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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
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    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.
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    Cited by:

    1. Magnus Værbak & Joy Dalmacio Billanes & Bo Nørregaard Jørgensen & Zheng Ma, 2024. "A Digital Twin Framework for Simulating Distributed Energy Resources in Distribution Grids," Energies, MDPI, vol. 17(11), pages 1-36, May.

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