Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/10/3/377 |
| _version_ | 1811307185276190720 |
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| author | Bharatiraja Chokkalingam Sanjeevikumar Padmanaban Pierluigi Siano Ramesh Krishnamoorthy Raghu Selvaraj |
| author_facet | Bharatiraja Chokkalingam Sanjeevikumar Padmanaban Pierluigi Siano Ramesh Krishnamoorthy Raghu Selvaraj |
| author_sort | Bharatiraja Chokkalingam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:58:59Z |
| format | Article |
| id | doaj.art-77bd472093c4492b9a9d06da65c1c574 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:58:59Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-77bd472093c4492b9a9d06da65c1c5742022-12-22T02:53:11ZengMDPI AGEnergies1996-10732017-03-0110337710.3390/en10030377en10030377Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy SystemsBharatiraja Chokkalingam0Sanjeevikumar Padmanaban1Pierluigi Siano2Ramesh Krishnamoorthy3Raghu Selvaraj4Department of Electrical and Electronics Engineering, SRM University, Chennai 603 203, IndiaDepartment of Electrical and Electronics Engineering, University of Johannesburg, Auckland Park, Johannesburg 2006, South AfricaDepartment of Industrial Engineering, University of Salerno, Salerno 84084, ItalyDepartment of Electronics and Communication Engineering, SRM University, Chennai 603 203, IndiaDepartment of Water Resource Development and Management, Indian Institute of Technology, Roorkee 247 667, IndiaThe 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.http://www.mdpi.com/1996-1073/10/3/377electric vehicle (EV)charging station (CS)state of charge (SOC)structured query language (SQL)personal home page (PHP) |
| spellingShingle | Bharatiraja Chokkalingam Sanjeevikumar Padmanaban Pierluigi Siano Ramesh Krishnamoorthy Raghu Selvaraj Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems Energies electric vehicle (EV) charging station (CS) state of charge (SOC) structured query language (SQL) personal home page (PHP) |
| title | Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems |
| title_full | Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems |
| title_fullStr | Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems |
| title_full_unstemmed | Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems |
| title_short | Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems |
| title_sort | real time forecasting of ev charging station scheduling for smart energy systems |
| topic | electric vehicle (EV) charging station (CS) state of charge (SOC) structured query language (SQL) personal home page (PHP) |
| url | http://www.mdpi.com/1996-1073/10/3/377 |
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