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dc.contributor.authorGözüküçük, M. A.
dc.contributor.authorAkdoğan, Taylan
dc.contributor.authorHussain, Waqas
dc.contributor.authorTasooji, Tohid Kargar
dc.contributor.authorŞahin, Mert
dc.contributor.authorÇelik, M.
dc.contributor.authorUğurdağ, Hasan Fatih
dc.date.accessioned2020-04-16T11:00:32Z
dc.date.available2020-04-16T11:00:32Z
dc.date.issued2018
dc.identifier.isbn978-1-5386-7641-7
dc.identifier.urihttp://hdl.handle.net/10679/6515
dc.identifier.urihttps://ieeexplore.ieee.org/document/8751923
dc.description.abstractEnergy management algorithms play a critical role in improving the energy efficiency of modern electric vehicles. In order to be desirable for the customer, electric vehicles should be capable of long distance driving on a single battery charge with a range which must be comparable to the values of their conventional counterparts. To achieve this goal, both the use of large-capacity battery and the development of a custom energy management algorithm are necessary. Thus, one must solve equations of vehicle dynamics, which is a part of conventional methods used in generalized energy management problems. In this paper, a Monte Carlo method is proposed for probabilistic prediction of the optimum energy to attain a given route. The route in question is obtained from the Google Maps and includes locations and road topologies. First, optimum speed set-points are generated for each state of the journey, and this generated speed array is imported into the vehicle control system to generate the required torque for vehicle propulsion. Then, this process is repeated with a constant average speed for comparison purposes. The simulation results show that an electric vehicle gains significant energy efficiency over a Hardware in the Loop (HIL) emulation, when it is being controlled with the proposed speed set-points generated by the Monte Carlo method.en_US
dc.description.sponsorshipTÜBİTAK
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relationinfo:turkey/grantAgreement/TUBITAK/115E097
dc.relationinfo:turkey/grantAgreement/TUBITAK/115E122
dc.relationinfo:turkey/grantAgreement/TUBITAK/115E127
dc.relation.ispartof2018 6th International Conference on Control Engineering & Information Technology (CEIT)
dc.rightsrestrictedAccess
dc.titleDesign and simulation of an optimal energy management strategy for plug-In electric vehiclesen_US
dc.typeConference paperen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-0646-4637 & YÖK ID 144754) Akdoğan, Taylan
dc.contributor.authorID(ORCID 0000-0002-6256-0850 & YÖK ID 118293) Uğurdağ, Fatih
dc.contributor.ozuauthorAkdoğan, Taylan
dc.contributor.ozuauthorUğurdağ, Hasan Fatih
dc.identifier.wosWOS:000491282100178
dc.identifier.doi10.1109/CEIT.2018.8751923en_US
dc.subject.keywordsApplication softwareen_US
dc.subject.keywordsElectric vehiclesen_US
dc.subject.keywordsEmbedded softwareen_US
dc.subject.keywordsEnergy managementen_US
dc.subject.keywordsMonte Carlo methoden_US
dc.subject.keywordsTorque controlen_US
dc.subject.keywordsSpeed controlen_US
dc.identifier.scopusSCOPUS:2-s2.0-85069230937
dc.contributor.ozugradstudentHussain, Waqas
dc.contributor.ozugradstudentTasooji, Tohid Kargar
dc.contributor.ozugradstudentŞahin, Mert
dc.contributor.authorMale5
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff and Graduate Student


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