The parameter identification of the Nexa 1.2 kW PEMFC's model using particle swarm optimization

被引:75
作者
Salim, Reem [1 ]
Nabag, Mahmoud [1 ]
Noura, Hassan [1 ]
Fardoun, Abbas [1 ]
机构
[1] United Arab Emirates Univ, Dept Elect Engn, Al Ain, U Arab Emirates
关键词
Fuel cells; Proton exchange membrane; Parameter identification; Voltage model; Thermal model; PSO;
D O I
10.1016/j.renene.2014.10.012
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
People's extensive and ignorant lifestyles impose an increasing amount of destruction on the environment, which lead to an increased governmental and research interest towards the development and use of green technology such as fuel cells. Fuel cells are recently receiving a major share of research interest due to their promising features. This paper presents an offline parameter identification approach based on particle swarm optimization (PSO) to identify the mathematical modeling parameters of the Nexa 1.2 kW proton exchange membrane fuel cell (PEMFC) system. The goal of this work is not to get a new technique in modeling, but rather to obtain a very good model of the PEMFC system using a simple and fast heuristic approach that requires minimal mathematical effort. This model can then be utilized to perform further analysis and fault diagnosis studies on PEMFCs. The proposed approach uses basic fitting to determine some of the initial values for the PSO, while the rest of the initial values are set to be chosen randomly. The developed model is then successfully validated using actual experimental data sets. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:26 / 34
页数:9
相关论文
共 20 条
[1]  
[Anonymous], 2024, P INT SCI CONFERENCE
[2]   Optimization of PEMFC model parameters with a modified particle swarm optimization [J].
Askarzadeh, Alireza ;
Rezazadeh, Alireza .
INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2011, 35 (14) :1258-1265
[3]   PSOt - a Particle Swarm Optimization Toolbox for use with Matlab [J].
Birge, B .
PROCEEDINGS OF THE 2003 IEEE SWARM INTELLIGENCE SYMPOSIUM (SIS 03), 2003, :182-186
[4]  
Chibante R, 2010, PROC IEEE INT SYMP, P2281, DOI 10.1109/ISIE.2010.5637642
[5]  
Eberhart R., P 6 INT S MICROMACHI, P39, DOI DOI 10.1109/MHS.1995.494215
[6]  
EG&G Technical Services Inc, 2004, FUEL CELL HDB
[7]   Comparison among five evolutionary-based optimization algorithms [J].
Elbeltagi, E ;
Hegazy, T ;
Grierson, D .
ADVANCED ENGINEERING INFORMATICS, 2005, 19 (01) :43-53
[8]  
Hu P., 2009, CHIN C PATT REC, V1, P1
[9]   Modeling of a proton exchange membrane fuel cell based on the hybrid particle swarm optimization with Levenberg-Marquardt neural network [J].
Hu, Peng ;
Cao, Guang-Yi ;
Zhu, Xin-Jian ;
Li, Jun .
SIMULATION MODELLING PRACTICE AND THEORY, 2010, 18 (05) :574-588
[10]   Hybrid Model of Fuel Cell System Using Wavelet Network and PSO Algorithm [J].
Li, Peng ;
Chen, Jie ;
Liu, Guoping ;
Rees, David ;
Zhang, Juan .
2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5, 2010, :2629-+