Neural network model of 100 W portable PEM fuel cell and experimental verification |
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Authors: | NS Sisworahardjo T Yalcinoz MY El-Sharkh MS Alam |
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Affiliation: | 1. School of Electrical Engineering and Informatics, Institute of Technology Bandung, Bandung 40132, Indonesia;2. Dept. of Electrical of Computer Engineering, University of South Alabama, Mobile, AL 36688, USA;3. Dept. of Electrical and Electronic Engineering, Meliksah University, Talas, Kayseri 38280, Turkey |
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Abstract: | The inherent properties of artificial neural networks (ANNs) such as low sensitivity to noise and incomplete information make the ANN a promising candidate to model the fuel cell system. In this paper, an ANN-based model of 100 W portable direct hydrogen fed proton exchange membrane fuel cell (PEMFC) is presented. The model is built based on experimentally collected data from a portable 100 W direct hydrogen fed PEMFC in the authors’ laboratory. A multilayer feedforward ANN with back-propagation training algorithm is used to model the portable PEMFC. The ANN consists of fully connected four layers network with two hidden layers. The PEMFC ANN model is trained using extracted data from experimentally measured and calculated parameters. To validate the model, the outputs of the PEMFC ANN are compared against experimental data and results from a dynamic model of portable direct hydrogen fed PEMFC. In addition, three statistical indices to measure variations, unbiasedness (precision), and accuracy in voltage, power, and hydrogen flow are used to evaluate the PEMFC ANN model performance. The indices indicate that the maximum variations, unbiasedness, and accuracy of the voltage, power, and hydrogen flow are 1.45%, 2.04%, and 1.90%, respectively, which shows a close agreement between the outputs of the PEMFC ANN and the experimental results. |
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Keywords: | Fuel cell PEMFC Neural network model Portable fuel cell Direct hydrogen fed fuel cell |
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