PEM fuel cell voltage-tracking using artificial neural network

2009 ◽  
Author(s):  
S.M. Rakhtala ◽  
R. Ghaderi ◽  
A. Ranjbar ◽  
T. Fadaeian ◽  
Ali Nabavi Niaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Cell ◽  
Cell Voltage ◽  
Pem Fuel Cell ◽  
Artificial Neural

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.

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

Modeling of Direct Methanol Fuel Cell Using the Artificial Neural Network

2013 ◽  
Vol 10 (4) ◽  
Author(s):  
Mehdi Tafazoli ◽  
Hamid Baseri ◽  
Ebrahim Alizadeh ◽  
Mohsen Shakeri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Neural Network Models ◽  
Input Parameters ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

The performance of a direct methanol fuel cell (DMFC) has complex nonlinear characteristics. In this paper, the performance of a DMFC has been modeled using a neural network approach. The input parameters of the DMFC model include cell geometrical and operational parameters such as the cell temperature, oxygen flow rate, channel depth of the bipolar plate, methanol concentration, cathode back pressure, and current density and the output parameter is the cell voltage. In order to predict the performance of a DMFC single cell, two types of artificial neural network (ANN) have been developed to correlate the input parameters of the DMFC to the cell voltage. The performance of the networks was investigated by varying the number of neurons, number of layers, and transfer function of the ANNs and the best one is selected based on the mean square error. The results indicated that the neural network models can predict the cell voltage with an acceptable accuracy.

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

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

High power fuel cell simulator based on artificial neural network

2010 ◽  
Vol 35 (21) ◽  
pp. 12125-12133 ◽  
Author(s):  
Abraham U. Chávez-Ramírez ◽  
Roberto Muñoz-Guerrero ◽  
S.M. Durón-Torres ◽  
M. Ferraro ◽  
G. Brunaccini ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Cell ◽  
High Power ◽  
Artificial Neural

Prediction of Cell Voltage and Chlorine Current Efficiency of Aqueous HCl Electrolysis Utilizing an Oxygen Reducing Cathode Based on Artificial Neural Network

2019 ◽  
Vol 25 (33) ◽  
pp. 27-42
Author(s):  
Seyed Nezamedin Ashrafizadeh ◽  
Fereydoon Mohammadi ◽  
Abolfazl Sattari ◽  
Narjes Shojaikaveh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Current Efficiency ◽  
Cell Voltage ◽  
Artificial Neural
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