State of Charge Estimation for Lithium-Ion Batteries Based on Informer-LSTM Hybrid Network

Accurate State of Charge (SoC) estimation is essential for efficient battery management systems (BMS). In this study, we propose a novel hybrid neural network architecture, combining the Informer and Long Short-Term Memory (LSTM) networks. Our hybrid network captures temporal dependencies and nonlinear characteristics inherent in battery data, enhancing sequence integration capabilities and computational efficiency. Experimental results on battery datasets demonstrate the effectiveness of our approach, with the proposed method achieving a maximum Mean Absolute Error (MAE) of 1.395% and a maximum Root Mean Square Error (RMSE) of 1.593%. Our findings suggest that the Informer-LSTM hybrid network holds promise for improving battery SoC estimation accuracy and enhancing battery management systems.