Intelligent Energy Management System for Hybrid Electric Vehicle Based on Optimization Wavelet Neural Network by PSO Algorithm

dc.authorscopusid58074007400
dc.authorscopusid55780618800
dc.authorscopusid33267553600
dc.contributor.authorKamoona,M.
dc.contributor.authorKivanc,O.C.
dc.contributor.authorAhmed,O.A.
dc.date.accessioned2024-05-25T12:18:25Z
dc.date.available2024-05-25T12:18:25Z
dc.date.issued2023
dc.departmentOkan Universityen_US
dc.department-tempKamoona M., Department of Power Electronics and Clean Energy Systems, Istanbul Okan University, Istanbul, 34959, Turkey; Kivanc O.C., Department of Electrical and Electronics Engineering, Istanbul Okan University, Istanbul, 34959, Turkey; Ahmed O.A., Department of Electrical Engineering, University of Technology, Baghdad, Iraqen_US
dc.description.abstractAn intelligent controller is proposed in this work for plug-in hydrogen Fuel Cell Hybrid Electric Vehicle (FCHEV) that integrated Fuel Cell (FC), Battery (BAT), and Ultracapacitor (UC) to reach a high dynamic response and keep high efficiency of energy storage resources. That controller manages the power flow of the system in an intelligent tracking to be optimal for FCHEV. Where the Fuzzy Logic Controller (FLC) and the Artificial Neural Networks (ANNs) are employed to meet the Energy Management System (EMS) requirements thereby efficient management has been developed for ensuring that three power sources are running at high efficiency with their mechanism performance and meets efficiently the load power demands and uses less hydrogen consumption. Moreover, the control strategy of the Wavelet Neural Network that is linked with the PI controller, named WNN-PI, and the parameters of WNN-PI are tuned by using the Particle Swarm Optimization (PSO) algorithm is also used. Considering the battery and ultra-capacitor state-of-charge (SOC) with power conditioning unit converters that control the FC and BAT output and provide the desired voltage of the DC bus as well as keep the voltage stable at 300 V. Analysis and evaluations of the system have been done by MATLAB/Simulink environment while various vehicle driving cycles have been applied by using ADVISOR Simulator. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.citationcount2
dc.identifier.doi10.1007/978-3-031-25344-7_51
dc.identifier.endpage573en_US
dc.identifier.isbn978-303125343-0
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85148016431
dc.identifier.scopusqualityQ4
dc.identifier.startpage558en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-25344-7_51
dc.identifier.urihttps://hdl.handle.net/20.500.14517/1706
dc.identifier.volume624 LNNSen_US
dc.institutionauthorKıvanç, Ömer Cihan
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systems -- 12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022 -- 26 August 2022 through 27 August 2022 -- Virtual, Online -- 289999en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.scopus.citedbyCount4
dc.subjectArtificial Neural Networksen_US
dc.subjectEMSen_US
dc.subjectFuzzy Logic Controlleren_US
dc.subjectPSO algorithmen_US
dc.subjectWavelet Neural Networken_US
dc.titleIntelligent Energy Management System for Hybrid Electric Vehicle Based on Optimization Wavelet Neural Network by PSO Algorithmen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
relation.isAuthorOfPublicationa8a28b97-f9e7-4486-8767-ddba23bc6fee
relation.isAuthorOfPublication.latestForDiscoverya8a28b97-f9e7-4486-8767-ddba23bc6fee

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