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Contract No. DE-AC36-08GO28308

Accelerating Battery Design 

Using Computer-Aided 

Engineering Tools 

Preprint 

Ahmad Pesaran, Gi-Heon Kim,

and Kandler Smith

Presented at the 25th World Battery, Hybrid and Fuel Cell Electric 

Vehicle Symposium & Exhibition

Shenzhen, China

November 5 – 9, 2010

Conference Paper

NREL/CP-5400-49316

January 2011
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1 

Accelerating Battery Design Using Computer-Aided Engineering Tools 

Ahmad Pesaran, Gi-Heon Kim, and Kandler Smith 

National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, Colorado 80401, USA 

E-mail: ahmad.pesaran@nrel.gov 

Abstract—Computer-aided engineering (CAE) is a proven pathway, especially in the automotive industry, to improve 

performance by resolving the relevant physics in complex systems, shortening the product development design cycle and 

thus reducing cost, and providing an efficient way to evaluate parameters for robust designs. Most CAE battery models 

can be enhanced. Academic models include the relevant physics details, but neglect engineering complexities. Industry 

models include the relevant macroscopic geometry and system conditions, but simplify the fundamental physics too 

much. Most of the CAE battery tools for in-house use are custom model codes and require expert users. There is a need 

to make these battery modeling and design tools more accessible to end users such as battery developers, pack 

integrators, and vehicle makers. Developing integrated and physics-based CAE battery tools can reduce the design, build, 

test, break, re-design, re-build, and re-test cycle and help lower costs. The National Renewable Energy Laboratory 

(NREL) has been involved in developing various models to predict the thermal and electrochemical performance of 

large-format cells and has used in commercial three-dimensional finite-element analysis and computational fluid 

dynamics to study battery pack thermal issues. These NREL cell and pack design tools can be integrated to help support 

the automotive industry and to accelerate battery design. Our approach and modeling results are provided in the 

following paper. 

Keywords—Lithium-ion battery, electric drive vehicles, modeling, simulation, design tool

1. Introduction 

 Batteries in electric drive vehicles (EDVs), such as 

hybrid electric, plug-in hybrid, and battery electric 

vehicles, are critical to achieve the desired fuel economy 

and affordability needed to significantly impact 

worldwide gasoline consumption and to reduce 

greenhouse gas emissions and dependence on oil. 

However, to do this, the cost, performance, and life of 

today’s batteries must improve. Government entities and 

industry officials have been investing in product 

development and manufacturing to produce batteries that 

meet the desired targets. One approach to accelerate 

scaling up these batteries from materials to cells to packs 

and to energy storage systems in vehicles is to use 

computer-aided engineering (CAE) tools. In many 

industries, including automotive and combustion engine 

development, CAE tools have been the proven pathway 

to: 

• Improving performance by resolving the relevant 

physics in complex systems 

• Shortening the product development design cycle, 

thus reducing cost 

• Providing an efficient manner for evaluating 

parameters for robust design. 

 It is important to integrate the existing models or newly 

developed ones into a suite of software tools that can be 

used by end-users in the industry to design the next 

generation of batteries faster. CAE for the battery industry 

needs to mature at par with modeling tools for internal 

combustion engines, conventional drive trains, and the 

like to make batteries competitive and affordable for use 

in advanced vehicles. 

 To develop EDV lithium ion batteries, the industry 

depends on custom-made codes, ad-hoc design 

approaches, and rules of thumb. The development takes 

place at various scales, from the angstrom size of 

molecules and the micron size of electrode layers to the 

millimeter size of electrode design, centimeter size of 

cells, decimeter size of modules and packs, and the meter 

size of packs and vehicles, as shown in Figure 1. Various 

modeling tools are available and used to predict the 

behavior of each material, cell, and vehicle. 

Figure 1: Multiple scales of physics exist in the lithium-ion 

battery, with various physics interacting across a wide range of 

length and time scales. 

2. Background—Modeling at Various Scales 

 Modeling can be conducted at various levels, including 

the material level, cell level, pack level, and vehicle level. 

In an ideal situation, each model level interacts with the 

other levels, and analysis is performed to provide input to 

the other levels using input from those levels. However, 

different level models have been developed by different 

institutions and written in different programming 

languages; therefore, it is challenging to make these 

models interact well together. There is a great need to 

improve this interaction. 

