Research On Estimation Method Of Key Parameters Of Power Lithium Battery

With the increasing shortage of non-renewable energy sources such as oil and increasing environmental pollution problems,new energy vehicles,especially e lectric vehicles,have become the development direction of the current global automotive industry.As the power source of electric vehicles,accurate estimation of its key parameters plays an important role in the development of electric vehicles.This paper takes lithium titanate battery as the research object,and studies the estimation of its two key parameters: SOC and SOH.The main research contents are as follows:(1)This paper analyzes and compares the advantages and disadvantages of the equivalent circuit model and electrochemical model of the power lithium battery,and considers the accuracy and ease of use of the model,the second-order RC equivalent circuit model is selected as the model of the lithium titanate battery in this project.Based on the model and circuit principle,the second-order RC equivalent circuit model of lithium titanate battery is established and the model parameters are identified offline using measured test data.The model is verified in MATLAB / Simulink under 10 C constant current discharge and HPPC conditions.The results show that the second-order RC equivalent circuit model has high reliability and accuracy.(2)Based on the basic principle of least squares method,this paper applies the forgetting factor recursive least squares method to identify the second-order RC equivalent circuit model parameters of lithium titanate battery.The model is verified in MATLAB / Simulink under HPPC and NEDC conditions,and the results of offline parameter model and online parameter model under NEDC condition are compared.The results show that the model based on online parameter identification used in this paper has higher precision.(3)This paper analyzes and compares the advantages and disadvantages of the current mainstream SOC estimation algorithm,and uses the extended Kalman filter algorithm as the SOC estimation algorithm of this project.The algorithm model is set up in MATLAB / Simulink,and the feasibility and accuracy of the algorithm are verified under 10 C constant current discharge condition.Based on the online parameter identification algorithm and the extended Kalman filter algorithm,a joint estimation algorithm is proposed.The algorithm model is also set up in MATLAB/Simulink and verified under HPPC condition and NEDC condition.The results of the extended Kalman filter SOC estimation model based on offline parameters and the joint estimation algorithm model based on online parameter identification under NEDC condition are compared.The results show that the joint estimation algorithm has higher precision.(4)This paper analyzes and compares the advantages and disadvantages of the current mainstream SOH estimation algorithm,selects the adaptive algorithm as the SOH estimation algorithm of this project,and sets up the model of the algorithm in MATLAB/Simulink.The feasibility and accuracy of the algorithm are verified under NEDC condition.(5)This paper sets up the test bench of the algorithm,and compiles and downloads the embedded C code of the Simulink model of SOC and SOH estimation algorithm into the actual BMS controller through automatic code generation technology,and carries out the NEDC test for the lithium titanate battery module.The running results of the two algorithms in the BMS are consistent with the running results in MATLAB/Simulink,which verifies the validity of the two algorithms.