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The local lithium plating caused by anode crack defect in Li-ion battery

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  • Yuan, Yuebo
  • Wang, Hewu
  • Han, Xuebing
  • Pan, Yue
  • Sun, Yukun
  • Kong, Xiangdong
  • Lu, Languang
  • Ouyang, Minggao

Abstract

Anode cracks are typical defects in Li-ion batteries, which lead to local lithium plating in the defect region. To avoid lithium plating, it is necessary to study the evolution mechanism, lithium plating condition, parameter sensitivity, and safety boundaries of defects. In this study, an artificial defect was implanted on the anode surface, and the appearance characteristic of dead lithium was observed. Based on finite element simulations, it was confirmed that potential heterogeneity is the core factor for lithium plating and that dead lithium can fill the defect region. The slope of the cathode equilibrium potential had the most significant influence on the lithium plating. The safety boundaries of the defect sizes for different cathode materials were determined. Moreover, based on simulations and experimental verification, the lithium iron phosphate battery working on the potential plateau was found to be capable of tolerating defects, thus providing a novel approach for improving battery safety.

Suggested Citation

  • Yuan, Yuebo & Wang, Hewu & Han, Xuebing & Pan, Yue & Sun, Yukun & Kong, Xiangdong & Lu, Languang & Ouyang, Minggao, 2024. "The local lithium plating caused by anode crack defect in Li-ion battery," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924003519
    DOI: 10.1016/j.apenergy.2024.122968
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    References listed on IDEAS

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    1. Feng, Xuning & Weng, Caihao & Ouyang, Minggao & Sun, Jing, 2016. "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Elsevier, vol. 161(C), pages 168-180.
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    3. Hildenbrand, Felix & Ditscheid, Dominik & Barbers, Elias & Sauer, Dirk Uwe, 2023. "Influence of the anode overhang on the open-circuit voltage and the ageing of lithium-ion batteries—A model based study," Applied Energy, Elsevier, vol. 332(C).
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    Cited by:

    1. Dan, Zhaohui & Song, Aoye & Yu, Xiaojun & Zhou, Yuekuan, 2024. "Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach," Energy, Elsevier, vol. 299(C).

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