TY - GEN
T1 - Generative Design and Optimization of Battery Packs with Active Immersion Cooling
AU - Liu, Zheng
AU - Wu, Jiaxin
AU - Fu, Wuchen
AU - Kabirazadeh, Pouya
AU - Kohtz, Sara
AU - Miljkovic, Nenad
AU - Li, Yumeng
AU - Wang, Pingfeng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Among different battery packaging technologies, cell-to-pack is a widely used method to reduce the cost and increase the volumetric density of battery packs. Unlike the traditional cell-to-module technology, it requires more robust management to keep the temperature uniformity of all cells within a desirable range to ensure good pack performances. Besides active cooling controls, the layout of cells within the battery pack plays an important role in cooling performances, and thus needs to be optimized for lower cooling costs considering the geometry limitations of the pack. This paper presents the layout optimization of the battery pack with active immersion cooling for the 21700 cylindrical battery pack under harsh loading conditions. Based on the experiment testing, the finite element model with electric and thermal couplings has been built in COMSOL Multiphysics. To reduce the high computational cost, a data-driven generative design method based on variational autoencoder has been developed, which could autonomously mine useful properties from the data set of existing battery layout designs and performance metrics. With the generative design method, candidate designs that optimize the layout decisions can be identified. Based on the computational studies, the cooling cost can be lowered by more than 90% with the identified optimal layout design.
AB - Among different battery packaging technologies, cell-to-pack is a widely used method to reduce the cost and increase the volumetric density of battery packs. Unlike the traditional cell-to-module technology, it requires more robust management to keep the temperature uniformity of all cells within a desirable range to ensure good pack performances. Besides active cooling controls, the layout of cells within the battery pack plays an important role in cooling performances, and thus needs to be optimized for lower cooling costs considering the geometry limitations of the pack. This paper presents the layout optimization of the battery pack with active immersion cooling for the 21700 cylindrical battery pack under harsh loading conditions. Based on the experiment testing, the finite element model with electric and thermal couplings has been built in COMSOL Multiphysics. To reduce the high computational cost, a data-driven generative design method based on variational autoencoder has been developed, which could autonomously mine useful properties from the data set of existing battery layout designs and performance metrics. With the generative design method, candidate designs that optimize the layout decisions can be identified. Based on the computational studies, the cooling cost can be lowered by more than 90% with the identified optimal layout design.
KW - Battery active cooling
KW - Battery management system
KW - Design optimization
KW - Generative model
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85163609311&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85163609311&partnerID=8YFLogxK
U2 - 10.1109/ITEC55900.2023.10187078
DO - 10.1109/ITEC55900.2023.10187078
M3 - Conference contribution
AN - SCOPUS:85163609311
T3 - 2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023
BT - 2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023
Y2 - 21 June 2023 through 23 June 2023
ER -