Research on SOC Estimation of Lithium-Ion Battery Based on CA-SVDUKF Algorithm

Because of the problem that the traditional unscented Kalman filter algorithm (UKF) may terminate the iteration due to the non-positive definite error covariance matrix during state of charge (SOC) estimation of lithium-ion battery, considering the unknown noise and current mutation during the actual operation of the battery, an SOC estimation method based on covariance adaptive singular value decomposition unscented Kalman filter (CA-SVDUKF) algorithm was proposed. Based on the singular value decomposition traceless Kalman filtering algorithm, the proposed CA-SVDUKF algorithm introduced an adaptive method of covariance matching to improve the algorithm’s anti-interference capability to unknown noise. Accordingly, an error covariance matrix adaptive method with adaptive scaling factor was proposed, which could reduce the influence of current mutation exerting on the estimated convergence rate. Taking the lithium-ion battery as the research object, the second-order RC equivalent circuit model of the lithium-ion battery was first built, and then the online parameters of the battery were identified. Finally, the CA-SVDUKF algorithm was used to complete the SOC estimation. The algorithm was simulated and verified under three working conditions: ordinary pulse condition, DST working condition, and US06 working condition. The experimental results showed that the algorithm had higher accuracy and stability compared with the traditional extended Kalman filter algorithm (EKF) and the UKF algorithm. The maximum absolute error was less than 0.6%, and the root mean square error was less than 0.3%, which could verify the effectiveness and superiority of the algorithm.