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Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods

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  • Tian, Yu
  • Lin, Cheng
  • Li, Hailong
  • Du, Jiuyu
  • Xiong, Rui

Abstract

Lithium plating on anodes, which can happen during fast charging and low-temperature charging, and/or after long-term cycling, plays a crucial role in the aging of lithium-ion batteries (LIBs) and leads to irreversible capacity fade and severe safety hazards. This study systematically reviews the recent progress in developing methods for in-situ detecting lithium plating in order to provide guidelines regarding selecting proper methods for on-board applications. In general, lithium plating can be divided into three stages according to the damage level. There are two categories of methods, electrochemical methods and physical methods, which can be used to detect lithium plating. Their principles, features, and limitations have been thoroughly analyzed. Trends for the prospective development of novel technologies are also discussed.

Suggested Citation

  • Tian, Yu & Lin, Cheng & Li, Hailong & Du, Jiuyu & Xiong, Rui, 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods," Applied Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:appene:v:300:y:2021:i:c:s0306261921007893
    DOI: 10.1016/j.apenergy.2021.117386
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    References listed on IDEAS

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    1. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
    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. Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Lu, Jiahuan, 2021. "State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach," Applied Energy, Elsevier, vol. 291(C).
    4. Yang, Fangfang & Wang, Dong & Zhao, Yang & Tsui, Kwok-Leung & Bae, Suk Joo, 2018. "A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries," Energy, Elsevier, vol. 145(C), pages 486-495.
    5. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    6. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    7. Yang, Fangfang & Song, Xiangbao & Dong, Guangzhong & Tsui, Kwok-Leung, 2019. "A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries," Energy, Elsevier, vol. 171(C), pages 1173-1182.
    8. 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:

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    3. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    4. Zhou, Boru & Fan, Guodong & Wang, Yansong & Liu, Yisheng & Chen, Shun & Sun, Ziqiang & Meng, Chengwen & Yang, Jufeng & Zhang, Xi, 2024. "Life-extending optimal charging for lithium-ion batteries based on a multi-physics model and model predictive control," Applied Energy, Elsevier, vol. 361(C).
    5. Yu, Xiao & Lin, Cheng & Zhao, Mingjie & Yi, Jiang & Su, Yue & Liu, Huimin, 2022. "Optimal energy management strategy of a novel hybrid dual-motor transmission system for electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    6. Liu, Yisheng & Fan, Guodong & Zhou, Boru & Chen, Shun & Sun, Ziqiang & Wang, Yansong & Zhang, Xi, 2023. "Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks," Applied Energy, Elsevier, vol. 351(C).

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