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Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network

Author

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  • Sibo Zeng

    (Zhuzhou CRRC Times Electric Co., Ltd., Zhuzhou 412001, China)

  • Sheng Chen

    (School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Babakalli Alkali

    (School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

Abstract

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.

Suggested Citation

  • Sibo Zeng & Sheng Chen & Babakalli Alkali, 2024. "Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network," Energies, MDPI, vol. 17(6), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1272-:d:1352708
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    References listed on IDEAS

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    1. Wu, Ji & Zhang, Chenbin & Chen, Zonghai, 2016. "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks," Applied Energy, Elsevier, vol. 173(C), pages 134-140.
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