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dc.contributor.authorKaya, Kamil
dc.contributor.authorPoyrazoğlu, Göktürk
dc.date.accessioned2021-06-15T12:49:40Z
dc.date.available2021-06-15T12:49:40Z
dc.date.issued2020
dc.identifier.isbn978-172817116-6
dc.identifier.urihttp://hdl.handle.net/10679/7437
dc.identifier.urihttps://ieeexplore.ieee.org/document/9179482
dc.description.abstractThis study reports a new e-mobility platform to construct effective usage of charging points by electric vehicle users to eliminate long charge durations. The e-mobility platform includes such subsystems as smartphones, databases, and IoT. A smartphone is used by the user to connect with an electric vehicle via Bluetooth, and the phone makes bidirectional communication with the database system by sending a car's location, charge level, and reservation request information and taking charge station status and most suitable station proposal. Also, the platform continuously checks the car's charge level and when the level falls in a critical range, automatically suggests the user navigate the car to the nearest charge station. Moreover, our platform is also designed for route forecasting concerning driver's past travel routines. In this forecasting module, the platform also offers the most suitable charge stations by controlling the charge level of the car, the density of the charging station, duration of total charging.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
dc.rightsrestrictedAccess
dc.titleA platform for personal e-mobility with route forecastingen_US
dc.typeConference paperen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-8503-1767 & YÖK ID 280588) Poyrazoğlu, Göktürk
dc.contributor.ozuauthorPoyrazoğlu, Göktürk
dc.identifier.doi10.1109/ICECCE49384.2020.9179482en_US
dc.subject.keywordsE-mobilityen_US
dc.subject.keywordsCharging stationsen_US
dc.subject.keywordsRoute trackeren_US
dc.subject.keywordsRoute forecastingen_US
dc.subject.keywordsMachine learningen_US
dc.identifier.scopusSCOPUS:2-s2.0-85091893078
dc.contributor.ozugradstudentKaya, Kamil
dc.contributor.authorMale2
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff and Graduate Student


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