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

被引：36

作者：

Chokkalingam, Bharatiraja ^{[1
]}

Padmanaban, Sanjeevikumar ^{[2
]}

Siano, Pierluigi ^{[3
]}

Krishnamoorthy, Ramesh ^{[4
]}

Selvaraj, Raghu ^{[5
]}

机构：

[1] SRM Univ, Dept Elect & Elect Engn, Madras 603203, Tamil Nadu, India

[2] Univ Johannesburg, Dept Elect & Elect Engn, ZA-2006 Johannesburg, South Africa

[3] Univ Salerno, Dept Ind Engn, I-84084 Salerno, Italy

[4] SRM Univ, Dept Elect & Commun Engn, Madras 603203, Tamil Nadu, India

[5] Indian Inst Technol, Dept Water Resource Dev & Management, Roorkee 247667, Uttar Pradesh, India

来源：

ENERGIES | 2017年 / 10卷 / 03期

关键词：

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

D O I：

10.3390/en10030377

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页数：16

共 24 条

[1]

Chang W. -Y, 2013, APPL MATH, V2013, DOI [10.1155/2013/953792, DOI 10.1155/2013/953792]

[2] A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles [J].

Chen, Zheng ;

Li, Xiaoyu ;

Shen, Jiangwei ;

Yan, Wensheng ;

Xiao, Renxin .

ENERGIES, 2016, 9 (09)

[3] Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network [J].

Chen, Zhijia ;

Zhu, Yuanchang ;

Di, Yanqiang ;

Feng, Shaochong .

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2015, 2015

[4] Battery-Management System (BMS) and SOC Development for Electrical Vehicles [J].

Cheng, K. W. E. ;

Divakar, B. P. ;

Wu, Hongjie ;

Ding, Kai ;

Ho, Ho Fai .

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2011, 60 (01) :76-88

[5]

Chynoweth J., 2014, INNOVATIVE SMART GRI, P1

[6]

Ciaravolo R. J., 2010, P 45 INT U POW ENG C, P1

[7] Rapid-Charge Electric-Vehicle Stations [J].

Etezadi-Amoli, Mehdi ;

Choma, Kent ;

Stefani, Jason .

IEEE TRANSACTIONS ON POWER DELIVERY, 2010, 25 (03) :1883-1887

[8] Managing the Charging of Electrical Vehicles: Impacts on the Electrical Grid and on the Environment [J].

Faria, Ricardo ;

Moura, Pedro ;

Delgado, Joaquim ;

de Almeida, Anibal T. .

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2014, 6 (03) :54-65

[9]

FOLEY A M, 2010, 2010 IEEE VEH POW PR, P1

[10]

Haghbin S., 2010, The XIX International Conference on Electrical Machines - ICEM 2010, P1, DOI [10.1109/ICELMACH.2010.5608152, DOI 10.1109/ICELMACH.2010.5608152]

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