State of Charge Estimation for Lithium-Ion Batteries: An Online Method Combining Deep Neural Network and Adaptive Kalman Filter

Electric vehicles (EVs) powered by lithium-ion batteries are crucial for sustainable transportation. Accurate State of Charge (SOC) estimation, a core function of Battery Management Systems (BMS), enhances battery performance, lifespan, and safety. This paper proposes a hybrid CNN-LSTM-AKF model integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) Neural Networks with an Adaptive Kalman Filter. CNN extracts spatial features from current, voltage, and temperature data, while LSTM processes temporal dependencies. AKF reduces output fluctuations. Trained on datasets under three operating conditions, the model was tested across various temperatures and initial SOC states. Results demonstrate that the proposed model significantly outperforms standalone LSTM and LSTM-AKF model, particularly at low temperatures. Within 0 °C to 50 °C, it achieves Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) below 1.51% and 1.18%, respectively. With an initial SOC of 80%, the model achieves an RMSE of 1.09% and MAE of 0.88%, showing rapid convergence. The model exhibits high accuracy, strong adaptability, and robust performance.