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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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    5. 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.
    6. 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).
    7. Tian, Jiaqiang & Xu, Ruilong & Wang, Yujie & Chen, Zonghai, 2021. "Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries," Energy, Elsevier, vol. 221(C).
    8. Kasper, Lukas & Schwarzmayr, Paul & Birkelbach, Felix & Javernik, Florian & Schwaiger, Michael & Hofmann, René, 2024. "A digital twin-based adaptive optimization approach applied to waste heat recovery in green steel production: Development and experimental investigation," Applied Energy, Elsevier, vol. 353(PB).
    9. Tian, Yong & Huang, Zhijia & Li, Xiaoyu & Tian, Jindong, 2022. "Parallel-connected battery module modeling based on physical characteristics in multiple domains and heterogeneous characteristic analysis," Energy, Elsevier, vol. 239(PB).
    10. Pang, Bo & Liu, Siyang & Zhu, Haijia & Feng, Yanbiao & Dong, Zuomin, 2024. "Real-time optimal control of an LNG-fueled hybrid electric ship considering battery degradations," Energy, Elsevier, vol. 296(C).
    11. Lai, Xin & Yi, Wei & Cui, Yifan & Qin, Chao & Han, Xuebing & Sun, Tao & Zhou, Long & Zheng, Yuejiu, 2021. "Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter," Energy, Elsevier, vol. 216(C).
    12. Li, Shuangqi & He, Hongwen & Su, Chang & Zhao, Pengfei, 2020. "Data driven battery modeling and management method with aging phenomenon considered," Applied Energy, Elsevier, vol. 275(C).
    13. Li, Yuanmao & Liu, Guixiong & Deng, Wei & Li, Zuyu, 2024. "Comparative study on parameter identification of an electrochemical model for lithium-ion batteries via meta-heuristic methods," Applied Energy, Elsevier, vol. 367(C).
    14. 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).
    15. 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).
    16. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).

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