Research On State Of Charge Estimation Of Power Battery Based On Kalman Filter

The problems of environmental pollution and lack of energy are becoming more and more serious.In order to build ecological civilization and achieve sustainable development,vigorous development of new energy vehicles is now a necessary way.There are still a series of problems in the use of new energy vehicles,and the estimation of the state of charge(SOC)of lithium-ion batteries is a technical difficulty faced by China in the application of pure electric vehicles.In this paper,we take lithium iron phosphate battery as the research object,and conduct research on the modeling method of the battery and the estimation method of SOC,focusing on.1.Preliminary preparatory work on power battery SOC estimation.Understanding the working principle,type,main parameters and equivalent circuit model of the battery.Conducting corresponding battery characteristics tests using a power battery tester and lithium iron phosphate batteries.Through the analysis of the Li-ion battery characteristics,the maximum available capacity of the Li-ion battery is determined and a 5th order polynomial equation is selected to illustrate the relationship between the battery open circuit voltage and the battery SOC.2.To improve the accuracy of the battery model,the algorithm of the battery model is improved.First,the first-order RC equivalent circuit model is constructed using offline parameter identification and online parameter identification.Secondly,the online parameter identification is improved and the voltage deviation is corrected by using the proportional control method.Finally,in MATLAB/Simulink,the models of offline parameter identification,online parameter identification and improved online parameter identification are built,and the accuracy of the battery models of these three identification methods is compared with the hybrid pulse characteristics test and city road cycle conditions,and the experimental results prove that the model composed of improved online parameter identification has higher accuracy.3.To improve the accuracy of battery SOC estimation,the Extended Kalman Filter(EKF)algorithm is improved.The EKF is combined with the BP(Back Propagation)neural network algorithm,i.e.,the SOC of the battery model is modified online on top of the EKF algorithm.The online parameter identification and BP-EKF algorithm are combined to achieve the real-time online estimation of SOC.The simulation results show that the improved BP-EKF algorithm has a higher accuracy using hybrid pulse characteristics test and city road cycle conditions.In this paper,the improved online parameter identification is used to improve the accuracy of the battery model,and the improved online parameter identification model based on the joint BP-EKF algorithm improves the accuracy of SOC estimation,which is important for the operation of the battery management system.