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On neural network modeling to maximize the power output of PEMFCs

Nanadegani, Fereshteh Salimi LU ; Lay, Ebrahim Nemati ; Iranzo, Alfredo ; Salva, J. Antonio and Sunden, Bengt LU (2020) In Electrochimica Acta 348.
Abstract

Optimum operating conditions of a fuel cell will provide its maximum efficiency and the operating cost will be minimized. Thus, operation optimization of the fuel cell is essential. Neural networks can simulate systems without using simplifying assumptions. Therefore, the neural network can be used to simulate complex systems. This paper investigates the effects of important parameters, i.e., temperature, relative humidity in the cathode and anode, stoichiometry on the cathode and anode sides, on the polarization curve of a PEMFC (Proton Exchange Membrane Fuel Cell) having MPL (Micro Porous Layer) by ANN (artificial neural network). For this purpose, an analytical model validated using laboratory data is applied for prediction of the... (More)

Optimum operating conditions of a fuel cell will provide its maximum efficiency and the operating cost will be minimized. Thus, operation optimization of the fuel cell is essential. Neural networks can simulate systems without using simplifying assumptions. Therefore, the neural network can be used to simulate complex systems. This paper investigates the effects of important parameters, i.e., temperature, relative humidity in the cathode and anode, stoichiometry on the cathode and anode sides, on the polarization curve of a PEMFC (Proton Exchange Membrane Fuel Cell) having MPL (Micro Porous Layer) by ANN (artificial neural network). For this purpose, an analytical model validated using laboratory data is applied for prediction of the operating conditions providing maximum (and/or minimum) output power of a PEM fuel cell for arbitrary values of the current. The mean absolute relative error was calculated to 1.95%, indicating that the network results represented the laboratory data very accurately. The results show 23.6% and 28.9% increase of the power by the model and the network, respectively, when comparing the maximum and minimum power outputs.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial neural network, Operation optimization, PEMFC, Polarization curve, Water management
in
Electrochimica Acta
volume
348
article number
136345
publisher
Pergamon Press Ltd.
external identifiers
  • scopus:85084109448
ISSN
0013-4686
DOI
10.1016/j.electacta.2020.136345
language
English
LU publication?
yes
id
c4396586-ec48-4b71-a9d0-bc146885cc66
date added to LUP
2020-05-08 13:55:11
date last changed
2023-11-20 04:08:05
@article{c4396586-ec48-4b71-a9d0-bc146885cc66,
  abstract     = {{<p>Optimum operating conditions of a fuel cell will provide its maximum efficiency and the operating cost will be minimized. Thus, operation optimization of the fuel cell is essential. Neural networks can simulate systems without using simplifying assumptions. Therefore, the neural network can be used to simulate complex systems. This paper investigates the effects of important parameters, i.e., temperature, relative humidity in the cathode and anode, stoichiometry on the cathode and anode sides, on the polarization curve of a PEMFC (Proton Exchange Membrane Fuel Cell) having MPL (Micro Porous Layer) by ANN (artificial neural network). For this purpose, an analytical model validated using laboratory data is applied for prediction of the operating conditions providing maximum (and/or minimum) output power of a PEM fuel cell for arbitrary values of the current. The mean absolute relative error was calculated to 1.95%, indicating that the network results represented the laboratory data very accurately. The results show 23.6% and 28.9% increase of the power by the model and the network, respectively, when comparing the maximum and minimum power outputs.</p>}},
  author       = {{Nanadegani, Fereshteh Salimi and Lay, Ebrahim Nemati and Iranzo, Alfredo and Salva, J. Antonio and Sunden, Bengt}},
  issn         = {{0013-4686}},
  keywords     = {{Artificial neural network; Operation optimization; PEMFC; Polarization curve; Water management}},
  language     = {{eng}},
  publisher    = {{Pergamon Press Ltd.}},
  series       = {{Electrochimica Acta}},
  title        = {{On neural network modeling to maximize the power output of PEMFCs}},
  url          = {{http://dx.doi.org/10.1016/j.electacta.2020.136345}},
  doi          = {{10.1016/j.electacta.2020.136345}},
  volume       = {{348}},
  year         = {{2020}},
}