Improving Lithium-Ion Battery State of Health Estimation with an Integrated Convolutional Neural Network, Gated Recurrent Unit, and Squeeze-and-Excitation Model

Abstract Estimating the State of Health (SOH) of lithium batteries is vital for the safe management of new energy systems. This study leverages voltage, current, and temperature data from the NASA battery dataset to extract health features. The relationship between these features and battery capacity is evaluated using mutual information analysis. An integrated Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Squeeze-and-Excitation (SE) model (CNN-GRU-SE) for estimating battery SOH is proposed. The CNN module identifies local features from the input data, the SE module emphasizes key features, and the GRU module captures temporal dependencies, effectively tracking the battery's health trend over time. The estimation results indicate that the CNN-GRU-SE model reduces the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) by approximately 3% to 10% compared to the CNN, GRU, and CNN-GRU models. These results confirm the superior estimation capability of the integrated CNN-GRU-SE model. Furthermore, the study underscores the effective integration of the strengths of CNN, SE modules, and GRU, demonstrating its potential application in battery health management.