A Comparative Analysis of Online Prognosis of State of Charge of Lithium-Ion Battery

Lithium-ion batteries have become an important part of energy storage systems. The prognosis of the parameters is important for the battery management system which effectively increases the life of the battery and protects it from fault. This study concentrates on the accurate state of charge (SoC) of Lithium-ion Batteries using the Extended Kalman Filter (EKF) and Particle Filter (PF) methods. The EKF is a signal-tracking algorithm but PF is a likelihood algorithm based on Bayesian probability theory. In this study, a forgetting factor recursive least square (FF-RLS) algorithm is used to estimate 1RC battery model parameters. The (FF-RLS) algorithm is embedded with the EKF filter for the prognosis of the SoC of the lithium-ion battery using terminal Voltage and Current Signals. The PF outperforms the EKF in terms of the accuracy of the SoC estimation of the battery.