Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 533

The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.

Towards the future of smart electric vehicles: Digital twin technology

Bhatti, Ghanishtha, Mohan, Harshit, Raja Singh, R.·2021·Renewable and Sustainable Energy Reviews

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.rser.2021.110801

Cites

79 of 79 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 79 references

Cited by

See all 533

Metadata

Authors
Bhatti, G., Mohan, H., & Raja Singh, R.
Venue
Renewable and Sustainable Energy Reviews
Publisher
Elsevier BV
Published
2021-05
Volume
141
Pages
110801
ISSN
1364-0321 (ISSN-L)
OpenAlex
W3129920713
Cited by
533
References
79
FWCI
46.34
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Towards the future of smart electric vehicles: Digital twin technology. https://app.cochat.ai/papers/10.1016/j.rser.2021.110801
The paper
Bhatti, G., Mohan, H., & Raja Singh, R. (2021). Towards the future of smart electric vehicles: Digital twin technology. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2021.110801

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.