Application of artificial neural network in performance prediction of PEM fuel cell

2011 ◽  
Vol 36 (13) ◽  
pp. 1215-1225 ◽  
Author(s):  
Yamini Sarada Bhagavatula ◽  
Maruthi T. Bhagavatula ◽  
K. S. Dhathathreyan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Cell ◽  
Performance Prediction ◽  
Pem Fuel Cell ◽  
Artificial Neural

Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network

2017 ◽  
Vol 42 (40) ◽  
pp. 25619-25629 ◽  
Author(s):  
Mehmet Seyhan ◽  
Yahya Erkan Akansu ◽  
Miraç Murat ◽  
Yusuf Korkmaz ◽  
Selahaddin Orhan Akansu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Cell ◽  
Performance Prediction ◽  
Pem Fuel Cell ◽  
Flow Channel ◽  
Artificial Neural ◽  
Serpentine Flow Channel

Artificial Neural Network Modeling of PEM Fuel Cells

2005 ◽  
Vol 2 (4) ◽  
pp. 226-233 ◽  
Author(s):  
Shaoduan Ou ◽  
Luke E. K. Achenie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Cells ◽  
Fuel Cell ◽  
Back Propagation ◽  
Pem Fuel Cell ◽  
Pem Fuel Cells ◽  
Optimal Operating ◽  
Cell Systems ◽  
Artificial Neural

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.

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