An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumpt...An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.展开更多
基金supported by the Renewable Energy Organization of Iran (SANA)
文摘An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.
