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A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system

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  • Zhang, Qisong
  • Yang, Lin
  • Guo, Wenchao
  • Qiang, Jiaxi
  • Peng, Cheng
  • Li, Qinyi
  • Deng, Zhongwei

Abstract

Accurate prediction of the battery remaining useful life (RUL) at different operating conditions is critical for the battery management system to guarantee safe and efficient operation. However, because of the complicated degradation mechanisms inside the battery, it is extremely challenging to predict the battery life by measuring the external variables. Due to the sparse and random segment data in practical applications, the existing methods are difficult to be applied for online prediction. In this paper, a hybrid parallel residual convolutional neural networks (HPR CNN) model for RUL prediction is proposed. By fusing the charging data of voltage, current and temperature curves in multiple cycles, the hidden feature information of different depths is effectively extracted through the residual network. Based on the sparse data corresponding to only 20% charging capacity, combined with a cloud computing system, this method is able to achieve online prediction in various practical applications. By calculating the difference between each cycle as supplementary input data, the method is able to predict the RUL of a battery with high accuracy and reliability. Validated by a public data set and compared with other methods, the proposed method achieves a low test error of 4.15%, which is promising to be applied in the conditions of random charging process.

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  • Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221029650
    DOI: 10.1016/j.energy.2021.122716
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    Cited by:

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    7. 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).
    8. Wang, Shunli & Wu, Fan & Takyi-Aninakwa, Paul & Fernandez, Carlos & Stroe, Daniel-Ioan & Huang, Qi, 2023. "Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-curren," Energy, Elsevier, vol. 284(C).
    9. Liu, Yunpeng & Hou, Bo & Ahmed, Moin & Mao, Zhiyu & Feng, Jiangtao & Chen, Zhongwei, 2024. "A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments," Applied Energy, Elsevier, vol. 358(C).
    10. Zheng, Jianfei & Ren, Jincheng & Zhang, Jianxun & Pei, Hong & Zhang, Zhengxin, 2023. "A lifetime prediction method for Lithium-ion batteries considering storage degradation of spare parts," Energy, Elsevier, vol. 282(C).
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    12. Yue Ren & Chunhua Jin & Shu Fang & Li Yang & Zixuan Wu & Ziyang Wang & Rui Peng & Kaiye Gao, 2023. "A Comprehensive Review of Key Technologies for Enhancing the Reliability of Lithium-Ion Power Batteries," Energies, MDPI, vol. 16(17), pages 1-38, August.
    13. Yao, Fang & He, Wenxuan & Wu, Youxi & Ding, Fei & Meng, Defang, 2022. "Remaining useful life prediction of lithium-ion batteries using a hybrid model," Energy, Elsevier, vol. 248(C).
    14. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
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    17. Huang, Congzhi & Li, Zhuoyong, 2023. "Data-driven modeling of ultra-supercritical unit coordinated control system by improved transformer network," Energy, Elsevier, vol. 266(C).

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