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Multivariable controller design for the cooling system of a PEM fuel cell by considering nearly optimal solutions in a multi-objective optimization approach

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Multivariable controller design for the cooling system of a PEM fuel cell by considering nearly optimal solutions in a multi-objective optimization approach

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dc.contributor.author Pajares-Ferrando, Alberto es_ES
dc.contributor.author Blasco, Xavier es_ES
dc.contributor.author Herrero Durá, Juan Manuel es_ES
dc.contributor.author Simarro Fernández, Raúl es_ES
dc.date.accessioned 2021-06-03T03:32:07Z
dc.date.available 2021-06-03T03:32:07Z
dc.date.issued 2020-08-06 es_ES
dc.identifier.issn 1076-2787 es_ES
dc.identifier.uri http://hdl.handle.net/10251/167205
dc.description.abstract [EN] This paper presents a design for the multivariable control of a cooling system in a PEM (proton exchange membrane) fuel cell stack. This system is complex and challenging enough: interactions between variables, highly nonlinear dynamic behavior, etc. This design is carried out using a multiobjective optimization methodology. There are few previous works that address this problem using multiobjective techniques. Also, this work has, as a novelty, the consideration of, in addition to the optimal controllers, the nearly optimal controllers nondominated in their neighborhood (potentially useful alternatives). In the multiobjective optimization problem approach, the designer must make decisions that include design objectives; parameters of the controllers to be estimated; and the conditions and characteristics of the simulation of the system. However, to simplify the optimization and decision stages, the designer does not include all the desired scenarios in the multiobjective problem definition. Nevertheless, these aspects can be analyzed in the decision stage only for the controllers obtained with a much less computational cost. At this stage, the potentially useful alternatives can play an important role. These controllers have significantly different parameters and therefore allow the designer to make a final decision with additional valuable information. Nearly optimal controllers can obtain an improvement in some aspects not included in the multiobjective optimization problem. For example, in this paper, various aspects are analyzed regarding potentially useful solutions, such as (1) the influence of certain parameters of the simulator; (2) the sample time of the controller; (3) the effect of stack degradation; and (4) the robustness. Therefore, this paper highlights the relevance of this in-depth analysis using the methodology proposed in the design of the multivariable control of the cooling system of a PEM fuel cell. This analysis can modify the final choice of the designer. es_ES
dc.description.sponsorship This study was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (Spain) (grant no. RTI2018-096904-B-I00) and by the Generalitat Valenciana regional government through project AICO/2019/055. es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation.ispartof Complexity es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Multivariable controller design for the cooling system of a PEM fuel cell by considering nearly optimal solutions in a multi-objective optimization approach es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1155/2020/8649428 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096904-B-I00/ES/HERRAMIENTAS DE OPTIMIZACION MULTIOBJETIVO PARA LA CARACTERIZACION Y ANALISIS DE CONCEPTOS DE DISEÑO Y SOLUCIONES SUB-OPTIMAS EFICIENTES EN PROBLEMAS DE INGENIERIA DE SISTEMAS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//AICO%2F2019%2F055/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Pajares-Ferrando, A.; Blasco, X.; Herrero Durá, JM.; Simarro Fernández, R. (2020). Multivariable controller design for the cooling system of a PEM fuel cell by considering nearly optimal solutions in a multi-objective optimization approach. Complexity. 2020:1-17. https://doi.org/10.1155/2020/8649428 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1155/2020/8649428 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 2020 es_ES
dc.relation.pasarela S\417001 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.description.references Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1), 1502242. doi:10.1080/23311916.2018.1502242 es_ES
dc.description.references Engau, A., & Wiecek, M. M. (2007). Generating ε-efficient solutions in multiobjective programming. European Journal of Operational Research, 177(3), 1566-1579. doi:10.1016/j.ejor.2005.10.023 es_ES
dc.description.references Loridan, P. (1984). ?-solutions in vector minimization problems. Journal of Optimization Theory and Applications, 43(2), 265-276. doi:10.1007/bf00936165 es_ES
dc.description.references White, D. J. (1986). Epsilon efficiency. Journal of Optimization Theory and Applications, 49(2), 319-337. doi:10.1007/bf00940762 es_ES
dc.description.references Pajares, A., Blasco, X., Herrero, J. M., & Reynoso-Meza, G. (2018). A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA. Complexity, 2018, 1-22. doi:10.1155/2018/1792420 es_ES
