Estimation of SOC of Battery Based on RVM-EKF Algorithm

In order to improve the accuracy of SOC estimation, a new algorithm based on the relevance vector machine (RVM) and an extended Kalman filter (EKF) method (RVM-EKF) is proposed. An error prediction model was built based on RVM, by which the measurement noise covariance of EKF was real time revised. When the predicted model error was small, the measurement model was updated, otherwise, the process model was updated only. The simulation and experimental results show that the proposed algorithm can effectively eliminate the SOC estimation error caused by the model error and the uncertain noise statistical properties. The proposed algorithm has good convergence and robustness, and is applicable to various complicated driving cycles for electric vehicles, with high application value.