Extracting Unknown Parameters of Proton Exchange Membrane Fuel Cells Using Quantum Encoded Pathfinder Algorithm

Abstract

Proton exchange membrane fuel cell (PEMFC) is one of the most widely used fuel cell types. Accurate modeling of PEMFC can better facilitate the research of PEMFC and guide designers to design FC products that meet people’s needs. The modeling problem of PEMFC can be transformed into a parametric optimization problem. In order to improve the exploration capability of the pathfinder algorithm, the concept of quantum computing is introduced and a new quantum coded pathfinder optimization algorithm (QPFA) is proposed. QPFA was applied to the extraction of parameters of NedStackPS6, BCS500W and 250W FC, and these models of PEMFC have been applied for commercial use. The experimental results were compared with seven recently proposed metaheuristics and recently published literature, showing the accuracy and high precision of QPFA in extracting PEMFC parameters.