dc.description.references Schutze, O., Vasile, M., & Coello, C. A. C. (2011). Computing the Set of Epsilon-Efficient Solutions in Multiobjective Space Mission Design. Journal of Aerospace Computing, Information, and Communication, 8(3), 53-70. doi:10.2514/1.46478 es_ES
dc.description.references Pajares, A., Blasco, X., Herrero, J. M., & Reynoso-Meza, G. (2019). A New Point of View in Multivariable Controller Tuning Under Multiobjective Optimization by Considering Nearly Optimal Solutions. IEEE Access, 7, 66435-66452. doi:10.1109/access.2019.2915556 es_ES
dc.description.references Fredriksson, A., Forsgren, A., & Hårdemark, B. (2011). Minimax optimization for handling range and setup uncertainties in proton therapy. Medical Physics, 38(3), 1672-1684. doi:10.1118/1.3556559 es_ES
dc.description.references Lee, J., & Johnson, G. E. (1993). Optimal tolerance allotment using a genetic algorithm and truncated Monte Carlo simulation. Computer-Aided Design, 25(9), 601-611. doi:10.1016/0010-4485(93)90075-y es_ES
dc.description.references Andújar, J. M., & Segura, F. (2009). Fuel cells: History and updating. A walk along two centuries. Renewable and Sustainable Energy Reviews, 13(9), 2309-2322. doi:10.1016/j.rser.2009.03.015 es_ES
dc.description.references Mehta, V., & Cooper, J. S. (2003). Review and analysis of PEM fuel cell design and manufacturing. Journal of Power Sources, 114(1), 32-53. doi:10.1016/s0378-7753(02)00542-6 es_ES
dc.description.references De las Heras, A., Vivas, F. J., Segura, F., Redondo, M. J., & Andújar, J. M. (2018). Air-cooled fuel cells: Keys to design and build the oxidant/cooling system. Renewable Energy, 125, 1-20. doi:10.1016/j.renene.2018.02.077 es_ES
dc.description.references Kandlikar, S. G., & Lu, Z. (2009). Thermal management issues in a PEMFC stack – A brief review of current status. Applied Thermal Engineering, 29(7), 1276-1280. doi:10.1016/j.applthermaleng.2008.05.009 es_ES
dc.description.references Yan, Q., Toghiani, H., & Causey, H. (2006). Steady state and dynamic performance of proton exchange membrane fuel cells (PEMFCs) under various operating conditions and load changes. Journal of Power Sources, 161(1), 492-502. doi:10.1016/j.jpowsour.2006.03.077 es_ES
dc.description.references Maghanki, M. M., Ghobadian, B., Najafi, G., & Galogah, R. J. (2013). Micro combined heat and power (MCHP) technologies and applications. Renewable and Sustainable Energy Reviews, 28, 510-524. doi:10.1016/j.rser.2013.07.053 es_ES
dc.description.references Notter, D. A., Kouravelou, K., Karachalios, T., Daletou, M. K., & Haberland, N. T. (2015). Life cycle assessment of PEM FC applications: electric mobility and μ-CHP. Energy & Environmental Science, 8(7), 1969-1985. doi:10.1039/c5ee01082a es_ES
dc.description.references Martinez, S., Michaux, G., Salagnac, P., & Bouvier, J.-L. (2017). Micro-combined heat and power systems (micro-CHP) based on renewable energy sources. Energy Conversion and Management, 154, 262-285. doi:10.1016/j.enconman.2017.10.035 es_ES
dc.description.references Elmer, T., Worall, M., Wu, S., & Riffat, S. B. (2015). Fuel cell technology for domestic built environment applications: State of-the-art review. Renewable and Sustainable Energy Reviews, 42, 913-931. doi:10.1016/j.rser.2014.10.080 es_ES
dc.description.references Hawkes, A., Staffell, I., Brett, D., & Brandon, N. (2009). Fuel cells for micro-combined heat and power generation. Energy & Environmental Science, 2(7), 729. doi:10.1039/b902222h es_ES
dc.description.references Ellamla, H. R., Staffell, I., Bujlo, P., Pollet, B. G., & Pasupathi, S. (2015). Current status of fuel cell based combined heat and power systems for residential sector. Journal of Power Sources, 293, 312-328. doi:10.1016/j.jpowsour.2015.05.050 es_ES
dc.description.references Strahl, S., & Costa-Castelló, R. (2017). Temperature control of open-cathode PEM fuel cells. IFAC-PapersOnLine, 50(1), 11088-11093. doi:10.1016/j.ifacol.2017.08.2492 es_ES
dc.description.references Zhang, G., & Kandlikar, S. G. (2012). A critical review of cooling techniques in proton exchange membrane fuel cell stacks. International Journal of Hydrogen Energy, 37(3), 2412-2429. doi:10.1016/j.ijhydene.2011.11.010 es_ES
dc.description.references Navarro Gimenez, S., Herrero Dura, J. M., Blasco Ferragud, F. X., & Simarro Fernandez, R. (2019). Control-Oriented Modeling of the Cooling Process of a PEMFC-Based $\mu$ -CHP System. IEEE Access, 7, 95620-95642. doi:10.1109/access.2019.2928632 es_ES
dc.description.references Herrero, J. M., García-Nieto, S., Blasco, X., Romero-García, V., Sánchez-Pérez, J. V., & Garcia-Raffi, L. M. (2008). Optimization of sonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm. Structural and Multidisciplinary Optimization, 39(2), 203-215. doi:10.1007/s00158-008-0323-7 es_ES
dc.description.references Bristol, E. (1966). On a new measure of interaction for multivariable process control. IEEE Transactions on Automatic Control, 11(1), 133-134. doi:10.1109/tac.1966.1098266 es_ES
dc.description.references Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.010 es_ES
dc.description.references Schmittinger, W., & Vahidi, A. (2008). A review of the main parameters influencing long-term performance and durability of PEM fuel cells. Journal of Power Sources, 180(1), 1-14. doi:10.1016/j.jpowsour.2008.01.070 es_ES


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