Predictive equivalent consumption minimization strategy for power-split hybrid electric vehicles using Monte Carlo algorithm

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dc.authoridCoskun, Serdar/0000-0002-7080-0340
dc.authoridYAZAR, OZAN/0000-0002-4593-0178
dc.contributor.authorGul, Merve Nur
dc.contributor.authorYazar, Ozan
dc.contributor.authorCoskun, Serdar
dc.contributor.authorZhang, Fengqi
dc.contributor.authorLi, Lin
dc.contributor.authorKaya, Irem Ersoz
dc.date.accessioned2025-03-17T12:25:26Z
dc.date.available2025-03-17T12:25:26Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description.abstractPurpose: The underlying research goal of this article is to put forward a reliable fuel saving performance based on the forecasted velocities of drive cycles for a power-split hybrid electric vehicle. Theory and Methods: The power distribution between energy sources is devised by utilizing the P-ECMS for the power-split hybrid electric vehicle using the uncertain drive cycle velocity estimation based on MC algorithm. Results: The effectiveness and accuracy of the method are evaluated under seven drive cycles. The MC provides good prediction results of the velocities. On the basis of it, the P-ECMS method decreases fuel consumption up to 6.01% under NEDC, up to 9.09% under WLTP, up to 6.33% under UDDS, up to 5.14% under HWFET, up to 1.96% under NYCC, up to 11.47% under LA-92, and up to 7.92% under ALL-CYC compared to a standard ECMS method. Conclusion: It is seen from the analysis results that battery SOC decreases slightly using the P-ECMS since the electric motor is actively used to meet power demand instead of the engine over the predicted speed profiles. In the end, the MC algorithm-based P-ECMS strategy can verify the optimal power distribution based on fuel-saving potentials as compared to the baseline ECMS strategy while keeping the battery SOC at a reasonable interval.
dc.description.sponsorshipTurkish Scientific and Technological Research Council [121E260]
dc.description.sponsorship~This work is supported by the Turkish Scientific and Technological Research Council with Project Number 121E260 under the grant name CAREER.
dc.identifier.doi10.17341/gazimmfd.1040940
dc.identifier.endpage1630
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85146682445
dc.identifier.scopusqualityQ2
dc.identifier.startpage1615
dc.identifier.trdizinid1159583
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.1040940
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1159583
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1684
dc.identifier.volume38
dc.identifier.wosWOS:000968663800025
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherGazi Univ, Fac Engineering Architecture
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectEquivalent consumption minimization
dc.subjectPredictive control
dc.subjectMonte Carlo algorithm
dc.subjectSpeed prediction
dc.subjectHybrid electric vehicles
dc.titlePredictive equivalent consumption minimization strategy for power-split hybrid electric vehicles using Monte Carlo algorithm
dc.typeArticle

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