Model structure optimization for fuel cell polarization curves
Ohenoja, Markku; Sorsa, Aki; Leiviskä, Kauko (2018-11-09)
Ohenoja, M., Sorsa, A., & Leiviskä, K. (2018). Model Structure Optimization for Fuel Cell Polarization Curves. Computers, 7(4), 60. doi:10.3390/computers7040060
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The applications of evolutionary optimizers such as genetic algorithms, differential evolution, and various swarm optimizers to the parameter estimation of the fuel cell polarization curve models have increased. This study takes a novel approach on utilizing evolutionary optimization in fuel cell modeling. Model structure identification is performed with genetic algorithms in order to determine an optimized representation of a polarization curve model with linear model parameters. The optimization is repeated with a different set of input variables and varying model complexity. The resulted model can successfully be generalized for different fuel cells and varying operating conditions, and therefore be readily applicable to fuel cell system simulations.
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