Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Accurately estimating the online state-of-charge (SOC) of battery is one crucial issues management system. In this paper, gas–liquid dynamics (GLD) model with direct temperature input selected to Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm elaborated couple offline and achieve goal quickly eliminating initial errors in SOC estimation. An implementation hybrid pulse power characterization test performed identify parameters determine open-circuit voltage vs. curve. Apart from standard cycles including Constant Current cycle, Federal Urban Driving Schedule Dynamometer cycle Dynamic Stress Test a combined constructed for experimental validation. Furthermore, study effect sampling time on estimation accuracy robustness analysis value are carried out. results demonstrate that proposed method realizes accurate maximum mean absolute error at 0.50% five working conditions shows strong against sparse error.