CLC number: TP301.6; TM911
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2011-07-06
Cited: 15
Clicked: 12440
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.
@article{title="A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters",
author="Alireza Askarzadeh, Alireza Rezazadeh",
journal="Journal of Zhejiang University Science C",
volume="12",
number="8",
pages="638-646",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000355"
}
%0 Journal Article
%T A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters
%A Alireza Askarzadeh
%A Alireza Rezazadeh
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 8
%P 638-646
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000355
TY - JOUR
T1 - A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters
A1 - Alireza Askarzadeh
A1 - Alireza Rezazadeh
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 8
SP - 638
EP - 646
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
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.
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