Study On Online Estimation Method Of State Of Charge For Power Battery

As the link between electric vehicles and power batteries,the battery management system plays a vital role in the safe operation of electric vehicles.Achieving accurate estimation of the state of charge(SOC)of power battery is a premise for ensuring the energy management of the battery management system.In order to improve the estimation accuracy of battery SOC,this paper studies the SOC estimation method based on battery model.Firstly,based on the analysis of the accuracy and complexity of the existing battery model,the second-order RC equivalent circuit model is selected;in order to obtain the open circuit voltage value under different SOC values,the Gaussian trinomial function is selected to fit the OCV-SOC relationship curve of the battery model,which improves the fitting accuracy and reduces the amount of calculation;the exponential fitting method is used to realize the offline parameters identification of battery model,and the battery model is built and the accuracy of offline parameter identification is verified in Mat lab/Simulink environment.Secondly,aiming at the problem that the low data utilization rate and poor anti-interference ability of the least squares algorithm with forgetting factor in the online parameter identification of battery model,the multi-innovation theory is introduced to the algorithm for improvement,and the identification effect of the algorithm is simulated and verified.Finally,the online identification algorithm is combined with the traditional Kalman filter algorithm and the cubature Kalman filter algorithm to realize the online estimation of battery SOC.Through the analysis of the estimation results of the algorithm,it can be seen that the cubature Kalman filter has higher estimation accuracy,but it has the disadvantage of limited estimation accuracy in high-dimensional state.Therefore,the high-order cubature Kalman filter algorithm is introduced into the battery SOC estimation.Aiming at the problems that the algorithm stops running due to the loss of positive definiteness of the error covariance matrix and the noise matrix cannot be updated in real time during the recursive process,square root filtering and Sage-Husa algorithm are used to improve it respectively,and verified through simulation by experimental data.The results show that the accuracy of the improved high-order cubature Kalman filter algorithm for battery SOC estimation is better than that of the cubature Kalman filter algorithm.