Research On The State Of Charge Estimation Of Battery For Electric Vehicle

With the energy shortage and environmental pollution is increasing,new energy vehicles increasingly become the focus of the development of the automotive industry.The state of charge(SOC)of the battery is an important part of the overall system,and its estimation accuracy is directly related to the use of new energy vehicles.In this paper,the high-capacity single-cell lithium battery for vehicle is taken as the research object.The EKF algorithm is used to estimate the state of charge of the battery.The problem of insufficient estimation accuracy of battery SOC is proposed for the extended Kalman filter algorithm(EKF).Estimated accuracy of battery SOC.First of all,this paper studies and analyzes the working principle and chemical principle of lithium batteries.By experimenting with the battery,the effects of parameters such as charge and discharge rate and battery cycle number on battery operation were analyzed.The functional relationship between the open circuit voltage and the battery SOC is obtained by the HPPC pulse discharge experimental data.After that,the existing common battery model was analyzed.After comparing and analyzing the advantages and disadvantages of various battery models,the Thevenin equivalent circuit model was selected as the battery model of this paper.Through the experimental data,the battery model is identified offline and online.Online and offline recognition results were validated using DST conditional experiments.The verification results show that the battery model using online recursive least squares with forgetting factor can better reflect the actual working state of the battery.Then,the research and analysis of the principle of EKF algorithm.The extended Kalman filter algorithm is used to estimate the SOC of the battery.The simulation results show that the noise and Jacobian matrix error have a great influence on the estimation results.An adaptive extended Kalman filter(AEKF)based on the principle of covariance matching is proposed to reduce the SOC.The effect of unknown noise.In order to estimate the accuracy fluctuation caused by the Jacobian matrix in the linearization process of SOC curve for EKF algorithm,an extended Kalman filter algorithm(MVEKF)with covariance correction principle is proposed to estimate the battery SOC.Finally,the simulation results of the three algorithms are compared and analyzed.The results show that the MVEKF algorithm and the AEKF algorithm have higher SOC estimation accuracy than the EKF algorithm,which can reduce the error of SOC estimation accuracy caused by noise and Jacobian matrix error.Extended Kalman Filter(EKF)algorithm will bring large linearization error when linearizing high-linear system equations,which has a great influence on battery SOC estimation accuracy.This paper proposes to use cubature Kalman filter algorithm(CKF).The battery SOC is estimated and simulated,and compared with the simulation results of the EKF algorithm and the unscented Kalman algorithm(UKF).The analysis found that in the estimation of battery SOC,the volume Kalman filter algorithm can solve the accuracy divergence caused by the linear equation of the EKF algorithm.And compared with the UKF algorithm,CKF is more accurate in battery SOC estimation.