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Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method

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  • Qiao, Dongdong
  • Wang, Xueyuan
  • Lai, Xin
  • Zheng, Yuejiu
  • Wei, Xuezhe
  • Dai, Haifeng

Abstract

Early internal short circuit (ISC) diagnosis is critical for a battery management system (BMS) to prevent the thermal runaway of lithium-ion batteries. However, it is difficult to be diagnosed owing to few obvious electric and thermoelectric characteristics in the early stage of ISC. In this study, a novel ISC diagnosis method based on the incremental capacity (IC) curves is proposed. Different charging rates on four ISC situations are carried out on a cell to verify the proposed method. The leakage current of the ISC battery can be obtained by the area difference between the normal cell and the ISC cell, and it can be converted into the ISC resistance. The experiments of different initial charging states of charge (SOC) in a series-connected battery pack are conducted to verify the method in the real EVs working environment. The diagnosis results of the battery cell and battery pack indicate the proposed method is feasible and effective to quantitatively diagnose the ISC.

Suggested Citation

  • Qiao, Dongdong & Wang, Xueyuan & Lai, Xin & Zheng, Yuejiu & Wei, Xuezhe & Dai, Haifeng, 2022. "Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221033314
    DOI: 10.1016/j.energy.2021.123082
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    8. Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
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    10. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
    11. Chenqiang Luo & Zhendong Zhang & Dongdong Qiao & Xin Lai & Yongying Li & Shunli Wang, 2022. "Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML," Energies, MDPI, vol. 15(13), pages 1-15, June.
    12. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
    13. Song, Youngbin & Park, Shina & Kim, Sang Woo, 2023. "Model-free quantitative diagnosis of internal short circuit for lithium-ion battery packs under diverse operating conditions," Applied Energy, Elsevier, vol. 352(C).
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