Estimation Of Electric Vehicle Power Battery Key State Parameters And Design Of Management System

In today’s world,energy and environmental issues have gradually attracted the attention of all countries in the world.At present,my country’s policy orientation and the development direction of various car companies clearly show the trend of de-fueling vehicles.With the advent of the era of electric transportation,the rapid development of electric vehicle power battery systems is inevitably promoted.Efficient management of the power battery is not only the key to ensuring the safety of the vehicle and the long-life and efficient operation of the power battery but also an essential factor in breaking through the obstacles to the development of the power battery system.This paper takes the electric vehicle power battery management system as the research object,and it mainly studies the estimation method of the critical state parameters of the power battery and the design of the battery management system.The estimation of the critical state parameters of the power battery mainly includes the estimation of the battery state of charge(SOC),the battery state of health(SOH),and the internal battery temperature(Stata of Temperature,SOT).The estimation of SOC is to use the second-order RC equivalent circuit model.First,the recursive least squares method with the forgetting factor is used to realize the online identification of model parameters.Then the results of the model parameter identification are combined with an extended Kalman filter to discover the estimation of battery SOC.Due to the time-varying model parameters,this method has low stability of SOC estimation.Therefore,this paper proposes a method of introducing ampere-hour integration combined with weighted data fusion to enhance the stability of SOC estimation.The SOC estimation method has the characteristics of enhancing the estimation stability while taking into account the estimation accuracy.Finally,MATLAB/Simulink is used to establish a simulation model to verify that the error of the estimation method is less than 2% and the stability of the SOC estimation result can be effectively enhanced.The SOH estimation is based on the estimated value of SOC,combined with the ampere-hour integration method,and iteratively calculates the current available capacity of the battery using the recursive weighted overall least squares method with a forgetting factor and then estimates the SOH value.Finally,MATLAB is used to verify that the error of the SOH estimation method is less than 1.58%.The SOT estimation is based on establishing an equivalent thermal model of the lumped parameters of the power battery,the model parameter identification method combining offline and online is adopted,and the joint Kalman filter is used to realize the adaptive estimation of the SOT.Finally,MATLAB/Simulink is used to establish a simulation model to verify that the estimation error of this method does not exceed 5%.The battery management system’s design mainly includes hardware and software design.Based on the system’s functional requirements,a modular hardware system with strong compatibility is designed,which includes primarily the creation of voltage,current,and temperature acquisition circuits,as well as the design of communication,insulation detection,balance,and other functional circuits.The software design is guided by the system’s functional requirements and based on the hardware circuit.It mainly includes the creation of subroutines such as voltage,current,and temperature acquisition,charge and discharge control,insulation detection,battery protection,state parameter estimation,battery balance,etc.The China Internet of Things One Net cloud platform realizes the visualization of battery cloud data.Finally,by building a battery management system test platform,the battery’s physical parameter acquisition accuracy and various functions are tested,which further verifies the effectiveness and reliability of the battery management system.