Research On Lithium Battery SOC Estimation Algorithm Based On Temperature Compensation Model

With the energy crisis and environmental crisis becoming more and more serious,all industries are developing in the direction of energy conservation,environmental protection and green sustainability,and automobiles are also transforming from traditional fuel vehicles to new energy vehicles,among which electric vehicles are the fastest growing and have become the main sales force of new energy vehicles.The power battery is the energy supply center of the electric vehicle.The accurate state of charge(SOC)of the battery is the guarantee of the safe operation of the electric vehicle for a long time.Based on the accuracy requirement of SOC in electric vehicle,this thesis studies the lithium battery model and SOC estimation algorithm.Firstly,this thesis designs a battery performance test scheme for SOC estimation,analyzes the battery output characteristics based on the test data,and lays the foundation for improving the battery model and SOC estimation algorithm.In the aspect of battery modeling,based on the influence of temperature on the battery model,the parameters and structure of the model are adjusted on the basis of Thevenin model,and the temperature compensation model is proposed to realize the temperature adaption,expand the temperature application range of the model,and improve the accuracy of the battery model.Finally,the correctness of the model is verified by simulation.In the aspect of SOC estimation algorithm,this thesis analyzes the application of classical Extended Kalman Filter algorithm in SOC estimation,and introduces the traditional Adaptive Extended Kalman Filter algorithm based on moving window according to the way of dealing with the noise in the algorithm.It is considered that the window size of the algorithm is fixed,and the adaptive effect of noise needs to be strengthened.Therefore,this thesis discusses an improved Adaptive Extended Kalman Filter algorithm,T-S fuzzy algorithm is introduced into the adaptive covariance matching algorithm.The window is adjusted by the adaptive factor to obtain more accurate noise information,and then the Kalman gain is adjusted to accelerate the convergence speed and improve the accuracy of the algorithm.Finally,the temperature compensation model is combined with three algorithms,and the SOC estimation accuracy of the three algorithms is compared by simulation.In order to improve the accuracy of SOC estimation,this thesis improves the battery model and SOC estimation algorithm.Finally,the MATLAB simulation platform combined with test data is used to verify the analysis,which shows that the improved temperature compensation model combined with the improved Adaptive Extended Kalman Filter algorithm has good temperature adaptability and can effectively improve the accuracy of SOC estimation.