 Figure 2 depicts an integrated design tool using a vehicle 

simulation tool with a battery pack simulation tool that 

This work was sponsored by the United States Department 

of Energy, Vehicle Technologies Program, Energy Storage 

R&D Technologies (David Howell). 
2 

with a detailed cell-level model embedded in it. The 

vehicle simulation tool helps identify the requirements for 

the battery pack, which identifies the size and design of a 

particular cell. Next, cell-level performance is predicted 

using different driving profiles, which are converted to 

battery power profiles using the vehicle simulator. 

Ambient conditions and battery load are used to identify 

the temperature rise, which provides feedback on the 

design of thermal management of the battery pack. The 

feedback provided could be used to study the impacts of 

various design parameters such as changes in the size, 

shape, design, and chemistry of the cell; vehicle size and 

performance; and the thermal management system. 

Virtual 

Battery Design

Vehicle Simulator

-10

0

10

20

30

40

50

60

70

80

90

0 100 200 300 400 500 600

Time (s)

Vehicle Speed

Vehicle Driving Profile Vehicle Design

Virtual

0 10 20 30 40 50 60 -600

-400

-200

0

200

400

600

800

Battery Power Profile

Multi-Scale

Multi-Dimensional 

Li-ion Battery Model

Battery Responses Feedback

Embedded 

Battery Model

CD: Charge Depleting

CS: Charge Sustaining 

Figure 2: Linking battery cell and pack modeling with vehicle 

simulations helps study many parameters for designing 

optimized components. 

 Optimization can be performed and an initial design of a 

battery pack and cell could be arrived at for a particular 

vehicle without conducting any prototyping or testing. Of 

course, one has to have confidence in these models and 

design tools; therefore, validation of the models and 

design tools is critical and must be carried out in parallel 

with model development. 

2.1 Cell-Level Modeling

 The National Renewable Energy Laboratory (NREL) is 

the leader in the thermal analysis of batteries and has now 

added electrochemical modeling capabilities to design 

large-format cells as the interaction between temperature 

and electrochemistry becomes paramount with larger 

format design. 

 The NREL model captures a three-dimensional aspect of 

a cell while capturing the electrochemistry as modeled by 

Dr. John Newman’s group. Newman’s model captures 

lithium diffusion dynamics and charge transfer kinetics in 

porous media; predicts the current/voltage response of a 

cell; and provides a design guide for thermodynamics, 

kinetics, and transport across electrodes [1]. The 

equations cover charge transfer kinetics at a reaction site, 

species conservation, charge conservation, and energy 

conservation. Although the model has been very 

successful for small cells, it is difficult to resolve heat and 

electron current transport in large cell systems. 

Newman’s model is often appropriate to predict the 

behavior of small cells. In large cells, however, 

particularly for automotive applications, working 

potential and temperature are non-uniform throughout the 

cell. The same is true for battery packs consisting of 

multiple cells. Potential and temperature imbalances cause 

certain locations of a cell to be cycled more excessively 

than the rest. The local excess use generates more heat 

and stress, causing severe localized degradation. 

Electrical current transfer in non-active conductive 

components of a cell and heat transfer through a highly 

anisotropic composite medium in a cell have a critical 

impact on the performance and degradation of largeformat batteries. 

 Through multi-year efforts supported by the U.S. 

Department of Energy, NREL has developed a modeling 

framework for predictive computer simulation of lithium 

ion batteries, namely the Multi-Scale Multi-Dimension 

(MSMD) model, which addresses the interplay among the 

various battery physics in varied scales [2–6]. This paper 

introduces NREL’s MSMD model and demonstrates how 

the model can be applied to the evaluation and study of 

large-format cell design. At NREL, Newman’s model has 

been extended to a thermal-electrochemical threedimensional model using the MSMD (domain) model 

approach. 

2.2 Pack-Level Modeling

 Designing battery modules and packs needs to take the 

cell-level models into account but simplifying them for 

the computational speed. Commercial CAE battery 

modeling tools have been used primarily for packaging 

and thermal analysis. Moving beyond thermal modeling, a 

more complete battery pack CAE tool must be developed 

to capture the physics related to electrochemistry, thermal, 

fluid flow, structural, mechanical, and chemical 

degradation, and finally, exothermic abuse behavior. 

