Abstract
Hydrogen based technologies are a growing field among the renewable energies options, being the single proton exchange membrane (PEM) fuel cells (FC) one of the most promising. There are a number of possibilities to model the electrical behavior of this kind of devices. Among them, in this paper authors have tackled the problem using an Artificial Neural Network based approach. As a proof of concept, a specific 2 KW PEM FC device has been used to obtain the data needed to carry out the process. Using these data, a model of only one signal at both the input and the output has been designed, i.e., the current \(I_{FC}\) at the input and the voltage \(V_{FC}\) at the output. As result of the modeling process that is explained along the paper, a relatively small model showing a medium squared error of 0.012 V\(^{2}\) with test data has been obtained.
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Acknowledgments
The work in this paper has been partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and project KK-2018/00071 of the Elkartek 2018 funding program of the Basque Government.
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Lopez-Guede, J.M., Graña, M., Estevez, J. (2020). Neural Model of a Specific Single Proton Exchange Membrane PEM Fuel Cell. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_31
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DOI: https://doi.org/10.1007/978-3-030-20055-8_31
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