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

Viktor Rjabtšikov (), Anton Rassõlkin (), Karolina Kudelina, Ants Kallaste and Toomas Vaimann
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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

Energies, 2023, vol. 16, issue 19, 1-17

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.

Keywords: digital twin; propulsion drive system; automotive; vehicle propulsion; powertrain; virtual modeling; simulation; performance optimization; real-time monitoring; vehicle modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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