State of Health Prediction of Pure Electric Vehicle Batteries Based on Patch Wavelet Transformer

Min Wei, Siquan Yuan, Lin Chen, Yuhang Liu and Jie Hu ()
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Min Wei: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Siquan Yuan: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Lin Chen: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Yuhang Liu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Jie Hu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2025, vol. 13, issue 13, 1-26

Abstract: The accuracy of onboard power battery capacity prediction is often limited due to excessive reliance on laboratory data and the neglect of complex usage environments. To address this issue, a Transformer-based model, named PWT, which integrates a patching strategy with wavelet decomposition, is proposed. By utilizing multi-scale temporal feature extraction and attention mechanisms, the model effectively enhances the capability of battery degradation modeling. To tackle the challenge of limited capacity label availability, a fuzzy Kalman filtering model based on ampere-hour integration is designed, reducing the relative error in SOH estimation to 0.906% and significantly improving label accuracy. Furthermore, a charging behavior scoring mechanism based on fuzzy membership functions and a current–temperature interaction feature matrix is constructed to enhance the model’s sensitivity to degradation factors. Experimental results show that the proposed method outperforms LSTM, Transformer, and PatchTST under various real-world operating conditions, achieving a worst-case RMSE of 0.0226, MAPE of 0.0725, and R 2 of 0.903, demonstrating higher accuracy, robustness, and computational efficiency. In conclusion, the proposed method exhibits promising prospects in both theoretical research and engineering applications, providing a novel and effective approach to power battery health management.

Keywords: SOH prediction; real-world vehicle data; data-driven approach; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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