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Internet of Vehicles for Intelligent Transportation System

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 9681

Special Issue Editors


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School of Computing, School of Computing, Faculty of Computing and Digital Technologies, Staffordshire University, Mellor Building, College Road, Stoke-on-Trent ST4 2DE, UK
Interests: technology learning; HCI; activity theory; big data; knowledge management; web engineering; multimedia; artificial intelligence; information systems; service science; emotional intelligence; data science; health care; e-business; service science and innovation; mobile computing; cloud computing; neuroscience; social media; intelligent transport systems; Internet of Things; human-centered design; sustainability; educational and cognitive psychology; problem-based learning
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Special Issue Information

Dear Colleagues,

The Internet of Vehicles (IoV) is a distributed network that supports the use of data created by connected cars and vehicular ad hoc networks (VANETs). The goal of the IoV is to allow vehicles to communicate in real time with their human drivers, pedestrians, other vehicles, roadside infrastructure, and fleet management systems. It is predicted that the global IoV market is expected to be worth over USD 200 billion by 2024. The IoV supports five types of network communication: Intra-Vehicle; Vehicle to Vehicle (V2V); Vehicle to Infrastructure (V2I); Vehicle to Cloud (V2C); and Vehicle to Pedestrian (V2P). These five types of networks mentioned above are sometimes referred to as Vehicle to Everything (V2X) communication. In this Special Issue, we would like to mainly focus on research, technology development, engineering policy, and management studies that are related to the field of electric vehicles where IoV is a significant medium. As IoV-focused systems need energy in the same way that all electronic devices do, potential authors are strongly encouraged to relate their research to energy supply, conversion, and dispatch technologies, though research on other technologies is welcome as well.

Autonomous Vehicles (electric vehicles) are technologies that co-exist and co-support  Intelligent Transportation Systems (ITS). IoV and V2X communication are considered to be frameworks of the ITS. It has been generally accepted that IoV together with ITS focuses on a certain branch of transport, namely road transport. However, some authors have defined the term Internal Intelligent Transport System as “an information and communication system aimed at providing services related to various subjects of transport/logistics/production engineering and management (…), allowing and ensuring safer, more coordinated, smarter use of internal transport and better information flow between various subjects” (https://doi.org/10.3390/en14164919). Therefore, this Special Issue welcomes fresh insights that relate to the space around us in the context of terms that are also complementary to intelligent transport.

This Special Issue aims at highlighting the latest developments in identifying the key challenges in IoV along with their available and potential solutions. The Special Issue is expected to provide a platform for academics and industry researchers to identify and debate technical problems and recent accomplishments that are associated with IoV.

We invite researchers and industry representatives in the global IT, transportation, logistics, and production communities to contribute original research papers as well as review articles and empirical studies that will stimulate debate in the examination of different aspects of smart vehicles and transport systems such as enabling technologies, human factors, analyses of social and economic impacts, resource management, high-performance computing, security, privacy, trust management, the complexity of routing algorithms, and environmental issues, as well as other relevant topics in the field. The topics of interest include but are not limited to the following keywords.


Prof. Dr. Lorna Uden
Dr. Mariusz Kostrzewski
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.

Published Papers (3 papers)

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Research

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17 pages, 1303 KiB  
Article
Electric Vehicle Charging Station Layout for Tourist Attractions Based on Improved Two-Population Genetic PSO
by Shuang Che, Yan Chen and Longda Wang
Energies 2023, 16(2), 983; https://doi.org/10.3390/en16020983 - 16 Jan 2023
Cited by 6 | Viewed by 1515
Abstract
In this paper, the optimization issue of electric vehicle charging station layout (EVCSL) for tourist attractions is addressed, and an improved PSO is used to solve the optimization issue. Specifically, the improved particle swarm optimization (PSO) is proposed to obtain an appreciative planning [...] Read more.
In this paper, the optimization issue of electric vehicle charging station layout (EVCSL) for tourist attractions is addressed, and an improved PSO is used to solve the optimization issue. Specifically, the improved particle swarm optimization (PSO) is proposed to obtain an appreciative planning solution of EVCSL, and dynamic weight adjustment strategy and integration into the two-population genetic mode are proposed to improve the optimization quality for PSO. Simulation results show that the proposed improvement strategies can increase the optimization quality for PSO effectively so that a more appreciative planning solution of EVCSL can be obtained. Full article
(This article belongs to the Special Issue Internet of Vehicles for Intelligent Transportation System)
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Review

