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F-DCS: FMI-Based Distributed CPS Simulation Framework with a Redundancy Reduction Algorithm

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dc.contributor.authorHong, Seokjoon-
dc.contributor.authorLim, Ducsun-
dc.contributor.authorJoe, Inwhee-
dc.contributor.authorKim, WonTae-
dc.date.accessioned2022-07-08T16:03:50Z-
dc.date.available2022-07-08T16:03:50Z-
dc.date.created2021-05-12-
dc.date.issued2020-01-
dc.identifier.issn1424-3210-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146341-
dc.description.abstractA cyber physical system (CPS) is a distributed control system in which the cyber part and physical part are tightly interconnected. A representative CPS is an electric vehicle (EV) composed of a complex system and information and communication technology (ICT), preliminary verified through simulations for performance prediction and a quantitative analysis is essential because an EV comprises a complex CPS. This paper proposes an FMI-based distributed CPS simulation framework (F-DCS) adopting a redundancy reduction algorithm (RRA) for the validation of EV simulation. Furthermore, the proposed algorithm was enhanced to ensure an efficient simulation time and accuracy by predicting and reducing repetition patterns involved during the simulation progress through advances in the distributed CPS simulation. The proposed RRA improves the simulation speed and efficiency by avoiding the repeated portions of a given driving cycle while still maintaining accuracy. To evaluate the performance of the proposed F-DCS, an EV model was simulated by adopting the RRA. The results confirm that the F-DCS with RRA efficiently reduced the simulation time (over 30%) while maintaining a conventional accuracy. Furthermore, the proposed F-DCS was applied to the RRA, which provided results reflecting real-time sensor information.-
dc.language영어-
dc.language.isoen-
dc.publisherMDPI-
dc.titleF-DCS: FMI-Based Distributed CPS Simulation Framework with a Redundancy Reduction Algorithm-
dc.typeArticle-
dc.contributor.affiliatedAuthorJoe, Inwhee-
dc.identifier.doi10.3390/s20010252-
dc.identifier.scopusid2-s2.0-85077639953-
dc.identifier.wosid000510493100252-
dc.identifier.bibliographicCitationSENSORS, v.20, no.1, pp.1 - 27-
dc.relation.isPartOfSENSORS-
dc.citation.titleSENSORS-
dc.citation.volume20-
dc.citation.number1-
dc.citation.startPage1-
dc.citation.endPage27-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaInstruments & Instrumentation-
dc.relation.journalWebOfScienceCategoryChemistry, Analytical-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryInstruments & Instrumentation-
dc.subject.keywordPlusSYSTEM-
dc.subject.keywordPlusMANAGEMENT-
dc.subject.keywordPlusVEHICLES-
dc.subject.keywordPlusDESIGN-
dc.subject.keywordAuthorcyber-physical system-
dc.subject.keywordAuthorelectric vehicle-
dc.subject.keywordAuthordistributed co-simulation-
dc.subject.keywordAuthorfunctional mock-up interface-
dc.subject.keywordAuthorzero crossing-
dc.subject.keywordAuthordriving cycle-
dc.identifier.urlhttps://www.mdpi.com/1424-8220/20/1/252-
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