Implementation of Unscented Kalman Filter-Based Online State-of-Charge Estimation in LiFePO4 Battery-Powered Electric Vehicle Applications

This paper proposes an online state-of-charge (SoC) estimation method based on a 2nd-order RC model. The open circuit voltage (OCV) of the battery is calculated through two adaptive filtering stage, which is then used to determine the SoC via a dynamic OCV-SoC curve. In the first stage a variable step-size least mean square (VSS-LMS) algorithm is employed to adaptively estimate the model parameters in real-time; in the second stage, an unscented Kalman filter (UKF) is used to estimate the OCV from the parameters. The proposed SoC estimation method with its simplified model was implemented in DSP system for real-time application. Plenty of experiments under different conditions were carried out to confirm the effectiveness and superiority of the proposed system.