Optimization of Proton Exchange Membrane Fuel Cell Parameters Based on Modified Particle Swarm Algorithms

Authors: M. Dezvarei, S. Morovati

Abstract:

In recent years, increasing usage of electrical energy provides a widespread field for investigating new methods to produce clean electricity with high reliability and cost management. Fuel cells are new clean generations to make electricity and thermal energy together with high performance and no environmental pollution. According to the expansion of fuel cell usage in different industrial networks, the identification and optimization of its parameters is really significant. This paper presents optimization of a proton exchange membrane fuel cell (PEMFC) parameters based on modified particle swarm optimization with real valued mutation (RVM) and clonal algorithms. Mathematical equations of this type of fuel cell are presented as the main model structure in the optimization process. Optimized parameters based on clonal and RVM algorithms are compared with the desired values in the presence and absence of measurement noise. This paper shows that these methods can improve the performance of traditional optimization methods. Simulation results are employed to analyze and compare the performance of these methodologies in order to optimize the proton exchange membrane fuel cell parameters.

Keywords: Clonal algorithm, proton exchange membrane fuel cell, particle swarm optimization, real valued mutation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126786

References:

[1] M. Ye, X. Wang, Y. Xu, “Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization,” International journal of hydrogen energy, 2008.
[2] M-T. Outeiro, R. Chibante, A. S. Carvalho, A. T. de Almeida, “A parameter optimized model of a proton exchange membrane fuel cell include temperature effects”, International journal of power sources, 2008.
[3] H. Lu, P. Striyanyong, Y. Hua Song, T. Dillon, “Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function”, International journal of electrical power and energy system, 2010.2010.
[4] L. Hu, “A novel particle swarm optimization method using clonal selection algorithm”, International Conference on Measuring Technology and Mechatronics Automation, 2009.
[5] Y. Tan, Z. M. Xiao, “Clonal particle swarm optimization and its applications”, IEEE Congress on Evolutionary Computation, 2007.
[6] S. J. Nanda, G. Panda, B. Majhi, “Improved identification of Hammerstein plants using new CPSO and ISPO algorithms”, Journal of Expert Systems with Applications, 2010.
[7] N. Higashi, H. Iba, “Particle swarm optimization with Gaussian mutation”, Proceeding of the IEEE swarm intelligence symposium, 2003.