Strong commercial interest in the deployment of autonomous vehicles is driving the development of smart tires that will be required for meeting the safety standards. Millions of self-driving cars are expected to be deployed in the near future, which emphasizes the urgent need for the design of precise control and communication subsystem. Smart tires provide the ability to dynamically sense road – tire interaction parameters which are critical towards the design of robust intelligent controls. Ideally, self-powered sensors should be embedded in the tires and sensed data should be securely transmitted at high frequencies to enable real-time control. Prior solutions have not been able to meet these requirements, often resulting in a cumbersome multi-step integration process which adds to the cost and management.
To address above mentioned issues, in present work, we provide a breakthrough in the 3D-printing process of the advanced functional materials, and demonstrated a cost-effective and viable solution for the next generation of smart tires. Here, we developed a novel aerosol-based 3D-printing process for printing graphene-based sensors for smart tires coupled with energy harvesting, machine learning, and secure data transmission. Our innovative 3D-printing process can be used to directly print sensors on the inner liner surface of a tire. The wrinkled microstructure of our 3D-printed graphene sheets allowed withstanding a large deformation without failure.
For demonstrating real-life applications, we integrated our 3D-printed graphene sensors into an actual tire of a mobile test rig to sense environmental conditions during motion. A substantial change was observed in the output signal due to changes in various parameters like normal load, speed, and tire pressure. We performed theoretical calculations to successfully model and simulate the experimental results of 3D printed sensors mounted on tire. To further demonstrate the practical feasibility of the 3D printed tire sensor, we developed machine learning algorithm for estimating the tire pressure condition using data collected from the 3D printed sensors. Remarkably, most of the data points were found to be within a close range of zero error line, which exhibited the viability of the printed sensor in monitoring the tire pressure successfully. We also demonstrated wireless data transfer by harvesting tire strain energy and developed an energy-efficient technique to employ the secure wireless data transfer.
We believe our transformative results will pave the path for the next generation of smart tires for autonomous vehicles. The leading authors of this manuscript are affiliated to the leading centers and institutes, namely, NSF I/UCRC: Center for Tire Research (CenTiRe) at Virginia Tech, Department of Mechanical Engineering at Virginia Tech, and the Department of Materials Science and Engineering (Penn State University).
You can find more details about this work in our research article published in Nature Communications: Maurya, D., Khaleghian, S., Sriramdas, R. et al. 3D Printed Graphene-based Self-Powered Strain Sensors for Smart Tires in Autonomous Vehicles. Nature Communications (2020). https://doi.org/0.1038/s41467-020-19088-y
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