Model structure optimization for fuel cell polarization curves
|Author:||Ohenoja, Markku1; Sorsa, Aki1; Leiviskä, Kauko1|
1Control Engineering, University of Oulu
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018121150373
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2018-12-11
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
215 Chemical engineering
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