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WEVJ
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Dynamic Control of Traction Motors for EVs
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Dynamic Control of Traction Motors for EVs

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 995

Special Issue Editors

Dr. Rezkallah Miloud

E-Mail Website
Guest Editor
1. Energy Intelligence Research and Innovation Center (CR2ie), 175, Rue De La Vérendrye, Sept-Îles, QC, Canada
2. Electrical Engineering Department, Ecole de Technologie Superieure (ETS), Montreal, QC H3C 1K3, Canada
Interests: control and design of microgrids; renewable energy generation and applications; energy storage systems; digital twin
Prof. Dr. Ambrish Chandra

E-Mail Website
Guest Editor
Electrical Engineering Department, Ecole de Technologie Superieure (ETS), Montreal, QC H3C 1K3, Canada
Interests: UPQC; power quality; harmonic compensation; active power filters; statcom; dstatcom; renewable energy; wind energy; solar energy; integration with grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite researchers, academicians, and industry experts to contribute to our Special Issue focusing on the "Dynamic Control of Traction Motors for EVs". As electric vehicles continue to revolutionize the automotive landscape, the efficient and dynamic control of traction motors becomes paramount for achieving optimal performance, energy efficiency, and overall sustainability.

This Special Issue aims to explore advancements in the dynamic control strategies, algorithms, and technologies employed in traction motors for electric vehicles. Topics of interest include, but are not limited to, the following:

  1. Advanced control algorithms:
    • Model predictive control (MPC);
    • Adaptive control strategies;
    • Machine learning-based approaches.
  2. Real-time optimization techniques:
    • Dynamic programming;
    • Sliding mode control;
    • Nonlinear control methods.
  3. Fault diagnosis and tolerance:
    • Fault detection and isolation;
    • Fault-tolerant control;
    • Reliability analysis.
  4. Integration with vehicle systems:
    • Powertrain control integration;
    • Energy management strategies;
    • Cooperative control with other vehicle components.
  5. Hardware and software solutions:
    • Embedded systems for traction motor control;
    • Hardware-in-the-loop (HIL) simulations;
    • Cyber–physical systems for EVs.
  6. Energy efficiency and sustainability:
    • Regenerative braking systems;
    • Energy harvesting techniques;
    • Life cycle assessment of control strategies.
  7. Battery charging technologies:
    • Onboard–offboard charging technologies;
    • Fast charging techniques;
    • Wireless charging technologies;
    • Green chargers.
  8. Battery management systems (BMSs):
    • Active and passive technologies for BMSs;
    • Architecture and advancements in BMSs;
    • Battery twinning systems for BMSs.
  9. Digital twins for EVs:
    • Intelligent transportation system;
    • Autonomous vehicles;
    • Internet of things and big data.

Dr. Rezkallah Miloud
Prof. Dr. Ambrish Chandra
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

  • dynamic control
  • motor
  • EV
  • autonomous vehicles

Published Papers (1 paper)

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Research

24 pages, 6162 KiB  
Article
Power Signal Analysis for Early Fault Detection in Brushless DC Motor Drivers Based on the Hilbert–Huang Transform
by David Marcos-Andrade, Francisco Beltran-Carbajal, Eduardo Esquivel-Cruz, Ivan Rivas-Cambero, Hossam A. Gabbar and Alexis Castelan-Perez
World Electr. Veh. J. 2024, 15(4), 159; https://doi.org/10.3390/wevj15040159 - 10 Apr 2024
Viewed by 561
Abstract
Brushless DC machines have demonstrated significant advantages in electrical engineering by eliminating commutators and brushes. Every year, these machines increase their presence in transportation applications. In this sense, early fault identification in these systems, specifically in the electronic speed controllers, is relevant for [...] Read more.
Brushless DC machines have demonstrated significant advantages in electrical engineering by eliminating commutators and brushes. Every year, these machines increase their presence in transportation applications. In this sense, early fault identification in these systems, specifically in the electronic speed controllers, is relevant for correct device operation. In this context, the techniques reported in the literature for fault identification based on the Hilbert–Huang transform have shown efficiency in electrical systems. This manuscript proposes a novel technique for early fault identification in electronic speed controllers based on the Hilbert–Huang transform algorithm. Initially, currents from the device are captured with non-invasive sensors in a time window during motor operation. Subsequently, the signals are processed to obtain pertinent information about amplitudes and frequencies using the Hilbert–Huang transform, focusing on fundamental components. Then, estimated parameters are evaluated by computing the error between signals. The existing electrical norms of a balanced system are used to identify a healthy or damaged driver. Through amplitude and frequency error analysis between three-phase signals, early faults caused by system imbalances such as current increasing, torque reduction, and speed reduction are detected. The proposed technique is implemented through data acquisition devices at different voltage conditions and then physical signals are evaluated offline through several simulations in the Matlab environment. The method’s robustness against signal variations is highlighted, as each intrinsic mode function serves as a component representation of the signal and instantaneous frequency computation provides resilience against these variations. Two study cases are conducted in different conditions to validate this technique. The experimental results demonstrate the effectiveness of the proposed method in identifying early faults in brushless DC motor drivers. This study provides data from each power line within the electronic speed controller to detect early faults and extend different approaches, contributing to addressing early failures in speed controllers while expanding beyond the conventional focus on motor failure analysis. Full article
(This article belongs to the Special Issue Dynamic Control of Traction Motors for EVs)
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