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

Nikolaos Milas, Dimitris Mourtzis and Emmanuel Tatakis
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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

Energies, 2020, vol. 13, issue 22, 1-28

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.

Keywords: analytic hierarchy process; AHP; charge scheduling; decision-making; electric vehicle; particle swarm optimisation; PSO; genetic algorithm; GA; OPC–UA (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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