Enhanced Accuracy in State-of-Charge Estimation for Lithium-Ion Batteries in Electric Vehicles Using Augmented Adaptive Extended Kalman Filter

Abstract

Accurate assessment of state of charge (SoC) is crucial for the safety and efficiency of lithium-ion batteries used in electric vehicles (EVs). For precise SoC estimation in EV BMSs, an Augmented Adaptive Extended Kalman Filter (AAEKF) has been proposed in this research. To improve the precision of SoC estimation, AAEKF approach combines Kalman filtering methods with adaptive robust control concepts. The effectiveness of the AAEKF algorithm is shown by simulation results and confirmed by comparison with data from LiFePO4 battery research. The proposed AAEKF SoC estimation matches measurement results with errors of less than 3%. Adaptive Extended Kalman Filter (AEKF) is quite sensitive to the accuracy of the battery model, as errors in this region can lead to large estimate errors. Faster convergence to actual SoC, improved real-time responsiveness, accuracy, and resilience to disturbances and parameter fluctuations are all results of AAEKF's efficient handling of nonlinearities in battery systems, adaptation to system changes, errors (Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and overall performance. The maximum estimate error stays under 5% at various external temperatures.