Nb-doped TiO2/carbon composite supports synthesized by ultrasonic spray pyrolysis for proton exchange membrane (PEM) fuel cell catalysts

2012 ◽  
Vol 220 ◽  
pp. 1-9 ◽  
Author(s):  
Keerthi Senevirathne ◽  
Vladimir Neburchilov ◽  
Vanesa Alzate ◽  
Ryan Baker ◽  
Roberto Neagu ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Spray Pyrolysis ◽  
Proton Exchange Membrane ◽  
Ultrasonic Spray Pyrolysis ◽  
Pem Fuel Cell ◽  
Carbon Composite ◽  
Proton Exchange ◽  
Ultrasonic Spray ◽  
Fuel Cell Catalysts ◽  
Exchange Membrane

scholarly journals An Artificial Intelligence Solution for Predicting Short-Term Degradation Behaviors of Proton Exchange Membrane Fuel Cell

2021 ◽  
Vol 11 (14) ◽  
pp. 6348
Author(s):  
Zijun Yang ◽  
Bowen Wang ◽  
Xia Sheng ◽  
Yupeng Wang ◽  
Qiang Ren ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cell ◽  
Activation Function ◽  
Proton Exchange ◽  
Cell Performance ◽  
Short Term ◽  
Degradation Behaviors ◽  
Hidden Layer ◽  
Exchange Membrane

The dead-ended anode (DEA) and anode recirculation operations are commonly used to improve the hydrogen utilization of automotive proton exchange membrane (PEM) fuel cells. The cell performance will decline over time due to the nitrogen crossover and liquid water accumulation in the anode. Highly efficient prediction of the short-term degradation behaviors of the PEM fuel cell has great significance. In this paper, we propose a data-driven degradation prediction method based on multivariate polynomial regression (MPR) and artificial neural network (ANN). This method first predicts the initial value of cell performance, and then the cell performance variations over time are predicted to describe the degradation behaviors of the PEM fuel cell. Two cases of degradation data, the PEM fuel cell in the DEA and anode recirculation modes, are employed to train the model and demonstrate the validation of the proposed method. The results show that the mean relative errors predicted by the proposed method are much smaller than those by only using the ANN or MPR. The predictive performance of the two-hidden-layer ANN is significantly better than that of the one-hidden-layer ANN. The performance curves predicted by using the sigmoid activation function are smoother and more realistic than that by using rectified linear unit (ReLU) activation function.

The Effect of Inlet Parameters on Fluid Flow and Cell Performance at Cathode of a Proton Exchange Membrane Fuel Cell

2010 ◽  
Vol 7 (4) ◽  
Author(s):  
Utku Gulan ◽  
Hasmet Turkoglu ◽  
Irfan Ar
Keyword(s):  
Reynolds Number ◽  
Fuel Cell ◽  
Power Density ◽  
Current Density ◽  
Proton Exchange Membrane ◽  
Catalyst Layer ◽  
Pem Fuel Cell ◽  
Proton Exchange ◽  
Exchange Membrane ◽  
Oxygen Mole Fraction

In this study, the fluid flow and cell performance in cathode side of a proton exchange membrane (PEM) fuel cell were numerically analyzed. The problem domain consists of cathode gas channel, cathode gas diffusion layer, and cathode catalyst layer. The equations governing the motion of air, concentration of oxygen, and electrochemical reactions were numerically solved. A computer program was developed based on control volume method and SIMPLE algorithm. The mathematical model and program developed were tested by comparing the results of numerical simulations with the results from literature. Simulations were performed for different values of inlet Reynolds number and inlet oxygen mole fraction at different operation temperatures. Using the results of these simulations, the effects of these parameters on the flow, oxygen concentration distribution, current density and power density were analyzed. The simulations showed that the oxygen concentration in the catalyst layer increases with increasing Reynolds number and hence the current density and power density of the PEM fuel cell also increases. Analysis of the data obtained from simulations also shows that current density and power density of the PEM fuel cell increases with increasing operation temperature. It is also observed that increasing the inlet oxygen mole fraction increases the current density and power density.

