Accurate battery modelling is crucial for Battery Management Systems (BMS) to ensure precise state estimation and dynamic characteristic prediction, optimizing power and energy management in Electric Vehicles (EVs). Among various models, the Electric Equivalent Circuit Model (EECM) is widely used due to its ability to represent dynamic battery behaviour. However, its accuracy in EV applications is impacted by time-varying parameters such as temperature, current, State of Charge (SoC), and State of Health (SoH). This study addresses the limitations of conventional RLS algorithms by proposing a systematic evaluation of seven Forgetting Factor Recursive Least Squares (FFRLS) methods under dynamic conditions. Two Open Circuit Voltage (OCV) assumptions-OCV as an estimated parameter and as a function of SoC-are considered. Using a second-order Thevenin model in MATLAB/Simulink and …