State-of-Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy Features and Fusion Interpretable Deep Learning Framework

Bohan Shao, Jun Zhong, Jie Tian, Yan Li, Xiyu Chen, Weilin Dou, Qiangqiang Liao, Chunyan Lai (), Taolin Lu () and Jingying Xie ()
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Bohan Shao: Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China
Jun Zhong: Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518001, China
Jie Tian: Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518001, China
Yan Li: Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518001, China
Xiyu Chen: Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China
Weilin Dou: Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China
Qiangqiang Liao: Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China
Chunyan Lai: Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China
Taolin Lu: Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China
Jingying Xie: Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China

Energies, 2025, vol. 18, issue 6, 1-25

Abstract: Monitoring and accurately predicting the state of health (SOH) of lithium-ion batteries (LIBs) is essential for ensuring safety, particularly in detecting early signs of potential failures such as overheating and incorrect charging and discharging practices. This paper introduces a network architecture called CGMA-Net (Convolutional Gated Multi-Attention Network), which is designed to effectively address the issue of battery capacity degradation. The network architecture performs initial feature extraction and filtering through convolutional layers, extracting potential key features from the raw input data. The multi-head attention mechanism is the core of this framework, enabling the model to perform weighted analysis of input features. This enables the model to provide a more transparent decision-making process, assisting in the discovery and interpretation of key features within battery SOH estimation. Moreover, a GRU (gated recurrent unit) architecture is introduced in the intermediate layers of the model to ensure its generalization ability, further improving overall prediction performance. A multiple cross-validation approach is adopted to ensure the model’s adaptability across different battery samples, enabling flexible estimation of battery SOH. The experimental results show that the average RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error) values are within 1 mAh, and the MAPE (Mean Absolute Percentage Error) is below 2.5%.

Keywords: lithium-ion batteries; electrochemical impedance spectroscopy; comprehensive deep learning framework; battery management systems (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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