 The physics of interest for battery CAE include 

interactions related to charge/discharge processes, 

localized heat generation, degradation of cell and 

electrode components, mechanical stress, and heat release 

during cell abuse and further propagation from cell to cell 

in a pack. All of these processes must be coupled with 

temperature, electrical load, and, in some cases, 

mechanical load on the pack. At the pack level, models 

must be able to accurately capture cell-to-cell nonuniformity, especially when it impacts system-level 

behavior and design. Cell-internal non-uniformities are 

also of interest as they relate to localized heat generation, 

cooling channel design, and volume expansion. 

 To design a battery pack that incorporates the 

implications of selecting a particular chemistry, cell 

geometry and aspect ratio, particularly the size, location 

of terminals, cooling methods, driving profiles, and 

ambient temperature conditions, etc., the existing models 

and CAE tools need to be further developed and 

interfaced with the existing cell models. This will be 

discussed later in this paper. 
3 

3. Model Description

3.1 Overview—Cell Level

 The MSMD model approach has achieved computational 

efficiency for resolving multi-physics interactions 

occurring over a wide range of length scales by 

introducing separate solution domains for particle physics, 

electrode-scale physics, and cell-scale physics. In 

addition, the MSMD approach provides a modularized 

framework enabling model flexibility by providing 

multiple sub-model solver options with various physical/ 

computational complexities and expandability to add new 

physics of interest or to drop physics of insignificance or 

indifference. The successfully integrated MSMD model 

can expand knowledge on the interplay of different scale 

battery physics to help speed development of high 

performing, long lasting, and safe lithium ion batteries for 

electrified vehicles. Figure 3 shows the modularized 

hierarchy of structure in NREL’s MSMD model.

 This modularized approach allows the end-user to pick 

and choose the desired physics and code for particle, 

electrode, or cell modeling. The solution technique in 

each domain depends on the accuracy and fidelity desired. 

The MSMD approach applies to any electrochemistry, 

cell shape (cylindrical or prismatic), and electrode 

configuration (rolled or stacked) with three-dimensional 

geometries. 

ξ1

ξ2

ξ3

x1

x2 x3

X1

X2 X3

ξ particle domain dimension x electrode domain dimension X cell domain dimension

p_module 1

p_module 2

p_module k

p_physics b

p_physics z

p_physics a

e_module 1

e_module 2

e_module k

e_physics b

e_physics z

e_physics a

c_module 1

c_module 2

c_module k

c_physics b

c_physics z

c_physics a

Figure 3: Modularized hierarchy of the model structure in 

NREL’s MSMD approach. 

3.1.1 Modeling of Large Stacked Prismatic Cells

 Large stacked prismatic cells are becoming of increased 

interest for EDVs. In this case, the impact of macroscopic 

cell design features such as the number of stacked layers

and the electrode area, size, and location of current tabs is 

explored. The same can be done for a given set of 

microscopic cell design parameters such as component 

materials, particle shape and size distribution, electrode

loadings, and thickness. Because the cell domain design 

features are of interest in this model, efficient particle and 

electrode domain models were chosen for this study. The 

cell domain model and solver resolve the full threedimensional geometry of the cells. The electrode domain 

model was a one-dimension transport model. The 

spherical particle model was selected to resolve particle 

domain physics. A state-variable model was used in the 

solution scheme for particle and electrode domain model 

solvers to achieve computational efficiency [8]. Figure 4

shows the schematics for the models and solver modules 

selected for the present study. The equations solved in this 

study are shown in the appendix. 

Figure 4: Models and solver modules were selected for this 

study to evaluate macroscopic designs of large stacked prismatic 

cells using the MSMD model. 

3.2 Overview—Pack Level

 Computer-aided design (CAD), computational fluid 

dynamics, and finite element analysis (FEA) software 

packages are already capable of capturing the complex 

three-dimensional geometry of battery packs, although 

they are generally limited in their capability to include 

battery-relevant physics. For example, FEA models can 

calculate the stress imposed on various pack components 

when the pack is exposed to external vibration. They 

cannot, however, predict localized stresses within the cell 

stack for a specific electrical cycling condition. The 

ability to quickly and accurately explore a wide range of 

thermal, duty cycle, and aging scenarios through 

computer simulation will substantially reduce battery 

pack development costs for automotive original 

equipment manufacturers.

