Neural network modeling strategy applied to a multi-stack PEM fuel cell system

2016 ◽  
Author(s):  
Francisco da Costa Lopes ◽  
Sousso Kelouwani ◽  
Loic Boulon ◽  
Kodjo Agbossou ◽  
Neigel Marx ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Network Modeling ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Neural Network Modeling ◽  
Fuel Cell System ◽  
Modeling Strategy

scholarly journals Neural network based model for a PEM fuel cell system

2009 ◽  
Vol 1 (07) ◽  
pp. 819-824 ◽  
Author(s):  
I. Zamora ◽  
J.I. San Martín ◽  
J.J. San Martín ◽  
V. Aperribay ◽  
P. Eguía
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Fuel Cell System

Electrical and Thermal Time Constants Fuel Cell System Identification: A Linear Versus Neural Network Approach

2008 ◽  
Author(s):  
M. T. Outeiro ◽  
Alberto J. L. Cardoso ◽  
R. Chibante ◽  
A. S. Carvalho
Keyword(s):  
Neural Network ◽  
Fuel Cells ◽  
Fuel Cell ◽  
Pem Fuel Cell ◽  
Pem Fuel Cells ◽  
Cell System ◽  
Thermal Time ◽  
Fuel Cell System ◽  
Time Constants ◽  
Made In

The energy generated by PEM fuel cells can be used in many different applications with emphasis to commercial power generation and automotive application. It requires the integration of various subsystems such as chemical, mechanical, fluid, thermal and electrical ones. Their electrical and thermal time constants are important variables to analyze and consider in the development of control strategies of electronic converters. For this purpose, a mathematical model of the PEM fuel cell system was developed in Matlab/Simulink based on a set of equations describing cell operation. The model considers static and dynamic operating conditions of the PEM. Using experimental measurements at different load conditions made in a Nexa™ PEM fuel cell system, analysis based on linear ARX (Autoregressive with Exogenous Input) and neural network methods were made in Matlab in order to identify the electrical and thermal time constant values. Both linear ARX and neural network approaches can successfully predict the values of the time constants variables. However, the identification by the linear ARX is appropriated around the most significant operation points of the PEM system while neural network allows at obtaining a nonlinear global model. The paper intends to be a contribution for the identification of the electrical and thermal time constants of PEM fuel cells through these two methodologies. The linear approach is simple but presents some limitations while the non-linear one is widespread but more complex to be implemented.

Neural network model for a commercial PEM fuel cell system

2007 ◽  
Vol 172 (2) ◽  
pp. 749-759 ◽  
Author(s):  
Anucha Saengrung ◽  
Amir Abtahi ◽  
Ali Zilouchian
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Network Model ◽  
Neural Network Model ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Fuel Cell System

scholarly journals Balanced version of Slime Mold Algorithm: A study on PEM fuel cell system parameters identification

2021 ◽  
Vol 7 ◽  
pp. 3199-3209
Author(s):  
Junlong Zheng ◽  
Yujie Xie ◽  
Xiaoping Huang ◽  
Zhongxing Wei ◽  
Bahman Taheri
Keyword(s):  
Fuel Cell ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Slime Mold ◽  
Parameters Identification ◽  
Fuel Cell System ◽  
System Parameters
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