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

Author

Listed:
  • Bharatiraja Chokkalingam

    (Department of Electrical and Electronics Engineering, SRM University, Chennai 603 203, India)

  • Sanjeevikumar Padmanaban

    (Department of Electrical and Electronics Engineering, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa)

  • Pierluigi Siano

    (Department of Industrial Engineering, University of Salerno, Salerno 84084, Italy)

  • Ramesh Krishnamoorthy

    (Department of Electronics and Communication Engineering, SRM University, Chennai 603 203, India)

  • Raghu Selvaraj

    (Department of Water Resource Development and Management, Indian Institute of Technology, Roorkee 247 667, India)

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.

Suggested Citation

  • Bharatiraja Chokkalingam & Sanjeevikumar Padmanaban & Pierluigi Siano & Ramesh Krishnamoorthy & Raghu Selvaraj, 2017. "Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:377-:d:93207
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    References listed on IDEAS

    as
    1. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2017. "On-line battery state-of-charge estimation based on an integrated estimator," Applied Energy, Elsevier, vol. 185(P2), pages 2026-2032.
    2. Su, Wencong & Chow, Mo-Yuen, 2012. "Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck," Applied Energy, Elsevier, vol. 96(C), pages 171-182.
    3. Caiping Zhang & Jiuchun Jiang & Linjing Zhang & Sijia Liu & Leyi Wang & Poh Chiang Loh, 2016. "A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery," Energies, MDPI, vol. 9(11), pages 1-16, November.
    4. Zheng Chen & Xiaoyu Li & Jiangwei Shen & Wensheng Yan & Renxin Xiao, 2016. "A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-15, September.
    5. Ming-Hui Chang & Han-Pang Huang & Shu-Wei Chang, 2013. "A New State of Charge Estimation Method for LiFePO 4 Battery Packs Used in Robots," Energies, MDPI, vol. 6(4), pages 1-24, April.
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    Cited by:

    1. Rosario Miceli & Fabio Viola, 2017. "Designing a Sustainable University Recharge Area for Electric Vehicles: Technical and Economic Analysis," Energies, MDPI, vol. 10(10), pages 1-24, October.
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    3. Kang Miao Tan & Vigna K. Ramachandaramurthy & Jia Ying Yong & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg, 2017. "Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling," Energies, MDPI, vol. 10(11), pages 1-21, November.
    4. Héricles Eduardo Oliveira Farias & Camilo Alberto Sepulveda Rangel & Leonardo Weber Stringini & Luciane Neves Canha & Daniel Pegoraro Bertineti & Wagner da Silva Brignol & Zeno Iensen Nadal, 2021. "Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(5), pages 1-27, March.
    5. Ramji Tiwari & Sanjeevikumar Padmanaban & Ramesh Babu Neelakandan, 2017. "Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System," Energies, MDPI, vol. 10(10), pages 1-17, September.
    6. Cuiyu Kong & Raka Jovanovic & Islam Safak Bayram & Michael Devetsikiotis, 2017. "A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations," Energies, MDPI, vol. 10(5), pages 1-20, May.
    7. Gheorghe Badea & Raluca-Andreea Felseghi & Mihai Varlam & Constantin Filote & Mihai Culcer & Mariana Iliescu & Maria Simona Răboacă, 2018. "Design and Simulation of Romanian Solar Energy Charging Station for Electric Vehicles," Energies, MDPI, vol. 12(1), pages 1-16, December.
    8. Dominic A. Savio & Vimala A. Juliet & Bharatiraja Chokkalingam & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Frede Blaabjerg, 2019. "Photovoltaic Integrated Hybrid Microgrid Structured Electric Vehicle Charging Station and Its Energy Management Approach," Energies, MDPI, vol. 12(1), pages 1-28, January.
    9. Bačeković, Ivan & Østergaard, Poul Alberg, 2018. "A smart energy system approach vs a non-integrated renewable energy system approach to designing a future energy system in Zagreb," Energy, Elsevier, vol. 155(C), pages 824-837.
    10. Hyukjoon Lee & Dongjin Ji & Dong-Ho Cho, 2019. "Optimal Design of Wireless Charging Electric Bus System Based on Reinforcement Learning," Energies, MDPI, vol. 12(7), pages 1-20, March.
    11. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.

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