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Sustainability
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Electric Vehicles: Production, Charging Stations, and Optimal Use
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Electric Vehicles: Production, Charging Stations, and Optimal Use

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (1 February 2024) | Viewed by 3699

Special Issue Editors

Dr. Amin Mahmoudi

E-Mail Website
Guest Editor
College of Science and Engineering, Flinders University, Adelaide, Australia
Interests: electrical machines and drives; energy conversion and renewable energy systems, energy storage; electric vehicle
Special Issues, Collections and Topics in MDPI journals
Dr. Solmaz Kahourzade

E-Mail Website
Guest Editor
SREM, University of South Australia, Adelaide 5042, Australia
Interests: electrical machines and drives; hybrid power networks; renewable energy systems; transmission and distribution networks
Special Issues, Collections and Topics in MDPI journals
Dr. Amirmehdi Yazdani

E-Mail Website
Guest Editor
Discipline of Engineering and Energy, College of Science, Health, Engineering and Education, Murdoch University, Perth, WA 6150, Australia
Interests: autonomous systems; intelligent control; optimization; AI in renewable systems
Special Issues, Collections and Topics in MDPI journals
Dr. Valeh Moghaddam

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Waurn Ponds Campus, VIC 3216, Australia
Interests: Internet of Things; electric vehicles application; machine learning and optimisation in energy management systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation is responsible for significant emission of the total pollution into the atmosphere. So, there is an urgent need to develop electric vehicles (EVs) which are not only efficient and reliable, but also cost-effective and scalable. The efficiency and performance of the EVs can be improved when they are designed, manufactured, operated in an optimum fashion. This Special Issue covers the technologies for optimal productions and applications of the EVs. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Charging stations and energy storage systems for electric vehicles;
  • Strategies for the optimum application of the EVs and charging stations;
  • Techniques for optimum design and manufacturing of the propulsion system for EVs;
  • Predictive maintenance, energy management, and control;
  • Analyzing charging patterns and user behavior to inform placement and sizing of electric vehicle charging stations;
  • Enhancing user experience and accessibility of electric vehicle charging stations through smart technologies and user-centered design.

Dr. Amin Mahmoudi
Dr. Solmaz Kahourzade
Dr. Amirmehdi Yazdani
Dr. Valeh Moghaddam
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. Sustainability 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 2400 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

  • electric vehicle
  • charging stations
  • manufacturing
  • optimization
  • technologies

Published Papers (2 papers)

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Research

26 pages, 16105 KiB  
Article
Electric Taxi Charging Load Prediction Based on Trajectory Data and Reinforcement Learning—A Case Study of Shenzhen Municipality
by Xiaojia Liu, Bowei Liu, Yunjie Chen, Yuqin Zhou and Dexin Yu
Sustainability 2024, 16(4), 1520; https://0-doi-org.brum.beds.ac.uk/10.3390/su16041520 - 10 Feb 2024
Viewed by 682
Abstract
In order to effectively solve the problem of electric taxi charging load prediction and reasonable charging behaviour discrimination, in this paper, we use taxi GPS trajectory data to mine the probability of operation behaviour in each area of the city, simulate the operation [...] Read more.
In order to effectively solve the problem of electric taxi charging load prediction and reasonable charging behaviour discrimination, in this paper, we use taxi GPS trajectory data to mine the probability of operation behaviour in each area of the city, simulate the operation behaviour of a day by combining it with reinforcement learning ideas, obtain the optimal operation strategy through training, and count the spatial and temporal distributions and power values at the time of charging decision making, so as to predict the charging load of electric taxis. Experiments are carried out using taxi travel data in Shenzhen city centre. The results show that, in terms of taxi operation behaviour, the operation behaviour optimized by the DQN algorithm shows the optimal effect in terms of the passenger carrying time, mileage, and daily net income; in terms of the charging load distribution, the spatial charging demand of electric taxis in each area shows obvious differences, and the charging demand load located in the city centre area and close to the traffic hub is higher. In time, the peak charging demand is distributed around 3:00 to 4:00 and 14:00 to 15:00. Compared with the operating habits of drivers based on the Monte Carlo simulation, the DQN algorithm is able to optimise the efficiency and profitability of taxi drivers, which is more in line with the actual operating habits of drivers formed through accumulated experience, thus achieving a more accurate charging load distribution. Full article
(This article belongs to the Special Issue Electric Vehicles: Production, Charging Stations, and Optimal Use)
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23 pages, 5605 KiB  
Article
Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities
by Fayez Alanazi, Talal Obaid Alshammari and Abdelhalim Azam
Sustainability 2023, 15(22), 16030; https://0-doi-org.brum.beds.ac.uk/10.3390/su152216030 - 16 Nov 2023
Cited by 1 | Viewed by 2604
Abstract
Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To [...] Read more.
Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to demographic disparities in the accessibility of EV charging stations (EVCSs). We analyze the impact of various factors, including EV percentage, geographic area, population density, available electric vehicle supply equipment (EVSE) ports, electricity sources, energy costs, per capita and average family income, traffic patterns, and climate, on the placement of EVCSs in nine selected US states. Furthermore, we employ predictive modeling techniques, such as linear regression and support vector machine, to explore unique nuances in EVCS installation. By leveraging real-world data from these states and the identified variables, we forecast the future distribution of EVCSs using machine learning. The linear regression model demonstrates exceptional effectiveness, achieving 90% accuracy, 94% precision, 89% recall, and a 91% F1 score. Both graphical analysis and machine learning converge on a significant finding: Texas emerges as the most favorable state for optimal EVCS placement among the studied areas. This research enhances our understanding of the multifaceted dynamics that govern the accessibility of EVCSs, thereby informing the development of policies and strategies to accelerate EV adoption, reduce emissions, and promote social inclusivity. Full article
(This article belongs to the Special Issue Electric Vehicles: Production, Charging Stations, and Optimal Use)
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