Authors:
D. S. Falcão
;
J. C. M. Pires
;
C. Pinho
;
A. M. F. R. Pinto
and
F. G. Martins
Affiliation:
Faculdade de Engenharia da Universidade do Porto, Portugal
Keyword(s):
Artificial neural networks (ANN), Proton Exchange Membrane Fuel Cell (PEMFC), Modelling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
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 cm2 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 (d2). 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.