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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: 25 March 2025 | Viewed by 2376

Special Issue Editors

Prof. Dr. Senyi Liu

E-Mail Website
Guest Editor
Institute of Rail Transit, Tongji University, Shanghai, China
Interests: multiphase machine; model predictive control
Dr. Zaixin Song

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
Interests: modern electric drives; electric machinery; fault-tolerant control; robot actuator design; multiphysics
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Yang

E-Mail Website
Guest Editor
School of Rail Transportation, Soochow University, Suzhou, China
Interests: power electronics; onboard charger; wireless power transfer
Special Issues, Collections and Topics in MDPI journals
Dr. Chunhua Liu

E-Mail Website
Guest Editor
School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
Interests: electric motor and drives; electric vehicle technologies; wireless power transfer; smart grid and micro grid
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

14 pages, 7786 KiB  
Article
Model Predictive Current Control for Six-Phase PMSM with Steady-State Performance Improvement
by Yongcan Huang, Senyi Liu, Rui Pang, Xingbang Liu and Xi Rao
Energies 2024, 17(21), 5273; https://doi.org/10.3390/en17215273 - 23 Oct 2024
Viewed by 580
Abstract
The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces [...] Read more.
The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range. Full article
(This article belongs to the Special Issue Data-Enabled Control and Design Solutions for Electric Machines and Power Electronics in Transportation Electrification)
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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 2 | Viewed by 1260
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
(This article belongs to the Special Issue Data-Enabled Control and Design Solutions for Electric Machines and Power Electronics in Transportation Electrification)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Data-Driven Model Predictive Control for Energy-Efficient Traction Systems
Authors: Prof. Kang jingsong
Affiliation: Tongji University

Title: Fault Diagnosis and Prognostics for Power Electronic Converters in Electric Transportation with Data Analysis
Authors: Dr. Fei Zhao
Affiliation: Harbin Institute of Technology (Shenzhen)

Title: Data-Enabled Adaptive Control for Electric Vehicles: Design, Simulation, and Field Trials
Authors: Prof. Ying Fan
Affiliation: Southeast University

Title: Big Data Analytics for Enhanced Energy Management and Grid Integration of Electric Transportation Systems
Authors: Dr. Patrick Hu
Affiliation: The University of Auckland

Title: A Comprehensive Review of Advanced Power Electronic Converters and Control Strategies for Electric Vehicle Charging Infrastructure
Authors: Dr. Feng Yu
Affiliation: NanTong University

Title: Optimal Design of Permanent Magnet Synchronous Machines for Electric Vehicles Using Genetic Algorithms
Authors: Dr. Shuangxia Niu
Affiliation: The Hong Kong Polytechnic University

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