A Mamba Model Fused with Residual Convolution and Attention Mechanism for SOC Prediction of Lithium-Ion Batteries

Abstract

State of charge (SOC) is one of the most critical state variables in lithium-ion batteries for energy storage systems and electric vehicles. SOC estimation under varying temperatures and operating conditions remains challenging due to the difficulty of simultaneously representing local dynamics, long-range temporal dependencies, and salient information. To this end, this study proposes a hybrid CNN–Mamba–Attention network. A convolution-based feature extraction and residual fusion module is introduced ahead of the Mamba backbone to enhance local variation information in the input signals and to facilitate the modelling of long-term temporal dependencies during battery operation. To further refine the learned representation, an attention mechanism is applied after the Mamba module, allowing important features to be highlighted for more accurate SOC estimation. The proposed method is validated using publicly available dynamic test data for lithium-ion batteries under three temperature settings and three operating conditions. Experimental results show that the model achieves a minimum root mean square error (RMSE) of 0.75%, a lowest mean absolute error (MAE) of 0.59%, and a maximum coefficient of determination (R²) of 0.9993, demonstrating strong generalization capability and robustness across different working conditions.