State of Charge Estimation for Ternary Battery in Electric Vehicles Using Spherical Simplex-Radial Cubature Kalman Filter

Abstract

State of charge (SOC) estimation is a core technology for battery management system (BMS), which plays an important role to make electric vehicles (EVs) operate safely, reliably and economically. In this paper, a new approach based on the Spherical Simplex-Radial Cubature Kalman Filter (SSRCKF) algorithm is presented to improve the accuracy of SOC estimation. The superiority of the proposed approach has been proved through the Worldwide harmonized Light Vehicles Test Procedure, which came into effect last year in the European Union. In addition, noise are added to the measured data of current and voltage to verify the its anti-interference ability. By comparing with the Unscented Kalman Filter (UKF) and the Cubature Kalman Filter (CKF), the experimental results show that the SSRCKF algorithm estimated the SOC more accurately than the UKF and CKF.