A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer

SUMMARY As an open and demanding problem, accurate modeling of polarization curve in proton exchange membrane fuel cell has become the main issue of various researches. In recent years, because of their great potentials, metaheuristic optimization algorithms have represented good performances in identification of the unknown parameters of the proton exchange membrane fuel cell model, but there is the possibility to obtain more accurate results with more capable algorithms. In the literature, many heuristic optimization algorithms have been developed on the basis of natural phenomena. However, there are still some possibilities to devise new ones. In this paper, evolution of bird species has been regarded, and the intelligent behavior of birds during mating season has become an inspiration to devise a new heuristic optimization algorithm, named bird mating optimizer. Moreover, in this paper, the whole unknown parameters of the model, even dimensional parameters, are included in the identification process. The proposed algorithm is used to model the Ballard Mark V FC, and its performance is compared with those of the recently published paper by the authors. Simulation results reveal the superior performance of bird mating optimizer algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

[1]  Mohammad Tariq Iqbal,et al.  Modelling and Analysis of Electro‐chemical, Thermal, and Reactant Flow Dynamics for a PEM Fuel Cell System , 2005 .

[2]  Alireza Rezazadeh,et al.  A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters , 2011, Journal of Zhejiang University SCIENCE C.

[3]  Anders Pape M oller Badge size in the house sparrow Passer domesticus , 1988 .

[4]  Luciane Neves Canha,et al.  An electrochemical-based fuel-cell model suitable for electrical engineering automation approach , 2004, IEEE Transactions on Industrial Electronics.

[5]  Kauko Leiviskä,et al.  Validation of genetic algorithm results in a fuel cell model , 2010 .

[6]  S. D. Strahl,et al.  Nesting Behavior of Sunbitterns (Eurypyga helias) in Venezuela , 1990 .

[7]  Alireza Rezazadeh,et al.  Optimization of PEMFC model parameters with a modified particle swarm optimization , 2011 .

[8]  M.G. Simoes,et al.  Sensitivity analysis of the modeling parameters used in Simulation of proton exchange membrane fuel cells , 2005, IEEE Transactions on Energy Conversion.

[9]  Pierre R. Roberge,et al.  Development and application of a generalised steady-state electrochemical model for a PEM fuel cell , 2000 .

[10]  Atilla Biyikoglu,et al.  Review of proton exchange membrane fuel cell models , 2005 .

[11]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[12]  Jing J. Liang,et al.  A self-adaptive global best harmony search algorithm for continuous optimization problems , 2010, Appl. Math. Comput..

[13]  X. D. Xue,et al.  Unified mathematical modelling of steady-state and dynamic voltage–current characteristics for PEM fuel cells , 2006 .

[14]  Xin-Jian Zhu,et al.  Parameter optimization for a PEMFC model with a hybrid genetic algorithm , 2006 .

[15]  Alireza Rezazadeh,et al.  A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell , 2011 .

[16]  Alireza Rezazadeh,et al.  Artificial immune system-based parameter extraction of proton exchange membrane fuel cell , 2011 .