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Advanced Electric Powertrain Technologies for Electric Vehicles
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Advanced Electric Powertrain Technologies for Electric Vehicles

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

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 3809

Special Issue Editors

Prof. Dr. Qingsong Wang

E-Mail Website
Guest Editor
The Power Electronics and Industrial Control Research Group (GRÉPCI), École de Technologie Supérieure (ÉTS), Montréal, QC H3C 1K3, Canada
Interests: electrical machines and drives; magnetic gear; wireless power transfer
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junnian Wang

E-Mail Website
Guest Editor
College of Automotive Engineering, Jilin University, Changchun 130015, China
Interests: advanced e-driveline design; vehicle dynamics control; energy management strategy; chassis-by-wire integrated design and autonomous driving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to increasing concerns regarding energy saving and environmental protection, electric vehicles have garnered significant attention in recent years. Electric powertrains, as the key systems utilized in electric vehicles, offer novel advantages, including a more compact size, lower cost, higher power density, enhanced efficiency, reduced noise and vibration, and improved reliability. This Special Issue is devoted to the latest developments in advanced electric powertrain technologies with applications in electric vehicles. It welcomes the submission of papers from both academia and industry that present the technical progress observed in advanced motor topologies, magnetic gears, integrated electric drive systems, multi-physics optimization, thermal management techniques and system-level design.

Topics of interest include, but are not limited to, the following:

  • New machine topologies
  • Magnetic gears
  • Integrated electric drive systems
  • Advanced motor control strategies
  • Multi-physics design optimization
  • Noise and vibration analysis and reduction
  • Advanced thermal management techniques
  • System-level design optimization of the electric powertrain

Prof. Dr. Qingsong Wang
Prof. Dr. Junnian Wang
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

  • electrical machines
  • magnetic gear
  • integrated electric drive systems
  • permanent magnet machines
  • flux modulated machines
  • multi-physics optimization
  • novel machine topology
  • noise and vibration

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Published Papers (3 papers)

