3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles

Deepam Maurya; Seyedmeysam Khaleghian; Rammohan Sriramdas; Prashant Kumar; Ravi Anant Kishore; Min Gyu Kang; Vireshwar Kumar; Hyun-Cheol Song; Seul-Yi Lee; Yongke Yan; Jung-Min Park; Saied Taheri; Shashank Priya

doi:10.1038/s41467-020-19088-y

Nature Communications (Oct 2020)

3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles

Deepam Maurya,
Seyedmeysam Khaleghian,
Rammohan Sriramdas,
Prashant Kumar,
Ravi Anant Kishore,
Min Gyu Kang,
Vireshwar Kumar,
Hyun-Cheol Song,
Seul-Yi Lee,
Yongke Yan,
Jung-Min Park,
Saied Taheri,
Shashank Priya

Affiliations

Deepam Maurya: Department of Mechanical Engineering, Virginia Tech
Seyedmeysam Khaleghian: Department of Engineering Technology, Texas State University
Rammohan Sriramdas: Department of Materials Science and Engineering, Penn State University
Prashant Kumar: Department of Mechanical Engineering, Virginia Tech
Ravi Anant Kishore: Department of Mechanical Engineering, Virginia Tech
Min Gyu Kang: Department of Materials Science and Engineering, Penn State University
Vireshwar Kumar: Department of Electrical and Computer Engineering, Virginia Tech
Hyun-Cheol Song: Center for Electronic Materials, Korea Institute of Science and Technology (KIST)
Seul-Yi Lee: Institute for Critical Technology and Applied Science (ICTAS), Virginia Tech
Yongke Yan: Department of Materials Science and Engineering, Penn State University
Jung-Min Park: Department of Electrical and Computer Engineering, Virginia Tech
Saied Taheri: Department of Mechanical Engineering, Virginia Tech
Shashank Priya: Department of Materials Science and Engineering, Penn State University

DOI: https://doi.org/10.1038/s41467-020-19088-y
Journal volume & issue: Vol. 11, no. 1
pp. 1 – 10

Abstract

Read online

Designing efficient sensors for smart tires for autonomous vehicles remains a challenge. Here, the authors present a tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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