@article{10272/16430,
year = {2019},
month = {2},
url = {http://hdl.handle.net/10272/16430},
abstract = {Afuel cell is a complex system,which produces electricity through an electrochemical reaction. For the formal application of control
strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen
fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel
cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a
hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell
in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared
error obtained for a global model of the fuel cell.},
organization = {This work has been funded by the Spanish Ministry of Economy Industry and Competitiveness through the H2SMART-mu GRID (DPI2017-85540-R) project.},
publisher = {Hindawi: Wiley Hindawi Partnership},
title = {Fuel Cell Output Current Prediction with a Hybrid Intelligent System},
doi = {10.1155/2019/6317270},
author = {Casteleiro Roca, José Luis and Barragán Piña, Antonio Javier and Segura Manzano, Francisca and Calvo Rolle, José Luis and Andújar Márquez, José Manuel},
}