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20 pages, 527 KiB  
Review
The Internet of Vehicles and Sustainability—Reflections on Environmental, Social, and Corporate Governance
by Mariusz Kostrzewski, Magdalena Marczewska and Lorna Uden
Energies 2023, 16(7), 3208; https://doi.org/10.3390/en16073208 - 2 Apr 2023
Cited by 9 | Viewed by 2443
Abstract
The Internet of Vehicles (IoV) has generated great interest among researchers from different disciplines as it is multidisciplinary research. Sustainability for the IoV requires solutions from different perspectives, particularly in the context of environmental, social, and corporate governance. This review paper examines each [...] Read more.
The Internet of Vehicles (IoV) has generated great interest among researchers from different disciplines as it is multidisciplinary research. Sustainability for the IoV requires solutions from different perspectives, particularly in the context of environmental, social, and corporate governance. This review paper examines each of the mentioned perspectives of IoV research which were conducted among at least one of these three perspectives. On the one hand, this allows determining how widely research on the IoV system has been conducted. Moreover, it shows the directions of research on the IoV. On the other hand, it determines whether and how the IoV research is linked to each of the perspectives separately and analyses this link from a global perspective as well; i.e., it analyses the survey data in terms of the data’s relationship to all the perspectives as a group. As one of the research results, a conceptual model of IoV systems allocating the ESG perspectives was developed. The current research has shown that consideration of IoV systems in the context of these three perspectives (treated both individually and collectively) is still limited. A balanced approach towards these IoV systems is still required. Therefore, the paper consists of a survey of the current research related to the sustainability of the IoV from the three mentioned perspectives, aiming to give a balanced view of the importance of the three perspectives for IoV systems. Full article
(This article belongs to the Special Issue Internet of Vehicles for Intelligent Transportation System)
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20 pages, 1577 KiB  
Review
A Review of Applications of Artificial Intelligence in Heavy Duty Trucks
by Sasanka Katreddi, Sujan Kasani and Arvind Thiruvengadam
Energies 2022, 15(20), 7457; https://doi.org/10.3390/en15207457 - 11 Oct 2022
Cited by 9 | Viewed by 4313
Abstract
Due to the increasing use of automobiles, the transportation industry is facing challenges of increased emissions, driver safety concerns, travel demand, etc. Hence, automotive industries are manufacturing vehicles that produce fewer emissions, are fuel-efficient, and provide safety for drivers. Artificial intelligence has taken [...] Read more.
Due to the increasing use of automobiles, the transportation industry is facing challenges of increased emissions, driver safety concerns, travel demand, etc. Hence, automotive industries are manufacturing vehicles that produce fewer emissions, are fuel-efficient, and provide safety for drivers. Artificial intelligence has taken a major leap recently and provides unprecedented opportunities to enhance performance, including in the automotive and transportation sectors. Artificial intelligence shows promising results in the trucking industry for increasing productivity, sustainability, reliability, and safety. Compared to passenger vehicles, heavy-duty vehicles present challenges due to their larger dimensions/weight and require attention to dynamics during operation. Data collected from vehicles can be used for emission and fuel consumption testing, as the drive cycle data represent real-world operating characteristics based on heavy-duty vehicles and their vocational use. Understanding the activity profiles of heavy-duty vehicles is important for freight companies to meet fuel consumption and emission standards, prevent unwanted downtime, and ensure the safety of drivers. Utilizing the large amount of data being collected these days and advanced computational methods such as artificial intelligence can help obtain insights in less time without on-road testing. However, the availability of data and the ability to apply data analysis/machine learning methods on heavy-duty vehicles have room for improvement in areas such as autonomous trucks, connected vehicles, predictive maintenance, fault diagnosis, etc. This paper presents a review of work on artificial intelligence, recent advancements, and research challenges in the trucking industry. Different applications of artificial intelligence in heavy-duty trucks, such as fuel consumption prediction, emissions estimation, self-driving technology, and predictive maintenance using various machine learning and deep learning methods, are discussed. Full article
(This article belongs to the Special Issue Internet of Vehicles for Intelligent Transportation System)
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