Sistema híbrido inteligente para la predicción de la tensión de una pila de combustible basada en hidrógeno

  1. Casteleiro-Roca, José-Luis 1
  2. Barragán, Antonio Javier 2
  3. Segura, Francisca 2
  4. Calvo-Rolle, José Luis 1
  5. Andújar, José Manuel 2
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  2. 2 Universidad de Huelva
    info

    Universidad de Huelva

    Huelva, España

    ROR https://ror.org/03a1kt624

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2019

Volumen: 16

Número: 4

Páginas: 492-501

Tipo: Artículo

DOI: 10.4995/RIAI.2019.10986 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Objetivos de desarrollo sostenible

Resumen

Due to some reasons like sustainability and energy strategy, there is a clear trend using new ways to obtain energy, more efficient and, usually, renewables. In addition, with other dierent objectives, many researchs are being carried out on energy storage systems; one of the most promising, in terms of capacity and mobility, is hydrogen-based. In the present work a model is obtained to predict the dynamic behavior of a hydrogen fuel cell, which will improve its control. The variables used in this research have been extracted from a test bench, where a fuel cell is monitored under several load conditions with a programmable load connected to its output. To perform this model, a hybrid intelligent model was chosen. This kind of models use clustering techniques to divide the data set and, after that, intelligent regression algorithm with artificial neural networks are used for each group. The proposal has been tested with two validation data set, obtaining highly satisfactory results.

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Información de financiación

Los autores de este trabajo quieren agradecer el soporte en materia de financiaci?n del Ministerio de Econom?a, Industria y Competitividad del Gobierno de Espa?a a trav?s del proyecto H2SMART- ?GRID (DPI2017-85540-R).

