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Computers 2018, 7(4), 60;

Model Structure Optimization for Fuel Cell Polarization Curves

Control Engineering, University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland
Author to whom correspondence should be addressed.
Received: 4 September 2018 / Revised: 30 October 2018 / Accepted: 6 November 2018 / Published: 9 November 2018
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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. View Full-Text
Keywords: model identification; genetic algorithms; fuel cell model identification; genetic algorithms; fuel cell

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ohenoja, M.; Sorsa, A.; Leiviskä, K. Model Structure Optimization for Fuel Cell Polarization Curves. Computers 2018, 7, 60.

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