Upload
vandat
View
234
Download
0
Embed Size (px)
Citation preview
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