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Quantitative failure analysis of lithium-ion batteries based on direct current internal resistance decomposition model

Author

Listed:
  • Xu, Ruhui
  • Li, Xinhai
  • Tang, Siqi
  • Wang, Zhixing
  • Guo, Huajun
  • Peng, Wenjie
  • Wang, Ding
  • Duan, Jianguo
  • Wang, Jiexi
  • Yan, Guochun

Abstract

Accurate failure analysis plays a pivotal role in the optimization design and lifetime prediction of 4.45 V high-voltage LiCoO2/Graphite (LCO/Gr) batteries. Multiphysics coupling model brings great opportunities to conduct battery failure analysis quantitatively, although it is quite challenging because many model parameters need to be handled properly. Herein, we systematically elaborate the differences of ion and electron transport properties before and after cycling ageing of LCO/Gr batteries by constructing direct current internal resistance (DCR) decomposition model. The key parameters acquisition method is established, and the mechanism of DCR growth is elucidated. Furthermore, the aforementioned model parameters are refined by using a hybrid power pulse characteristics (HPPC) curve optimization algorithm based on DCR decomposition results obtained from the three-electrode battery system. Through analyzing the influence of single-factor parameter ageing on battery voltage output capacity and discharge temperature rise, the main factors affecting battery failure process are identified. For instance, the account of the positive electrochemical reaction resistance related to the kpos increased from the initial 22.9% of total DCR to 37.3% after ageing. This work provides a reliable quantitative analysis basis for the global optimization design of advanced LIBs.

Suggested Citation

  • Xu, Ruhui & Li, Xinhai & Tang, Siqi & Wang, Zhixing & Guo, Huajun & Peng, Wenjie & Wang, Ding & Duan, Jianguo & Wang, Jiexi & Yan, Guochun, 2024. "Quantitative failure analysis of lithium-ion batteries based on direct current internal resistance decomposition model," Applied Energy, Elsevier, vol. 371(C).
    • Handle: RePEc:eee:appene:v:371:y:2024:i:c:s0306261924010134
    DOI: 10.1016/j.apenergy.2024.123630
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    References listed on IDEAS

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