An Adaptive Sigma Point Kalman Filter Hybridized by Support Vector Machine Algorithm for Battery SoC and SoH Estimation

Abstract

This paper considers the issue of Li-Ion batteries State of Health (SoH) and State of Charge (SoC) accurate and robust estimation for electric vehicle applications. SoC and SoH are two monitoring indicators of primary importance that are used by the Battery Management System (BMS) , amongst other benefits, to manage and equalize the battery cells. Improving the estimation precision and reliability of the SoC and the SoH indicators is highly beneficial during operation and maintenance of the vehicle. We propose in this paper a new scheme of SoC and SoH estimation using an hybridization of Kalman filtering, Recursive Least Squares approach and Support Vector Machines learning. The battery SoC and SoH indicators are estimated using an adaptive-Sigma Point Kalman Filter. The battery cell impedance equivalent filter is obtained in real-time by a Recursive Least Square. Furthermore, the cell capacity evolution tracking is achieved by using a Support Vector Machine (SVM) method. Finally, the battery cell capacity and impedance equivalent filter are provided to the SoC estimator in order to update its state and observation models. This architecture yields to a complete SoC and SoH algorithmic solution exhibiting a high level of accuracy and robustness. The SVM method which requires the highest computational load in the architecture is designed to be used only for estimating the variable with the lowest evolution dynamics.