Research On Model Parameter Identification And State Of Charge Estimation Of Ternary Lithium-ion Battery For Electric Vehicle

With the shortage of energy and the deterioration of the environment,new energy vehicles have been widely concerned.Among them,electric vehicles with its advantages of low energy consumption,no emission pollution and other advantages have become the focus of the automotive industry.As an important parameter in battery management system,state of charge plays an important role in the energy management of electric vehicle.However,the battery state of charge cannot be measured directly.A battery model is established based on the battery parameters,and then related algorithms are used for estimation.Therefore,how to identify the parameters of the battery and estimate the state of charge accurately is still one of the research hotspots.This paper takes ternary lithium batteries as the research object,and the algorithm of battery parameter identification and state of charge estimation is analyzed.Firstly,the working principle of lithium battery is introduced,then the battery test platform used in the experiment is introduced,and then the battery charge and discharge experiment is carried out to analyze the open circuit voltage characteristics,charge and discharge characteristics of the battery,and to identify the parameters and SOC estimates Research provides data sources.Secondly,compare several commonly battery models,select the second-order RC equivalent circuit model for analysis,and build the state equation and observation equation of the battery.Then,the relationship curve between battery SOC and OCV is fitted according to the measured data,and the battery parameters are identified offline.Then,according to the identification results of the parameters,the battery model is built under Simulink,and the dynamic data of the battery is used to prove the accuracy of the model.Thirdly,in order to reduce the accuracy of SOC caused by model parameters under complex working conditions,the battery model is identified on-line.In order to improve the identification accuracy of battery model parameters under uncertainty noise,a deviation term is added on the basis of recursive least square(RLS)on-line identification,and bias recursive least square(BCRLS)with deviation compensation is used for identification Identify battery parameters.The battery parameter identification results of two identification methods,RLS and BCRLS are compared and analyzed to verify the rationality and correctness of BCRLS and RLS algorithm parameter identification.On this basis,two algorithms are used to identify and test the battery parameters.The error results of the output voltage of the battery model and the actual measured terminal voltage are analyzed.The correctness and rationality of the identification results are further proved,and the accuracy of the model can be improved by updating the parameters in real time.Finally,the basic theory of extended Kalman filter and unscented Kalman filter is introduced.Considering the influence of noise in the application of battery and the deficiency of EKF algorithm,the method of joint estimation of AUKF and BCRLS with noise covariance matching is selected to estimate SOC of battery,and the algorithm model is built according to the state equation and observation equation of battery.Then using the battery charge and discharge data,this paper analyzes the estimation accuracy of the SOC estimation algorithm combined with BCRLS and AUKF and then analyzes the estimation of this method in the case of uncertain initial SOC value and noise interference,and compares with the estimation results of BCRLS＿EKF and BCRLS＿UKF.The results show that the joint estimation method of BCRLS＿AUKF has good performance,not only can effectively suppress noise,but also has higher estimation accuracy.