Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13542
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLee, WC-
dc.creatorTsang, KF-
dc.creatorChi, HR-
dc.creatorHung, FH-
dc.creatorWu, CK-
dc.creatorChui, KT-
dc.creatorLau, WH-
dc.creatorLeung, YW-
dc.date.accessioned2015-07-13T10:35:10Z-
dc.date.available2015-07-13T10:35:10Z-
dc.identifier.urihttp://hdl.handle.net/10397/13542-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Lee, W.C.; Tsang, K.F.; Chi, H.R.; Hung, F.H.; Wu, C.K.; Chui, K.T.; Lau, W.H.; Leung, Y.W. A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm. Sensors 2015, 15, 1245-1251 is available at https://dx.doi.org/10.3390/s150101245en_US
dc.subjectAdaptive genetic algorithmen_US
dc.subjectFuel efficiency managementen_US
dc.subjectPHEVen_US
dc.titleA high fuel consumption efficiency management scheme for PHEVs using an adaptive genetic algorithmen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1245en_US
dc.identifier.epage1251en_US
dc.identifier.volume15en_US
dc.identifier.issue1en_US
dc.identifier.doi10.3390/s150101245en_US
dcterms.abstractA high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSensors, Jan. 2015, v. 15, no. 1, p. 1245-1251-
dcterms.isPartOfSensors-
dcterms.issued2015-
dc.identifier.scopus2-s2.0-84921284087-
dc.identifier.pmid25587974-
dc.identifier.eissn1424-8220en_US
dc.identifier.rosgroupid2014003234-
dc.description.ros2014-2015 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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