 A particular challenge for the battery pack CAE tool is 

to be able to include the appropriate physics and/or 

approximations to answer engineering design questions 

across a range of length scales. To solve the 

electrochemical-thermal-fluid behavior at the cell or 

module level, it may be appropriate to fully discretize the 

cell and flow channel regions with a fine mesh. Direct 

scaling of the same model to the pack level, however, 

may not be possible, as the number of volume elements in 

the mesh could increase more than 100-fold. Timely 

execution of such a large-scale model may require parallel 

implementation on a supercomputer. Other alternatives 

include using reduced order or simplified physics models 

for individual cells or other pack subcomponents.

ξ1

ξ2

ξ3

x1

x2 x3

X1

X2 X3

1D

ξ particle domain dimension x electrode domain dimension X cell domain dimension

r

1D 3D

15 mm

 

 

 

SVM

 

 

 

 

 

H

W

θ

L

Cu

Separator

Al

δCu δAl

NegativeNegative

PositivePositive

3D
4 

4. Results and Discussion

4.1 Large Stacked Prismatic Cells

 To show the utility of battery CAE for designing cells, 

an example for designing the tab location (Case 1) and

aspect ratio (Case 2) for a large stacked prismatic cell are

provided for a lithium-ion battery with nickel-cobaltaluminum (NCA) cathode and graphite anode. 

4.1.1 Impact of Tab Location for a 40-Ah Cell (Case1)

 Figures 5 and 6 show the example results of an MSMD

model of a 40-Ah stacked-cell design with prismatic 

configuration after discharging the cell at a constant 

current rate of 200 A for 2 minutes. Two cases were 

studied, one with positive and negative terminals on the 

same end (Figure 5), and the other with terminals on both 

ends (Figure 6). The model predicts the working potential, 

electrochemical current production, and the resulting 

temperature. In this case, the temperature near the tabs of 

the cell with terminals on both sides is about 4°C–5°C 

higher than in the other cell. 

Figure 5: Example results of battery modeling multi-physics 

interaction for 40-Ah prismatic cells with terminals on the same 

end after 2 minutes of 200-A constant discharge (compare with 

Figure 6). 

 The temperature distribution in the cell with terminals on 

both ends was much more uniform and thus provides a 

better way to cool the battery. This stems from the 

differences in working potential that leads to a different 

current production and thus temperature distribution, 

which in turn results in difference in the state of charge 

(SOC) of each cell. Having this non-uniformity in 

temperature, current distribution, and SOC in a cell over 

the many years and cycles expected from the large-format 

cells could lead to an inconsistent usage of the active 

material. As a result, the areas close to the tabs in the cell 

with terminals on the same side can have more usage and

potentially degrade faster, as shown by Smith et al. [5]. 

Figure 6: Example results of battery modeling multi-physics 

interaction for 40-Ah prismatic cells with terminals on both ends 

after 2 minutes of 200-A constant discharge (compare with 

Figure 5). 

4.1.2 Impact of Aspect Ratio on a 20-Ah Cell (Case 2)

Using a vehicle simulation of a plug-in hybrid electric 

vehicle (PHEV10), a 20-Ah cell with NCA/graphite 

chemistry was selected for further analysis. In this study, 

many parameters, such as current collector thickness (δAl

= 1.6 x δCu), electrode loadings, and electrode thickness, 

were kept constant while other parameters, such as aspect 

ratio (H/W), number of electrode layers, and tab width 

(θ/W), were changed. The initial cell temperature and the 

air cooling temperature were both assumed to be 30°C. 

The heat transfer coefficient for all surfaces (tabs and cell 

surfaces) was assumed to be 20 W/m2

/°C. Four alternative 

designs were considered to deliver the same 20-Ah 

capacity. Figure 7 shows the different designs. Design 1 

(nominal) has terminals on the same end, and design 4 has 

terminals on both sides. Design 2 is similar to design 1, 

except it has one-third the capacity of design 1, so three of 

them should be used in parallel to have the same capacity. 

Design 2 has three times the surface area of design 1, and

design 3 is similar to design 1 except it is thinner and has 

wider sides to have the same 20-Ah capacity.

