Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Abstract

Summary The aim of this study is to analyze the exergy efficiency of a high‐temperature proton exchange membrane fuel cell. To do this purpose, meta‐heuristic technique has been used. First, the model of a membrane fuel cell is simulated and the polarization diagram shows a potent agreement with empirical data. Then, a new improved version of collective animal behavior algorithm is utilized for evaluating and optimizing the thermodynamic irreversibility, exergy efficiency, and work of the fuel cell. The algorithm uses opposition‐based learning and Lévy flight for improving the algorithm's premature convergence shortcoming. The result of this study shows that by comparison with standard collective animal behavior algorithm, genetic algorithm, and empirical results, the proposed algorithm has better achievements for both terms of optimal value finding and convergence strength.

Ecological Performance Optimization of a High Temperature Proton Exchange Membrane Fuel Cell

“…Lin et al [27] investigated the exergy efficiency of HT-PEMFC using the meta-heuristic technique, and an improved collective animal behavior algorithm was utilized to evaluate and optimize the thermodynamic irreversibility, exergy efficiency and output power. Compared with the standard collective animal behavior algorithm and genetic algorithm, the proposed improved collective animal behavior algorithm increased the output power density by 1.2 and 12.1% and the exergy efficiency increased by 22.9%.…”