Improved unscented Kalman filter for state of charge estimation of lithium-ion battery with one-step randomly measurement loss and inaccurate noise covariance matrices

Abstract

Accurate state of charge estimation is of great significance for optimizing the management of lithium batteries. Due to the influence of actual environmental factors, random measurement loss and inaccurate noise covariance matrices will appear in the estimation process. Especially when the two situations occur at the same time, the estimation of SOC will be seriously affected. Thus, an improved unscented Kalman filter is presented to solve the above problems in this paper. First, the state space model of nonlinear system is constructed, which can fully reflect one-step random measurement loss, and the nominal noise covariance matrix is used to reflect the uncertainty. Second, as the prior probability density function of the parameters, the Beta prior and inverse Wishart prior are used in the derivation of the joint probability density function. Finally, these parameters are derived based on variational Bayesian theory, the updated state vector is obtained. Simulation results shows that the proposed method can deal with random measurement loss and inaccurate noise covariance matrices and the high accuracy in dynamic stress test and federal urban driving schedule.