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Electric Mobility and Smart Cities

A topical collection in Energies (ISSN 1996-1073). This collection belongs to the section "E: Electric Vehicles".

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Collection Editor
Algoritmi Research Centre, Department of Industrial Electronics, University of Minho, 4800-058 Guimarães, Portugal
Interests: power electronics converters; electric mobility; renewable energy sources; digital control techniques; smart grids
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Collection Editor
Department of Industrial Electronics, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal
Interests: power electronics; power quality; active power filters; renewable energy; energy efficiency; electric vehicles; energy storage systems; battery charging systems; smart grids; smart cities; smart homes; technologies for innovative railway systems
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Electrical power systems are facing a revolutionary shift to a smart grid paradigm aimed at sustainability. To achieve such an ambitious objective, a set of pertinent technologies have already been identified and are ongoing, including the various trends of electric mobility offering disruptive operation modes, decentralized renewable energy production, and energy storage technologies. These technologies can be viewed from a holistic perspective of smart grids, but other scenarios are also gaining increased attention, such as the case of a cooperative and dynamic integration of these technologies in smart cities. In fact, cities are responsible for around 80% of the world's energy consumption and 75% of all carbon emissions, which highlights the importance of focusing on ushering smart cities toward sustainable electrical power systems. Therefore, the importance of ensuring a strong synergism between electric mobility and smart cities and maintaining a close connection with energy production from renewables, together with energy storage systems, is recognized. The central theme of this Collection is “Electric Mobility and Smart Cities”, for which we are accepting submissions of original papers, including review papers, from researchers, Ph.D. students, and professional communities. The topics of interestinclude, but are not limited to:

  • Life-cycle assessment of electric vehicles;
  • Economic analysis of electric mobility;
  • Environmental impact of electric mobility;
  • End-of-life assessment of electric mobility;
  • Charging infrastructures and operation modes;
  • Electric mobility in distribution power grids and smart grids;
  • Power electronics for electric mobility;
  • Renewable energy technologies integrated with electric mobility;
  • Energy storage technologies integrated with electric mobility;
  • Electric mobility and power quality;
  • Electric mobility as support for smart cities;
  • Demand response and optimized energy management.

Dr. Vítor Monteiro
Dr. Joao L. Afonso
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)


27 pages, 4254 KiB  
Electric Vehicle Routing Problem with an Enhanced Vehicle Dispatching Approach Considering Real-Life Data
by Meryem Abid, Mohamed Tabaa and Hanaa Hachimi
Energies 2024, 17(7), 1596; https://doi.org/10.3390/en17071596 - 27 Mar 2024
Cited by 1 | Viewed by 652
Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging [...] Read more.
Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging cost is less than the cost of fuel, this charge time adds to the overall travel time and may overlap with customers’ time windows. All these factors increased the computational time exponentially and resulted in the need to overlook some constraints such as traffic congestion, road conditions, weather impact on energy consumption, and driving style, to name a few, in order to speed up execution time. While this method proved effective in accelerating the process of the EVRP, it did, however, render the approach unrealistic, as it steered far from real-life settings and made the approach unpredictable when facing dynamic and changing parameters. In this paper, we try to remedy this issue by proposing an approach in which we try to replicate real-life parameters such as heterogenous fleets, energy consumption, and infrastructure data. The objective of our approach was to minimize the total travel time, travel distance, energy consumed, and the number of vehicles deployed. To solve this problem, we propose a three-stages approach, in which the first stage consists of a newly developed dispatching approach where customers are assigned to vehicles. The second stage uses the genetic algorithm to find a set of optimal paths, and, finally, in the third stage, charging stations are inserted in the selected paths. Upon testing our approach on Solomon’s instances, our approach proved effective in finding optimal solutions in a reasonable time for five- to fifteen-customer datasets. However, when trying to solve larger datasets, the approach was slowed down by the extreme number of constraints it had to satisfy. Full article
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