 The MSMD model was used to simulate the 

electrochemical and thermal responses of the four designs 

under a US-06 driving profile for a mid-size sedan 

PHEV10 design consisting of charge-depleting (CD) and 

charge-sustaining (CS) driving. The initial SOC of the 

battery was 90%. The results of the thermal behavior are 

presented in more detail for the electrochemical behavior

in Kim et al. [4] and Smith et al. [6]. 
5 

1. Nominal Design • 140 x 100 x 15 mm3

• Tabs on the same side

• 20 Ah

• Nominal area and thickness

2. Small Capacity

3. Thinner & Wider

4. Tabs both-sides 

• 3 x (140 x 100 x 5) mm3

• Same tab design

• 3 x 6.67 Ah

• Same electrode area/stack layer

• 1/3 thickness

• ~ 3x surface area

• 200 x 140 x 7.5 mm3

• Same tabs

• 20 Ah

• 2x electrode area/stack layer

• 1/2 thickness

• ~ 2x surface area

• 250 x 120 x 7 mm3

• Wide-counter tab design

• 20 Ah

• ~2x electrode area/stack layer

• ~1/2 thickness

• ~2x surface area

Figure 7: Alternative cell designs were used to study the impact 

of aspect ratio using the electrochemical-thermal MSMD model. 

 The results of the thermal response of the four designs to 

CD and CS profiles are shown in Figure 8. The top graph 

shows the resulting US-06 power profile for the first 20 

minutes of driving (the first 10 minutes is mostly CD). 

The middle graph shows the average volumetric 

temperature of each cell design. As can be seen, the 

thickest cell (design 1) reaches the highest average 

temperature, while the small-capacity cells (design 2) 

have the lowest temperature rise. The lower graph shows 

the temperature difference (maximum – minimum) for the 

four designs. Design 3 (thin and wide) has the highest 

temperature difference while design 4 (thin with tabs on 

both sides) has the lowest. From these analyses, it can be 

concluded that from the perspective of thermal and 

electrochemical uniformity and in the attempt to retain the 

lowest temperature from the three large-format cells, 

design 4 (thin with tabs on both sides) is the preferred 

design. Of course, packaging, structural, and cost analyses 

must be performed to see if design 4 is the optimum cell 

configuration, which is why it is necessary to perform the 

battery pack modeling studies using CAE. 

4.2. Pack-Level Modeling—Prismatic Cells 

 The following example provides information from 

Compact Power, Inc. [7] using CAD, computational fluid 

dynamics, and FEA software packages for capturing the 

complex three-dimensional geometry of battery packs. As 

shown in Figure 9, the cells are prismatic with stacked 

layers of Mn-spinel/layered mixed cathodes and graphite/ 

amorphous-carbon anodes. The positive and negative 

terminals are on opposite sides of the cell. The cells are 

stacked horizontally in a module and then assembled in an 

air-cooled pack. 

0 5 10 15 0

2

4

6

8

10

12

14

t [min]

ΔT(t) [°C]

0 5 10 15 25

30

35

40

45

50

55

t [min]

T

avg(t) [°C]

2. Small Capacity 

3. Thin & Wide

4. Tabs Both Sides 

1. Nominal

2. Small Capacity 

3. Thin & Wide

4. Tabs Both Sides 

1. Nominal

20

-600

-400

-200

0

200

400

600

800

0 10

Time (minute)

Scaled Power (W)

Avg Temperature (0C) Delta Temp (0C) 

Time (minute)

Time (minute)

CD CS

Figure 8: Results of electrochemical-thermal MSMD modeling 

for four stacked prismatic cells under the US-06 power profile 

(top graph). Volumetric average temperature rise above 

initial/ambient temperature of 30°C is shown in the middle 

graph. The bottom graph shows the temperature difference 

(maximum – minimum) in each design.

Figure 9: Compact Power, Inc. integrated prismatic cells in an 

air-cooled pack using existing CAD tools [7].
6 

 The cells were assumed to be isothermal for the battery 

pack studies with the heat source estimated using direct 

current resistance and the root mean square of the current. 

To use an integrated approach including electrochemical 

and thermal performance of cells in CAE tools for a 

battery pack, further work is needed. The objective of 

such a pack-level modeling development could include

capturing physics and geometry that are important for 

operation of the energy storage in an EDV environment; 

achieving good computational efficiency through model 

reduction, parallelization, etc.; interfacing with 

commercial software to automate robust design 

optimization processes; interfacing with appropriate celllevel and (vehicle) system-level models; and encouraging 

usability by battery non-experts. In addition, it would be 

beneficial if the battery pack-level CAE tool provided the 

ability to: 

• Rapidly define common cell and battery pack 

geometries

• Import existing CAD geometries from other 

commercial packages 

• Predict thermal management system performance

• Capture cell-to-cell variations due to parallel/series 

electrical connections, location in pack, cell 

manufacturing variability, and degradation at various 

states of life 

• Predict internal cell heat generation and temperature 

variation 

• Evaluate the effects of duty cycle (e.g., aggressive 

driving, fast charge, etc.)

