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Development of Intelligent Electric Vehicles and Smart Transportation

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 8175

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Special Issue Editors

Prof. Dr. Yi-Hsuan Hung

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Guest Editor

Undergraduate Program of Vehicle and Energy Engineering, National Taiwan Normal University, Taipei, Taiwan
Interests: intelligent control; optimal energy management; vehicle system dynamics; hybrid and electric vehicles

Dr. Hwa-Dong Liu

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Guest Editor

Undergraduate Program of Vehicle and Energy Engineering, National Taiwan Normal University, Taipei 10610, Taiwan
Interests: solar power; maximum power point tracking algorithm; rail vehicle auxiliary power system; power quality
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Special Issue Information

Dear Colleagues,

Due to stringent environmental policies and the issue of global warming, the technology of green energy sources, which are employed to advanced vehicles, as well as the technology of smart transportation, have become essential. For intelligent electric vehicles, which are different from internal-combustion-engine vehicles, traction motors and green energy systems (such as batteries and fuel cells) cause vehicles to produce zero (or little) pollutants, as well as having low-vibration, low-noise, and energy-saving characteristics. Moreover, multiple energy sources or power sources, such as fuel cell/battery vehicles and engine/motor hybrid electric vehicles, maximize output performance while minimizing the inherent drawbacks of single power (or energy) sources. Therefore, proper intelligent control and energy management are crucial considerations. Moreover, with the intelligent control of vehicle dynamics, steering, brakes and chassis, vehicles can be more efficient and more stable in their operation. With the signals from sensors and actuators, vehicles must respond rapidly to maintain optimal conditions. Besides the outstanding performance of a single vehicle, intelligent transportation systems (ITSs) have recently attracted increasing research attention due to growing communication and information technologies. By establishing proper infrastructures and implementing a rapid information exchange between vehicles and users, traffic management and vehicle supervision can be conducted. Therefore, a highly efficient traffic network can be established. This Special Issue will consider high-quality research and review papers that address the theoretical and application aspects of intelligent vehicles and smart transportation systems. Specific topics of interest for this Special Issue include, but are not limited to, the following topics:

Electric vehicles and hybrid vehicles;
Green energy sources and hybrid powertrains;
Key components of electric vehicles;
Intelligent vehicle control and energy management;
Control of vehicle dynamics and steering;
Intelligent vehicle systems design and control;
Applications of neural and fuzzy control systems;
Vehicle modeling and performance evaluation;
Information and communication system;
Real-time simulation and hardware-in-the-loop system;
X-by-wire control;
Advanced driver assistance system;
Autonomous vehicle system;
Smart traffic management;
Intelligent transportation system;
Human interface and safety enhancement;
Sensor and actuator technology;
Transportation policy and traffic planning.

Prof. Dr. Yi-Hsuan Hung
Dr. Hwa-Dong Liu
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 (5 papers)

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Research

19 pages, 2629 KiB

Open AccessArticle

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

by Luca Pulvirenti, Luigi Tresca, Luciano Rolando and Federico Millo

Energies 2023, 16(10), 4121; https://doi.org/10.3390/en16104121 - 16 May 2023

Cited by 1 | Viewed by 1135

Abstract

In a context in which the connectivity level of last-generation vehicles is constantly on the rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the synergistic optimization of the route and the speed trajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connected environment: the virtual test rig of a premium segment passenger car was used for generating the simulation scenarios and to assess the benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem was formulated and solved by means of a Variable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on the basis of the V2X information allows to drastically reduce the DP computation burden by more than 95%. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world urban route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization. Full article

(This article belongs to the Special Issue Development of Intelligent Electric Vehicles and Smart Transportation)

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20 pages, 6492 KiB

Open AccessArticle

Shared Driving Assistance Design Considering Human Error Protection for Intelligent Electric Wheelchairs

by Hsin-Han Chiang, Wan-Ting You and Jin-Shyan Lee

Energies 2023, 16(6), 2583; https://doi.org/10.3390/en16062583 - 09 Mar 2023

Cited by 2 | Viewed by 1314

Abstract

To effectively provide the handicapped with mobility aids, studies on the shared autonomy of robotic systems have been widely cultivated. This study proposes an adaptive shared control strategy to realize reliable and safe driving assistance on an intelligent electric wheelchair with protection against human errors. The theoretical framework of the system is analyzed by the linearized reference wheelchair model and stable characteristics of obstacle avoidance behavior can be subsequently derived according to the Lyapunov analysis and Liénard-Chipart criterion. Based on the convex analysis, the relationships between human input and robot control are investigated to determine shared control weights. As such, safety and reliability can be guaranteed. To verify the performances of the proposed approach, human errors including skill-based errors, decision errors, and violations are considered in the experiments. The experimental results based on a comprehensive study show that the proposed method is capable of enhancing driving safety and reducing operation burden in terms of the designed criteria with fluency, smoothness, and time efficiency while protecting the user from human manual errors. Full article

