Engineering

Engineering

ISSN Print: 1947-3931
ISSN Online: 1947-394X
www.scirp.org/journal/eng
E-mail: eng@scirp.org
"Mechanistic Model versus Artificial Neural Network Model of a Single-Cell PEMFC"
written by Brigitte Grondin-Perez, Sébastien Roche, Carole Lebreton, Michel Benne, Cédric Damour, Jean-Jacques Amangoua Kadjo,
published by Engineering, Vol.6 No.8, 2014
has been cited by the following article(s):
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[1] A Review on Modeling of Proton Exchange Membrane Fuel Cell
Chemical Industry & Chemical Engineering …, 2023
[2] Modelling of Fuel Cells and Related Energy Conversion Systems
ChemEngineering, 2022
[3] Optimal parameter estimation of polymer electrolyte membrane fuel cells model with chaos embedded particle swarm optimization
2021
[4] Precise PEM fuel cell parameter extraction based on a self-consistent model and SCCSA optimization algorithm
2021
[5] PV Cell Parameter Extraction Using Data Prediction–Based Meta-Heuristic Algorithm via Extreme Learning Machine
2021
[6] Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm
2020
[7] A critical survey on proton exchange membrane fuel cell parameter estimation using meta-heuristic algorithms
2020
[8] Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells
2019
[9] Parametric Optimization of Proton Exchange Membrane Fuel Cell using Successive Trust Region Algorithm
2018
[10] A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling
Renewable and Sustainable Energy Reviews, 2018
[11] Development of Adaptive and Factorized Neural Models for MPC of Industrial Systems
2017
[12] Fault Tolerant Control Strategy applied to PEMFC water management
International Journal of Hydrogen Energy, 2015
[13] Contrôle tolérant aux fautes en ligne d'une pile à combustible de type PEM. Contribution à la gestion de l'eau
Thesis, 2015
[14] Contribution à la modélisation optimale des piles à combustible de type PEM par utilisation des méthodes méta-heuristiques
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