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Fractional-Derivative Enhanced LSTM for Accurate SOH Prediction of Lithium-Ion Batteries

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  • Jing Han

    (School of Automation, Central South University, Changsha 410017, China
    China Aluminum Ningxia Energy Group Co., Ltd., Yinchuan 750002, China)

  • Bingbing Luo

    (School of Automation, Central South University, Changsha 410017, China)

  • Chunsheng Wang

    (School of Automation, Central South University, Changsha 410017, China)

Abstract

Accurate estimation of the State-of-Health (SOH) of lithium-ion batteries is crucial for ensuring the safety and longevity of electric vehicles and energy storage systems. However, conventional LSTM models often fail to capture the nonlinear degradation dynamics and long-term dependencies of battery aging. This study proposes a Fractional-Derivative Enhanced LSTM (F-LSTM), which incorporates fractional parameters α and Δt into the cell state update to model multi-scale memory effects. Experiments conducted on the CALCE LiCoO 2 dataset and the Tongji University NCA dataset demonstrate that, compared with the standard LSTM, the proposed F-LSTM reduces RMSE and MAE by more than 40% while maintaining robust performance across different chemistries, temperatures, and dynamic conditions. These results highlight the potential of integrating fractional calculus with deep learning to achieve accurate SOH prediction and support intelligent battery management.

Suggested Citation

  • Jing Han & Bingbing Luo & Chunsheng Wang, 2025. "Fractional-Derivative Enhanced LSTM for Accurate SOH Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 18(17), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4697-:d:1741920
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    References listed on IDEAS

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    1. Zhu, Rong & Hu, Jiawen & Peng, Weiwen, 2025. "Bayesian calibrated physics-informed neural networks for second-life battery SOH estimation," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    2. Xinfeng Zhang & Xiangjun Li & Kaikai Yang & Zhongyi Wang, 2023. "Lithium-Ion Battery Modeling and State of Charge Prediction Based on Fractional-Order Calculus," Mathematics, MDPI, vol. 11(15), pages 1-15, August.
    3. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
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