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Recent Advancement in Electric Vehicles
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Recent Advancement in Electric Vehicles

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: 21 August 2024 | Viewed by 3826

Special Issue Editors

Dr. Irfan Ullah

E-Mail Website
Guest Editor
Transportation Engineering College, Dalian Maritime University, Dalian, China
Interests: transport modeling and simulation; sustainable cities; sharing mobility (car sharing, ride sharing, customized bus, electric vehicles, autonomous vehicles); traffic safety; traffic engineering; travelers’ behavior; intelligent transportation systems; logistics and supply chain management; operational research
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Zahid

E-Mail Website
Guest Editor
Department of Civil, Geological and Mining Engineering, École Polytechnique de Montréal, Montreal, QC H3C 3A7, Canada
Interests: Traffic Engineering; Road Safety; ITS; Transportation Planning; Accident Analysis; Travel Behavior; Spatial Analysis; Renewable Energy and Environment Protection
Dr. Arshad Jamal

E-Mail Website
Guest Editor
Traffic and Transportation Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
Interests: road safety; accident analysis; traffic simulation; transportation planning shared mobility; traffic flow and operations; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) are the most essential components of smart transportation systems. Limited driving range, prolonged charging times, and inadequate charging infrastructure are among the few key barriers to EV adoption. Accurate energy consumption prediction under real-world driving conditions is essential for alleviating the ‘range anxiety’ that can support optimal battery sizing, energy-efficient route planning, and charging infrastructure deployment and operation. In addition, improper layout of charging facilities and illogical charging arrangements cause unexpected queuing of EVs for some facilities, while for others, it means they remain unvisited. To tackle these issues, it is necessary to accurately predict EVs’ driving range and charging duration time, which can assist drivers in effectively planning their trips, thereby alleviating range anxiety enroute. Moreover, suitable planning and deployment for EV charging stations can help towards travel arrangements.

This Special Issue is focused on recent advances in EVs, and includes but is not limited to the following topics.

  • Modeling and optimization of EVs
  • Energy consumption
  • Driving range
  • Charging duration time
  • Charging behavior
  • Charging demand
  • Advanced charging technologies
  • Deployment of charging stations

Dr. Irfan Ullah
Dr. Muhammad Zahid
Dr. Arshad Jamal
Guest 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 special issue 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.

Keywords

  • modeling and optimization of EVs
  • energy consumption
  • driving range
  • charging duration time
  • charging behavior
  • charging demand
  • advanced charging technologies
  • deployment of charging stations

Published Papers (2 papers)

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Research

18 pages, 1057 KiB  
Article
Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach
by Imen Azzouz and Wiem Fekih Hassen
Energies 2023, 16(24), 8102; https://doi.org/10.3390/en16248102 - 16 Dec 2023
Viewed by 991
Abstract
The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the [...] Read more.
The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min. Full article
(This article belongs to the Special Issue Recent Advancement in Electric Vehicles)
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24 pages, 13940 KiB  
Article
Employing Bibliometric Analysis to Identify the Current State of the Art and Future Prospects of Electric Vehicles
by Irfan Ullah, Muhammad Safdar, Jianfeng Zheng, Alessandro Severino and Arshad Jamal
Energies 2023, 16(5), 2344; https://doi.org/10.3390/en16052344 - 28 Feb 2023
Cited by 10 | Viewed by 2138
Abstract
Electric vehicles (EVs) are rapidly becoming a sustainable and viable mode of future transportation due to their multitude of advantages, such as reduced CO2 emissions, local air pollutants, and vehicular noise. This study aims to identify and analyze the scientific literature using [...] Read more.
Electric vehicles (EVs) are rapidly becoming a sustainable and viable mode of future transportation due to their multitude of advantages, such as reduced CO2 emissions, local air pollutants, and vehicular noise. This study aims to identify and analyze the scientific literature using bibliometric analysis to determine the main topics of authors, their sources, and the most-cited articles, countries, journals, and institutes in the literature on EVs. This bibliometric analysis included scientific work that was published from 2011 to 2022 to provide a thorough analysis of EVs, which will assist researchers and policymakers in understanding the most current global EV advancements. This analysis extracted all bibliometric information about EVs from the Scopus database, collecting 17,150 articles published between 2011 and 2022. The data were sorted for analysis by publication year, document type, author, institute, country, cited author, keyword, and keyword co-occurrence of the EVs. The VOSviewer software was employed to examine the sorted data due to its excellent analysis and visualization capabilities. We used VOSviewer to graphically represent the density, co-occurrence, trends, and linkage of the aforementioned data comprehensibly. The publishing patterns of EVs indicate that the research field is evolving, with a yearly increase in the number of publications. The analysis showed that China, the United States, and the United Kingdom are leading in EV research and large-scale applications. Furthermore, China is the leading country in terms of research institutions and authors involved in EVs. The journal Energies is the most prominent publication periodical. Keyword analysis revealed that during the past decade, EV research has concentrated on battery-management systems, energy storage, charging infrastructure, environmental concerns, etc. The bibliometric study offered pertinent details on the main themes explored concerning EVs and current technological developments. Full article
(This article belongs to the Special Issue Recent Advancement in Electric Vehicles)
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