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

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.