Financiadores

    • DPI2017-85540-R

Referencias bibliográficas

  • Alaiz Moretón, H., Calvo Rolle, J., García, I., Alonso Alvarez, A., 2011. Formalization and practical implementation of a conceptual model for pid controller tuning. Asian Journal of Control 13 (6), 773-784. https://doi.org/10.1002/asjc.264
  • Alique, A., Haber, R. E., Haber, R. H., Ros, S., Gonzalez, C., 2000. A neural network-based model for the prediction of cutting force in milling process. A progress study on a real case. In: Intelligent Control, 2000. Proceedings of the 2000 IEEE International Symposium on. IEEE, pp. 121-125. https://doi.org/10.1109/ISIC.2000.882910
  • Amphlett, J. C., Baumert, R. M., Mann, R. F., Peppley, B. A., Roberge, P. R., Harris, T. J., Jan. 1995. Performance modeling of the Ballard Mark IV solid polymer electrolyte fuel cell i. Mechanistic model development. Journal of the Electrochemical Society 142 (1), 1-8. https://doi.org/10.1149/1.2043866
  • Amphlett, J. C., Mann, R. F., Peppley, B. A., Roberge, P. R., Rodrigues, A., Feb. 1996. A model predicting transient responses of proton exchange membrane fuel cells. Journal of Power Sources 61 (1-2), 183-188, cited By (since 1996) 216. https://doi.org/10.1016/S0378-7753(96)02360-9
  • Andújar, J. M., Segura, F., Dec. 2009. Fuel cells: History and updating. A walk along two centuries. Renewable and Sustainable Energy Reviews 13 (9), 2309-2322. https://doi.org/10.1016/j.rser.2009.03.015
  • Andújar, J. M., Segura, F., Durán, E., Rentería, L. A., Nov. 2011. Optimal interface based on power electronics in distributed generation systems for fuel cells. Renewable Energy 36 (11), 2759-2770. https://doi.org/10.1016/j.renene.2011.04.005
  • Andújar, J. M., Segura, F., Vasallo, M. J., 2008. A suitable model plant for control of the set fuel cell-DC/DC converter. Renewable Energy 33 (4), 813-826. https://doi.org/10.1016/j.renene.2007.04.013
  • Ballard, 2009. FCgenTM-1020ACS/FCvelocityTM-1020ACS Fuel Cell Stack. Ballard Product Manual and Integration Guide. Document Number MAN5100192-0GS.
  • Ballard, 2018. FCgen1020-ACS fuel cell from Ballard Power Systems. URL: http://www.ballard.com/docs/default-source/backup-power-documents/fcgen-1020acs.pdf
  • Barragán, A. J., Al-Hadithi, B. M., Andújar, J. M., Jiménez, A., 2015. Formal methodology for analyzing the dynamic behavior of nonlinear systems using fuzzy logic. Revista Iberoamericana de Automática e Informática Industrial (RIAI) 12 (4), 434-445. https://doi.org/10.1016/j.riai.2015.09.005
  • Barragán, A. J., Al-Hadithi, B. M., Jiménez, A., Andújar, J. M., 2014. A general methodology for online TS fuzzy modeling by the extended kalman filter. Applied Soft Computing 18 (0), 277-289. https://doi.org/10.1016/j.asoc.2013.09.005
  • Baruque, B., Porras, S., Jove, E., Calvo-Rolle, J. L., 2019. Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization. Energy 171, 49–60. https://doi.org/10.1016/j.energy.2018.12.207
  • Bertoluzzo, M., Buja, G., Aug. 2011. Development of electric propulsion systems for light electric vehicles. Industrial Informatics, IEEE Transactions on 7 (3), 428-435. https://doi.org/10.1109/TII.2011.2158840
  • Calvo-Rolle, J. L., Casteleiro-Roca, J. L., Quintián, H., del Carmen Meizoso-Lopez, M., 2013. A hybrid intelligent system for PID controller using in a steel rolling process. Expert Systems with Applications 40 (13), 5188-5196. https://doi.org/10.1016/j.eswa.2013.03.013
  • Calvo-Rolle, J. L., Fontenla-Romero, O., Pérez-Sánchez, B., Guijarro-Berdinas, B., 2014. Adaptive inverse control using an online learning algorithm for neural networks. Informatica 25 (3), 401-414. https://doi.org/10.15388/Informatica.2014.20
  • Calvo-Rolle, J. L., Quintian-Pardo, H., Corchado, E., del Carmen Meizoso-López, M., García, R. F., 2015. Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. Journal of Applied Logic 13 (1), 37-47. https://doi.org/10.1016/j.jal.2014.11.010
  • Casteleiro-Roca, J.-L., Barragan, A. J., Segura, F., Calvo-Rolle, J. L., Andújar, J. M., 2019. Fuel cell output current prediction with a hybrid intelligent system. Complexity 2019.
  • Casteleiro-Roca, J. L., Calvo-Rolle, J. L., Meizoso-López, M.-C., Piñón-Pazos, A., Rodríguez-Gómez, B. A., 2015. Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump. Neurocomputing 150, 90-98. https://doi.org/10.1016/j.neucom.2014.02.075
  • Casteleiro-Roca, J.-L., Jove, E., Gonzalez-Cava, J. M., Pérez, J. A. M., Calvo- Rolle, J. L., Alvarez, F. B., 2018. Hybrid model for the ANI index prediction using remifentanil drug and EMG signal. Neural Computing and Applications, 1-10. https://doi.org/10.1007/s00521-018-3605-z
