Abstract
The use of Proton-Exchange Membranes Fuel Cells is presented as a key alternative to face the increasing and concerning problems related to global warming. The international expansion of green policies, has resulted in the need of ensuring their quality and reliability performance. Although fuel cells can get to play a significant role, this technology is still under development, paying special attention to the problems related to gas starvation and degradation. In this context, the present work deals with the virtual sensor implementation of one of the voltage cells present in a stack, whose operation is subjected to several degradation cycles. The proposal predicts indirectly the voltage of one cell from the current state of the rest of the cells by means of an intelligent model.
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References
Alaiz-Moreton, H., Fernández-Robles, L., Alfonso-Cendón, J., Castejón-Limas, M., Sánchez-González, L., Pérez, H.: Data mining techniques for the estimation of variables in health-related noisy data. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds.) SOCO/CISIS/ICEUTE -2017. AISC, vol. 649, pp. 482–491. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67180-2_47
Alaiz-Moretón, H., et al.: Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system. Processes 7(11), 825 (2019)
Barreras, F., Lozano, A., Roda, V., Barroso, J., Martín, J.: Optimal design and operational tests of a high-temperature PEM fuel cell for a combined heat and power unit. Int. J. Hydrogen Energy 39(10), 5388–5398 (2014)
Baruque, B., Porras, S., Jove, E., Calvo-Rolle, J.L.: Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization. Energy 171, 49–60 (2019)
Casteleiro-Roca, J.L., Barragán, A.J., Segura, F., Calvo-Rolle, J.L., Andújar, J.M.: Fuel cell output current prediction with a hybrid intelligent system. Complexity 2019, Article ID 6317270 (2019)
Casteleiro-Roca, J.L., Gómez-González, J.F., Calvo-Rolle, J.L., Jove, E., Quintián, H., Gonzalez Diaz, B., Mendez Perez, J.A.: Short-term energy demand forecast in hotels using hybrid intelligent modeling. Sensors 19(11), 2485 (2019)
Casteleiro-Roca, J.L., Javier Barragan, A., Segura, F., Luis Calvo-Rolle, J., Manuel Andujar, J.: Intelligent hybrid system for the prediction of the voltage-current characteristic curve of a hydrogen-based fuel cell. Revista Iberoamericana de Automática e Informática Industrial 16(4), 492–501 (2019)
Casteleiro-Roca, J.L., Jove, E., Sánchez-Lasheras, F., Méndez-Pérez, J.A., Calvo-Rolle, J.L., de Cos Juez, F.J.: Power cell SOC modelling for intelligent virtual sensor implementation. J. Sensors 2017, Article ID 9640546 (2017)
Chen, Y.S., Yang, C.W., Lee, J.Y.: Implementation and evaluation for anode purging of a fuel cell based on nitrogen concentration. Appl. Energy 113, 1519–1524 (2014)
Chukwuka, C., Folly, K.: Batteries and super-capacitors. In: Power Engineering Society Conference and Exposition in Africa (PowerAfrica). IEEE, pp. 1–6, July 2012. https://doi.org/10.1109/PowerAfrica.2012.6498634
Fernández-Serantes, L.A., Estrada Vázquez, R., Casteleiro-Roca, J.L., Calvo-Rolle, J.L., Corchado, E.: Hybrid intelligent model to predict the SOC of a LFP power cell type. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 561–572. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07617-1_49
González, S., Sedano, J., Villar, J.R., Corchado, E., Herrero, Á., Baruque, B.: Features and models for human activity recognition. Neurocomputing 167, 52–60 (2015). https://doi.org/10.1016/j.neucom.2015.01.082
Jove, E., Aláiz-Moretón, H., Casteleiro-Roca, J.L., Corchado, E., Calvo-Rolle, J.L.: Modeling of bicomponent mixing system used in the manufacture of wind generator blades. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds.) IDEAL 2014. LNCS, vol. 8669, pp. 275–285. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10840-7_34
