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Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging

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  • Ruan, Haokai
  • Wei, Zhongbao
  • Shang, Wentao
  • Wang, Xuechao
  • He, Hongwen

Abstract

State of health (SOH) estimation is essential to the health diagnostic of lithium-ion battery. The data-driven approach with charging feature extraction is promising for online SOH estimation and has been widely explored over years. However, their deployment can be barriered by the lack of complete charging data in real-world applications. Motivated by this, this paper proposes an artificial intelligence-based SOH estimator using the transient phase between constant current (CC) and constant voltage (CV) charging, which is easily obtained in real-world charging scenarios. Specifically, a convolutional neural network (CNN) model is proposed to explain the relationship between the charging data and the SOH. Following this endeavor, the transfer learning is exploited for model mitigation and SOH estimation on different battery types, relying on much reduced amount of data for efficient CNN model re-training. The validation experiments are conducted based on the aging data obtained on LiNiCoAlO2 (NCA) and LiCoO2 (LCO) cells. Results suggest that the proposed method realizes accurate SOH estimation requiring only a short segment from the CC-CV transient phase, so that can meet a broad range of real-world charging scenarios. Moreover, the efficient model transfer promises expected performance with different battery types.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:336:y:2023:i:c:s0306261923001150
    DOI: 10.1016/j.apenergy.2023.120751
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    References listed on IDEAS

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    2. Li, Fang & Min, Yongjun & Zhang, Ying & Zhang, Yong & Zuo, Hongfu & Bai, Fang, 2024. "State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. 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).
    4. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    5. Guo, Wenchao & Yang, Lin & Deng, Zhongwei & Li, Jilin & Bian, Xiaolei, 2023. "Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment," Energy, Elsevier, vol. 281(C).
    6. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    7. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features," Energy, Elsevier, vol. 283(C).
    8. Khosravi, Nima & Dowlatabadi, Masrour & Abdelghany, Muhammad Bakr & Tostado-Véliz, Marcos & Jurado, Francisco, 2024. "Enhancing battery management for HEVs and EVs: A hybrid approach for parameter identification and voltage estimation in lithium-ion battery models," Applied Energy, Elsevier, vol. 356(C).
    9. Li, Qingbo & Lu, Taolin & Lai, Chunyan & Li, Jiwei & Pan, Long & Ma, Changjun & Zhu, Yunpeng & Xie, Jingying, 2024. "Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation," Energy, Elsevier, vol. 290(C).

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