The Research Of Lithium-ion Battery Health Status And Remaining Life

In order to reduce vehicle exhaust emissions and alleviate the global energy crisis,the electric vehicle industry has developed rapidly in China in recent years,and lithium-ion batteries have gradually become the primary choice for electric vehicle power batteries.Battery safety is particularly important for electric vehicles.The state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries are indispensable functions of the battery management system(BMS).Accurately estimating the SOH of the battery and predicting the RUL of the battery can help improve the reliability and safety of the battery.Regarding the estimation of the health status of lithium-ion batteries and the prediction of remaining service life,this paper has done the following work:Firstly,the structure,working principle,and degradation mechanism of lithiumion batteries are introduced,and the influencing factors of battery degradation are analyzed.The discharge capacity was used as the evaluation standard of the lithium ion battery SOH.According to the voltage and current curves of the battery during the charge and discharge cycle,several failure factors were extracted.The Spearman correlation analysis method was used to solve these failure factors.Correlation analysis was performed between the factors and the actual SOH,and the failure factors were screened according to the results of the correlation analysis.Finally,three failure characteristic parameters were screened.Due to the non-linear characteristics of the capacity decay curve of lithium-ion batteries,a support vector regression(SVR)method was selected for SOH estimation.Based on the previously determined failure characteristic parameters,the SVR model was used to establish its relationship with the health status of the battery,and the Gaussian radial basis function(RBF)was used as the kernel function of the SVR model.The differential evolution algorithm(DE)was used to optimize the parameters of the SVR model,and the experimental data of the lithium ion battery cycle life experiments of the NASA PCo E center and the CACLE center were used for verification.The simulation results show that the DE-SVR model can estimate SOH more accurately.Use the Genetic Algorithms(GA)-based Traceless Particle Filtering Algorithm(UPF)to predict the remaining useful life of lithium-ion batteries.Since the standard particle filtering algorithm has the problem of loss and degradation of particle diversity,the Unscented Kalman filter is used to generate the proposed density distribution function,and GA is used to optimize the resampling process.After comparing three empirical equations for battery capacity degradation,the integrated model was finally selected as the capacity degradation model for lithium-ion batteries.Verification under MATLAB environment shows that GA-UPF model can predict RUL more accurately than standard particle filtering algorithm.