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Aging-induced, rate-independent Lithium plating: A complete mechanism analysis throughout the battery lifecycle

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  • Wang, Peng
  • Xiong, Rui
  • Shen, Weixiang
  • Sun, Fengchun

Abstract

Lithium-ion battery aging poses a critical challenge, and understanding its mechanisms is key to extending battery life. While most aging side reactions are largely influenced by current rate (C-rate), some reactions, such as aging-induced lithium plating, occur independently of C-rate and play a major role in battery aging. A rate-independent lithium plating has a profound impact on battery performance and longevity. To investigate this phenomenon, we conducted systematic aging tests including battery disassembly, scanning electron microscope imaging, and half-cell testing. Thermodynamic analysis revealed that aging is accompanied by the formation of a distinct open circuit voltage (OCV) plateau, which contracts over time as lithium deposition on the anode initially increases and then stabilizes. Additionally, we introduced an innovative phase-based scaling technique to segment and scale the anode's over-discharge potential curve. This technique enabled precise alignment of the electrode open circuit potential with battery OCV throughout its lifecycle, achieving a root mean square error below 10 mV under both plating and non-plating conditions. Furthermore, a strong correlation was identified between lithium plating and capacity degradation inflection point, underscoring its critical role in accelerating performance degradation. These findings provide valuable insights into battery aging mechanisms and contribute to developing more effective strategies for optimizing battery performance and extending battery life.

Suggested Citation

  • Wang, Peng & Xiong, Rui & Shen, Weixiang & Sun, Fengchun, 2025. "Aging-induced, rate-independent Lithium plating: A complete mechanism analysis throughout the battery lifecycle," Applied Energy, Elsevier, vol. 393(C).
    • Handle: RePEc:eee:appene:v:393:y:2025:i:c:s0306261925008244
    DOI: 10.1016/j.apenergy.2025.126094
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    References listed on IDEAS

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    1. Jun Lu & Tianpin Wu & Khalil Amine, 2017. "State-of-the-art characterization techniques for advanced lithium-ion batteries," Nature Energy, Nature, vol. 2(3), pages 1-13, March.
    2. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    3. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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    Cited by:

    1. Xiong, Xin & Wang, Yujie & Jiang, Cong & Sun, Zhendong & Chen, Zonghai, 2025. "Multi-physics data and model feature fusion for lithium-ion battery capacity estimation by transformer-based deep learning," Energy, Elsevier, vol. 335(C).
    2. Du, Xinghao & Meng, Jinhao & Amirat, Yassine & Gao, Fei & Benbouzid, Mohamed, 2025. "Real-time lithium plating onset detection for lithium-ion batteries via dynamic impedance spectra analysis," Applied Energy, Elsevier, vol. 402(PA).
    3. Zhang, Zhihang & Wang, Hewu & Lu, Languang & Li, Yalun & Xu, Wenqiang & Liu, Haoran & Li, Desheng & Ouyang, Minggao, 2025. "State-of-charge and capacity estimation for MWh-scale LiFePO4 peak-shaving battery energy storage stations based on real-world operating data," Energy, Elsevier, vol. 339(C).

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