Online Parameter Identification for Lithium-Ion Cell in Battery Management System

Battery parameter identification is a foundation of dynamic estimations for battery performance states, such as state of charge (SOC) and state of health (SOH), which are key factors in ensuring a battery system's effectiveness, safety and reliability. In this study, a real-time and training- free method is developed to identify such parameters as open-circuit voltage, internal resistances, and polarization capacitance of a first- order resistance-capacitance (RC) circuit model on line. The state-space equations are formulated according to model's dynamic characteristics, and an extended Kalman filter (EKF) algorithm is employed for the parameter identification only using the measured current and voltage of batteries. Federal Urban Driving Schedule (FUDS) cycles are performed in validation experiments on LiFePO
₄
cells. The results demonstrate the accuracy and regression of the proposed algorithm. Additionally, the algorithm was successfully applied in a battery management system (BMS) with a low computational complexity.