State of Health Prediction for Lithium-Ion Batteries Using Transformer–LSTM Fusion Model

With the widespread use of lithium-ion batteries in various application fields, accurate prediction of battery state of health (SOH) has become an important research topic to ensure battery performance and safety. To improve the accuracy of SOH prediction, this paper proposes a novel approach that combines multidimensional feature extraction and a transformer–LSTM fusion model. This method extracts time domain, frequency domain, and time dimension features from voltage, energy, and temperature curves. It evaluates feature importance, removes redundancy, and focuses on key features most relevant to SOH. Then, using the self-attention mechanism of transformer and the long-term dependency capture ability of LSTM, an efficient fusion model is constructed to further improve the accuracy and stability of SOH prediction. The proposed method is validated based on the cycling data from 124 commercial lithium iron phosphate/graphite batteries under fast-charging conditions. Compared with existing methods, the proposed approach effectively extracts key features closely related to SOH and builds models based on these features. It achieves a prediction accuracy exceeding 50% and demonstrates superior generalization performance relative to current methods.