The effective parameter identification for a PEMFC based on Archimedes optimization algorithm

Abstract

Designing and developing an accurate model of fuel cell systems, particularly for Proton Exchange Membrane Fuel Cells (PEMFCs), is critical for effective simulation, control, and management of these systems. One of the key challenges in this process is the accurate identification of unknown parameters. Delivering higher accuracy, fast and robust decision-making in parameter identification is crucial for enhancing the performance and efficiency of PEMFCs. In this study, the Archimedes Optimization Algorithm (AOA) is introduced to determine the unidentified parameters of the PEMFC model. By framing the parameter identification as an optimization problem, the study applies AOA to solve it based on the fundamental equations governing the fuel cell's operation. The proposed AOA-based model is rigorously tested using commercial PEMFCs' data. Compared to various optimization methods, this approach achieved the lowest Sum of Squared Errors (SSE) criteria value of 0.01229, highlighting its superior performance in parameter identification.