Proton exchange membrane fuel cell stack model,Parameter optimization,Artificial bee swarm optimization algorithm," /> A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters" /> A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters" /> Proton exchange membrane fuel cell stack model,Parameter optimization,Artificial bee swarm optimization algorithm,"/> <span style="font-size:13.3333px;">A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters</span>
Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (8): 638-646    DOI: 10.1631/jzus.C1000355
    
A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters
Alireza Askarzadeh, Alireza Rezazadeh
Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Evin 1983963113, Tehran, Iran
A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters
Alireza Askarzadeh, Alireza Rezazadeh
Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Evin 1983963113, Tehran, Iran
 全文: PDF(463 KB)  
摘要： 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.
关键词： Proton exchange membrane fuel cell stack model')" href="#">Proton exchange membrane fuel cell stack model;  Parameter optimization;  Artificial bee swarm optimization algorithm    
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.
Key words: Proton exchange membrane fuel cell stack model    Parameter optimization    Artificial bee swarm optimization algorithm
收稿日期: 2010-10-11 出版日期: 2011-08-03
CLC:  TP301.6  
服务　　
把本文推荐给朋友 A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters”的文章，特向您推荐。请打开下面的网址：http://www.zjujournals.com/xueshu/fitee/CN/abstract/abstract15075.shtml" name="neirong"> A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters">
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Alireza Askarzadeh
Alireza Rezazadeh

引用本文:

Alireza Askarzadeh, Alireza Rezazadeh. A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters. Front. Inform. Technol. Electron. Eng., 2011, 12(8): 638-646.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1000355    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I8/638

[1] Lai TENG, Zhong-he JIN. A composite optimization method for separation parameters of large-eccentricity pico-satellites[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(5): 685-698.
[2] Peng Chen, Yong-zai Lu. Extremal optimization for optimizing kernel function and its parameters in support vector regression[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(4): 297-306.