Artificial Neural Network Modeling of PEM Fuel Cells

[+] Author and Article Information
Shaoduan Ou, Luke E. Achenie

 Department of Chemical Engineering, Unit 3222, 191 Auditorium Road, Storrs, CT 06269

J. Fuel Cell Sci. Technol 2(4), 226-233 (May 23, 2005) (8 pages) doi:10.1115/1.2039951 History: Received April 14, 2004; Revised May 23, 2005

Artificial neural network (ANN) approaches for modeling of proton exchange membrane (PEM) fuel cells have been investigated in this study. This type of data-driven approach is capable of inferring functional relationships among process variables (i.e., cell voltage, current density, feed concentration, airflow rate, etc.) in fuel cell systems. In our simulations, ANN models have shown to be accurate for modeling of fuel cell systems. Specifically, different approaches for ANN, including back-propagation feed-forward networks, and radial basis function networks, were considered. The back-propagation approach with the momentum term gave the best results. A study on the effect of Pt loading on the performance of a PEM fuel cell was conducted, and the simulated results show good agreement with the experimental data. Using the ANN model, an optimization model for determining optimal operating points of a PEM fuel cell has been developed. Results show the ability of the optimizer to capture the optimal operating point. The overall goal is to improve fuel cell system performance through numerical simulations and minimize the trial and error associated with laboratory experiments.

Copyright © 2005 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 1

Feed-forward multilayer neural network with a single hidden layer

Grahic Jump Location
Figure 2

Schematic of the applications of the ANN model for PEM fuel cells

Grahic Jump Location
Figure 3

Schematic diagram of the optimization solution

Grahic Jump Location
Figure 4

Experimental and predicted cell voltages for a DMFC running at various operating conditions (data not used for training)

Grahic Jump Location
Figure 5

Comparison of predicted and measured cell polarization curves (temperature: 313.15K, methanol solution flow rate: 5ml∕min, methanol solution concentration: 1M)

Grahic Jump Location
Figure 6

Comparison of different ANN approaches

Grahic Jump Location
Figure 7

ANN predictions of the effects of Pt loading on cell performance

Grahic Jump Location
Figure 8

Comparison of ANN predictions to experimental data from CGFCC

Grahic Jump Location
Figure 9

Validation of the ANN model for optimization (hydrogen feed PEM fuel celoperated at 323K)

Grahic Jump Location
Figure 10

Comparison of the simulated result and experimental data for the cell power density (hydrogen feed PEM fuel cell operated at 323K)



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In