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Data-Enabled Control and Design Solutions for Electric Machines and Power Electronics in Transportation Electrification

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

Deadline for manuscript submissions: closed (22 May 2024) | Viewed by 1294

Special Issue Editors


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Guest Editor
Institute of Rail Transit, Tongji University, Shanghai, China
Interests: multiphase machine; model predictive control

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Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
Interests: electric machines; energy efficiency; smart transport propulsion; smart manufacturing and robotics; advanced control of motor drives;
Special Issues, Collections and Topics in MDPI journals
School of Rail Transportation, Soochow University, Suzhou, China
Interests: power electronics; onboard charger; wireless power transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing transition to electric transportation represents a significant step towards achieving a more sustainable future, reducing greenhouse gas emissions, and improving air quality. This Special Issue aims to explore the latest advancements in data-enabled control and design solutions for electric machines and power electronics in transportation electrification.

Data-enabled control and design solutions leverage the power of data analytics, artificial intelligence, and machine learning to optimize the performance, reliability, and efficiency of whole systems consisting of electric machines and power electronics.

Topics of interest include, but are not limited to:

  1. Advanced control techniques for electric machines and power electronics, including model predictive control, adaptive control, etc.
  2. Data-driven design optimization of electric machines and power electronics components for enhanced performance, efficiency, and reliability.
  3. Integration of artificial intelligence and machine learning algorithms for fault diagnosis, prognostics, and health management of electric machines and power electronics in transportation electrification.
  4. Design and control of electric machines for special applications in electrical transportation, such as electric buses, e-bikes, railways, and electric aircraft.
  5. Cybersecurity and communication challenges in data-enabled control and design solutions for electric transportation systems.
  6. Big data analytics for performance evaluation and energy management in electric transportation systems.

This Special Issue aims to collect research articles that provide valuable insights and contribute to the data-enabled control and design solutions in transportation electrification. By fostering a multidisciplinary dialogue among researchers, this Special Issue will promote the development of innovative data-enabled technologies and strategies.

Dr. Senyi Liu
Dr. Zaixin Song
Dr. Xiao Yang
Dr. Chunhua 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.

Keywords

  • electric machines
  • power electronics
  • data-driven control
  • data-driven design optimization
  • model predictive control.

Published Papers (1 paper)

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Research

21 pages, 11548 KiB  
Article
Model Predictive Direct Speed Control of Permanent-Magnet Synchronous Motors with Voltage Error Compensation
by Lixiao Gao and Feng Chai
Energies 2023, 16(13), 5128; https://doi.org/10.3390/en16135128 - 3 Jul 2023
Cited by 1 | Viewed by 936
Abstract
Traditional strategies for model predictive direct speed control of permanent-magnet synchronous motors are known to be vulnerable to voltage errors. In this paper, we present a novel approach that compensates for voltage errors arising from inverter nonlinearity and bus voltage uncertainties, while remaining [...] Read more.
Traditional strategies for model predictive direct speed control of permanent-magnet synchronous motors are known to be vulnerable to voltage errors. In this paper, we present a novel approach that compensates for voltage errors arising from inverter nonlinearity and bus voltage uncertainties, while remaining unaffected by parameter errors. Initially, we conducted a detailed analysis to assess the impact of inverter nonlinearity and bus voltage uncertainties. Subsequently, we proposed a voltage error compensation strategy based on bus voltage identification. Using this strategy, the identified voltage error is effectively compensated within candidate voltage vectors. To validate the effectiveness of our proposed method, we conducted comprehensive experiments. The results demonstrate notable improvements compared with traditional model predictive control. Specifically, our method successfully reduces the total harmonic distortion of phase currents from 23.2% and 49.6% to 11.6% and 13.9%, respectively. Additionally, it accurately identifies voltage errors, even in the presence of parameter errors. Overall, our proposed method presents a robust and reliable solution for addressing voltage errors, thereby enhancing the performance and stability of the system. Full article
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