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

Authors :
Mechanical Engineering
Materials Science and Engineering
Electrical and Computer Engineering
Institute for Critical Technology and Applied Science
Center for Tire Research
Maurya, Deepam
Khaleghian, Seyedmeysam
Sriramdas, Rammohan
Kumar, Prashant
Kishore, Ravi Anant
Kang, Min-Gyu
Kumar, Vireshwar
Song, Hyun-Cheol
Lee, Seul-Yi
Yan, Yongke
Park, Jung-Min (Jerry)
Taheri, Saied
Priya, Shashank
Mechanical Engineering
Materials Science and Engineering
Electrical and Computer Engineering
Institute for Critical Technology and Applied Science
Center for Tire Research
Maurya, Deepam
Khaleghian, Seyedmeysam
Sriramdas, Rammohan
Kumar, Prashant
Kishore, Ravi Anant
Kang, Min-Gyu
Kumar, Vireshwar
Song, Hyun-Cheol
Lee, Seul-Yi
Yan, Yongke
Park, Jung-Min (Jerry)
Taheri, Saied
Priya, Shashank
Publication Year :
2020

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. 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.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1417052196
Document Type :
Electronic Resource