Research On SOC Estimation Of Ternary Lithium Battery Based On Kalman Filter

With the rapid development of the economy,the problems of massive energy consumption and environmental pollution have gradually become prominent,and electric vehicles have become a way to solve the energy and environmental crisis with the advantages of low energy consumption and environmental protection.As one of the most important equipment of electric vehicles,the battery management system(BMS)needs to be based on the battery state of charge(SOC)parameters for most of its core functions.The premise of vehicle system service.This paper discusses the SOC estimation of ternary lithium batteries in common power batteries.The main work is as follows:(1)The internal structure,chemical reaction principle and main battery parameters of the ternary lithium battery were analyzed,and the battery capacity characteristics,rate characteristics,and temperature characteristics were analyzed through relevant experiments.Estimates lay the groundwork.In order to fully simulate the dynamic characteristics of the battery during operation and avoid complex operations caused by excessively high derivative orders,after comprehensive evaluation of various battery models,the battery SOC estimation is finally determined based on the second-order RC equivalent circuit model.Through the hybrid power pulse characteristic(HPPC)experiment,the terminal voltage change under a certain current pulse was obtained,and the relationship between the state of charge and the open circuit voltage of the battery was analyzed based on this.Finally,Matlab software was used to build the battery simulation model,and the single process parameter verification under HPPC condition and the whole process parameter verification under variable current condition were completed.(2)The extended Kalman filter(EKF)algorithm is used to estimate the battery SOC.Through simulation and analysis,it is found that the EKF algorithm has a certain correction effect on the initial value error,but the tracking effect of the estimated value in the whole process is not good enough;the estimation results Depends on how accurate the battery model is;accumulation of historical measurement data can cause cumulative errors.In order to reduce the influence of the above problems on the estimation accuracy,combined with the idea of strong tracking,the adaptive fading extended Kalman(AFEKF)algorithm is proposed to be applied to the battery SOC estimation,and the method of determining the fading factor in the AFEKF algorithm is emphatically analyzed.Then,the simulation verification is carried out in three cases: the American urban cycle condition(UDDS),the variable current condition and the new European driving cycle condition(NEDC).The results show that the adaptive fading extended Kalman filter algorithm is more efficient The filtering algorithm has higher estimation accuracy and better tracking effect.(3)The battery SOC test platform is built,each hardware module in the test platform is designed,and the corresponding software programs are written.The experiments show that the battery voltage,current and other data collected by the test platform basically meet the needs of battery SOC estimation accuracy.At the same time,the operation results of AFEKF algorithm on this platform show that the maximum error of battery SOC estimation is 1.81%,which has good application value.