= - 1 x 100%

BOL

Power 

Margin

BOL Total Power

EOL Available Power 

Requirement

= - 1 x 100%

BOL = Beginning of Life

EOL = End of Life

Next slides give results for typical Li-ion NCA 

chemistry and include fade for a chosen 

ΔDOD window (1 cycle/day, 30oC).

Battery Sizing Metrics:
National Renewable Energy Laboratory 26 Innovation for Our Energy Future

Example Results: Life-Cost Trade-Off Study 

(Energy & Power Margin, 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

• Too much power is 

preferable to too little

• small increase in cost

• reduces mass

• PHEV10 batteries can 

require >100% excess 

power at BOL

Allows ~60% usable ΔDOD

(More useable ΔDOD is 

possible with even more 

excess power)

* 3.9 EoCV (90% SOCmax) ** Excess power and energy relative to 50kW and 3.4 kWh PHEV 10 requirements

$440/kWh, P/E=15 hr-1 Today’s costs at 

volume production

NCA chemistry
National Renewable Energy Laboratory 27 Innovation for Our Energy Future

Example Results: Life-Cost Trade-Off Study 

(Energy & Power Margin, Usable ΔDOD)

PHEV40 battery sized for 

10 years at 30oC, 1 cycle/day*

Scenario 

limited by 

deep 

cycling

• Higher excess power 

is not advantageous 

as it does not allow 

deeper ΔDOD cycling 

(Unlike PHEV10)

• PHEV40 batteries 

can require ~25% 

excess power at BOL

Less power sensitivity 

compared with PHEV10

* 3.9 EoCV (90% SOCmax) ** Excess power and energy relative to 46kW and 11.6 kWh PHEV 40 requirements

Scenario 

limited by 

calendar

$320/kWh, P/E=4 hr-1

Lowest cost

Today’s costs at 

volume production

NCA chemistry
National Renewable Energy Laboratory 28 Innovation for Our Energy Future

Example Results: Life-Cost Trade-off Study

(Sensitivity to Years of Life)

Increasing life 

requirement from 

10 to 15 years 

means:

•10% less ΔDOD 

is usable

•$250 greater cost

* 1 cycle/day, 3.9 EoCV (90% SOCmax)

PHEV10 battery sized for

10, 15 years at 30oC*

** Excess power relative to 50kW PHEV 10 requirement

NCA chemistry
National Renewable Energy Laboratory 29 Innovation for Our Energy Future

Example Results: Life-Cost Trade-Off Study

(Temperature Sensitivity)

• Temperature 

exposure drastically 

impacts system size 

necessary to meet 

goals at end of life

•25oC: 70% ΔDOD 

is usable

•35oC: 50% 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)

PHEV10 battery sized for 

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

** Excess power relative to 50kW PHEV 10 requirement

NCA chemistry
National Renewable Energy Laboratory 30 Innovation for Our Energy Future

Summary: Comparison of Battery Minimum Cost Designs 

for Varying Years of Life and Temperature

• Battery replacement not economically justified

• Cost can be more sensitive to temperature than years life

(Especially true for small PHEV batteries with high power requirement)

30

15 years

10 years

71⁄2 years

15 years

10 years

71⁄2 years

PHEV40

PHEV10

NCA chemistry
National Renewable Energy Laboratory 31 Innovation for Our Energy Future

Temperature Impacts Cost (Sizing & Life)

Power

Limits

15°C 35°C

discharge

charge

Rated

Power

T

Degradation Sluggish

Electrochemistry

Power

Limits

15°C 35°C

discharge

charge

Rated

Power

T

Degradation Sluggish

Electrochemistry

Dictates power 

capability through 

cold cranking

Dictates the size 

depending on the 

power and capacity 

fade rate

Limiting power to 

reduced T increase 

and degradation 
National Renewable Energy Laboratory 32 Innovation for Our Energy Future

Impact of Temperature on Battery in a Parked Car 

(Battery T = Ambient T)

• Used typical metrological year (TMY) as the hourly temperature

• Power fade model reformulated as rate law, integrated for temperature profile.

• PHEV10 with a typical quality NCA chemistry.

Phoenix

44oC max, 24oC avg

Houston

39oC max, 20oC avg

Minneapolis

37oC max, 8oC avg

Ambient 

Effects Only

Most passenger 

vehicles are parked 

>90% of time.

0oC

10oC

20oC

30oC

40oC
National Renewable Energy Laboratory 33 Innovation for Our Energy Future

Phoenix

49oC max, 27oC avg

Houston

42oC max, 23oC avg

Minneapolis

41oC max, 10oC avg

Ambient 

Effects +

Solar Gain 

(Vehicle/Battery 

Thermal 

Interactions)

Impact of Temperature on Battery in a Parked Car 

(Battery T = Ambient T + Solar Gain)

• The same as previous slide (PHEV10, NCA chemistry and TYM weather) 

• Developed a vehicle-battery-ambient model to predict the battery temperature

• Results show significant fade due to the ambient temperature and solar gain

0oC

10oC

20oC

30oC

40oC
National Renewable Energy Laboratory 34 Innovation for Our Energy Future

Analysis Shows Keeping Peak Battery Temperature below 

Extremes Could 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.

Typical Quality Current NCA Li-ion Technology

How much is it worth to spend on thermal control (parked too)?

Phoenix Houston Minneapolis
National Renewable Energy Laboratory 35 Innovation for Our Energy Future

Summary

• Battery cost, cycle life, and calendar life must be optimized to 

achieve maximum value for PHEV commercialization.

– A process/approach such as the one discussed here is needed.

• Useful life of a given pack design is dictated by complex interaction 

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

– Different chemistries have different behaviors.

• Battery life is extremely sensitive to temperature exposure; solar 

loading can cause further battery heating and lower life.

• Thermal control (when parked or driving) could be a cost-effective 

method to reduce oversizing of battery for the beginning of life.

• PHEV battery “standby” thermal control can reduce power loss, 

particularly for PHEV10.

• Accurate degradation prediction requires a large experimental 

matrix (for different chemistries).
National Renewable Energy Laboratory 36 Innovation for Our Energy Future

Thank You!

www.nrel.gov/vehiclesandfuels/energystorage/