Lithium-Ion Battery State of Health Estimation Based on Feature Reconstruction and Transformer-GRU Parallel Architecture

Bing Chen, Yongjun Zhang, Jinsong Wu, Hongyuan Yuan and Fang Guo ()
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Bing Chen: School of Electric Power, South China University of Technology, Guangzhou 510641, China
Yongjun Zhang: School of Electric Power, South China University of Technology, Guangzhou 510641, China
Jinsong Wu: School of Electric Power, South China University of Technology, Guangzhou 510641, China
Hongyuan Yuan: China Energy Engineering Group Guangdong Teway Energy Storage Technology Co., Ltd., Guangzhou 510660, China
Fang Guo: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China

Energies, 2025, vol. 18, issue 5, 1-19

Abstract: Estimating the state of health of lithium-ion batteries in energy storage systems is a key step in their subsequent safety monitoring and energy optimization management. This study proposes a method for estimating the state of health of lithium-ion batteries based on feature reconstruction and Transformer-GRU parallel architecture to solve the problems of noisy feature data and the poor applicability of a single model to different types and operating conditions of batteries. First, the incremental capacity curve was constructed based on the charging data, smoothed using Gaussian filtering, and the diverse health features were extracted in combination with the charging voltage curve. Then, this study used the CEEMDAN algorithm to reconstruct the IC curve features, which reduces noisy data due to the process of data collection and processing. Lastly, this study used the cross-attention mechanism to fuse the Transformer and GRU neural networks, which constructed a Transformer-GRU parallel model to improve its ability to mine time-dependent features and global features for state of health estimation. This study conducted experiments using three datasets from Oxford, CALCE, and NASA. The results show that the RMSE of the state of health estimation by the proposed method is 0.0071, which is an improvement of 61.41% in the accuracy of its baseline model.

Keywords: lithium-ion battery; state of health; feature reconstruction; data-driven; Transformer-GRU; parallel architecture (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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