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State of health estimation of lithium-ion batteries based on the constant voltage charging curve

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  • Wang, Zengkai
  • Zeng, Shengkui
  • Guo, Jianbin
  • Qin, Taichun

Abstract

State of health estimation is critical for ensuring the safety and dependability of lithium-ion batteries. In practical usage, batteries are seldom completely discharged. With a constant current-constant voltage charging mode, the incomplete discharging process influences the initial charging voltage and the charging time of the subsequent constant current charging, greatly hindering the applications of many traditional health indicators that require a full cycling process. However, the charging data of the constant voltage charging is fully reserved, and is not affected by the previous incomplete discharging process. Furthermore, the charging current curve during the constant voltage profile is discovered to relate with the battery state of health in this study. Therefore, a new health indicator is extracted only from the monitoring parameters of the constant voltage profile for state of health estimation. The battery aging phenomena during the constant voltage profile are firstly characterized by the equivalent circuit model, and a new indicator is then constructed. A framework for the online extraction of this indicator of is proposed. Additionally, the correlation analysis and performance assessment prove the adaptability and effectiveness of the proposed method for estimating state of health of lithium-ion batteries.

Suggested Citation

  • Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:661-669
    DOI: 10.1016/j.energy.2018.11.008
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    6. Yang, Jufeng & Cai, Yingfeng & Pan, Chaofeng & Mi, Chris, 2019. "A novel resistor-inductor network-based equivalent circuit model of lithium-ion batteries under constant-voltage charging condition," Applied Energy, Elsevier, vol. 254(C).
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    14. Song, Ziyou & Hou, Jun & Li, Xuefeng & Wu, Xiaogang & Hu, Xiaosong & Hofmann, Heath & Sun, Jing, 2020. "The sequential algorithm for combined state of charge and state of health estimation of lithium-ion battery based on active current injection," Energy, Elsevier, vol. 193(C).
    15. 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).
    16. Zheng, Yuejiu & Qin, Chao & Lai, Xin & Han, Xuebing & Xie, Yi, 2019. "A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    17. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    18. Chen, Si-Zhe & Liang, Zikang & Yuan, Haoliang & Yang, Ling & Xu, Fangyuan & Fan, Yuanliang, 2023. "A novel state of health estimation method for lithium-ion batteries based on constant-voltage charging partial data and convolutional neural network," Energy, Elsevier, vol. 283(C).
    19. Haochen Qin & Xuexin Fan & Yaxiang Fan & Ruitian Wang & Qianyi Shang & Dong Zhang, 2023. "A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(14), pages 1-23, July.
    20. Kong, Fanhou & Liang, Xue & Yi, Lanlin & Fang, Xiaohui & Yin, Zhongbin & Wang, Yulong & Zhang, Ruixiang & Liu, Longyang & Chen, Qing & Li, Minghan & Li, Changjiu & Jiang, Hong & Chen, Yongjun, 2021. "Multi-electron reactions for the synthesis of a vanadium-based amorphous material as lithium-ion battery cathode with high specific capacity," Energy, Elsevier, vol. 219(C).
    21. Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
    22. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    23. 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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