Year: 2025
Type: article
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 LiCoO2 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.
Source: Energies
DOI: 10.3390/en18174697
Authors: Jing Han, Bingbing Luo, Chunsheng Wang
Institution: Central South University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4413992221