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Research Paper

Modeling of Direct Methanol Fuel Cell Using the Artificial Neural Network

[+] Author and Article Information
Hamid Baseri

e-mail: h.baseri@nit.ac.ir

Mohsen Shakeri

Mechanical Engineering Department,
Babol University of Technology,
Babol 47148-71167, Iran

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF FUEL CELL SCIENCE AND TECHNOLOGY. Manuscript received March 23, 2012; final manuscript received January 16, 2013; published online July 5, 2013. Editor: Nigel M. Sammes.

J. Fuel Cell Sci. Technol 10(4), 041007 (Jul 05, 2013) (9 pages) Paper No: FC-12-1024; doi: 10.1115/1.4024859 History: Received March 23, 2012; Revised January 16, 2013

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.

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Figures

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Fig. 1

Experimental setup

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Fig. 2

BPNN structure for the DMFC

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Fig. 3

Flow chart of the best network selection

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Fig. 4

RBF-NN structure for the DMFC

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Fig. 8

Comparison of the V–I curves of the fuel cell at different channel depths for the conditions of: methanol concentration = 1 M, methanol flow rate = 10 ml min−1, oxygen flow rate = 2 SLPM, cathode back pressure = 0.5 bar for (a) temperature = 40 °C, and (b) temperature = 65 °C

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Fig. 7

Comparison of the V–I curves of the fuel cell at different operating temperatures for the conditions of: methanol concentration = 1 M, methanol flow rate = 10 ml min−1, oxygen flow rate = 2 SLPM, cathode back pressure = 0.5 bar for (a) channel depth = 1 mm, (b) channel depth = 1.5 mm, and (c) channel depth = 2 mm

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Fig. 6

Predicted values by the (6-17-15-1) BPNN model with the tansig transfer function versus the measured data of the cell voltage in the (a) training, (b) validation, and (c) testing process

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Fig. 5

MSEtest for different numbers of hidden nodes in the RBF-NN

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Fig. 9

Comparison of the V–I curves of the fuel cell at different oxygen flows for the conditions of: cell temperature = 65 °C, methanol concentration = 1 M, methanol flow rate = 10 ml min−1, channel depth = 2 mm, and cathode back pressure = 0.5 bar

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Fig. 10

Comparison of the V–I curves of the fuel cell at different methanol concentrations for the conditions of: cell temperature = 65 °C, methanol flow rate = 10 ml min−1, channel depth = 2 mm, cathode back pressure = 0.5 bar, and cathode oxygen flow rate = 2 SLPM

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Fig. 11

Comparison of the V–I curves of the fuel cell at different cathode back pressures for the conditions of: cell temperature = 65 °C, methanol concentration = 1 M, methanol flow rate = 10 ml min−1, channel depth = 1.5 mm, and cathode oxygen flow rate = 2 SLPM

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