State of Charge Estimation of Lithium-Ion Based on VFFRLS-Noise Adaptive CKF Algorithm

The complex and changeable noises in application often bring uncertainty to the battery model and the algorithm for the State of Charge (SOC) estimation. If the prior noise knowledge is still selected, the estimation accuracy is reduced. On the basis of the improved Cubature Kalman Filter (CKF) proposed by our research group, this paper introduces an adaptive noise algorithm to automatically update the observation noise matrix and then proposes an adaptive noise CKF algorithm. In addition, considering the change of the model parameters, combined with the Variable Forgetting Factor Recursive Least Square (VFFRLS) algorithm, a VFFRLS-Noise adaptive CKF algorithm is proposed. In order to verify the accuracy and adaptability of the proposed algorithm, the influence of current and voltage white noise on voltage are analyzed. Results show that the noise adaptive CKF algorithm can deal with external disturbance better than the traditional CKF algorithm. It can maintain good stability with voltage white noise, and the error can be kept within 2% under most working conditions. Meanwhile, the estimation results of offline and online identification parameters under low temperature, high temperature, and aging conditions are compared. Results show that the estimation accuracy of online identification is improved compared with that of offline identification. The maximum SOC estimation errors are within 2.8%.

Automated summary

This paper introduces an adaptive noise CKF algorithm based on the improved Cubature Kalman Filter (CKF) and an adaptive noise algorithm. The results show that the proposed algorithm can deal with external disturbance better and maintain good stability under most working conditions. The estimation accuracy of online identification parameters is improved compared to offline identification.