Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

Journal Browser

Connection, Automation, and Electrification for More Sustainable Mobility

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 14959

Share This Special Issue

Special Issue Editors

Dr. Hongbo Gao

E-Mail Website
Guest Editor

Department of Automotive Engineering, Tsinghua University, Beijing, China
Interests: Artificial intelligence technology and its application in education, automated system platforms and robotics, machine learning, deep reinforcement learning, decision support systems for sustainable unmanned systems, transportation systems and intelligent cyber-physical systems
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoxiang Na

E-Mail Website
Guest Editor

Department of Engineering, University of Cambridge, UK
Interests: modelling of vehicle emissions and energy consumption; in-service monitoring of vehicle freight operation; modelling of driver control of vehicle energy consumption; evaluation of sustainability of driver–vehicle systems; vehicle platooning and autonomous vehicles

Prof. Dr. Xiaosong Hu

E-Mail Website
Guest Editor

Department of Automotive Engineering, Chongqing University, Chongqing 400044, China
Interests: electrified vehicles; alternative powertrains; energy storage systems; battery management; vehicle-grid-home interactions; energy management optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Some of today’s vehicles already have automated safety features that can help drivers avoid drifting into adjacent lanes or making unsafe lane changes, and brake automatically if a vehicle ahead of them stops or slows down suddenly, etc. However, there is still a long way to go toward a fully intelligent mobility with automated driving systems that can handle the whole task of driving when we do not want to or cannot do it by ourselves. In particular, the next-generation intelligent transportation system, featured by strong connection, automation, and electrification, requires advanced automotive technologies to deliver greater safety and more mobility benefits.

Connected and automated vehicles (CAVs) are one of such new technologies, which are strengthened by the ability of gathering and sharing traffic information and vehicle state with other neighboring vehicles. Enabled by CAVs and other technologies like vehicular ad hoc networks (VANETS), smart roads, and electrified propulsion. It is feasible to significantly improve vehicular safety, energy efficiency, and pollutant emissions by autonomous driving, cooperative driving, accurate traffic control, and comfortable driving experiences.

In the context of Cyber-Physical-Social Systems (CPSS), transportation has become highly multidisciplinary and requires an ever-increasing combination of mechanical, electrical/electronic, artificial intelligence, control and information disciplines. In particular, connected, automated, and electrified vehicles can also bring new perspectives and innovations to the transportation and logistics sector, since they become smarter and smarter via increasing utilization of advanced technologies such as machine learning, deep reinforcement learning, artificial intelligence, advanced computing, V2V and V2I communications, among many others. This Special Issue seeks to explore the areas related to these challenges. Papers selected for this Special Issue will be subject to a peer-review procedure with the aim of rapid and wide dissemination of their contents.

Dr. Hongbo Gao
Dr. Xiaoxiang Na
Prof. Dr. Xiaosong Hu
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

Connected and automated vehicles
autonomous decision-making
control and optimization
vehicular cyber-physical systems
electrified vehicles
energy conversion and storage

Published Papers (4 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

15 pages, 2520 KiB

Open AccessArticle

Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian–Vehicle Collision

by Renfei Wu, Xunjia Zheng, Yongneng Xu, Wei Wu, Guopeng Li, Qing Xu and Zhuming Nie

Sustainability 2019, 11(22), 6254; https://doi.org/10.3390/su11226254 - 7 Nov 2019

Cited by 18 | Viewed by 3009

Abstract

Pedestrian–vehicle collision is an important component of traffic accidents. Over the past decades, it has become the focus of academic and industrial research and presents an important challenge. This study proposes a modified Driving Safety Field (DSF) model for pedestrian–vehicle risk assessment at an unsignalized road section, in which predicted positions are considered. A Dynamic Bayesian Network (DBN) model is employed for pedestrian intention inference, and a particle filtering model is conducted to simulate pedestrian motion. Driving data collection was conducted and pedestrian–vehicle scenarios were extracted. The effectiveness of the proposed model was evaluated by Monte Carlo simulations running 1000 times. Results show that the proposed risk assessment approach reduces braking times by 18.73%. Besides this, the average value of TTC⁻¹ (the reciprocal of time-to-collision) and the maximum TTC⁻¹ were decreased by 28.83% and 33.91%, respectively. Full article

(This article belongs to the Special Issue Connection, Automation, and Electrification for More Sustainable Mobility)

► Show Figures

Figure 1

19 pages, 3739 KiB

Open AccessArticle

A Control Strategy for Driving Mode Switches of Plug-in Hybrid Electric Vehicles

by Yuping Zeng, Zhikai Huang, Yang Cai, Yonggang Liu, Yue Xiao and Yang Shang

Sustainability 2018, 10(11), 4237; https://doi.org/10.3390/su10114237 - 16 Nov 2018

