Energy harvesting and strain sensing in smart tire for next generation autonomous vehicles. (15th December 2018)

• Record Type: Journal Article
• Authors: Maurya, Deepam; Kumar, Prashant; Khaleghian, Seyedmeysam; Sriramdas, Rammohan; Kang, Min Gyu; Kishore, Ravi Anant; Kumar, Vireshwar; Song, Hyun-Cheol; Park, Jung-Min (Jerry); Taheri, Saied; Priya, Shashank
• Is Part Of: Applied energy. Volume 232(2018)
• Page Start: 312
• Page End: 322
• Publication Date: 2018-12-15
• Subjects: Energy harvesting -- Piezoelectric sensor -- Smart tires; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.09.183
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Developed high energy density harvester-cum-sensor to power wireless data transfer. The generated energy from tire strain harvester was enough to directly power 78 LEDs. A mathematical model for tire-road interaction and piezoelectric response. Real environment test for sensing tire-road interaction under different variables. Demonstrated the self-powered smart tire sensor for autonomous vehicles. Abstract: We demonstrate the feasibility of the strain energy harvesting from the automobile tires, powering wireless data transfer with enhanced frame rates, and self-powered strain sensing. For this, we used a flexible organic piezoelectric material for continuous power generation and monitoring of the variable strain experienced by a tire under different driving conditions. Power output of ∼580 µW at 16 Hz (∼112 km/h) from the energy-harvester and mounted on a section of a tire, is sufficient to power 78 LEDs. We further demonstrate that the stored energy was sufficient to power the wireless system that transmits tire deformation data with an enhanced frame rate to control system of a vehicle. Using sensors mounted on a tire of a mobile test rig, measurements were conducted on different terrains with varying normal loads and speeds to quantify the sensitivity and self-powered sensing operation. These results provide a foundation for self-powered real-time sensing and energy efficient data transfer in autonomous vehicles.