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Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries

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  • Tian, Jiaqiang
  • Xu, Ruilong
  • Wang, Yujie
  • Chen, Zonghai

Abstract

Lithium-ion battery is a complex thermoelectric coupling system, which has complicated internal reactions. It is difficult to investigate the aging mechanism due to the lack of direct observation of side reaction. In response, a method of aging mode identification based on open-circuit voltage matching analysis is proposed in this work. Firstly, the LiCoO2 and graphite half cells are made to measure the open-circuit voltage for electrodes. The open-circuit voltage model of the full cell is established based on the state of charge matching relationship between the full cell and electrodes. Then, a non-destructive aging mechanism identification method is developed, which can quantify the loss of lithium inventory, the loss of active materials of electrodes. Whereafter, the aging semi-empirical models of the three aging modes are established respectively, and the mapping models with state of health, ohmic resistance and polarization resistance evolution are developed. Besides, the short-term state of health and remaining useful life prediction method is proposed based on the particle filter algorithm and established models. Finally, the developed models and methods are validated by the battery data. The experimental results show that the root mean square error and mean absolute error of the calculated voltage are kept within 38 mV and 51 mV. The root mean square error of RUL and short-term SOH prediction are maintained within 5.549 and 1.31%, respectively. And the predicted RUL remains within the 95% confidence interval. The results further prove that the established models and methods have high accuracy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544220327894
    DOI: 10.1016/j.energy.2020.119682
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    References listed on IDEAS

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    5. Yan, Lisen & Peng, Jun & Gao, Dianzhu & Wu, Yue & Liu, Yongjie & Li, Heng & Liu, Weirong & Huang, Zhiwu, 2022. "A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery," Energy, Elsevier, vol. 243(C).
    6. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    7. Sun, Tao & Wang, Shaoqing & Jiang, Sheng & Xu, Bowen & Han, Xuebing & Lai, Xin & Zheng, Yuejiu, 2022. "A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning," Energy, Elsevier, vol. 239(PC).
    8. Che, Yunhong & Deng, Zhongwei & Li, Penghua & Tang, Xiaolin & Khosravinia, Kavian & Lin, Xianke & Hu, Xiaosong, 2022. "State of health prognostics for series battery packs: A universal deep learning method," Energy, Elsevier, vol. 238(PB).
    9. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
    10. Liu, Xinyang & Zheng, Zhuoyuan & Büyüktahtakın, İ. Esra & Zhou, Zhi & Wang, Pingfeng, 2021. "Battery asset management with cycle life prognosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. Zhou, Danhua & Wang, Bin & Zhu, Chao & Zhou, Fang & Wu, Hong, 2023. "A light-weight feature extractor for lithium-ion battery health prognosis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
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    14. Lin, Mingqiang & Wu, Jian & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "State of health estimation with attentional long short-term memory network for lithium-ion batteries," Energy, Elsevier, vol. 268(C).

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