Preprint Article Version 1 This version is not peer-reviewed

Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

Version 1 : Received: 15 March 2017 / Approved: 16 March 2017 / Online: 16 March 2017 (06:36:11 CET)

A peer-reviewed article of this Preprint also exists.

Chokkalingam, B.; Padmanaban, S.; Siano, P.; Krishnamoorthy, R.; Selvaraj, R. Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems. Energies 2017, 10, 377. Chokkalingam, B.; Padmanaban, S.; Siano, P.; Krishnamoorthy, R.; Selvaraj, R. Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems. Energies 2017, 10, 377.

Journal reference: Energies 2017, 10, 377
DOI: 10.3390/en10030377

Abstract

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

Subject Areas

electric vehicle (EV); charging station (CS); state of charge (SOC); structured query language (SQL); personal home page (PHP)

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