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Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications

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  • Astaneh, Majid
  • Andric, Jelena
  • Löfdahl, Lennart
  • Stopp, Peter

Abstract

Large-scale commercialization of electric vehicles (EVs) seeks to develop battery systems with higher energy efficiency and improved thermal performance. Integrating simulation-based design optimization in battery development process expands the possibilities for novel design exploration. This study presents a dual-stage multiphysics simulation optimization methodology for comprehensive concept design of Lithium-ion (Li-ion) battery packs for EV applications. At the first stage, multi-objective optimization of electrochemical thermally coupled cells is performed using genetic algorithm considering the specific energy and the maximum temperature of the cells as design objectives. At the second stage, the energy efficiency and the thermal performances of each optimally designed cell are evaluated under pack operation to account for cell-to-pack interactions under realistic working scenarios. When operating at 1.5 C discharge current, the battery pack comprising optimally designed cells for which the specific energy and the maximum temperature are equally weighted delivers the highest specific energy with enhanced thermal performance. The most favorable pack design shows 8% reduction in maximum pack temperature and 16.1% reduction in module-to-module temperature variations compared to commercially available pack. The methodology for design optimization presented in this work is generic, providing valuable knowledge for future cell and pack designs that employ different chemistries and configurations.

Suggested Citation

  • Astaneh, Majid & Andric, Jelena & Löfdahl, Lennart & Stopp, Peter, 2022. "Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023409
    DOI: 10.1016/j.energy.2021.122092
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    3. Ma, Chen & Chang, Long & Cui, Naxin & Duan, Bin & Zhang, Yulong & Yu, Zhihao, 2022. "Statistical relationships between numerous retired lithium-ion cells and packs with random sampling for echelon utilization," Energy, Elsevier, vol. 257(C).
    4. Jie Liu & Santosh Chavan & Sung-Chul Kim, 2023. "Investigation of the Electrochemical and Thermal Characteristics of NCM811-21700 Cylindrical Lithium-Ion Battery: A Numerical Study and Model Validation," Energies, MDPI, vol. 16(17), pages 1-16, September.
    5. Zha, Yunfei & Meng, Xianfeng & Qin, Shuaishuai & Hou, Nairen & He, Shunquan & Huang, Caiyuan & Zuo, Hongyan & Zhao, Xiaohuan, 2023. "Performance evaluation with orthogonal experiment method of drop contact heat dissipation effects on electric vehicle lithium-ion battery," Energy, Elsevier, vol. 271(C).

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