Fully Informed Search Algorithm for Estimating the Parameters of Li-Ion Battery Model under UDDS Drive Cycle Profile

Accurate parameter extraction for Li-ion Batteries (LiBs) is crucial for evaluating the performance of battery energy storage systems in e-mobility and smart grids. This study investigates the Fully Informed Search Algorithm (FISA) as a recent method for optimizing the parameters of the extended Thevenin Equivalent Circuit Model (ECM) for LiBs. As an evolution of the Rao algorithm, FISA is noted for its commendable performance in solving real-world optimization challenges while retaining the original algorithm’s simplicity and non-parametric characteristics. The primary aim of this research is to minimize the disparity between the voltage values predicted by the model and the actual battery voltage measurements. To evaluate the effectiveness of this strategy, the real-world UDDS drive cycle profile is utilized. Additionally, a comparative analysis is performed, contrasting FISA with the established Rao Algorithm and other modern optimization algorithms. Simulation results highlight the proficiency of the proposed method in accurately and with stability estimating ECM parameters, outperforming alternative techniques in terms of precision and speed.