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Multiphysical modeling for life analysis of lithium-ion battery pack in electric vehicles

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  • Xia, Quan
  • Yang, Dezhen
  • Wang, Zili
  • Ren, Yi
  • Sun, Bo
  • Feng, Qiang
  • Qian, Cheng

Abstract

With the continuous improvement in battery life requirements, the modeling, analysis and management of battery pack life become an important topic in the design of electric vehicles. A more realistic and generic modeling method coupling the electrochemical, thermal, SEI formation model of cells, fluid dynamics and the series-parallel circuit model is proposed to accurately describe the coupling relationships and quantify the inconsistencies, including temperature and current. A life model including capacity fade and reliability is established to evaluate the life of lithium-ion battery pack system (LIBPs). And a model implementation method is established to raise the computational accuracy and efficiency, then, it is validated from the perspective of electrochemical, degradation, thermal performance and coupling effect using experiments. Moreover, the multiphysical behavior and life of LIBPs in different situations are analyzed and predicted, followed by the analysis of coupling effect, inconsistency, reliability and economy. The results show the degradation rate of LIBPs first decreases and then accelerates in the whole life. Furthermore, adding series cells is uneconomical to extend the life of LIBPs. Finally, a SoH balanced management method of changing the airflow direction is proposed to extend the life of LIBPs, and the optimal strategy for different SoH inconsistency are obtained.

Suggested Citation

  • Xia, Quan & Yang, Dezhen & Wang, Zili & Ren, Yi & Sun, Bo & Feng, Qiang & Qian, Cheng, 2020. "Multiphysical modeling for life analysis of lithium-ion battery pack in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    • Handle: RePEc:eee:rensus:v:131:y:2020:i:c:s1364032120302847
    DOI: 10.1016/j.rser.2020.109993
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    References listed on IDEAS

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    1. Ren, Yi & Fan, Dongming & Feng, Qiang & Wang, Zili & Sun, Bo & Yang, Dezhen, 2019. "Agent-based restoration approach for reliability with load balancing on smart grids," Applied Energy, Elsevier, vol. 249(C), pages 46-57.
    2. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    3. Ouyang, Minggao & Feng, Xuning & Han, Xuebing & Lu, Languang & Li, Zhe & He, Xiangming, 2016. "A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery," Applied Energy, Elsevier, vol. 165(C), pages 48-59.
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    Cited by:

    1. Guo, Zengjia & Xu, Qidong & Wang, Yang & Zhao, Tianshou & Ni, Meng, 2023. "Battery thermal management system with heat pipe considering battery aging effect," Energy, Elsevier, vol. 263(PE).
    2. Xie, Lin & Ustolin, Federico & Lundteigen, Mary Ann & Li, Tian & Liu, Yiliu, 2022. "Performance analysis of safety barriers against cascading failures in a battery pack," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. Xia, Quan & Ren, Yi & Wang, Zili & Yang, Dezhen & Yan, Peiyu & Wu, Zeyu & Sun, Bo & Feng, Qiang & Qian, Cheng, 2023. "Safety risk assessment method for thermal abuse of lithium-ion battery pack based on multiphysics simulation and improved bisection method," Energy, Elsevier, vol. 264(C).
    4. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo & Xie, Jiale, 2022. "A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry," Energy, Elsevier, vol. 239(PC).
    5. Tae-Hwan Jin & Ki-Yeol Shin & Mo Chung & Geon-Pyo Lim, 2022. "Development and Performance Verification of Frequency Control Algorithm and Hardware Controller Using Real-Time Cyber Physical System Simulator," Energies, MDPI, vol. 15(15), pages 1-24, August.

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