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CLC number: TP301.6; TM911

On-line Access: 2011-08-03

Received: 2010-10-11

Revision Accepted: 2010-12-16

Crosschecked: 2011-07-06

Cited: 15

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.8 P.638-646

http://doi.org/10.1631/jzus.C1000355


A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters


Author(s):  Alireza Askarzadeh, Alireza Rezazadeh

Affiliation(s):  Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Evin 1983963113, Tehran, Iran

Corresponding email(s):   askarzadeh_a@yahoo.com

Key Words:  Proton exchange membrane fuel cell stack model, Parameter optimization, Artificial bee swarm optimization algorithm


Alireza Askarzadeh, Alireza Rezazadeh. A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters[J]. Journal of Zhejiang University Science C, 2011, 12(8): 638-646.

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author="Alireza Askarzadeh, Alireza Rezazadeh",
journal="Journal of Zhejiang University Science C",
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doi="10.1631/jzus.C1000355"
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DOI - 10.1631/jzus.C1000355


Abstract: 
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, optimization 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 algorithm (GA) and particle swarm optimization (PSO). The results show that the ABSO algorithm outperforms the other algorithms.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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