Vehicle Technologies for Sustainable Smart Cities and Societies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 1240

Special Issue Editors


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Faculty of Electrical and Electronics Engineering, Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
Interests: energy harvesting; interactive electronic systems; electric vehicles; integrated information systems; indirect measurement methods; reinforcement learning
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Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: electric machines; control theory and applications; power systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The process of introducing electric vehicles, which represents one of the largest political projects in recent decades, also poses an extraordinary challenge for engineers and scientists. Governments and industry are investing enormous amounts of resources in this project. This project's importance and expectations correspond to immense activities in developing and researching electric vehicles. Therefore, it is crucial that those involved in working in the fields of electric vehicles have enough state-of-the-art knowledge and that they have access to the experiences gained by engineers in neighboring fields. This is also the purpose of this Special Issue: that engineers and scientists who have conjured up new knowledge and findings while working in the field of electric vehicles can transfer this knowledge to others.

In this Special Issue, we invite contributions from the fields of research, development, design, and manufacturing of electric vehicles and the necessary infrastructure, as well as from the fields of application of electric vehicles and their technical, economic, and social impact on other systems and the environment. Although the term electric vehicles mainly refers to electric cars, this Special Issue is not limited in scope to cars alone. Articles from the fields of aircraft and electric boats and submarines are also welcome.

The scope of the Special Issue is vast; we invite contributions which deal with the following topics:

  • Powertrains for electric vehicles (motors, generators, frequency converters, and control algorithms);
  • Batteries and battery management systems;
  • Sensors and sensor networks;
  • Fuel cells in electric vehicles;
  • Charging infrastructure for electric vehicles;
  • The influence of electric vehicles on the power system (stabilization and V2G);
  • Autonomous driving solutions;
  • Activities for the public promotion of electric vehicles;
  • Seaborne and airborne electric vehicles;
  • Energy harvesting;
  • IoT for sustainable mobility;
  • Cybersecurity;
  • Internet of vehicles (IoV).

Dr. Nikolay Hinov
Prof. Dr. Darius Andriukaitis
Dr. Jožef Ritonja
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. Electronics 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 vehicles
  • electric vehicle (EV) powertrains
  • EV energy sources
  • EV components
  • autonomous drive
  • charging stations
  • EV in power systems
  • Internet of vehicles (IoV)

Published Papers (1 paper)

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Research

13 pages, 3035 KiB  
Article
Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model
by Boyu Wang, Wan Li and Zulqarnain H. Khattak
Electronics 2024, 13(7), 1251; https://doi.org/10.3390/electronics13071251 - 28 Mar 2024
Viewed by 766
Abstract
Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe [...] Read more.
Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field. Full article
(This article belongs to the Special Issue Vehicle Technologies for Sustainable Smart Cities and Societies)
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