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Towards the future of smart electric vehicles: Digital twin technology

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  • Bhatti, Ghanishtha
  • Mohan, Harshit
  • Raja Singh, R.

Abstract

Worldwide, transportation accounts for 18% of global carbon dioxide emissions (as of 2019). In order to battle the impending threat of climate change, consumers and industry must adopt sustainable transport that complies with the United Nations Sustainable Development Goals of increased energy efficiency and reduced greenhouse gas emissions. To fulfil these objectives, a new class of vehicles has recently emerged, smart electric vehicles, which is forecasted to reduce carbon dioxide emissions up to 43% as compared to diesel engine vehicles. However, to bring these vehicles to the mainstream, supporting architecture is needed to optimize them in a sustainable manner. One such novel architecture is Digital Twin Technology, which is a virtual mapping technology, extending from it, capable of investigating the lifecycle of multisystem bodies in a digital environment. In recent years, digital twin technology is becoming an underpinning area of research globally. As a result, novel individual research covering digital twin implementation on various aspects of smart vehicles has transpired in research and industrial studies, consequently allowing digital twin technology to evolve over the years. This work aims to bridge the gap between individual research to provide a comprehensive review from a technically-informed and academically neutral standpoint. Conceptual groundwork of digital twin technology is built systematically for the reader, to allow insight into its inception and evolution. The study sifts the digital twin domain for contributions in smart vehicle systems, exploring its potential and contemporaneous challenges to realization. The study then proceeds to review recent research and commercial projects for innovation within this domain. To the knowledge of the authors, this is the first extensive review of the application of digital twin technology in smart electric vehicles. The review has been systematically classified into specific domains within the smart vehicle system such as autonomous navigation control, advanced driver assistance systems, vehicle health monitoring, battery management systems, vehicle power electronics, and electrical power drive systems. An in-depth discussion of each vehicle subsystem is undertaken to present this review as an eclectic panorama of the smart vehicle system. This review further facilitates appreciation of the role of digital twin technology within each classification from a holistic technical perspective. Finally, the work ends with an inspection of the techno-socio-economic impact of digital twin technology that will revolutionize mainstream vehicle technology and the obstacles for further development.

Suggested Citation

  • Bhatti, Ghanishtha & Mohan, Harshit & Raja Singh, R., 2021. "Towards the future of smart electric vehicles: Digital twin technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:rensus:v:141:y:2021:i:c:s1364032121000964
    DOI: 10.1016/j.rser.2021.110801
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    References listed on IDEAS

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    1. Qiu, Dawei & Wang, Yi & Hua, Weiqi & Strbac, Goran, 2023. "Reinforcement learning for electric vehicle applications in power systems:A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    2. Antônio Rufino Júnior, Carlos & Sanseverino, Eleonora Riva & Gallo, Pierluigi & Koch, Daniel & Schweiger, Hans-Georg & Zanin, Hudson, 2022. "Blockchain review for battery supply chain monitoring and battery trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    3. Feng, Hailin & Lv, Haibin & Lv, Zhihan, 2023. "Resilience towarded Digital Twins to improve the adaptability of transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    4. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    5. Huang, Yufeng & Tao, Jun & Sun, Gang & Wu, Tengyun & Yu, Liling & Zhao, Xinbin, 2023. "A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis," Energy, Elsevier, vol. 270(C).
    6. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    7. Almansour, Mohammed, 2022. "Electric vehicles (EV) and sustainability: Consumer response to twin transition, the role of e-businesses and digital marketing," Technology in Society, Elsevier, vol. 71(C).

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