Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/69515
Author(s): D. S. Falcão
J. C. M. Pires
C. Pinho
A. M. F. R. Pinto
F. G. Martins
Title: Artificial neural network model applied to a PEM fuel cell
Issue Date: 2009
Abstract: This study proposes the simulation of PEM fuel cell polarization curves using artificial neural networks (ANN). Fuel cell performance can be affected by numerous parameters, namely, reactants pressure, humidification temperature, stoichiometric flow ratios and fuel cell temperature. In this work, the influence of relative humidity (RH) of the gases, as well as gases and fuel cell temperatures was studied. A feedforward ANN with three layers was applied to predict the influence of those parameters, simulating the voltage of a fuel cell of 25 cm(2) area. Different ANN models were tested, varying the number of neurons in the hidden layer (1 to 6). The model performance was evaluated using the Pearson correlation coefficient (R) and the index of agreement of the second order (d(2)). The results showed that feedforward ANN can be used with success in order to obtain the optimal operating conditions to improve PEM fuel cell performance.
Subject: Ciências da computação e da informação
Computer and information sciences
Scientific areas: Ciências exactas e naturais::Ciências da computação e da informação
Natural sciences::Computer and information sciences
URI: https://repositorio-aberto.up.pt/handle/10216/69515
Source: IJCCI 2009: PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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