• Estimate battery pack cost and distribution among 

various components. 

5. Conclusion 

 Non-uniform battery physics, which are more likely to 

occur in large-format cells, can cause unexpected 

performance and life degradations in lithium-ion batteries. 

Robust CAE design tools are needed to evaluate and 

predict the performance of batteries from material to cell, 

pack, and vehicle levels. These tools will also shorten the 

battery design and product development cycle time, thus 

reducing the battery cost.

 The MSMD model was used to evaluate large-format 

prismatic automotive cell designs by integrating microscale electrochemical processes and macro-scale 

transports. Thin-form factor prismatic cells with a wide 

counter tab design would be preferable to manage cell 

internal heat and electron current transport and 

consequently to achieve uniform electrochemical kinetics 

across a system. 

 To transfer and integrate a range of battery modeling 

tools, various organizations need to collaborate to 

incorporate existing and new models into software battery 

modeling suites/tools with the overall goal of shortening

the design cycle and optimizing batteries (cells and packs) 

for improved thermal uniformity, safety, long life, and

lower cost. 

6. References

[1]. M. Doyle, T. Fuller, J. Newman, “Modeling of galvanostatic 

charge and discharge of the lithium/polymer/insertion cell,” J. 

Electrochem. Soc., 140 (6), 1993.

[2]. G-H. Kim, K.A. Smith, “Multi-Dimensional 

Electrochemical-Thermal Coupled Model of Large Format 

Cylindrical Lithium Ion Cells,” presented at 212th ECS Meeting, 

Oct. 7–12, 2007, Washington, DC.

[3]. G-H. Kim, K.A. Smith, “Multi-Scale Multi-Dimensional 

Lithium-Ion Battery Model,” presented at 214th ECS Meeting, 

Oct. 12–17, 2008, Honolulu, HI.

[4]. G-H. Kim, K.A. Smith, “Evaluation Study for Large 

Prismatic Lithium-Ion Cell Designs Using Multi-Scale MultiDimensional Battery Model,” presented at 215th ECS Meeting, 

May 24–29, 2009, San Francisco, CA.

[5]. K.A. Smith, G-H. Kim, A. Pesaran, “Modeling of 

Nonuniform Degradation in Large-Format Li-ion Batteries,” 

presented at 215th ECS Meeting, May. 24–29, 2009, San 

Francisco, CA.

[6]. K.A. Smith, A. Vlahinos, G-H. Kim, A. Pesaran, 

“Computer-Aided Optimization of Macroscopic Design Factors 

for Lithium-Ion Cell Performance and Life,” presented at 217th

ECS Meeting, April 29, 2010, Vancouver, Canada. 

[7]. M. Alamgir, “A High-Performance PHEV Battery Pack,” 

Compact Power, Inc., presentation at the 2010 DOE Annual 

Merit Review, Washington, DC, June 8–10, 2010. 

[8]. K.A. Smith, C.D. Rahn, C.-Y. Wang, “Control oriented 1D 

electrochemical model of lithium ion battery,” Energy 

Conversion and Management, 48, 2007.

7. Authors 

Principal Engineer, Ahmad Pesaran

National Renewable Energy Laboratory 

1617 Cole Blvd., Golden, CO 80401, USA

Tel: (303) 275-4441, Fax: (303) 275-4415 

Email: ahmad.pesaran@nrel.gov 

URL: nrel.gov/vehiclesandfuels/energystorage 

Dr. Pesaran leads the energy storage team at 

NREL. He manages several projects for the Department of 

Energy and industrial partners, which include thermal testing 

and analysis of batteries, and modeling and simulation of 

electric drive vehicles and their energy storage. He received his 

Ph.D. in thermal engineering from UCLA.

Senior Engineer, Gi-Heon Kim

National Renewable Energy Laboratory

1617 Cole Blvd., Golden, CO 80401, USA

Tel: (303) 275-4437, Fax: (303) 275-4415 

Email: Gi-heon.kim@nrel.gov 

URL: nrel.gov/vehiclesandfuels/energystorage 

Dr. Kim’s recent research interests in advanced 

vehicle energy storage system tasks include development of a 

three-dimensional lithium ion battery thermal abuse model, 

three-dimensional electrochemical modeling of lithium-ion cells, 

and modeling hybrid electric vehicle/electric vehicle battery 

thermal management systems. He holds a Ph.D. in mechanical 

engineering from Colorado State University.

