Optimization of Lithium-Ion Battery Packs using automation of ANSYS Workbench

Felipe Gana Ortega

Centro de Energía, Universidad de Chile

PRESENTATION TOPICS

• Centro de Energía and Project EOBLi;

• Problem Description;

• Methodology;

• Results;

• Conclusion and next steps.

Centro de Energía

• Founded in 2009

• Has the objective of contributing in the energy area, developing and introducing new technological solutions relevant to chilean towns and cities, and competitive in the world.

Centro de Energía – Some projects

ESUSCON - Huatacondo

Projects in DeepEdit

Electromobility: Electric and solar vehicles

Intelligent networks and distributed generation

Eolian III

Decision support tools

Joint Venture: EOBLi

Problem Description

Thermal Management For Hybrid

Vehicles BEHR, 2009.A123 Systems Grid Solutions*NanophosphateTM Lithium Ion Enabling

New Possibilities for the Electric Grid

Battery Management

System

Thermal Management

SystemCell Module Pack

Recent Progresses of LG Chem’s Large-Format Li ion polymer batteriesMohamed Alamgir, Satish Ketkar and Kwangho

YooLG Chem Power, Inc., Troy, Michigan, USA

Problem description

• Optimization of battery pack designs

– A lot of possibilities for the optimal design

– Goals are usually conflicting, so we don’t need

to find just one solution but a Pareto Front of

multiple solutions

– Simulations are required for obtaining the

values of the objective functions that evaluate

each design

Problem description

Problem description

• How can we run thousands and thousands of ANSYS simulations in order to find the best solutions after an optimization?

• Is ANSYS capable of working correctly after a large amount of simulations?

• How can we do that automatically, without human effort?

Methodology

• Using ANSYS Workbench in a Linux environment, we were able to automizeANSYS via scripts in the Command Shell, Python and IronPython.

• All runs with one single order.

• Possible to do it in Windows, but need at least a couple more command lines to make it work.

Methodology

Shell

Console

Interface:

Activates

ANSYSPython

Code

Executable

File .sh

Project Vesuscon

Vesuscon optimization

• 528 cells ICR 26650

• Power: 5 kW

• Energy: 8 kWh

• Configuration: 176 s, 3p

Vesuscon optimization

…

Filter

Fan

Battery

module

Vesuscon optimization

Variables:

Variables:

d = separation with the borders (mm)

k = vertical separation between cells (mm)

k1 = horizontal separation between cells (mm)

Vesuscon Optimization

• Optimal design after optimization and simulation:

• Max. Temperature: 33,55 °C

• Mean Temperature: 28,216 °C

• Min. Temperature: 26,862 °C

• Temperature Difference: 6,688 °C

Vesuscon optimization: Results

Vesuscon optimization: Results

Conclusions and next steps

• It is possible to automize ANSYS via batchmode and scripts in Linux. And it’s not toughto do. This allows us to run multiplesimulations without human interaction, keyfor optimization of battery packs and manyother applications.

• Work has to be done in order to reduce thetime of each simulation: Mesh optimization, for example

Annex

6 cells optimization

Vesuscon optimization: Results

Separación Y

(k)

Separación X

(k1) Separación d Tamaño X Tamaño Y % X % Y Tmax Tmean Tmin DifT Potencia Eficiencia

1,25 1,03 5 250,24 491 100% 94% 30,55 27,639 26,268 4,282 22,788 99,43%

1,25 1,03 4,3 248,64 489,4 99% 94% 31,35 27,564 26,345 5,005 22,4117 99,44%

1,2 1,03 5 250,24 472,8 100% 91% 31,85 26,804 25,719 6,131 37,5116 99,06%

1,33 1,03 5 250,24 520 100% 100% 33,55 28,216 26,862 6,688 12,737 99,68%

1,31 1,03 4,2 248,64 511,24 99% 98% 33,75 28,222 26,957 6,793 15,412 99,61%

1,25 1,03 14,5 250,24 510 100% 98% 33,75 27,315 26,653 7,097 16,6601 99,58%

1,31 1,03 5 250,24 511,24 100% 98% 33,95 28,084 26,653 7,297 14,2294 99,64%

1,32 1,03 6,76 250,24 520 100% 100% 34,05 28,306 26,992 7,058 12,7016 99,68%

1,3 1,03 10,5 250,24 520 100% 100% 34,35 27,802 26,765 7,585 13,0562 99,67%

1,31 1,03 5 250,24 512,84 100% 99% 34,65 27,850 26,793 7,857 13,9804 99,65%

1,28 1,03 14,04 250,24 520 100% 100% 35,25 27,807 26,592 8,658 13,6967 99,66%

1,31 1,01 4,2 244,48 511,24 98% 98% 35,35 30,057 28,057 7,293 12,6324 99,68%

Results

• Eolian IV Battery Pack design

Results

• Eolian IV Battery Pack Design

– Chosen cell: ICR 26650 4000 mAh

– 28 electric modules of 32 cells

each.

– 4 electric modules for each

physical module.

– 7 physical modules of 128 cells

each.

– Total Energy: 13,2 kWh

– Total Power: 26 kW

Results

• Eolian IV battery pack design

Results

• Eolian IV battery pack design

Results

• Eolian IV battery pack design, best results

Configuration

Max.

Temperature

Mean

Temperature

Temperature

Difference

5,7-7,7-9,2 27,55 26,339 2,854

4,7-8,7-9,2 27,75 26,347 3,387

4,2-9,2-9,2 27,85 26,274 3,468

3,2-11,2-7,2 27,95 26,295 4,027

Compare with “easy” designs:

Configuration

Max.

Temperature

Mean

Temperature

Temperature

Difference

7,2-7,2-7,2 28,65 26,292 4,18

0-12-12 33,35 28,386 12,227

0-18-0 33,65 30,267 12,259

• ESUSCON

– Energización Sustentable Cóndor