  • Casteleiro-Roca, J.-L., Jove, E., Sánchez-Lasheras, F., Méndez-Pérez, J.-A., Calvo-Rolle, J.-L., de Cos Juez, F. J., 2017. Power cell SOC modelling for intelligent virtual sensor implementation. Journal of Sensors 2017. https://doi.org/10.1155/2017/9640546
  • De las Heras, A., Vivas, F., Segura, F., Andújar, J., 2018a. From the cell to the stack. a chronological walk through the techniques to manufacture the pefcs core. Renewable and Sustainable Energy Reviews 96, 29-45. https://doi.org/10.1016/j.rser.2018.07.036
  • De las Heras, A., Vivas, F., Segura, F., Redondo, M., Andújar, J., 2018b. Aircooled fuel cells: Keys to design and build the oxidant/cooling system. Renewable Energy 125, 1-20. https://doi.org/10.1016/j.renene.2018.02.077
  • del Brío, B., Molina, A., 2006. Redes neuronales y sistemas borrosos. Ra-Ma.
  • Famouri, P., Gemmen, R., Jul. 2003. Electrochemical circuit model of a PEM fuel cell. In: Power Engineering Society General Meeting, 2003, IEEE. Vol. 3. pp. 1436-1440. https://doi.org/10.1109/PES.2003.1267364
  • Fontanet, J. G. G., Cervantes, A. L., Ortiz, I. B., 2016. Alternatives of control for a furuta's pendulum. Revista Iberoamericana de Autom'atica e Informática Industrial RIAI 13 (4), 410 - 420, alternativas de control para un Péndulo de Furuta. https://doi.org/10.1016/j.riai.2016.05.008
  • Galipienso, M., Quevedo, M., Pardo, O., Ruiz, F., Ortega, M., 2003. Inteligencia artificial. Modelos, técnicas y áreas de aplicación. Editorial Paraninfo.
  • García, R. F., Rolle, J. L. C., Castelo, J. P., Gomez, M. R., 2014. On the monitoring task of solar thermal fluid transfer systems using NN based models and rule based techniques. Engineering Applications of Artificial Intelligence 27 (0), 129-136. https://doi.org/10.1016/j.engappai.2013.06.011
  • García, R. F., Rolle, J. L. C., Gomez, M. R., Catoira, A. D., 2013. Expert condition monitoring on hydrostatic self-levitating bearings. Expert Systems with Applications 40 (8), 2975-2984. https://doi.org/10.1016/j.eswa.2012.12.013
  • Ghanghermeh, A., Roshan, G., Orosa, J. A., Calvo-Rolle, J. L., Costa, A. M., 2013. New climatic indicators for improving urban sprawl: A case study of tehran city. Entropy 15 (3), 999-1013. https://doi.org/10.3390/e15030999
  • Gordillo, F., Aracil, J., Alamo, T., Jul. 1997. Determining limit cycles in fuzzy control systems. In: IEEE International Conference on Fuzzy Systems. Vol. 1. pp. 193-198. https://doi.org/10.1109/FUZZY.1997.616367
  • Harston, A. M. C., Pap, R., 2014. Handbook of Neural Computing Applications. Elsevier Science.
  • Hilera Gonzalez, J. R., Martínez Hernando, V. J., 2000. Redes neuronales artificiales: fundamentos, modelos y aplicaciones. Ra-Ma.
  • Hou, Y., Yang, Z., Fang, X., 2011. An experimental study on the dynamic process of PEM fuel cell stack voltage. Renewable Energy 36 (1), 325-329. https://doi.org/10.1016/j.renene.2010.06.046
  • Irigoyen, E., Miñano, G., 2013. A narx neural network model for enhancing cardiovascular rehabilitation therapies. Neurocomputing 109, 9 - 15, new trends on Soft Computing Models in Industrial and Environmental Applications. https://doi.org/10.1016/j.neucom.2012.07.031
  • Jove, E., Antonio Lopez-Vazquez, J., Isabel Fernandez-Ibanez, M., Casteleiro-Roca, J.-L., Luis Calvo-Rolle, J., 2018a. Hybrid intelligent system to predict the individual academic performance of engineering students. INTERNATIONAL JOURNAL OF ENGINEERING EDUCATION 34 (3), 895-904.
  • Jove, E., Blanco-Rodríguez, P., Casteleiro-Roca, J. L., Moreno-Arboleda, J., Lopez-V ázquez, J. A., de Cos Juez, F. J., Calvo-Rolle, J. L., 2018b. Attempts prediction by missing data imputation in engineering degree. In: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 Leon, Spain, September 6–8, 2017, Proceeding. Springer International Publishing, Cham, pp. 167–176.
  • Jove, E., Gonzalez-Cava, J. M., Casteleiro-Roca, J.-L., Méndez-Pérez, J.-A., Antonio Reboso-Morales, J., Javier Pérez-Castelo, F., Javier de Cos Juez, F., Luis Calvo-Rolle, J., 2018b. Modelling the hypnotic patient response in general anaesthesia using intelligent models. Logic Journal of the IGPL 00(0). https://doi.org/10.1093/jigpal/jzy032
  • Kim, J., Lee, S.-M., Srinivasan, S., Chamberlin, C. E., Aug. 1995. Modeling of proton exchange membrane fuel cell performance with an empirical equation. Journal of the Electrochemical Society 142 (8), 2670-2674. https://doi.org/10.1149/1.2050072
  • Kirubakaran, A., Jain, S., Nema, R., Dec. 2009. A review on fuel cell technologies and power electronic interface. Renewable and Sustainable Energy Reviews 13 (9), 2430-2440. https://doi.org/10.1016/j.rser.2009.04.004
