Lithium Battery State Of Charge Estimation Based On Improved Kalman Filter Algorithm

With the depletion of oil resources and the continuous deterioration of the environment,governments of various countries have successively introduced encouraging policies to promote the development of new energy vehicles.Power battery,as one of the core components of new energy vehicles,is mainly affected by the state of charge(SOC)of the battery.Accurate SOC estimates can not only provide control strategies for the entire vehicle system,but also accurately reflect the remaining battery power.Therefore,it has always been a hot topic of research.This article compares the commonly used power batteries,selects lithium iron phosphate batteries as the research object,and introduces the working principle of lithium ion batteries.Then,the definition of SOC is given from the perspective of electricity,and by comparing and analyzing the main SOC estimation methods,it is determined that the model-based method is used to estimate the battery SOC in this paper.Based on the experimental data,a second-order Thevenin equivalent circuit model is established to simulate the operating characteristics of the lithium battery.A polynomial fitting method is used to establish the OCV-SOC function relationship,and the model parameters are implemented online based on the least square identification method with forgetting factor.Identify.Based on the extended Kalman filter algorithm and multiple innovation identification theory,a multiinnovative extended Kalman filter algorithm is proposed.Based on this,based on the particle filter weight calculation idea,a more reasonable weighted multiple-innovative extended Kalman filter is proposed Weighted Multiple Innovation Kalman Filter(WMI-EKF)algorithm to estimate battery SOC.Finally,the validity of the proposed WMI-EKF algorithm is verified by intermittent discharge and DST experiments.The experimental results show that the SOC estimation method proposed in this paper can be applied to the high-precision estimation of SOC under multiple operating conditions.This method greatly improves the accuracy of SOC estimation,and the algorithm is simple and practical.