A novel battery state estimation model based on unscented Kalman filter

Abstract

Accurate estimation of the state of charge (SOC) of batteries is very important for real-time monitoring and safety control of electric vehicles. Four aspects of efforts are applied to promote the accuracy of SOC estimation. Firstly, the state-space equation of the battery model based on the Thevenin model is established and the parameters of the model are identified by the forgetting factor recursive least square method. Secondly, aiming at the nonlinear relationship between the open-circuit voltage (OCV) and SOC, the least square support vector machine is proposed to establish the mapping relationship between OCV and SOC. Thirdly, the influence of fitting accuracy of the OCV-SOC curve on SOC estimation is analyzed. Based on this, an error model is proposed, and a joint estimator using an adaptive unscented Kalman filter algorithm combining the error model is proposed. Finally, compared with the estimated SOC results of the traditional SOC estimation method, the experimental results show that the proposed model has better estimation ability and robustness.