  • Li, X., Deng, Z.-H., Wei, D., Xu, C.-S., Cao, G.-Y., 2011. Parameter optimization of thermal-model-oriented control law for pem fuel cell stack via novel genetic algorithm. Energy Conversion and Management 52 (11), 3290-3300. https://doi.org/10.1016/j.enconman.2011.05.012
  • López, R., Fernández, J., 2008. Las Redes Neuronales Artificiales. Netbiblo.
  • López-Baldán, M. J., García-Cerezo, A., Cejudo, J. M., Romero, A., Apr. 2002. Fuzzy modeling of a thermal solar plant. International Journal of Intelligent Systems 17 (4), 369-379. https://doi.org/10.1002/int.10026
  • Machón-González, I., López-García, H., Calvo-Rolle, J. L., 2010. A hybrid batch som-ng algorithm. In: Neural Networks (IJCNN), The 2010 International Joint Conference on. pp. 1-5. https://doi.org/10.1109/IJCNN.2010.5596812
  • MacQueen, J., 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics. pp. 281-297.
  • Márquez, J. M. A., Piña, A. J. B., Arias, M. E. G., 2009. A general and formal methodology for designing stable nonlinear fuzzy control systems. IEEE Transactions on Fuzzy Systems 17 (5), 1081-1091. https://doi.org/10.1109/TFUZZ.2009.2021984
  • Mehta, V., Cooper, J., 2003. Review and analysis of pem fuel cell design and manufacturing. Journal of Power Sources 114 (1), 32-53. https://doi.org/10.1016/S0378-7753(02)00542-6
  • Moody, J., Darken, C., 6 1989. Fast learning in networks of locally-tuned processing units. Neural Computation 1 (2), 281-294. https://doi.org/10.1162/neco.1989.1.2.281
  • Moreira, M. V., da Silva, G. E., Jul. 2009. A practical model for evaluating the performance of proton exchange membrane fuel cells. Renewable Energy 34 (7), 1734-1741. https://doi.org/10.1016/j.renene.2009.01.002
  • Orallo, J., Quintana, M., Ramírez, C., 2004. Introducción a la miner'ıa de datos. Editorial Alhambra S.A.
  • Paska, J., Biczel, P., Kłos, M., Nov. 2009. Hybrid power systems - an efective way of utilising primary energy sources. Renewable Energy 34 (11), 2414- 2421. https://doi.org/10.1016/j.renene.2009.02.018
  • Quintián, H., Calvo-Rolle, J. L., Corchado, E., 2014. A hybrid regression system based on local models for solar energy prediction. Informatica 25 (2), 265-282. https://doi.org/10.15388/Informatica.2014.14
  • Quintian Pardo, H., Calvo Rolle, J. L., Fontenla Romero, O., 2012. Application of a low cost commercial robot in tasks of tracking of objects. Dyna 79 (175), 24-33.
  • Ralph, T., Hards, G., Keating, J., Campbell, S., Wilkinson, D., Davis, M., St-Pierre, J., Johnson, M., 1997. Low cost electrodes for proton exchange membrane fuel cells: Performance in single cells and ballard stacks. Journal of the Electrochemical Society 144 (11), 3845-3857. https://doi.org/10.1149/1.1838101
  • Rolle, J., Gonzalez, I., Garcia, H., 2011. Neuro-robust controller for non-linear systems. Dyna 86 (3), 308-317. https://doi.org/10.6036/3949
  • Ross, D., Jul. 2003. Power struggle [power supplies for portable equipment]. IEE Review 49 (7), 34-38. https://doi.org/10.1049/ir:20030705
  • Segura, F., Andújar, J. M., Durán, E., april 2011. Analog current control techniques for power control in PEM fuel-cell hybrid systems: A critical review and a practical application. IEEE Transactions on Industrial Electronics 58 (4), 1171-1184. https://doi.org/10.1109/TIE.2010.2049710
  • Segura, F., Andújar, J., 2015a. Modular pem fuel cell scada & simulator system. Resources 4 (3), 692-712. https://doi.org/10.3390/resources4030692
  • Segura, F., Andújar, J., 2015b. Step by step development of a real fuel cell system. Design, implementation, control and monitoring. International Journal of Hydrogen Energy 40 (15), 5496-5508. https://doi.org/10.1016/j.ijhydene.2015.01.178
  • Segura, F., Bartolucci, V., Andújar, J., 2017. Hardware/software data acquisition system for real time cell temperature monitoring in air-cooled polymer electrolyte fuel cells. Sensors (Switzerland) 17 (7). https://doi.org/10.3390/s17071600
  • Van Bussel, H., Koene, F., Mallant, R. K., Mar. 1998. Dynamic model of solid polymer fuel cell water management. Journal of Power Sources 71 (1-2), 218-222. https://doi.org/10.1016/S0378-7753(97)02744-4
  • Viñuela, P., León, I., 2004. Redes de neuronas artificiales: un enfoque práctico. Pearson Educaci'on - Prentice Hall.
  • Vivas, F., De las Heras, A., Segura, F., And'ujar, J., 2018. A review of energy management strategies for renewable hybrid energy systems with hydrogen backup. Renewable and Sustainable Energy Reviews 82, 126-155. https://doi.org/10.1016/j.rser.2017.09.014
  • Ziogou, C., Voutetakis, S., Papadopoulou, S., Georgiadis, M., 2011. Modeling, simulation and experimental validation of a pem fuel cell system. Computers and Chemical Engineering 35 (9), 1886-1900. https://doi.org/10.1016/j.compchemeng.2011.03.013
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