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

• Main Authors: Nikolaos Milas, Dimitris Mourtzis, Emmanuel Tatakis
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Energies
• Subjects: analytic hierarchy process; AHP; charge scheduling; decision-making; electric vehicle; particle swarm optimisation
• Online Access: https://www.mdpi.com/1996-1073/13/22/6120

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.