State of charge estimation of lithium-ion battery under time-varying noise based on Variational Bayesian Estimation Methods

Non-linear filters such as UKF, CKF are often used to estimate the State of Charge (SoC) of lithium batteries, but the premise is that the noise is Gaussian noise, but the actual noise cannot be Gaussian noise, such as the process noise or measurement noise changed with time, which will affect the estimated effect. In this paper, a second-order battery model is established, and the Kalman filter is used to identify the model parameters, and then three methods are used to obtain the SOC-OCV mapping curve. Next, to improve the performance of SoC estimation under the influence of time-varying measurement noise or process noise, the idea of Variable Bayesian iteration is introduced into the nonlinear filter to obtain the Variable Bayesian unscented Kalman filter and the Variable Bayesian square-root cubature filter. Then the two methods are used to estimate the SoC under four working conditions, two temperatures, and two kinds of covariance time-varying noise. Experimental results show that the proposed method can effectively improve the estimation performance of SoC, and the SOC-OCV mapping curve used in this paper is more stable. •We combine the low current and incremental test to obtain the Multisampling point fusion SOC-OCV curve.•We introduce the idea of variational and Bayesian analysis iteration into the unscented Kalman filter to obtain a nonlinear filtering method that can simultaneously deal with Prediction error covariance and measurement noise covariance, which is written as VBUKF.•We introduce the iterative idea into the square-root cubature Kalman filter to obtain a nonlinear filtering method, which is written as VBSRCKF.•We use these methods to estimate the SOC of the battery, and then compare and analyze them with the usual UKF and SRCKF experiments.•The noise in the experiment are: Gaussian noise and two covariance time-varying noise; Working conditions: DST, FUDS, US06, BJDST; Temperatures: 0 °C, 25 °C.