Multi-state Estimation Of Lithium-ion Batteries

With the continuous development of battery technology,lithium-ion batteries have been widely used in civil,military and aerospace fields due to their advantages of high energy,long service life and large energy storage density.However,as the number of lithium-ion battery usage increases,the battery’s internal characteristic parameters are also continuously degraded,and sometimes unexpected failures will occur and the battery will fail.Therefore,studying the state of charge(SOC),state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries is of great significance for monitoring the degradation of battery parameters and the diagnosis of battery failures.This article takes lithium-ion batteries as the research object and conducts prediction research on the battery’s state of charge and remaining useful life.The main work is as follows:Based on the extended Kalman filter algorithm,the state of charge of the lithium-ion battery is estimated.The effects of the initial values of the state covariance matrix,state noise covariance matrix,and observed noise covariance matrix on the battery’s state of charge estimation are analyzed.Through simulation,the results show that the initial value of the state covariance matrix has little effect on the battery’s state of charge estimation results.When the initial value of the observed noise variance is small,the simulation results are better.Predict the remaining useful life of lithium-ion batteries based on Gaussian process regression.The parameters with a high degree of correlation with the battery capacity are selected,and the battery isobaric discharge time series is used as the health factor as the basis for the prediction of the remaining life of the lithium-ion battery.Gray correlation analysis was performed on the remaining capacity of the battery and the discharge time series of constant voltage drop,and there was a great correlation between the two.Then the two were curve-fitted,and the resulting health factor sequence was used as the input for prediction The mean and variance of the equal pressure drop discharge time series are used as output,and the uncertainty expression of the prediction result is given to obtain the predicted interval range,then the remaining capacity of the battery is obtained by fitting the formula,and finally the remaining useful life of the battery is obtained.Through simulation,the results show that the algorithm can effectively predict the remaining useful life of the battery.The significance of this model is to predict the remaining useful life of the battery in time and protect the battery.