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A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver

Author

Listed:
  • Nikolaos Milas

    (Laboratory of Electromechanical Energy Conversion, Department of Electrical and Computer Engineering, University of Patras, 26504 Rion-Patras, Greece)

  • Dimitris Mourtzis

    (Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Rion-Patras, Greece)

  • Emmanuel Tatakis

    (Laboratory of Electromechanical Energy Conversion, Department of Electrical and Computer Engineering, University of Patras, 26504 Rion-Patras, Greece)

Abstract

During the last decade, the technologies related to electric vehicles (EVs) have captured both scientific and industrial interest. Specifically, the subject of the smart charging of EVs has gained significant attention, as it facilitates the managed charging of EVs to reduce disturbances to the power grid. Despite the presence of an extended literature on the topic, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The framework consists of a heuristic algorithm that facilitates the charge scheduling within a charging station (CS), and the analytic hierarchy process (AHP) to support the driver of the EV selecting the most appropriate charging station based on their needs of transportation and personal preferences. The communications are facilitated by the Open Platform Communications–Unified Architecture (OPC–UA) standard. For the selection of the scheduling algorithm, the genetic algorithm and particle swarm optimisation have been evaluated, where the latter had better performance. The performance of the charge scheduling is evaluated, in various charging tasks, compared to the exhaustive search for small problems.

Suggested Citation

  • Nikolaos Milas & Dimitris Mourtzis & Emmanuel Tatakis, 2020. "A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver," Energies, MDPI, vol. 13(22), pages 1-28, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6120-:d:449178
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    References listed on IDEAS

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    Cited by:

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    2. Vitor Monteiro & Joao L. Afonso, 2022. "Power Electronics Technologies and Applicationsfor EV Battery Charging Systems," Energies, MDPI, vol. 15(3), pages 1-4, January.
    3. Algafri, Mohammed & Alghazi, Anas & Almoghathawi, Yasser & Saleh, Haitham & Al-Shareef, Khaled, 2024. "Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming," Applied Energy, Elsevier, vol. 372(C).
    4. Adam Kaizer & Tomasz Neumann, 2021. "The Model of Support for the Decision-Making Process, While Organizing Dredging Works in the Ports," Energies, MDPI, vol. 14(9), pages 1-15, May.
    5. 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.

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