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Research

27 pages, 5623 KiB  
Article
Torque Ripple Minimization for Switched Reluctance Motor Drives Based on Harris Hawks–Radial Basis Function Approximation
by Jackson Oloo and Szamel Laszlo
Energies 2025, 18(4), 1006; https://doi.org/10.3390/en18041006 - 19 Feb 2025
Viewed by 336
Abstract
Switched reluctance motor drives are becoming attractive for electric vehicle propulsion systems due to their simple and cheap construction. However, their operation is degraded by torque ripples due to the salient nature of the stator and rotor poles. There are several methods of [...] Read more.
Switched reluctance motor drives are becoming attractive for electric vehicle propulsion systems due to their simple and cheap construction. However, their operation is degraded by torque ripples due to the salient nature of the stator and rotor poles. There are several methods of mitigating torque ripples in switched reluctance motors (SRMs). Apart from changing the geometrical design of the motor, the less costly technique involves the development of an adaptive switching strategy. By selecting suitable turn-on and turn-off angles, torque ripples in SRMs can be significantly reduced. This work combines the benefits of Harris Hawks Optimization (HHO) and Radial Basis Functions (RBFs) to search and estimate optimal switching angles. An objective function is developed under constraints and the HHO is utilized to perform search stages for optimal switching angles that guarantee minimal torque ripples at every speed and current operating point. In this work, instead of storing the θon, θoff  values in a look-up table, the values are passed on to an RBF model to learn the nonlinear relationship between the columns of data from the HHO and hence transform them into high-dimensional outputs. The values are used to train an enhanced neural network (NN) in an adaptive switching strategy to address the nonlinear magnetic characteristics of the SRM. The proposed method is implemented on a current chopping control-based SRM 8/6, 600 V model. Percentage torque ripples are used as the key performance index of the proposed method. A fuzzy logic switching angle compensation strategy is implemented in numerical simulations to validate the performance of the HHO-RBF method. Full article
(This article belongs to the Special Issue Advanced Electric Powertrain Technologies for Electric Vehicles)
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24 pages, 6772 KiB  
Article
Nine-Switch Multiport Converter Applied to Battery-Powered Tramway with Reduced Leakage Current
by Antonio D. D. Almeida, Fabrício Bradaschia, Cassiano Rech, Carolina A. Caldeira, Rafael C. Neto and Gustavo M. S. Azevedo
Energies 2024, 17(6), 1434; https://doi.org/10.3390/en17061434 - 16 Mar 2024
Cited by 2 | Viewed by 1433
Abstract
Electrically powered rail transport is constantly increasing in order to meet the high demand for people and cargo transportation, whether with high-speed trains, subways, suburban trains, or electric tramways. In these types of applications, power electronics solutions such as integrated and efficient converters [...] Read more.
Electrically powered rail transport is constantly increasing in order to meet the high demand for people and cargo transportation, whether with high-speed trains, subways, suburban trains, or electric tramways. In these types of applications, power electronics solutions such as integrated and efficient converters with multiple functionalities are highly desirable. Among these converters, one family stands out for its ability to generating multiple output terminals with reduced number of switches, namely, the nine-switch converter. Therefore, this paper proposes a multiport converter solution based on the nine-switch converter topology that integrates multiple functionalities with a reduced switch count. The converter, responsible for the power drive of the electric tramway, is exclusively powered by a battery. Moreover, it presents a strategically connected passive filter that provides a low-impedance path for high-frequency currents, avoiding leakage of the current circulation in the induction motor. Its phase-shift pulse-width modulation is capable of reducing the high-frequency components of the current delivered by the battery. The energy storage system is designed to optimize the system capacity based on the known real load profile of a public tramway, with a maximum power of 532.1 kW. The control system is designed and applied considering the battery as the energy source. Simulations were performed in the Matlab/Simulink environment to validate the proposed system, along with experiments using a reduced-scale prototype controlled by the dSPACE platform. The results present the converter’s proper operation with integration of the source and AC load, presenting improved features compared with conventional solutions in terms of reduced leakage of the current circulation from the AC load and reduced battery current ripple. Full article
(This article belongs to the Special Issue Advanced Electric Powertrain Technologies for Electric Vehicles)
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18 pages, 14757 KiB  
Article
Surrogate-Based Multi-Objective Optimization of Flux-Focusing Halbach Coaxial Magnetic Gear
by Aran Shoaei, Farnam Farshbaf-Roomi and Qingsong Wang
Energies 2024, 17(3), 608; https://doi.org/10.3390/en17030608 - 26 Jan 2024
Cited by 5 | Viewed by 1203
Abstract
Due to their contact-free and low-maintenance features, magnetic gears (MGs) have been increasingly investigated to amplify the torque of electric motors in electric vehicles (EVs). In order to meet the requirements of propelling EVs, it is essential to design an MG with a [...] Read more.
Due to their contact-free and low-maintenance features, magnetic gears (MGs) have been increasingly investigated to amplify the torque of electric motors in electric vehicles (EVs). In order to meet the requirements of propelling EVs, it is essential to design an MG with a high torque density. In this paper, a novel flux-focusing Halbach coaxial MG (FFH-CMG) is proposed, which combines the advantages of flux focusing and Halbach permanent magnet (PM) arrays. The proposed structure has a higher torque performance and greater efficiency than conventional structures. A multi-objective design optimization based on a surrogate model is implemented to achieve the maximum volumetric torque density (VTD), torque-per-PM volume (TPMV), and efficiency, as well as the minimum torque ripple, in the proposed FFH-CMG. The employed optimization approach has a higher accuracy and is less time-consuming compared to the conventional optimization methods based on direct finite-element analysis (FEA). The performance of the proposed FFH-CMG is then investigated through 2D-FEA. According to the simulation results, the optimized FFH-CMG can achieve a VTD of 411 kNm/m3, and a TPMV of 830 kNm/m3, which are significantly larger than those of the existing MGs and make the proposed FFH-CMG very suitable for EV applications. Full article
(This article belongs to the Special Issue Advanced Electric Powertrain Technologies for Electric Vehicles)
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