QoE-based assignment of EVs to charging stations in metropolitan environments

Aljaidi, M, Aslam, N, Kaiwartya, O ORCID: https://orcid.org/0000-0001-9669-8244, Chen, X, Sadiq, AS ORCID: https://orcid.org/0000-0002-5746-0257, Kumar, S and Alsarhan, A, 2024. QoE-based assignment of EVs to charging stations in metropolitan environments. IEEE Transactions on Intelligent Vehicles. ISSN 2379-8858

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Abstract

With the recent advances in battery technology enabling fast charging, public Charging Stations (CSs) are becoming a viable choice for Electric Vehicles (EVs). However , the distribution of EVs relies on strategic assignment of EVs to CSs. EVs drivers' Quality of Experience (QoE) is an significant impact factor that should be considered to find the optimal assignment of EVs to CSs. In this context, a novel framework to find the optimal assignment of EVs to CSs has been proposed based on optimization of QoE. Our proposed approach considers the travel time of EVs towards CSs taking into account the distance between EVs and CSs, the impact of congestion level on the roads resulted from the Internal Combustion Engine Vehicles (ICEVs) and EVs, queuing time at the CSs, and the time required to fully charge the EVs battery when connected to any charging slot at a CSs. The adjacency between the different zones in a city environment is also considered in order to minimize the potential number of CSs for each EVs. Specifically, the assignment problem is formulated as Mixed Integer Nonlinear Programming (MINLP), and a heuristic solution is developed using the Genetic Algorithm (GA) technique. The performance evaluation in realistic metropolitan environment attests the benefits of the proposed CSs assignment framework considering range of charging metrics.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Intelligent Vehicles
Creators:	Aljaidi, M., Aslam, N., Kaiwartya, O., Chen, X., Sadiq, A.S., Kumar, S. and Alsarhan, A.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	8 June 2024
ISSN:	2379-8858
Identifiers:	Number Type 10.1109/TIV.2024.3412372 DOI 1899987 Other
Rights:	© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	04 Jun 2024 07:52
Last Modified:	04 Jul 2024 15:50
URI:	https://irep.ntu.ac.uk/id/eprint/51518

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