Research On SOC Estimation Of Electric Vehicle Battery Management System

Since the beginning of the 21 st century,as the number of fuel-fueled vehicles has increased rapidly,global warming and the energy crisis have become more and more serious.Electric vehicles drive cars with electrical energy,reducing the consumption caused by fuel vehicles,and thus attracting widespread attention.As an important part of electric vehicles,the BMS is responsible for collecting and processing battery information and protecting the battery.Accurate estimation of battery SOC has always been the focus and difficulty of BMS technology research.Because it can avoid longterm battery overload,achieve the purpose of improving the battery life of the vehicle,and ensuring the safety and stability of the electric vehicle.In this paper,a second-order Thevenin equivalent circuit model is used to establish a lithium-ion battery simulation model.The model parameters were obtained through a HPPC discharge experiment,and a mathematical simulation model was established in MATLAB/Simulink,and the accuracy of the model was verified by DST.The battery SOC is estimated using the UKF algorithm.This algorithm obtains a series of sigma points with the same probability density function as the system state through an unscented transformation.Using sigma points to map the updated state of the nonlinear system,and then using the Kalman filter algorithm to estimate the battery SOC at the next moment.The algorithm has low complexity and high estimation accuracy.In order to improve the adaptability of the algorithm under complex working conditions,the STF algorithm and AF algorithm are integrated into the UKF algorithm to form a STFAUKF algorithm.The algorithm maintains the good estimation ability of the UKF algorithm,and has good convergence and robustness.The simulation experiment of the STF-AUKF algorithm in MATLAB/Simulink shows that the STF-AUKF algorithm has high estimation accuracy,strong convergence ability,and the maximum error is within 1%.Build a lithium-ion battery experiment platform and write the STF-AUKF algorithm into the BMS,and verify the experiment in different SOC initial value intervals.The results show that under different SOC initial value conditions,the average error of the STF-AUKF algorithm is 4.1% and the maximum estimated error is 4.3%,which meets the accuracy requirements of the SOC estimation of the BMS.