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Multi-scale short circuit resistance estimation method for series connected battery strings

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  • Xu, Jun
  • Wang, Haitao
  • Shi, Hu
  • Mei, Xuesong

Abstract

Short circuit (SC) fault in battery systems is considered as one of the most severe problems, which may result in thermal runaway and fire. This paper tries to utilize the multi-scale technology to estimate the short circuit resistance to give a quantitative analysis of short circuit fault. With the value of the short circuit resistance, it is able to determine to light a warning or stop using the battery immediately. To solve this problem, the multi-scale short circuit resistance estimation method is proposed. Not only the hard short circuit with small resistance but also the soft short circuit with large resistance can be estimated accurately. Additionally, to reduce the computation complexity, only two battery cells in the battery string are needed for the estimation. The experimental test platform is established and different short circuit resistance is applied to the battery string. The results show that the fast estimation of hard short circuit resistance can be realized. Moreover, the soft short circuit resistance is able to be estimated accurately. The hard short circuit resistance can be estimated in 3 s and the estimation error standard deviation for the soft one is less than 4%.

Suggested Citation

  • Xu, Jun & Wang, Haitao & Shi, Hu & Mei, Xuesong, 2020. "Multi-scale short circuit resistance estimation method for series connected battery strings," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220307544
    DOI: 10.1016/j.energy.2020.117647
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    Cited by:

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    2. Chang, Chun & Wang, Qiyue & Jiang, Jiuchun & Jiang, Yan & Wu, Tiezhou, 2023. "Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram," Energy, Elsevier, vol. 278(PB).
    3. Jiang, Yihui & Xu, Jun & Hou, Wenlong & Mei, Xuesong, 2021. "A stack pressure based equivalent mechanical model of lithium-ion pouch batteries," Energy, Elsevier, vol. 221(C).
    4. Jiang, Yihui & Xu, Jun & Liu, Mengmeng & Mei, Xuesong, 2022. "An electromechanical coupling model-based state of charge estimation method for lithium-ion pouch battery modules," Energy, Elsevier, vol. 259(C).
    5. Huang, Peifeng & Yao, Caixia & Mao, Binbin & Wang, Qingsong & Sun, Jinhua & Bai, Zhonghao, 2020. "The critical characteristics and transition process of lithium-ion battery thermal runaway," Energy, Elsevier, vol. 213(C).
    6. Li, Da & Deng, Junjun & Zhang, Zhaosheng & Liu, Peng & Wang, Zhenpo, 2023. "Multi-dimension statistical analysis and selection of safety-representing features for battery pack in real-world electric vehicles," Applied Energy, Elsevier, vol. 343(C).
    7. Arkadiusz Hulewicz & Krzysztof Dziarski & Łukasz Drużyński & Grzegorz Dombek, 2023. "Thermogram Based Indirect Thermographic Temperature Measurement of Reactive Power Compensation Capacitors," Energies, MDPI, vol. 16(5), pages 1-18, February.

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