Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter Method for State-of-Charge Estimation of Li-Ion Battery Cells

Abstract

State-of-charge estimation is an essential part of a battery management system. Charging and discharging batteries involve complex chemical processes that could lead to undesired consequences, such as premature end of life or fire hazards if the battery's state-of-charge is not closely monitored. This work proposes a state-of-charge estimation method that compensates for noisy measurements and parametric uncertainties in a battery cell to improve the estimation accuracy. In this work, a robust and adaptive estimation scheme based on a Cubature Kalman filter is proposed. The algorithm utilizes the Variational Bayesian method to identify the measurements noise covariance magnitude online. Additionally, the algorithm is augmented with the ability to suppress outliers based on the Maximum Correntropy Criterion. The proposed filter is experimentally verified on standard tests including aggressive electric vehicle drive cycles, and it is compared against the extended Kalman filter, the Cubature Kalman filter, and the Variational Bayesian Cubature Kalman filter.