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Energies 2017, 10(3), 377; doi:10.3390/en10030377

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

1
Department of Electrical and Electronics Engineering, SRM University, Chennai 603 203, India
2
Department of Electrical and Electronics Engineering, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
3
Department of Industrial Engineering, University of Salerno, Salerno 84084, Italy
4
Department of Electronics and Communication Engineering, SRM University, Chennai 603 203, India
5
Department of Water Resource Development and Management, Indian Institute of Technology, Roorkee 247 667, India
*
Authors to whom correspondence should be addressed.
Academic Editor: Chunhua Liu
Received: 7 November 2016 / Revised: 11 March 2017 / Accepted: 13 March 2017 / Published: 16 March 2017
(This article belongs to the Special Issue Innovative Methods for Smart Grids Planning and Management)
View Full-Text   |   Download PDF [5790 KB, uploaded 17 March 2017]   |  

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 user’s “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 the road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested. View Full-Text
Keywords: electric vehicle (EV); charging station (CS); state of charge (SOC); structured query language (SQL); personal home page (PHP) electric vehicle (EV); charging station (CS); state of charge (SOC); structured query language (SQL); personal home page (PHP)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

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.

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