Research On Power Battery SOC Estimation Based On Two Types Of Compound Algorithms

The development of new energy vehicles is very rapid,and the performance of power batteries is related to the range of new energy vehicles and the convenience of supplementing energy.Optimizing the power battery management system(BMS)can effectively improve the performance of the power battery.This paper studies the key indicator parameter of BMS--the state of charge(SOC)of power battery,and studies two kinds of algorithms to improve the accuracy of power battery SOC estimation.Analyzed and studied the performance characteristics of lithium-ion power batteries,selected ternary lithium batteries as the research target,analyzed and compared the advantages and disadvantages of three different battery models,and considered the real-time nature of estimating the SOC value of the power battery,the accuracy of the model,and the complexity of calculations Select the Thevenin’s second-order RC equivalent circuit model for issues such as performance,analyze and study traditional empirical battery SOC estimation methods,combine the strengths and weaknesses of various algorithms,and select two types of composite algorithms for power battery SOC estimation research,based on genetic algorithm optimization The least squares vector regression machine combined with extended Kalman filter method and current scoring method improves the estimation accuracy.(1)The vector regression machine is suitable for dealing with the nonlinear problem of the optimal hyperplane.The least square method is used to convert the calculation inequality into an equation solution,reducing the amount of calculation,and because the sum of the parameter values ??in the output equation is an interval value,it is necessary to find To obtain the optimal parameter in this interval,the "evolution theory" of genetic algorithm is used to select the optimal value.(2)The extended Kalman filter method can solve nonlinear system problems more accurately,but the expressions of its state equation and observation equation are highly dependent on the accuracy of the model establishment.Before and after the end of the battery operation,the terminal voltage changes greatly,which will affect In order to solve this problem,the accuracy of the battery model is estimated in the initial and final stages of the charging and discharging of the power battery in combination with the current integration method;finally,an experimental platform is built to measure the five different daily temperatures and three types of charging and discharging.The parameter characteristics of the battery under the working condition of the multiplying current are simulated and simulated by matlab.The algorithm studied was simulated,and the simulation results showed that the error of using the above two composite algorithms to estimate the power battery SOC value was less than 3%,and the index was higher than the traditional single algorithm to estimate the power battery SOC.The results obtained have certain reference significance in improving the accuracy of estimating the SOC value of power batteries and the optimization of power battery management.