Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) parameters using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC converter for Improving the performance of proton-exchange membrane fuel cells

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dc.authorid0000-0003-3236-213Xen_US
dc.contributor.authorYakut, Yurdagül Benteşen
dc.date.accessioned2024-04-17T08:20:56Z
dc.date.available2024-04-17T08:20:56Z
dc.date.issued2024en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this article, the control of a DC/DC converter was carried out using the proposed methods of conventional PI, PSO-based PI, PSO-based FOPI, GA-based PI, and GA-based FOPI controllers in order to improve the performance of PEMFCs. Simulink models of a PEMFC model with two inputs—hydrogen consumption and oxygen air flow—and with controllers were developed. Then, the outputs of a system based on conventional PI were compared with the proposed methods. IAE, ISTE, and ITAE were employed as fitness functions in optimization algorithms such as PSO and GA. Fitness function value, maximum overshoot, and rising time were utilized as metrics to compare the performance of the methods. PI and FOPI parameters were optimized with the proposed methods and the results were compared with traditional PI in which the optimum parameters were determined by an empirical approach. This research study indicates that the proposed methods perform better than the conventional PI method. However, it becomes apparent that the GA-FOPI approach outperforms the others. The simulation result also shows that the PEMFC model with conventional PI and FOPI controllers in which the controller parameters are tuned using PSO and GA has an acceptable control performance.en_US
dc.identifier.citationYakut, Y. B. (2024). Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) parameters using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC converter for Improving the performance of proton-exchange membrane fuel cells. Energies, 17(4), 1-19.en_US
dc.identifier.doi10.3390/en17040890
dc.identifier.endpage19en_US
dc.identifier.issn1996-1073
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85185541043
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.mdpi.com/1996-1073/17/4/890
dc.identifier.urihttps://hdl.handle.net/11468/13899
dc.identifier.volume17en_US
dc.identifier.wosWOS:001172287700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorYakut, Yurdagül Benteşen
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofEnergies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGAen_US
dc.subjectImproving performanceen_US
dc.subjectOptimizationen_US
dc.subjectPEM fuel cellen_US
dc.subjectPI/FOPI controlleren_US
dc.subjectPSOen_US
dc.titleOptimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) parameters using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC converter for Improving the performance of proton-exchange membrane fuel cellsen_US
dc.titleOptimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) parameters using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC converter for Improving the performance of proton-exchange membrane fuel cells
dc.typeArticleen_US

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