Cited by 12 | Viewed by 4695

Abstract

Driving mode switches of hybrid vehicles are significant events. Due to the different dynamic characteristics of the engine, motor, and wet clutch, it is difficult to coordinate torque fluctuations caused by mode switches. This paper focused on a control strategy for driving mode switches of plug-in hybrid electric vehicles (PHEVs) with a multi-disk wet clutch. First, the dynamic model of the PHEV was established, and a rule-based control strategy was proposed to divide the working mode regions and distribute the torque between engine and motor. Second, the dual fuzzy control strategy for a wet clutch and the coordinated torque control strategy for driving mode switches were proposed. The dual fuzzy logic control system consisted of the initial pulse-width modulation (PWM)’s duty cycle control and the changing rate of the PWM’s duty cycle control. Considering the difference in the dynamic characteristics between engine, motor, and wet clutch, a coordinated control strategy for the driving mode switches of PHEVs was put forward. Third, simulations of driving mode switches between pure electric driving mode and only engine driving mode were conducted. The results showed that the proposed control strategy could reduce the torque ripple and the jerk of the vehicle, completely satisfying the requirements of China. Finally, the control strategy for the motor-assisted engine starting process was tested on the bench. The experiment results indicated that the proposed control strategy was effective. Full article

(This article belongs to the Special Issue Connection, Automation, and Electrification for More Sustainable Mobility)

► Show Figures

Figure 1

16 pages, 3619 KiB

Open AccessArticle

Open-Circuit Fault-Tolerant Characteristics of a New Four-Phase Doubly Salient Electro-Magnetic Generator

by Liwei Shi, Bing Yan, Xiaoyu Zhou and Xueyi Zhang

Sustainability 2018, 10(11), 4136; https://doi.org/10.3390/su10114136 - 10 Nov 2018

Cited by 1 | Viewed by 2850

Abstract

In order to improve the reliability of a more sustainable mobility generator, a four-phase Doubly Salient Electro-Magnetic Generator (DSEG) and its phase-isolated rectifier are proposed in this paper. The mathematical model of the machine and fault-tolerant rectifiers is proposed, which indicates that the four-phase fault-tolerant DSEG should have symmetric phases. With the asymmetry analysis of the traditional 8/6-pole DSEG, a new 12/9-pole DSEG with symmetric phases is proposed. The four-phase full bridge rectifier, positive half-wave rectifier and four-phase H bridge rectifier are presented. The voltage waveforms, no-load characteristics and loading characteristics with different rectifiers will be given based on the simulation and the experiment on a prototype of DSEG, and the results show that the four-phase H bridge rectifier has the best fault tolerant no-load characteristic and external characteristic, except that it needs more diodes. Full article

(This article belongs to the Special Issue Connection, Automation, and Electrification for More Sustainable Mobility)

► Show Figures

Figure 1

23 pages, 5252 KiB

Open AccessArticle

Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle

by Shaobo Xie, Xiaosong Hu, Kun Lang, Shanwei Qi and Tong Liu

Sustainability 2018, 10(10), 3758; https://doi.org/10.3390/su10103758 - 18 Oct 2018

Cited by 16 | Viewed by 3369

Abstract

Pontryagin’s Minimum Principle (PMP) has a significant computational advantage over dynamic programming for energy management issues of hybrid electric vehicles. However, minimizing the total energy consumption for a plug-in hybrid electric vehicle based on PMP is not always a two-point boundary value problem (TPBVP), as the optimal solution of a powering mode will be either a pure-electric driving mode or a hybrid discharging mode, depending on the trip distance. In this paper, based on a plug-in hybrid electric truck (PHET) equipped with an automatic mechanical transmission (AMT), we propose an integrated control strategy to flexibly identify the optimal powering mode in accordance with different trip lengths, where an electric-only-mode decision module is incorporated into the TPBVP by judging the auxiliary power unit state and the final battery state-of-charge (SOC) level. For the hybrid mode, the PMP-based energy management problem is converted to a normal TPBVP and solved by using a shooting method. Moreover, the energy management for the plug-in hybrid electric truck with an AMT involves simultaneously optimizing the power distribution between the auxiliary power unit (APU) and the battery, as well as the gear-shifting choice. The simulation results with long- and short-distance scenarios indicate the flexibility of the PMP-based strategy. Furthermore, the proposed control strategy is compared with dynamic programming (DP) and a rule-based charge-depleting and charge-sustaining (CD-CS) strategy to evaluate its performance in terms of computational accuracy and time efficiency. Full article

(This article belongs to the Special Issue Connection, Automation, and Electrification for More Sustainable Mobility)

► Show Figures

Journal Menu

Journal Browser

Connection, Automation, and Electrification for More Sustainable Mobility

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI