Water Transport in a PEMFC Based on the Difference in Capillary Pressure Between the Cathode Catalyst Layer and Microporous Layer

2015 ◽  
Vol 12 (5) ◽  
Author(s):  
Enju Nishiyama ◽  
Masaya Hara ◽  
Toshiaki Murahashi ◽  
Kazushige Nakao
Keyword(s):  
Capillary Pressure ◽  
Water Transport ◽  
Proton Exchange Membrane ◽  
Effective Diffusivity ◽  
Catalyst Layer ◽  
Gas Diffusion ◽  
Proton Exchange ◽  
Diffusion Resistance ◽  
Cathode Catalyst ◽  
Cathode Catalyst Layer

The water transport behavior of the cathode catalyst layer (CCL) in a proton exchange membrane fuel cell (PEMFC) was investigated by comparing the performance of several cells containing different microporous layers (MPLs). The capillary pressure and effective diffusivity of the cathode gas diffusion layer (GDL) and the CCL play an important role in the transport of water generated in the PEMFC. Experimental data for various inlet humidities and air stoichiometries were evaluated using the modified water vapor activity with the capillary pressure of the MPL. The capillary pressures in the MPLs and CCL are approximated using a polynomial function of liquid saturation. There was a significant increase in the diffusion resistance of oxygen in the CCL, while that in the MPLs and CCL was moderate, which indicates that the CCL is susceptible to flooding.

Ionomer content effect on charge and gas transport in the cathode catalyst layer of proton-exchange membrane fuel cells

2021 ◽  
Vol 490 ◽  
pp. 229531
Author(s):  
Yurii V. Yakovlev ◽  
Yevheniia V. Lobko ◽  
Maryna Vorokhta ◽  
Jaroslava Nováková ◽  
Michal Mazur ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Proton Exchange Membrane ◽  
Gas Transport ◽  
Catalyst Layer ◽  
Proton Exchange ◽  
Cathode Catalyst ◽  
Cathode Catalyst Layer ◽  
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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