Author
Kaya, Kamil, Poyrazoğlu, Göktürk
Publication Date
2020
Publication Place
-
IEEE
Subject
E-mobility, Charging stations, Route tracker, Route forecasting, Machine learning
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-172817116-6
Record ID
605ea6e5-42e1-44cd-8370-9258ace7a1e0
Library Location
Electrical & Electronics Engineering
Date
2020
Sample Text
This 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.
DOI
10.1109/ICECCE49384.2020.9179482
