PEMFC PARAMETER ESTIMATION FOR RENEWABLE ENERGY APPLICATIONS USING STARFISH OPTIMIZATION

Abstract

This research develops and evaluates the Starfish Optimization Algorithm (SFOA) for finding the best parameters of a Proton Exchange Membrane Fuel Cell (PEMFC). The SFOA is a metaheuristic optimization algorithm, which is applied for accurate estimating the model parameters for the minimization of the error between the experimental and calculated values. The performance of SFOA algorithm was tested on four different types of commercial fuel cells: 250W PEMFC, BCS-500 PEMFC, AVISTA SR-12 PEMFC and Temasek 1kW PEMFC, under various operating conditions, including varying temperature and reactant gas pressures, to ensure the reliability of the proposed models in different operating environments. The results showed that the performance of the SFOA algorithm varied depending on the cell type. SFOA algorithm achieved: 0.000607079 RMSE and Efficiency: 99.96166766% for the 250 W PEMFC; 0.000291RMSE and 98.5639177% Efficiency for BCS-500 W PEMFC; 0.00510784 RMSE and 99.8043958% Efficiency for AVISTA SR-12 (500 W) PEMFC; and 0.00094235 RMSE and 99.8989187% Efficiency for Temasek 1 kW PEMFC. Results demonstrate the ability of the SFOA algorithm to have a good balance between search accuracy and convergence speed. This study confirms that the nature-inspired optimization algorithms are an effective and accurate tool in estimating PEMFC fuel cell parameters, contributing to improved mathematical models and the development of more efficient control systems for various clean energy applications.