Study On Nonlinear Factor Of SOC Estimation Of Lithium-ion Battery Based On EKF Observer

The demand for power batteries has increased year by year,which puts higher demands on the production and application of batteries.The BMS plays an important role in the application of the battery,and the accurate estimation of the battery states is of great significance to the power performance and safety of the vehicle.In the states,the SOC directly affects the battery performance and determines the driving range of the vehicle,which is a state of great concern.This paper aims to analyze the influence of battery nonlinearity on the EKF-based SOC estimation algorithm and explore the potential of improving the performance of the algorithm.To this end,this paper starts from the working principle of lithium-ion battery and analyzes the influence of battery parameter characteristics and nonlinear characteristics on battery modeling.The first-order RC model is analyzed and selected as the basis of the research.Based on the experimental data,the least-squares method with genetic factors is used to obtain the parameters of the first-order RC model,which reduces the influence of model error.Secondly,comparing various SOC estimation algorithms,the commonly used EKF algorithm is selected as the research object.As a SOC state observer,the influencing factors of EKF algorithm are analyzed.Based on the first-order RC model,the linearization method is used for analysis,and the conclusion that the system satisfies the observable and controllable conditions is obtained.Furthermore,through the comparative analysis of the existing EKF algorithm stability analysis method,a suitable stability analysis method is obtained.Starting from the stability theorem,the stability of EKF algorithm is qualitatively analyzed in two aspects: the model parameters and the nonlinearity.The stability of EKF algorithm is obtained for the firstorder RC model.Finally,on the premise of the stability of the algorithm,compared with the KF algorithm,the system noise has obvious influence on the algorithm under the nonlinear characteristics of the system.Then based on the MATLAB/Simulink platform to establish the battery model,the conclusion of this analysis is verified,and the main effect of the algorithm in the battery system is voltage noise.Furthermore,the nonlinear factor is analyzed.For the system with inaccurate initial conditions,the possibility of using the calculated values of the error covariance to characterize the performance of the algorithm is demonstrated.Then,the properties of the steadystate KF algorithm are introduced.The steady-state value of the error covariance is used to characterize the ability to constrain the error.Based on the above conclusions,the influence of nonlinear factors on the algorithm is analyzed.And from the above analysis,the direction of algorithm improvement is obtained.The algorithm effect is improved by adjusting the sampling duration and matrices Q and R during the running of the algorithm.It is concluded that increasing the sampling duration and R,decreasing the parameters Q within a certain range can improve the algorithm effect.