Senior Engineer, Kandler Smith

National Renewable Energy Laboratory

1617 Cole Blvd., Golden, CO 80401, USA

Tel: (303) 275-4423, Fax: (303) 275-4415 

Email: kandler.smith@nrel.gov 

URL: nrel.gov/vehiclesandfuels/energystorage 

Dr. Smith is working in the areas of battery 
7 

thermal management and electrochemical modeling. He holds a 

Ph.D. in mechanical engineering from Penn State. His Ph.D. 

research developed electrochemistry-based real-time algorithms 

that enable expanded battery power and energy capability.

8. Appendix

 The governing equations for battery functional physics 

in the demonstrated cases shown above are given below. 

However, model and solver choice in the multi-scale 

multi-dimension approach can be altered according to the 

purpose of the model simulation and the domain.

Particle Domain

Solid phase diffusion: 

asF

Li j

Rr s r

sc Ds rsc Dr s r r t

sc − =

= ∂

∂ 



 





∂

∂

∂

∂ = ∂

∂ , 2

2

1

Charge transfer kinetics: 

















 





−−



 



 = η

α

η

α

RT

F

RT

F

iaj a c os

Li exp exp

( ) ( ) ( ) a a c

seo eses ccccki α αα = ,max − ,,

Electrode Domain

Charge balance in solid and liquid phases: 

( ) =−∇∇ 0 Li

s

eff φσ j , and

( ) ( ) ln =+∇∇+∇∇ 0 Li e

eff

e D

eff κφκ jc

Transport in electrolyte phase: 

( ) ( ) F

ti

j F

t cD

t

c o Li e o

e

eff

e ee + ∇⋅ + − − +∇∇=

∂

∂  ε 1

Heat generation in electrochemically active domain: 

ee

eff

ee D

eff

ss

eff

es

Li

c

T

U jq TU

κφφκ φ

φφ φφσ

∇⋅∇+∇⋅∇+

 ∇⋅∇+

 





∂

∂ ′′′ +−−=

ln

Cell Domain

Temperature: 

( ) ( ) qTk

t

Tc p +∇∇= ′′′ ∂

∂ ρ

Electric potential in non-active components outside of 

composite electrodes: 

( ) +Φ∇∇ ik ′′′ = 0 e
F1147-E(10/2008) 

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1. REPORT DATE (DD-MM-YYYY)

January 2011

2. REPORT TYPE

Conference Paper

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4. TITLE AND SUBTITLE

Accelerating Battery Design Using Computer-Aided Engineering 

Tools: Preprint

5a. CONTRACT NUMBER

DE-AC36-08GO28308

5b. GRANT NUMBER

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6. AUTHOR(S)

A. Pesaran, G.-H. Kim, and K. Smith

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NREL/CP-5400-49316

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FC10.6510

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National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

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NREL/CP-5400-49316

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14. ABSTRACT (Maximum 200 Words)

Computer-aided engineering (CAE) is a proven pathway, especially in the automotive industry, to improve 

performance by resolving the relevant physics in complex systems, shortening the product development design 

cycle, thus reducing cost, and providing an efficient way to evaluate parameters for robust designs. Academic models 

include the relevant physics details, but neglect engineering complexities. Industry models include the relevant 

macroscopic geometry and system conditions, but simplify the fundamental physics too much. Most of the CAE 

battery tools for in-house use are custom model codes and require expert users. There is a need to make these 

battery modeling and design tools more accessible to end users such as battery developers, pack integrators, and 

vehicle makers. Developing integrated and physics-based CAE battery tools can reduce the design, build, test, 

break, re-design, re-build, and re-test cycle and help lower costs. NREL has been involved in developing various 

models to predict the thermal and electrochemical performance of large-format cells and has used in commercial 

three-dimensional finite-element analysis and computational fluid dynamics to study battery pack thermal issues. 

These NREL cell and pack design tools can be integrated to help support the automotive industry and to accelerate 

battery design. 

15. SUBJECT TERMS

Li-ion battery; electric drive vehicles; modeling; simulation; design tool 

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