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Electronics
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Vehicle Technologies for Sustainable Smart Cities and Societies
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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: 31 December 2024 | Viewed by 5537

Special Issue Editors

Dr. Nikolay Hinov

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Guest Editor
Department of Power Electronics, Technical University of Sofia, Ohridski blvd., 1000 Sofia, Bulgaria
Interests: artificial intelligence; electric vehicles; energy storage; mathematical modeling; control theory and applications; smart cities and smart grids; power electronic converters; power electronic systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Darius Andriukaitis

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Jožef Ritonja

E-Mail Website
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)

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

18 pages, 4094 KiB  
Article
Proposing an Efficient Deep Learning Algorithm Based on Segment Anything Model for Detection and Tracking of Vehicles through Uncalibrated Urban Traffic Surveillance Cameras
by Danesh Shokri, Christian Larouche and Saeid Homayouni
Electronics 2024, 13(14), 2883; https://doi.org/10.3390/electronics13142883 - 22 Jul 2024
Viewed by 931
Abstract
In this study, we present a novel approach leveraging the segment anything model (SAM) for the efficient detection and tracking of vehicles in urban traffic surveillance systems by utilizing uncalibrated low-resolution highway cameras. This research addresses the critical need for accurate vehicle monitoring [...] Read more.
In this study, we present a novel approach leveraging the segment anything model (SAM) for the efficient detection and tracking of vehicles in urban traffic surveillance systems by utilizing uncalibrated low-resolution highway cameras. This research addresses the critical need for accurate vehicle monitoring in intelligent transportation systems (ITS) and smart city infrastructure. Traditional methods often struggle with the variability and complexity of urban environments, leading to suboptimal performance. Our approach harnesses the power of SAM, an advanced deep learning-based image segmentation algorithm, to significantly enhance the detection accuracy and tracking robustness. Through extensive testing and evaluation on two datasets of 511 highway cameras from Quebec, Canada and NVIDIA AI City Challenge Track 1, our algorithm achieved exceptional performance metrics including a precision of 89.68%, a recall of 97.87%, and an F1-score of 93.60%. These results represent a substantial improvement over existing state-of-the-art methods such as the YOLO version 8 algorithm, single shot detector (SSD), region-based convolutional neural network (RCNN). This advancement not only highlights the potential of SAM in real-time vehicle detection and tracking applications, but also underscores its capability to handle the diverse and dynamic conditions of urban traffic scenes. The implementation of this technology can lead to improved traffic management, reduced congestion, and enhanced urban mobility, making it a valuable tool for modern smart cities. The outcomes of this research pave the way for future advancements in remote sensing and photogrammetry, particularly in the realm of urban traffic surveillance and management. Full article
(This article belongs to the Special Issue Vehicle Technologies for Sustainable Smart Cities and Societies)
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17 pages, 2395 KiB  
Article
Using the Buckingham π Theorem for Multi-System Transfer Learning: A Case-Study with 3 Vehicles Sharing a Database
by William Therrien, Olivier Lecompte and Alexandre Girard
Electronics 2024, 13(11), 2041; https://doi.org/10.3390/electronics13112041 - 23 May 2024
Viewed by 690
Abstract
Many advanced driver assistance schemes or autonomous vehicle controllers are based on a motion model of the vehicle behavior, i.e., a function predicting how the vehicle will react to a given control input. Data-driven models, based on experimental or simulated data, are very [...] Read more.
Many advanced driver assistance schemes or autonomous vehicle controllers are based on a motion model of the vehicle behavior, i.e., a function predicting how the vehicle will react to a given control input. Data-driven models, based on experimental or simulated data, are very useful, especially for vehicles difficult to model analytically, for instance, ground vehicles for which the ground-tire interaction is hard to model from first principles. However, learning schemes are limited by the difficulty of collecting large amounts of experimental data or having to rely on high-fidelity simulations. This paper explores the potential of an approach that uses dimensionless numbers based on Buckingham’s π theorem to improve the efficiency of data for learning models, with the goal of facilitating knowledge sharing between similar systems. A case study using car-like vehicles compares traditional and dimensionless models on simulated and experimental data to validate the benefits of the new dimensionless learning approach. Preliminary results from the case study presented show that this new dimensionless approach could accelerate the learning rate and improve the accuracy of the model prediction when transferring the learned model between various similar vehicles. Prediction accuracy improvements with the dimensionless scheme when using a shared database, that is, predicting the motion of a vehicle based on data from various different vehicles was found to be 480% more accurate for predicting a simple no-slip maneuver based on simulated data and 11% more accurate to predict a highly dynamic braking maneuver based on experimental data. A modified physics-informed learning scheme with hand-crafted dimensionless features was also shown to increase the improvement to precision gains of 917% and 28% respectively. A comparative study also shows that using Buckingham’s π theorem is a much more effective preprocessing step for this task than principal component analysis (PCA) or simply normalizing the data. These results show that the use of dimensionless variables is a promising tool to help in the task of learning a more generalizable motion model for vehicles, and hence potentially taking advantage of the data generated by fleets of vehicles on the road even though they are not identical. Full article
(This article belongs to the Special Issue Vehicle Technologies for Sustainable Smart Cities and Societies)
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20 pages, 2100 KiB  
Article
Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling
by Vincent Roberge, Katerina Brooks and Mohammed Tarbouchi
Electronics 2024, 13(9), 1783; https://doi.org/10.3390/electronics13091783 - 5 May 2024
Viewed by 1439
Abstract
Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. [...] Read more.
Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. This paper presents a metaheuristic-based approach parallelized on multicore processors (CPU) and graphics processing units (GPU) to optimize the scheduling of EV charging in a single smart parking lot. The proposed method uses a particle swarm optimization algorithm that takes as input the arrival time, the departure time, and the power demand of the vehicles and produces an optimized charging schedule for all vehicles in the parking lot, which minimizes the overall charging cost while respecting the chargers’ capacity and the parking lot feeder capacity. The algorithm exploits task-level parallelism for the multicore CPU implementation and data-level parallelism for the GPU implementation. The proposed algorithm is tested in simulation on parking lots containing 20 to 500 EVs. The parallel implementation on CPUs provides a speedup of 7.1x, while the implementation on a GPU provides a speedup of up to 247.6x. The parallel implementation on a GPU is able to optimize the charging schedule for a 20-EV parking lot in 0.87 s and a 500-EV lot in just under 30 s. These runtimes allow for real-time computation when a vehicle arrives at the parking lot or when the electricity cost profile changes. Full article
(This article belongs to the Special Issue Vehicle Technologies for Sustainable Smart Cities and Societies)
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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 1609
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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