Proton Exchange Membrane Fuel Cell Degradation and Remaining Useful Life Prediction based on Artificial Neural Network

2018 ◽  
Author(s):  
Kui CHEN ◽  
Salah Laghrouche ◽  
Abdesslem Djerdir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Cell ◽  
Life Prediction ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Artificial Neural ◽  
Exchange Membrane

Parametric Analysis of the Cathode Catalyst Layer of Proton Exchange Membrane Fuel Cells Using Artificial Neural Network

2011 ◽  
Author(s):  
N. Khajeh-Hosseini-Dalasm ◽  
S. Ahadian ◽  
K. Fushinobu ◽  
K. Okazaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Proton Exchange Membrane ◽  
Structural Parameters ◽  
Catalyst Layer ◽  
Proton Exchange ◽  
Cathode Catalyst ◽  
Cathode Catalyst Layer ◽  
Artificial Neural ◽  
Exchange Membrane

A mathematical model was developed to study the cathode catalyst layer (CL) performance of a proton exchange membrane fuel cell (PEMFC). A number of CL parameters affecting its performance are implemented into the CL agglomerate model. These parameters are: saturation and eight structural parameters, i.e., ionomer film thickness covering the agglomerate, agglomerate radius, platinum and carbon loading, membrane content, gas diffusion layer penetration content and CL thickness. An artificial neural network (ANN) approach along with statistical methods was used for modeling, prediction, and analysis of the CL performance, which is determined by activation over-potential. The ANN was constructed to develop a relationship between the named (input) parameters and activation overpotential. An statistical analysis, namely, analysis of means (ANOM) was performed on the data obtained by the trained ANN and resulted in the main effect of each input parameter, sensitivity factors of structural parameters and their mutual combination.

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