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

Jing Han, Bingbing Luo and Chunsheng Wang ()
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Jing Han: School of Automation, Central South University, Changsha 410017, China
Bingbing Luo: School of Automation, Central South University, Changsha 410017, China
Chunsheng Wang: School of Automation, Central South University, Changsha 410017, China

Energies, 2025, vol. 18, issue 17, 1-16

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.

Keywords: State-of-Health; lithium-ion battery; long short-term memory; fractional derivative; fractional calculus (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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