Theoretical Analysis on the Autothermal Reforming Process of Ethanol as Fuel for a Proton Exchange Membrane Fuel Cell System

2006 ◽  
Vol 4 (4) ◽  
pp. 468-473 ◽  
Author(s):  
Alessandra Perna
Keyword(s):  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Autothermal Reforming ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Hydrogen Yield ◽  
Fuel Cell System ◽  
Exchange Membrane

The purpose of this work is to investigate, by a thermodynamic analysis, the effects of the process variables on the performance of an autothermal reforming (ATR)-based fuel processor, operating on ethanol as fuel, integrated into an overall proton exchange membrane (PEM) fuel cell system. This analysis has been carried out finding the better operating conditions to maximize hydrogen yield and to minimize CO carbon monoxide production. In order to evaluate the overall efficiency of the system, PEM fuel cell operations have been analyzed by an available parametric model.

Pore Structure Modeling of Flow in Gas Diffusion Layers of Proton Exchange Membrane Fuel Cells

2010 ◽  
Author(s):  
Zhongying Shi ◽  
Xia Wang
Keyword(s):  
Fuel Cell ◽  
Pore Structure ◽  
Proton Exchange Membrane ◽  
Cell Model ◽  
Transport Phenomena ◽  
Pem Fuel Cell ◽  
Gas Diffusion ◽  
Proton Exchange ◽  
Structure Model ◽  
Exchange Membrane

The gas diffusion layer (GDL) in a proton exchange membrane (PEM) fuel cell has a porous structure with anisotropic and non-homogenous properties. The objective of this research is to develop a PEM fuel cell model where transport phenomena in the GDL are simulated based on GDL’s pore structure. The GDL pore structure was obtained by using a scanning electron microscope (SEM). GDL’s cross-section view instead of surface view was scanned under the SEM. The SEM image was then processed using an image processing tool to obtain a two dimensional computational domain. This pore structure model was then coupled with an electrochemical model to predict the overall fuel cell performance. The transport phenomena in the GDL were simulated by solving the Navier-Stokes equation directly in the GDL pore structure. By comparing with the testing data, the fuel cell model predicted a reasonable fuel cell polarization curve. The pore structure model was further used to calculate the GDL permeability. The numerically predicted permeability was close to the value published in the literature. A future application of the current pore structure model is to predict GDL thermal and electric related properties.

Developing Hydrogen (H2) Specification Guidelines for Proton Exchange Membrane (PEM) Fuel Cell Vehicles

2005 ◽  
Author(s):  
Stella Papasavva ◽  
Chris Sloane ◽  
Fred Wagner ◽  
Mike Steele ◽  
Gerald Voecks ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cell ◽  
Proton Exchange ◽  
Fuel Cell Vehicles ◽  
Exchange Membrane

Pore Structure Modeling of Flow in Gas Diffusion Layers of Proton Exchange Membrane Fuel Cells

2012 ◽  
Vol 9 (2) ◽  
Author(s):  
Z. Shi ◽  
X. Wang
Keyword(s):  
Fuel Cell ◽  
Pore Structure ◽  
Proton Exchange Membrane ◽  
Cell Model ◽  
Transport Phenomena ◽  
Pem Fuel Cell ◽  
Gas Diffusion ◽  
Proton Exchange ◽  
Structure Model ◽  
Exchange Membrane

The gas diffusion layer (GDL) in a proton exchange membrane (PEM) fuel cell has a porous structure with anisotropic and non-homogenous properties. The objective of this research is to develop a PEM fuel cell model where transport phenomena in the GDL are simulated based on GDL’s pore structure. The GDL pore structure was obtained by using a scanning electron microscope (SEM). GDL’s cross-section view instead of surface view was scanned under the SEM. The SEM image was then processed using an image processing tool to obtain a two-dimensional computational domain. This pore structure model was then coupled with an electrochemical model to predict the overall fuel cell performance. The transport phenomena in the GDL were simulated by solving the Navier-Stokes equation directly in the GDL pore structure. By comparing with the testing data, the fuel cell model predicted a reasonable fuel cell polarization curve. The pore structure model was further used to calculate the GDL permeability. The numerically predicted permeability was close to the value published in the literature. A future application of the current pore structure model is to predict GDL thermal and electric related properties.

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