Publication
Identification of PEM fuel cells based on support vector regression and orthonormal base
Conference Article
Conference
IEEE International Symposium on Intelligent Control (ISIC)
Edition
2016
Pages
173-178
Doc link
http://dx.doi.org/10.1109/ISIC.2016.7579981
File
Abstract
Polymer Electrolyte Membrane Fuel Cells (PEMFC) are efficient devices that convert the chemical energy of the reactants in electricity. In this type of fuel cells, the performance of the air supply system is fundamental to improve their efficiency. An accurate mathematical model representing the air filling dynamics for a wide range of operating points is then necessary for control design and analysis. In this paper, a new Wiener model identification method based on Support Vector (SV) Regression and orthonormal bases is introduced and used to estimate a nonlinear dynamical model for the air supply system of a laboratory PEMFC from experimental data. The method is experimentally validated using a PEMFC system based on a ZB 8-cell stack with Nafion 115 membrane electrode assemblies
Categories
power system control.
Author keywords
Atmospheric modeling, Mathematical model, Fuel cells, Data models, Support vector machines, Linear systems, Kernel
Scientific reference
V. Roda, D. Feroldi and J.C. Gomez. Identification of PEM fuel cells based on support vector regression and orthonormal base, 2016 IEEE International Symposium on Intelligent Control, 2016, Buenos Aires, pp. 173-178.
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