(This article belongs to the Special Issue Development of Intelligent Electric Vehicles and Smart Transportation)

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20 pages, 4009 KiB

Open AccessArticle

A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)

by Ghassan Husnain, Shahzad Anwar, Gulbadan Sikander, Armughan Ali and Sangsoon Lim

Energies 2023, 16(3), 1456; https://doi.org/10.3390/en16031456 - 01 Feb 2023

Cited by 20 | Viewed by 2238

Abstract

Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf Optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole. Full article

(This article belongs to the Special Issue Development of Intelligent Electric Vehicles and Smart Transportation)

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17 pages, 765 KiB

Open AccessArticle

Federated System for Transport Mode Detection

by Iago C. Cavalcante, Rodolfo I. Meneguette, Renato H. Torres, Leandro Y. Mano, Vinícius P. Gonçalves, Jó Ueyama, Gustavo Pessin, Georges D. Amvame Nze and Geraldo P. Rocha Filho

Energies 2022, 15(23), 9256; https://doi.org/10.3390/en15239256 - 06 Dec 2022

Cited by 3 | Viewed by 1347

Abstract

Data on transport usage is important in a wide range of areas. These data are often obtained manually through costly and inaccurate interviews. In the last decade, several researchers explored the use of smartphone sensors for the automatic detection of transport modes. However, such works have focused on developing centralized machine learning mechanisms. This centralized approach requires user data to be transferred to a central server and, therefore, does not satisfy a transport mode detection mechanism’s practical response time and privacy needs. This research presents the Federated System for Transport Mode Detection (FedTM). The main contribution of FedTM is exploring Federated Learning on transport mode detection using smartphone sensors. In FedTM, both the training and inference process is moved to the client side (smartphones), reducing response time and increasing privacy. The FedTM was designed using a Neural Network for the classification task and obtained an average accuracy of 80.6% in three transport classes (cars, buses and motorcycles). Other contributions of this work are: (i) The use of data collected only on the curves of the route. Such reduction in data collection is important, given that the system is decentralized and the training and inference phases take place on smartphones with less computational capacity. (ii) FedTM and centralized classifiers are compared with regard to execution time and detection performance. Such a comparison is important for measuring the pros and cons of using Federated Learning in the transport mode detection task. Full article

(This article belongs to the Special Issue Development of Intelligent Electric Vehicles and Smart Transportation)

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18 pages, 8299 KiB

Open AccessArticle

A Novel LCOT Control Strategy for Self-Driving Electric Mobile Robots

by Hwa-Dong Liu, Guo-Jyun Gao, Shiue-Der Lu and Yi-Hsuan Hung

Energies 2022, 15(23), 9178; https://doi.org/10.3390/en15239178 - 03 Dec 2022

Cited by 2 | Viewed by 1060

Abstract

This study proposes a novel logarithm curve and operating time (LCOT) control strategy for a self-driving electric mobile robot. This new LCOT control strategy enables the mobile robot to speed up and slow down mildly when running longitudinally, turning left, turning right, and encountering an obstacle based on the relationship between the logarithm curve and operating time. This novel control strategy can enhance the comfort and stability of the self-driving electric mobile robot and reduce its vibrations and instabilities in the operation process. The proposed LCOT control strategy and the fixed duty cycle method were verified experimentally. The results showed that the LCOT control strategy spent 300 s running on a 3000 cm road, whereas the fixed duty cycle method spent 450 s. Because this novel method controls the acceleration and deceleration of the self-driving electric mobile robot gently and flexibly, the proposed LCOT control strategy has better working efficiency than the fixed duty cycle method. This novel control strategy is simple and easy to be implemented. As it can reduce the working load of the controller, increase system efficiency, and require low cost, it can be effectively used in a self-driving electric mobile robot. Full article

(This article belongs to the Special Issue Development of Intelligent Electric Vehicles and Smart Transportation)

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