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Advanced Operation and Control of Electrical Power Systems in Transportation...
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Advanced Operation and Control of Electrical Power Systems in Transportation Applications

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 3340

Special Issue Editors

Dr. Yuan Gao

E-Mail Website
Guest Editor
School of Engineering, University of Leicester, Leicester, UK
Interests: model predictive control; electrical power system; more electric aircraft; motor design; motor drive control; machine learning; artificial intelligence
Prof. Dr. Pat Wheeler

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK
Interests: power electronics; matrix converters; multi-level converters/multi-cellular converters; more electric aircraft
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tomislav Dragicevic

grade E-Mail Website
Guest Editor
Department of Wind and Energy Systems, Technical University of Denmark, Anker Engelunds Vej 101 2800 Kongens, Lyngby, Denmark
Interests: design, control and diagnostics of power electronics intensive electrical systems; electric vehicles; EV charging stations; water flow networks; electrolyzer systems; microgrids

Special Issue Information

Dear Colleagues,

The global net-zero emission target brings ever-lasting challenges to all areas of the transportation sector: aircraft, railway, ship, and road vehicles. One of the solutions to net-zero in transportation is electrification, requiring an onboard electrical power system. Advanced control and operation techniques for these electrical power systems are always essential to achieving environmentally friendly and high-performance transportation systems with good reliability, robustness, and efficiency.

With the rapid development of data science and computing technology, emerging technologies, such as artificial intelligence and digital twin, have attracted much attention and are still growing in recent years. These technologies have accelerated the development of both data-based and model-based intelligent algorithms in the control and operation of power systems. This provides potential solutions for more reliable and green electrical power systems for transportation applications.

This Special Issue aims to present and disseminate the most recent signs of progress in the advanced control and operation techniques of the electrical power system on all types of transportation platforms.

Topics of interest for publication include, but are not limited to:

  • Electric drives
  • Smart grids
  • Machine control
  • Electric power management and distribution
  • Condition monitoring and maintenance
  • Machine learning and artificial intelligence
  • Digital twin

Dr. Yuan Gao
Prof. Dr. Pat Wheeler
Prof. Tomislav Dragicevic
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.

Keywords

  • control
  • energy management
  • transportation electrification
  • more electric aircraft
  • railway system
  • shipboard microgrid
  • electric vehicle
  • machine learning
  • artificial intelligence
  • digital twin

Published Papers (3 papers)

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Research

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15 pages, 2945 KiB  
Article
Reliability Prediction of Distribution Network Using IAO-Optimized Mixed-Kernel LSSVM
by Chen Pan, Lijia Ren and Junjie Wan
Energies 2023, 16(21), 7448; https://doi.org/10.3390/en16217448 - 04 Nov 2023
Viewed by 855
Abstract
For the sake of conducting distribution network reliability prediction in an accurate and efficient manner, a model for distribution network reliability prediction (IAO-LSSVM) based on an improved Aquila Optimizer (IAO) optimized mixed-kernel Least Squares Support Vector Machine (LSSVM) is thus proposed in this [...] Read more.
For the sake of conducting distribution network reliability prediction in an accurate and efficient manner, a model for distribution network reliability prediction (IAO-LSSVM) based on an improved Aquila Optimizer (IAO) optimized mixed-kernel Least Squares Support Vector Machine (LSSVM) is thus proposed in this paper. First, the influencing factors that greatly affect the distribution network reliability are screened out through grey relational analysis. Afterwards, the radial basis kernel function and polynomial kernel function are combined and a mixed kernel LSSVM model is constructed, which has better generalization ability. However, for the AO algorithm, it is easy to fall into local extremum. In such case, the AO algorithm is innovatively improved after both the improved tent chaotic initialization strategy and adaptive t-distribution strategy are introduced. Next, the parameters of the mixed-kernel LSSVM model are optimized and the IAO-LSSVM distribution network reliability prediction model is established through using the improved AO algorithm. In the end, the prediction results and errors of the IAO-LSSVM prediction model and other models are compared in the actual distribution network applications. It is revealed that the IAO-LSSVM prediction model proposed in this paper features higher accuracy and better stability. Full article
(This article belongs to the Special Issue Advanced Operation and Control of Electrical Power Systems in Transportation Applications)
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19 pages, 12236 KiB  
Article
Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)
by Guihua Liu, Lichen Kui, Yuan Gao, Wanqiang Cui, Fei Liu and Wei Wang
Energies 2023, 16(18), 6690; https://doi.org/10.3390/en16186690 - 18 Sep 2023
Viewed by 748
Abstract
For recycling waste batteries, high-gain DC-DC provides a great solution. In this article, the design and optimization of a high-gain converter–synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) is studied. The optimization objective of this article is to propose an automatic design method [...] Read more.
For recycling waste batteries, high-gain DC-DC provides a great solution. In this article, the design and optimization of a high-gain converter–synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) is studied. The optimization objective of this article is to propose an automatic design method for passive components and the switching frequency of the converter to improve efficiency and power density. A machine learning-integrated optimization method is proposed to minimize the converter mass and power loss of the converter. In this method, first, a component-based automatic design model is built with embedded SLIC-QBC simulation. Then, a series of design schemes is generated within a large parameter range, and training data for machine learning are collected. Support vector machine (SVM) and artificial neural network (ANN) are used to validate the converter design scheme, where ANN establishes the mapping relationship from design parameters to optimization objectives. After the optimization, an experimental prototype is built for experimental verification. The simulation and experimental results verify the practicability of the proposed method. Full article
(This article belongs to the Special Issue Advanced Operation and Control of Electrical Power Systems in Transportation Applications)
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Review

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26 pages, 2953 KiB  
Review
Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications
by Zhen Huang, Xuechun Xiao, Yuan Gao, Yonghong Xia, Tomislav Dragičević and Pat Wheeler
Energies 2023, 16(17), 6269; https://doi.org/10.3390/en16176269 - 29 Aug 2023
Cited by 1 | Viewed by 1052
Abstract
The global objective of achieving net-zero emissions drives a significant electrified trend by replacing fuel-mechanical systems with onboard microgrid (OBMG) systems for transportation applications. Energy management strategies (EMS) for OBMG systems require complicated optimization algorithms and high computation capabilities, while traditional control techniques [...] Read more.
The global objective of achieving net-zero emissions drives a significant electrified trend by replacing fuel-mechanical systems with onboard microgrid (OBMG) systems for transportation applications. Energy management strategies (EMS) for OBMG systems require complicated optimization algorithms and high computation capabilities, while traditional control techniques may not meet these requirements. Driven by the ability to achieve intelligent decision-making by exploring data, artificial intelligence (AI) and digital twins (DT) have gained much interest within the transportation sector. Currently, research on EMS for OBMGs primarily focuses on AI technology, while overlooking the DT. This article provides a comprehensive overview of both information technology, particularly elucidating the role of DT technology. The evaluation and analysis of those emerging information technologies are explicitly summarized. Moreover, this article explores potential challenges in the implementation of AI and DT technologies and subsequently offers insights into future trends. Full article
(This article belongs to the Special Issue Advanced Operation and Control of Electrical Power Systems in Transportation Applications)
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