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Sustainable Eco-Driving: The Development of Connected and Automated Vehicles

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 1675

Special Issue Editors

The Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Interests: vehicles; energy efficiency; intelligent control; transportation electrification; sustainable drives

Special Issue Information

Dear Colleagues,

Connected and automated vehicles (CAVs) are high-tech products at present, and will play a crucial role in the future. CAVs reshape transportation and mobility by replacing human drivers and service providers with self-driving and car-networking technologies. CAVs not only improve driving safety and road mobility but also contribute much to energy consumption reduction and traffic emission reduction, becoming one of the most important technologies for eco-driving. For example, to enable vehicles to communicate with roadside infrastructures, such as traffic signals, driving efficiency and vehicle fuel consumption will be improved, while emissions will be reduced. The scope of CAVs is wide, including intelligent control, transportation electrification, sensors, service provision and algorithm optimization, etc. Many scholars and engineers are dedicated to them, promoting technical innovation and the application of CAVs. The importance of CAVs is reflected in the following aspects. 1) From the perspective of transportation, they will greatly improve production efficiency and traffic efficiency, and may become the first breakthrough field of artificial intelligence (AI). 2) From an environmental point of view, they can alleviate the energy crisis and reduce the pollution of automobiles to the environment. 3) From a social perspective, intelligent driving will alleviate the contradiction of labor shortage. 4) From the perspective of industrial development, they can lead the innovation of the business model of the automobile industry and reshape the industrial ecology. Therefore, the topic concerning the development of connected and automated vehicles deserves much more attention globally.

The aim of this Special Issue is to encourage the scholars working on CAVs- or eco-driving-related areas to submit their research papers to Sustainability, sharing their latest findings with peer scholars. Considering the subject complies totally with the scope of Sustainability, the journal will support any submissions in the relevant fields.

In this Special issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Connected and automated vehicles
  • Eco-driving strategies
  • Motion planning and control
  • Edge computing in mobility
  • Car networking
  • Optimization control
  • Development, test, and validation of connected and automated vehicles
  • Devices of connected automated vehicles including multi-sensors, processors and drives
  • Development of vehicle electrification in connected and automated vehicles

We look forward to receiving your contributions.

Dr. Chao Gong
Dr. Xing Zhao
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
  • eco-driving strategy
  • energy saving
  • transportation electrification
  • car networking
  • intelligent control
  • information infusion
  • optimization
  • data engineering

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Published Papers (1 paper)

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Research

18 pages, 4798 KiB  
Article
Parameter Robustness Enhanced Deadbeat Control for DFIG with ESO-Based Disturbance Estimation
by Kai Ni, Haochen Shi, Jin Zhang, Chong Zhang, Hongzhe Wang and Yizhou Sun
Sustainability 2023, 15(15), 12020; https://doi.org/10.3390/su151512020 - 5 Aug 2023
Cited by 1 | Viewed by 1044
Abstract
Doubly fed induction generators (DFIGs) are widely applied in wind energy conversion systems, where the harsh service environment and long-lasting operation can bring about motor parameter deviations, deteriorating the system performance. In this paper, an extended state observer (ESO)-based deadbeat control strategy that [...] Read more.
Doubly fed induction generators (DFIGs) are widely applied in wind energy conversion systems, where the harsh service environment and long-lasting operation can bring about motor parameter deviations, deteriorating the system performance. In this paper, an extended state observer (ESO)-based deadbeat control strategy that enhances the system parameter robustness is proposed. Firstly, the effects of motor parameter inaccuracy are analyzed to reflect the control errors and degradation of the system performance. Secondly, a lumped disturbance represented by an additional state extended from the system mathematical model is derived with the parameter inaccuracy taken into consideration. Finally, the parameter robustness enhanced deadbeat control method with the ESO-based disturbance estimation is developed to realize accurate prediction and control, even when the inductance of DFIG deviates under various operation conditions. To verify the effectiveness of the proposed method, simulations are carried out in MATLAB/Simulink for a 1.5 MW DFIG with a 30% stator and rotor inductance deviation. Compared to the conventional control method, smooth and fast dynamic performance is maintained, and the current ripple for the proposed control strategy can be reduced by approximately 40%, where the steady-state tracking performance and parameter robustness of the system are significantly enhanced. Full article
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