SOC Estimation And Residual Life Prediction Of Lithium-ion Batteries

Lithium-ion batteries are widely used in the field of electric vehicles due to their high energy density,slow aging and no memory effect.Monitoring the working State of the battery and accurately estimating the State of charge(SOC)and Remaining useful life(RUL)of the battery are crucial to the safe operation and prolonging the service life of the battery.In this paper,the ternary lithium battery is taken as the research object.Firstly,the internal structure of lithium-ion,electrochemical principle,equivalent model and battery performance degradation mechanism are discussed.The first-order RC circuit,the second-order RC circuit and the PNGV circuit were taken as the equivalent circuit models of lithium battery,respectively.The parameters of the model were identified by using the data of battery charge and discharge current and voltage combined with least square fitting.Based on Matlab/Simulink,the simulation models of the above three kinds of battery equivalent circuits are built.Through comparison,it is found that the error between the output voltage of the second-order RC equivalent circuit model and the measured voltage is the least.Therefore,the second-order RC circuit is finally selected as the battery equivalent model to carry out the SOC estimation of the battery.Based on the established second-order equivalent circuit model,the estimation of battery SOC is realized by Kalman filter algorithm.Considering that the traditional Extend Kalman filter(EKF)uses a single information to update the state variables of the system,which leads to the problem of large estimation error,A SOC estimation method based on Multi-Innovation Extended Kalman Filter(MIEKF)is proposed,which improves the estimation accuracy of SOC by reusing old information.At the same time,considering that the fixed system noise model parameters in the MIEKF algorithm will affect the accuracy of SOC estimation,the method of SOC estimation based on the Adaptive Multiple Information Extended Kalman Filter(AMIEKF)is adopted,and the SOC estimation experiment is carried out in the American urban circulating system(UDDS).The root mean square error and mean absolute error of SOC estimates based on AMIEKF and MIEKF are 0.23%and 0.19%,respectively,and 0.25%and 0.22%.The error comparison results verify the accuracy of battery SOC estimation based on AMIEKF algorithm.The convergence performance of the estimation algorithm is tested and compared by setting the wrong initial SOC value.The results show that the Amiekf algorithm can quickly track the correct SOC value.Aiming at the prediction of battery remaining useful life(RUL),this paper takes the battery capacity attenuation to 70%of the standard capacity as the battery failure threshold,and combines the dual exponential battery capacity attenuation model with particle filter(PF)algorithm to achieve the prediction of battery RUL.The capacity data of three groups of batteries were fitted according to the mathematical formula of double exponential battery capacity attenuation model to identify the model parameters,and the influence of the model parameters on the predicted RUL results was analyzed.The influence of particle number on RUL prediction of battery was studied,and it was found that the PF algorithm achieved the best estimation accuracy when the particle number was 200.In order to make the particle set approximate the probability density function of the parameters of the dual exponential attenuation model better,the RUL of the battery was predicted by using the untrace particle filter(UPF)algorithm.The results show that the prediction accuracy of UPF algorithm is higher than that of PF algorithm when the initial battery charge-discharge cycles are zero and non-zero.Figure[60]table[23]reference[64]...