Enabling EV Charging at varied locations using Smart Meters Paper No.: 2024-GI-08 Section Research Papers

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Ankith Rama
Dr. D. Hari Krishna
Shreya Gampa
Jhanavi Puli

Abstract

Electric vehicles primarily run on stored electrical energy, which needs to be replenished at regular intervals of time. Commercially, EVs are charged at charging stations, however they can be charged anywhere whether it be at home, public charging stations or even at workplaces. The commercial charging stations are highly costly and require huge setup. The main aim of this paper is to enable charging at any commercial and non-commercial establishment and optimize the cost of charging and grid usage by using an IOT (Internet of Things) device for bill generation and grid usage analysis.The charge enabling process is based on a QR based activation of the device. The user scans the QR code and registers his details, this enables the charge cycle. The bill is generated based on the start time and the end time of the charge cycle and the energy consumed in that cycle and a text message is sent to the consumer. The effect of charging load is also analyzed and hence an appropriate action is taken to optimize the peak load or demand.


Keywords: Internet of Things (IoT), Electric Vehicles (EVs), Smart Energymeter, QR-based activation

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How to Cite
Ankith Rama, Dr. D. Hari Krishna, Shreya Gampa, & Jhanavi Puli. (2024). Enabling EV Charging at varied locations using Smart Meters: Paper No.: 2024-GI-08. ARAI Journal of Mobility Technology, 4(3), 1249–1259. https://doi.org/10.37285/ajmt.4.3.8

References

  1. A. M. M. Foley, I. J. Winning, B. P. O Gallachoir, and M. Ieee, “Stateof-the-art in electric vehicle charging infrastructure,” in IEEE Vehicle Power and Propulsion Conference, 2010, pp. 1–6
  2. Marc-Olivier Metais, O. Jouini, Yannick Perez, Jaâfar Berrada, Emilia Suomalainen. Planning EV Charging Infrastructures: A Literature Review. 23rd EURO Working Group on Transportation Meeting, EWGT 2020, Sep 2020, Paphos, Greece. ffhal-03127577
  3. Sylvia Y. He, Yong Hong Kuo, Dan Wu, 2016, Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing, China
  4. Upchurch, C., & Kuby, M. (2010). Comparing the p-median and flow-refueling models for locating alternative-fuel stations. Journal of Transport Geography, 18(6), 750–758. https://doi.org/10.1016/j.jtrangeo.2010.06.015
  5. Xi, X., Sioshansi, R., & Marano, V. (2013). Simulation–optimization model for location of a public electric vehicle charging infrastructure. Transportation Research. Part D, Transport and Environment, 22, 60–69. https://doi.org/10.1016/j.trd.2013.02.014
  6. Sun, Z., Gao, W., Li, B., & Wang, L. (2020). Locating charging stations for electric vehicles. Transport Policy, 98, 48–54. https://doi.org/10.1016/j.tranpol.2018.07.009
  7. Andrews, M., Dogru, M.K., Hobby, J.D., & Tucci, G.H. (2012). Modeling and Optimization for Electric Vehicle Charging Infrastructure. Available: https://pomsmeetings.org/confpapers/043/043-0554.pdf
  8. J. Chynoweth, Ching-Yen Chung, C. Qiu, P. Chu, and R. Gadh, “Smart electric vehicle charging infrastructure overview,” in Innovative Smart Grid Technology (ISGT), 2014, pp. 1–5.
  9. L. K. Lam, K. T. Ko, H. Y. Tung, H. C. Tung, W. C. Lee, K. F. Tsang, and L. L. Lai, “Advanced Metering Infrastructure for Electric Vehicle Charging,” Smart Grid Renew. Energy, vol. 02, no. 04, pp. 312–323, 2011.
  10. M. Neaimeh, R. Wardle, A. M. Jenkins, J. Yi, G. Hill, P. F. Lyons, Y. Hübner, P. T. Blythe, and P. C. Taylor, “A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts,” Appl. Energy, vol. 157, pp. 688–698, Nov. 2015