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Advanced Electrical Machine and Power Electronics for the Charging and...
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Advanced Electrical Machine and Power Electronics for the Charging and Drive System of Electric Vehicles (EVs)

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2270

Special Issue Editors

Prof. Dr. Jianguo Zhu

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW 2006, Australia
Interests: computational electromagnetics; measurement and modeling of magnetic properties of materials; electrical machines and drives; power electronics; renewable energy systems; smart microgrids; digital energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yu Wang

E-Mail Website
Guest Editor
Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai, China
Interests: renewable energy generation and drive technology
Dr. Weiwei Geng

E-Mail Website
Guest Editor
Department of Electrical Engineering, Nanjing University of Science and Technology, Nanjing, China
Interests: high power density permanent magnet motors for all-electric propulsion and traction systems

Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) are rapidly transforming the automotive landscape, offering sustainable and energy-efficient transportation options. The effectiveness and efficiency of EVs hinge on the design and control of their drive systems, encompassing motors, power electronics, energy management, and integration with other vehicle components. In the realm of electric vehicles, electrical machines have evolved into sophisticated and highly efficient devices designed to meet the demands of modern transportation. These machines play a critical role in converting electrical energy into mechanical power, whether for propelling the vehicle or regenerating energy during deceleration. Power electronics, on the other hand, form the bridge that connects the vehicle to the charging infrastructure and ensures the safe, efficient conversion of electrical energy. They enable fast charging, bidirectional energy flow, and power management, revolutionizing how we charge our EVs and manage energy in the grid. 

This Special Issue seeks to advance the understanding of electric vehicle drive systems, exploring the latest innovations and addressing the challenges. We invite researchers, engineers, and experts in this field to submit their original research, review articles, and insights to foster knowledge exchange and shape the future of electric mobility.

Prof. Dr. Jianguo Zhu
Dr. Yu Wang
Dr. Weiwei Geng
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • new principles and novel topologies of motors for drive systems
  • coupled and intelligent analysis of multi-discipline fields in drive motor systems
  • advanced and data-driven control strategy/analysis for drive and charging systems
  • fault diagnosis and health management of drive/charging systems
  • integration technology for drive/charging system
  • noise and vibration control
  • sustainability and environmental Impact
  • multilevel converters for charging system
  • energy management for V2G systems

Published Papers (2 papers)

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Research

21 pages, 4797 KiB  
Article
Sliding Mode Control of an Electric Vehicle Driven by a New Powertrain Technology Based on a Dual-Star Induction Machine
by Basma Benbouya, Hocine Cheghib, Daniela Chrenko, Maria Teresa Delgado, Yanis Hamoudi, Jose Rodriguez and Mohamed Abdelrahem
World Electr. Veh. J. 2024, 15(4), 155; https://doi.org/10.3390/wevj15040155 - 09 Apr 2024
Viewed by 647
Abstract
This article examines a new powertrain system for electric vehicles based on the dual-star induction machine, presented as a promising option due to its significant advantages in terms of performance, energy efficiency, and reliability. This system could play a key role in the [...] Read more.
This article examines a new powertrain system for electric vehicles based on the dual-star induction machine, presented as a promising option due to its significant advantages in terms of performance, energy efficiency, and reliability. This system could play a key role in the evolution of electro-mobility technology. The dual-star induction machine reduces electromagnetic torque fluctuations, limits current harmonics, improves power factor, and enables half-speed operation. Our study focuses on the control strategy and operation of the traction chain for electric vehicles propelled by the dual-star induction machine (DSIM) using Matlab software with version 2017. We integrate the battery as the main energy source, along with three-level static converters for energy conversion in the vehicle’s four operating quadrants. We have opted for sliding mode control, which has proven to be feasible and robust against external disturbances. Although we have modeled driver behavior, we consider it as an aspect of control, to which we add the driving profile to guide our evaluation of the control to be used for vehicle operation. The results of our study demonstrate the reliability and robustness of DSIM for electric vehicle motorization and speed control. Promoting this technology is essential to improve the overall performance and efficiency of electric vehicles, especially in traction and braking modes for energy recovery. This underscores the importance of DSIM in the sustainable development of the electric transportation system. Full article
(This article belongs to the Special Issue Advanced Electrical Machine and Power Electronics for the Charging and Drive System of Electric Vehicles (EVs))
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17 pages, 6709 KiB  
Article
An Open-Circuit Fault Diagnosis System Based on Neural Networks in the Inverter of Three-Phase Permanent Magnet Synchronous Motor (PMSM)
by Kenny Sau Kang Chu, Kuew Wai Chew, Yoong Choon Chang and Stella Morris
World Electr. Veh. J. 2024, 15(2), 71; https://doi.org/10.3390/wevj15020071 - 16 Feb 2024
Viewed by 1132
Abstract
Three-phase motors find extensive applications in various industries. Open-circuit faults are a common occurrence in inverters, and the open-circuit fault diagnosis system plays a crucial role in identifying and addressing these faults to enhance the safety of motor operations. Nevertheless, the current open-circuit [...] Read more.
Three-phase motors find extensive applications in various industries. Open-circuit faults are a common occurrence in inverters, and the open-circuit fault diagnosis system plays a crucial role in identifying and addressing these faults to enhance the safety of motor operations. Nevertheless, the current open-circuit fault diagnosis system faces challenges in precisely detecting specific faulty switches. The proposed work presents a neural network-based open-circuit fault diagnosis system for identifying faulty power switches in inverter-driven motor systems. The system leverages trained phase-to-phase voltage data from the motor to recognize the type and location of faults in each phase with high accuracy. Employing separate neural networks for each of the three phases in a three-phase permanent magnet synchronous motor, the system achieves an outstanding overall fault detection accuracy of approximately 99.8%, with CNN and CNN-LSTM architectures demonstrating superior performance. This work makes two key contributions: (1) implementing neural networks to significantly improve the accuracy of locating faulty switches in open-circuit fault scenarios, and (2) identifying the optimal neural network architecture for effective fault diagnosis within the proposed system. Full article
(This article belongs to the Special Issue Advanced Electrical Machine and Power Electronics for the Charging and Drive System of Electric Vehicles (EVs))
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