Design and simulation of an optimal energy management strategy for plug-In electric vehicles
dc.contributor.author | Gözüküçük, M. A. | |
dc.contributor.author | Akdoğan, Taylan | |
dc.contributor.author | Hussain, Waqas | |
dc.contributor.author | Tasooji, Tohid Kargar | |
dc.contributor.author | Şahin, Mert | |
dc.contributor.author | Çelik, M. | |
dc.contributor.author | Uğurdağ, Hasan Fatih | |
dc.date.accessioned | 2020-04-16T11:00:32Z | |
dc.date.available | 2020-04-16T11:00:32Z | |
dc.date.issued | 2018 | |
dc.identifier.isbn | 978-1-5386-7641-7 | |
dc.identifier.uri | http://hdl.handle.net/10679/6515 | |
dc.identifier.uri | https://ieeexplore.ieee.org/document/8751923 | |
dc.description.abstract | Energy 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.sponsorship | TÜBİTAK | |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation | info:turkey/grantAgreement/TUBITAK/115E097 | |
dc.relation | info:turkey/grantAgreement/TUBITAK/115E122 | |
dc.relation | info:turkey/grantAgreement/TUBITAK/115E127 | |
dc.relation.ispartof | 2018 6th International Conference on Control Engineering & Information Technology (CEIT) | |
dc.rights | restrictedAccess | |
dc.title | Design and simulation of an optimal energy management strategy for plug-In electric vehicles | en_US |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin 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.ozuauthor | Akdoğan, Taylan | |
dc.contributor.ozuauthor | Uğurdağ, Hasan Fatih | |
dc.identifier.wos | WOS:000491282100178 | |
dc.identifier.doi | 10.1109/CEIT.2018.8751923 | en_US |
dc.subject.keywords | Application software | en_US |
dc.subject.keywords | Electric vehicles | en_US |
dc.subject.keywords | Embedded software | en_US |
dc.subject.keywords | Energy management | en_US |
dc.subject.keywords | Monte Carlo method | en_US |
dc.subject.keywords | Torque control | en_US |
dc.subject.keywords | Speed control | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85069230937 | |
dc.contributor.ozugradstudent | Hussain, Waqas | |
dc.contributor.ozugradstudent | Tasooji, Tohid Kargar | |
dc.contributor.ozugradstudent | Şahin, Mert | |
dc.contributor.authorMale | 5 | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff and Graduate Student |
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