An Adaptive Double Extended Kalman Filter Algorithm Based on Incremental Change Rate for Co-estimation of Battery SOC and Capacity

Abstract

The battery state of charge (SOC) and capacity are important state management indicators of the battery management system, and their estimation accuracy directly affects the safety of power battery use and the driver's driving experience. Since the increment change rate of the estimated variable can reflect the changing trend of the estimated variable, an extended Kalman filter algorithm based on the increment change rate is proposed, on this basis, and an adaptive double-extended Kalman filter algorithm based on incremental change rate is constructed for the co-estimation of SOC and capacity of batteries. The tests under various operating conditions show that the target algorithm proposed in this paper has greater advantages over the traditional adaptive double-extended Kalman filter algorithm, and the maximum absolute error value (MAE) and root mean square error (RMSE) of the target algorithm can be reduced by 36.3% and 74.4% (SOC), 95.5% and 97.6% (capacity) compared with the traditional adaptive double-extended Kalman filter algorithm under DST operating conditions; The MAE and RMSE of the target algorithm can be reduced by 79.1% and 92.3% (SOC), 95.4% and 96.2% (capacity) under BBDST operating conditions.

A Robust State of Charge Estimator Based on the Fourier Neural Operator for xEV Batteries

“…The accuracy of these model based methods heavily depends on the goodness of fit of the models. [4][5][6][7][8][9] Although the refined models that take into account the hysteresis effect [10][11][12] can provide more accurate estimations, the model based methods generally require expert domain knowledge, feature engineering and long development time.…”