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Parallel-connected battery module modeling based on physical characteristics in multiple domains and heterogeneous characteristic analysis

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  • Tian, Yong
  • Huang, Zhijia
  • Li, Xiaoyu
  • Tian, Jindong

Abstract

Cell inconsistencies inevitably occur inside a battery module. Particularly, the inconsistencies in current distribution and heat generation in a parallel-connected battery module may lead to battery degradation and potential safety issues. Consequently, it is imperative to evaluate and reduce cell inconsistencies in a battery module. In this paper, an extended single particle model of a battery cell is constructed using the Pade approximation and the first-order Taylor expansion to simplify the conventional electrochemical mechanism model. On this basis, a multidomain electrochemical mechanism simulation model of a parallel-connected battery module is attained. Then, the influence of cell inconsistencies on the battery module voltage, internal current distribution and heat generation under different aging situation is assessed by a parameter sensitivity analysis method. Moreover, based on the contribution of each battery model parameter to the inconsistency of the parallel-connected battery module, a battery cell sorting method is proposed. Finally, this proposed sorting method for secondary applications of batteries is validated based on 15 aged batteries. Results indicate that the average standard deviation of the cell current in the NCM parallel-connected module can be reduced from 0.209 A to 0.060 A. The proposed approach is helpful to the fault analysis of electric vehicle battery modules, module level grading or the secondary applications of retired batteries.

Suggested Citation

  • Tian, Yong & Huang, Zhijia & Li, Xiaoyu & Tian, Jindong, 2022. "Parallel-connected battery module modeling based on physical characteristics in multiple domains and heterogeneous characteristic analysis," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221024294
    DOI: 10.1016/j.energy.2021.122181
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    References listed on IDEAS

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    Cited by:

    1. Wang, Shumao & Bao, Wenkang & Sun, Yuedong & Li, Xiangjun & Dai, Feng & Hua, Jianfeng & Zheng, Yuejiu, 2024. "Current sensorless diagnosis of the cell internal resistance consistency in a parallel module using relaxation voltage," Energy, Elsevier, vol. 301(C).
    2. Li, Yuming & Wang, Tingyu & Li, Xinxi & Zhang, Guoqing & Chen, Kai & Yang, Wensheng, 2022. "Experimental investigation on thermal management system with flame retardant flexible phase change material for retired battery module," Applied Energy, Elsevier, vol. 327(C).
    3. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).

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