Enhanced accuracy in state-of-charge estimation for lithium-ion batteries in electric vehicles using augmented adaptive extended Kalman filter

• Proposed Augmented Adaptive Extended Kalman Filter (AAEKF) for precise State-of-Charge (SoC) estimation in Electric Vehicle (EV) Battery Management Systems (BMS). • Combines Kalman filtering methods with adaptive robust control concepts to enhance SoC estimation precision. • Demonstrated effectiveness through simulation results and comparison with LiFePO4 battery data, yielding SoC estimation errors of less than 3%. • AAEKF outperforms conventional methods, showcasing smaller average root mean square error (RMSE) and mean absolute error (MAE), with maximum estimate error staying below 5% across various temperatures. 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 LiFePO 4 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.