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ISA Trans. 2019 Aug 6. pii: S0019-0578(19)30347-7. doi: 10.1016/j.isatra.2019.08.011. [Epub ahead of print]

Internet of Things based real-time electric vehicle load forecasting and charging station recommendation.

Author information

1
SRM Institute of Science and Technology, Chennai 603-203, India.
2
Electrical Engineering Department, College of Engineering at Wadi Addawaser, 11991, Prince Sattam bin Abdulaziz University, Saudi Arabia; Electrical Engineering Department, Aswan faculty of Engineering, 81542, Aswan University, Egypt.
3
15th of May Higher Institute of Engineering, Mathematical and Physical Sciences, Helwan, Cairo, Egypt. Electronic address: engyshady@ieee.org.

Abstract

Electric vehicles (EVs) are emerging as a favorable strategy to meet the increasing environmental concerns and energy insufficiency, and this trend is expected to grow in the near future. However, the inadequate charging infrastructure is becoming a major barrier to the wide acceptance of EVs. Deployment of this infrastructure is expected to maximize the adoption of EVs to facilitate users' range anxiety. Therefore, connectivity between the charging stations (CS) is mandatory. Understanding the real-time status of CSs can provide valuable information to users such as availability of charging provisions, reserves and the time to reach the CS. The intent of this paper is to provide a better EV charging system by utilizing the advantages of the Internet of Things (IoT) technology. The IoT paradigm offers the present facilities a real-time interactional view of the physical world by a variety of sensors and broadcasting tools. This research article proposes a real-time server-based forecasting application: i) to provide scheduling management to avoid waiting time; and ii) to provide a real-time CS recommendation for EVs with an economic cost and reduced charging time. In addition, the proposed scheme avoids third-party intervention and protects EV user privacy and complex information exchange between the user and CS. The end users can easily use the CS based on their requirements. This synergetic application is built up through the PHP programming language in the Linux UBUNTU 16.04 LTS operating system, and all relevant information is processed and managed through Cloud Structured Query Language (CSQL) from a Google cloud platform. The effectiveness of this application is also validated through a low-cost test system using LTC 4150, ESP 8266 Wi-Fi module and Arduino.

KEYWORDS:

Charging stations; Cloud storage; EV charging; Forecasting application; Internet of Things; Renewable energy resources

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