IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6952-d1253734.html
   My bibliography  Save this article

Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis

Author

Listed:
  • Viktor Rjabtšikov

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Anton Rassõlkin

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Karolina Kudelina

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Ants Kallaste

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Toomas Vaimann

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

This article explores the transformative potential of digital twin (DT) technology in the automotive sector, focusing on its applications in enhancing propulsion drive systems. DT technology, a virtual representation of physical objects, has gained momentum due to its real-time monitoring and analysis capabilities. Within the automotive industry, where propulsion systems dictate vehicle performance, DTs offer a game-changing approach. Propulsion drive systems encompass electric motors, transmissions, and related components, significantly impacting efficiency and power delivery. Traditional design and testing methods need help addressing these systems’ intricate interactions. This article aims to investigate how DTs can revolutionize propulsion systems. The study examines various applications of DTs, ranging from predictive maintenance to performance optimization and energy efficiency enhancement. The article underscores the technology’s potential by reviewing case studies and real-world implementations. It also outlines challenges tied to integration and validation. In unveiling the capabilities of DT technology for propulsion systems, this article contributes to a comprehensive understanding of its role in shaping a more data-driven and efficient automotive industry.

Suggested Citation

  • Viktor Rjabtšikov & Anton Rassõlkin & Karolina Kudelina & Ants Kallaste & Toomas Vaimann, 2023. "Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis," Energies, MDPI, vol. 16(19), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6952-:d:1253734
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6952/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/6952/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. R. Al-Ali & Ragini Gupta & Tasneem Zaman Batool & Taha Landolsi & Fadi Aloul & Ahmad Al Nabulsi, 2020. "Digital Twin Conceptual Model within the Context of Internet of Things," Future Internet, MDPI, vol. 12(10), pages 1-15, September.
    2. Hadi Ashraf Raja & Karolina Kudelina & Bilal Asad & Toomas Vaimann & Ants Kallaste & Anton Rassõlkin & Huynh Van Khang, 2022. "Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines," Energies, MDPI, vol. 15(24), pages 1-16, December.
    3. Sakdirat Kaewunruen & Jessada Sresakoolchai & Wentao Ma & Olisa Phil-Ebosie, 2021. "Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    4. Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mikko Tahkola & Áron Szücs & Jari Halme & Akhtar Zeb & Janne Keränen, 2022. "A Novel Machine Learning-Based Approach for Induction Machine Fault Classifier Development—A Broken Rotor Bar Case Study," Energies, MDPI, vol. 15(9), pages 1-23, May.
    2. Krzysztof Kolano & Artur Jan Moradewicz & Bartosz Drzymała & Jakub Gęca, 2022. "Influence of the Placement Accuracy of the Brushless DC Motor Hall Sensor on Inverter Transistor Losses," Energies, MDPI, vol. 15(5), pages 1-13, March.
    3. Ahmed Belkhadir & Remus Pusca & Driss Belkhayat & Raphaël Romary & Youssef Zidani, 2023. "Analytical Modeling, Analysis and Diagnosis of External Rotor PMSM with Stator Winding Unbalance Fault," Energies, MDPI, vol. 16(7), pages 1-23, April.
    4. Roberta Avanzato & Francesco Beritelli & Alfio Lombardo & Carmelo Ricci, 2023. "Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors," Future Internet, MDPI, vol. 15(7), pages 1-16, June.
    5. Siddique Akbar & Toomas Vaimann & Bilal Asad & Ants Kallaste & Muhammad Usman Sardar & Karolina Kudelina, 2023. "State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions," Energies, MDPI, vol. 16(17), pages 1-44, September.
    6. Milena Kajba & Borut Jereb & Tina Cvahte Ojsteršek, 2023. "Exploring Digital Twins in the Transport and Energy Fields: A Bibliometrics and Literature Review Approach," Energies, MDPI, vol. 16(9), pages 1-23, May.
    7. Toomas Vaimann & Jose Alfonso Antonino-Daviu & Anton Rassõlkin, 2023. "Novel Approaches to Electrical Machine Fault Diagnosis," Energies, MDPI, vol. 16(15), pages 1-4, July.
    8. Jose R. Huerta-Rosales & David Granados-Lieberman & Juan P. Amezquita-Sanchez & Arturo Garcia-Perez & Maximiliano Bueno-Lopez & Martin Valtierra-Rodriguez, 2022. "Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions," Energies, MDPI, vol. 15(22), pages 1-15, November.
    9. Sebastian Berhausen & Tomasz Jarek, 2022. "Analysis of Impact of Design Solutions of an Electric Machine with Permanent Magnets for Bearing Voltages with Inverter Power Supply," Energies, MDPI, vol. 15(12), pages 1-19, June.
    10. Hazrathosseini, Arman & Moradi Afrapoli, Ali, 2023. "The advent of digital twins in surface mining: Its time has finally arrived," Resources Policy, Elsevier, vol. 80(C).
    11. Yuriy Zhukovskiy & Aleksandra Buldysko & Ilia Revin, 2023. "Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current," Energies, MDPI, vol. 16(8), pages 1-23, April.
    12. Hadi Ashraf Raja & Karolina Kudelina & Bilal Asad & Toomas Vaimann & Ants Kallaste & Anton Rassõlkin & Huynh Van Khang, 2022. "Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines," Energies, MDPI, vol. 15(24), pages 1-16, December.
    13. Hoffstaedt, J.P. & Truijen, D.P.K. & Fahlbeck, J. & Gans, L.H.A. & Qudaih, M. & Laguna, A.J. & De Kooning, J.D.M. & Stockman, K. & Nilsson, H. & Storli, P.-T. & Engel, B. & Marence, M. & Bricker, J.D., 2022. "Low-head pumped hydro storage: A review of applicable technologies for design, grid integration, control and modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    14. João Vieira & João Poças Martins & Nuno Marques de Almeida & Hugo Patrício & João Gomes Morgado, 2022. "Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    15. Muhammad Usman Sardar & Toomas Vaimann & Lauri Kütt & Ants Kallaste & Bilal Asad & Siddique Akbar & Karolina Kudelina, 2023. "Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques," Energies, MDPI, vol. 16(15), pages 1-41, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6952-:d:1253734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.