Jove, E., et al.: Missing data imputation over academic records of electrical engineering students. Logic J. IGPL 28(4), 487–501 (2020)
Jove, E., Casteleiro-Roca, J.L., Quintián, H., Méndez-Pérez, J.A., Calvo-Rolle, J.L.: A fault detection system based on unsupervised techniques for industrial control loops. Expert Syst. 36(4), e12395 (2019)
Jove, E., Casteleiro-Roca, J.L., Quintián, H., Méndez-Pérez, J.A., Calvo-Rolle, J.L.: Virtual sensor for fault detection, isolation and data recovery for bicomponent mixing machine monitoring. Informatica 30(4), 671–687 (2019)
Jove, E., Casteleiro-Roca, J.L., Quintián, H., Méndez-Pérez, J.A., Calvo-Rolle, J.L.: A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections. Inf. Fusion 65, 50–57 (2021)
Jove, E., Casteleiro-Roca, J.L., Quintián, H., Simić, D., Méndez-Pérez, J.A., Luis Calvo-Rolle, J.: Anomaly detection based on one-class intelligent techniques over a control level plant. Logic J. IGPL 28(4), 502–518 (2020)
Jove, E., Casteleiro-Roca, J.L., Quintián, H., Zayas-Gato, F., Vercelli, G., Calvo-Rolle, J.L.: A one-class classifier based on a hybrid topology to detect faults in power cells. Logic Journal of the IGPL (2021)
Kaltschmitt, M., Streicher, W., Wiese, A.: Renewable Energy. Springer, Heidelberg (2007)
Niakolas, D.K., Daletou, M., Neophytides, S.G., Vayenas, C.G.: Fuel cells are a commercially viable alternative for the production of “clean” energy. Ambio 45(1), 32–37 (2016)
Park, J., Oh, H., Ha, T., Lee, Y.I., Min, K.: A review of the gas diffusion layer in proton exchange membrane fuel cells: durability and degradation. Appl. Energy 155, 866–880 (2015)
Pinzón, C., de Paz, J.F., Bajo, J., Herrero, Á., Corchado, E.: AIIDA-SQL: an adaptive intelligent intrusion detector agent for detecting SQL injection attacks. In: 10th International Conference on Hybrid Intelligent Systems (HIS 2010), Atlanta, 23–25 August 2010, pp. 73–78 (2010). https://doi.org/10.1109/HIS.2010.5600026
Quintián, H., Corchado, E.: Beta Hebbian learning as a new method for exploratory projection pursuit. Int. J. Neural Syst. 27(06), 1750024 (2017)
Quintián, H., Corchado, E.: Beta scale invariant map. Eng. Appl. Artif. Intell. 59, 218–235 (2017)
Rosli, R., Sulong, A., Daud, W., Zulkifley, M., Husaini, T., Rosli, M., Majlan, E., Haque, M.: A review of high-temperature proton exchange membrane fuel cell (HT-PEMFC) system. Int. J. Hydrogen Energy 42(14), 9293–9314 (2017)
Sebastián, D., Baglio, V.: Advanced materials in polymer electrolyte fuel cells (2017)
Contribution of Working Group Team: Contribution of working groups i, ii and iii to the fourth assessment report of the intergovernmental panel on climate change. In: IPCC 2007: Climate Change 2007: Synthesis Report 104 (2007)
Wasserman, P.: Advanced Methods in Neural Computing, 1st edn. Wiley, New York (1993)
Zhang, L., Chae, S.R., Hendren, Z., Park, J.S., Wiesner, M.R.: Recent advances in proton exchange membranes for fuel cell applications. Chem. Eng. J. 204, 87–97 (2012)
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CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).
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Jove, E., Lozano, A., Manso, Á.P., Barreras, F., Costa-Castelló, R., Calvo-Rolle, J.L. (2022). A Virtual Sensor for a Cell Voltage Prediction of a Proton-Exchange Membranes Based on Intelligent Techniques. In: Corchado, J.M., Trabelsi, S. (eds) Sustainable Smart Cities and Territories. SSCTIC 2021. Lecture Notes in Networks and Systems, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-030-78901-5_21
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