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DEVELOPMENT OF EQUIVALENT FUEL CONSUMPTION MINIMIZATION STRATEGY FOR HYBRID ELECTRIC VEHICLES

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dc.contributor.authorPark, J.-
dc.contributor.authorPark, Jahng Hyon-
dc.date.accessioned2022-07-16T14:23:52Z-
dc.date.available2022-07-16T14:23:52Z-
dc.date.issued2012-08-
dc.identifier.issn1229-9138-
dc.identifier.issn1976-3832-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/164987-
dc.description.abstractPower distribution between an internal combustion engine and electric motors is one of main features of hybrid electric vehicles that improves their fuel economy. An equivalent fuel consumption minimization strategy can instantaneously identify the optimal power distribution by converting the battery power into the equivalent fuel power and minimizing the overall fuel consumption. To guarantee the effectiveness of the strategy, it is essential to find the proper value of the conversion factor used to obtain the equivalent fuel power. However, finding the proper value is not a straightforward process because it is necessary to consider the overall power conversion efficiencies and battery charge sustaining strategy for the target driving cycle in advance. In this study, a model-based parameter optimization method is introduced to find the optimal conversion factor. A hybrid electric vehicle simulation model capable of estimating fuel consumption was developed, and the optimal conversion factor was discovered using a genetic algorithm that evaluates its population members using the simulation model. A series of simulations and vehicle tests was conducted to verify the effectiveness of the optimized strategy, and the results show a distinct improvement in fuel economy.-
dc.format.extent9-
dc.language영어-
dc.language.isoENG-
dc.publisher한국자동차공학회-
dc.titleDEVELOPMENT OF EQUIVALENT FUEL CONSUMPTION MINIMIZATION STRATEGY FOR HYBRID ELECTRIC VEHICLES-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.1007/s12239-012-0084-6-
dc.identifier.scopusid2-s2.0-84864440585-
dc.identifier.wosid000306880000017-
dc.identifier.bibliographicCitationInternational Journal of Automotive Technology, v.13, no.5, pp 835 - 843-
dc.citation.titleInternational Journal of Automotive Technology-
dc.citation.volume13-
dc.citation.number5-
dc.citation.startPage835-
dc.citation.endPage843-
dc.type.docTypeArticle-
dc.identifier.kciidART001683257-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaTransportation-
dc.relation.journalWebOfScienceCategoryEngineering, Mechanical-
dc.relation.journalWebOfScienceCategoryTransportation Science & Technology-
dc.subject.keywordPlusENERGY MANAGEMENT-
dc.subject.keywordAuthorHybrid electric vehicle-
dc.subject.keywordAuthorSupervisory control-
dc.subject.keywordAuthorEquivalent factor-
dc.subject.keywordAuthorParameter optimization-
dc.subject.keywordAuthorGenetic algorithm-
dc.identifier.urlhttps://link.springer.com/article/10.1007%2Fs12239-012-0084-6-
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서울 공과대학 > 서울 미래자동차공학과 > 1. Journal Articles

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