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

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

Abstract

The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating 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. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability.

Original languageEnglish (US)
Article number5392
JournalNature communications
Volume11
Issue number1
DOIs
StatePublished - Dec 1 2020
Externally publishedYes

Bibliographical note

Funding Information:
D.M. and S.P. gratefully acknowledge financial support from National Science Foundation through I/UCRC: Center for Energy-Harvesting Materials and Systems (CEHMS). P.K. acknowledges the financial support through Office of Naval Research through grant number N000141712520. Y.Y. acknowledges the financial support through the National Science Foundation through grant number ECCS-1832865. M.G.K. acknowledges the support through the Air Force Office of Scientific Research under award number FA9550-17-1-0341. R.S. acknowledges the support through Office of Naval Research through grant number N000141613043. H.C.S. acknowledges support through the NSF-CREST grant number HRD-1547771, the Energy Technology Development Project (KETEP) grant funded by the Ministry of Trade, Industry and Energy, Republic of Korea (2018201010636A), and the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CAP-17-04-KRISS). R.A.K. acknowledges the financial support through DARPA MATRIX program.

Publisher Copyright:
© 2020, The Author(s).

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