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In situ monitoring of lithium-ion battery degradation using an electrochemical model

Author

Listed:
  • Lyu, Chao
  • Song, Yankong
  • Zheng, Jun
  • Luo, Weilin
  • Hinds, Gareth
  • Li, Junfu
  • Wang, Lixin

Abstract

Lithium-ion batteries (LIBs) are increasingly popular for electric vehicle and grid storage applications, but degradation mechanisms remain poorly understood and are difficult to characterize accurately. Here a new method for in situ monitoring of internal degradation in LIBs using an electrochemical model (EM) is introduced. The main contributions of this work can be summarized as follows: (1) An EM is developed based on a single particle model, the parameters of which are reorganized from the original physical property parameters for convenience of identification, with each new parameter assigned a specific physical meaning. (2) Identification methods for all parameters are determined through activation-and-response analysis, and a combined load profile for parameter identification is developed. (3) Cyclic aging experiments are carried out and the aforementioned model and parameter identification methods are applied at appropriate intervals. The predicted trends of 7 (internal resistance included) of the 10 model parameters show strong correlations with the experimentally observed degradation.

Suggested Citation

  • Lyu, Chao & Song, Yankong & Zheng, Jun & Luo, Weilin & Hinds, Gareth & Li, Junfu & Wang, Lixin, 2019. "In situ monitoring of lithium-ion battery degradation using an electrochemical model," Applied Energy, Elsevier, vol. 250(C), pages 685-696.
  • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:685-696
    DOI: 10.1016/j.apenergy.2019.05.038
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    1. K. Ogata & E. Salager & C.J. Kerr & A.E. Fraser & C. Ducati & A.J. Morris & S. Hofmann & C.P. Grey, 2014. "Revealing lithium–silicide phase transformations in nano-structured silicon-based lithium ion batteries via in situ NMR spectroscopy," Nature Communications, Nature, vol. 5(1), pages 1-11, May.
    2. M. Sathiya & J.-B. Leriche & E. Salager & D. Gourier & J.-M. Tarascon & H. Vezin, 2015. "Electron paramagnetic resonance imaging for real-time monitoring of Li-ion batteries," Nature Communications, Nature, vol. 6(1), pages 1-7, May.
    3. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
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    5. Gu, Yuxuan & Wang, Jianxiao & Chen, Yuanbo & Xiao, Wei & Deng, Zhongwei & Chen, Qixin, 2023. "A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control," Energy, Elsevier, vol. 264(C).
    6. Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Ke, Yuzhi & Qiu, Zhiqiang & Luo, Jian & Tang, Yong & Wang, Chun & Yuan, Yuhang & Huang, Yao, 2020. "A review on structuralized current collectors for high-performance lithium-ion battery anodes," Applied Energy, Elsevier, vol. 276(C).
    7. Li, Junfu & Wang, Lixin & Lyu, Chao & Wang, Dafang & Pecht, Michael, 2019. "Parameter updating method of a simplified first principles-thermal coupling model for lithium-ion batteries," Applied Energy, Elsevier, vol. 256(C).
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    9. Gao, Yizhao & Liu, Chenghao & Chen, Shun & Zhang, Xi & Fan, Guodong & Zhu, Chong, 2022. "Development and parameterization of a control-oriented electrochemical model of lithium-ion batteries for battery-management-systems applications," Applied Energy, Elsevier, vol. 309(C).
    10. Kaizhi Liang & Zhaosheng Zhang & Peng Liu & Zhenpo Wang & Shangfeng Jiang, 2019. "Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-17, December.
    11. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    12. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
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