State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

The accuracy of state charge estimation results will directly affect the performance battery management system. Due to such, we focus in this article on SOC Lithium-Ion batteries based a fractional second-order RC model with free noninteger differentiation orders. For such an estimation, three Kalman filters are employed: adaptive extended filter (AEKF), (EKF), and Unscented Filter (UKF). Fractional-Order Model (FOM) parameters orders identified by Particle Swarm Optimization (PSO) algorithm, pulsed-discharge test is implemented verify parameter identification. output voltage error FOM much less than that Integer-Order (IOM). has lower root-mean square (RMSE), mean absolute (MAE), maximum (MAXAE) IOM during regardless AEKF, EKF or UKF. Experimental show can simulate polarisation effect charge–discharge characteristics more realistically, demonstrating accurate promising one when using same filters.