In any automotive system, the tires play a very crucial role in defining both the safety and performance of the vehicle. The interaction between the tire and the road surface determines the vehicle's ability to accelerate, decelerate and steer. Having information about this interaction in real-time can be very valuable for the on-board advanced active safety systems to mitigate the risks ahead of time and keep the vehicle stable. The crucial information which can be obtained from the tire includes but are not limited to tire-road friction, tire forces (longitudinal, lateral), normal load, road surface characteristics and tire pressure. This information can be acquired through indirect vehicle dynamics based estimation algorithms or through direct measurements using sensors inside the tire. However, the indirect estimations fail to give an accurate measure of the vehicle state in certain conditions (e.g. side winds, road banking, surface change) and require ABS or VSC activation before the estimation begins. Therefore, to improve the performance of these active stability systems, direct measurement based approaches must be explored.
This research expands the applications of Intelligent tire and focuses on using the sensor based measurement approach to develop estimation algorithms relating to tire force measurement. A tri-axial accelerometer is attached to the inner liner of the tire (Intelligent Tire) and two of such tires are placed on an instrumented (MSW, VBox, IMU, Encoders) VW Jetta. Different controlled tests are carried out on the instrumented vehicle and the Intelligent tire signal is analyzed to extract features related to the tire forces and pressure. Due to unavailability of direct force measurements at the wheel, a VW Jetta simulation model is developed in CarSim and the extracted features are validated with a good correlation. / Master of Science / The automotive industry is heading towards autonomous vehicles driven at various levels of autonomy. Autonomous vehicles require a thorough understanding of the vehicle characteristics such as load, current state of the vehicle (speed, heading). It also requires a good grasp of the tire-road interaction to be able to estimate the future state of the vehicle.
This research focuses on exploring the tire-road interaction using sensor based approach. The tires are instrumented using a tri- axial accelerometer and different algorithms have been developed using signal processing techniques to estimate parameters such as Tire forces, tire pressure and load of the vehicle. The experiments are conducted on an instrumented VW Jetta vehicle which also has other sensors such as Inertial Measurement Unit, GPS based speed estimation sensor and steering angle measurement sensor. The results obtained from the sensor signal are processed using a code developed in MatLab software and validated using a simulation model in CarSim. Knowing the Tire Characteristics such as Tire force, pressure is essential for accurate estimation of the vehicle state which in turn will refine the autonomous capability of the vehicle.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/86858 |
| Date | 01 August 2017 |
| Creators | Cherukuri, Anup |
| Contributors | Mechanical Engineering, Taheri, Saied, Kennedy, Ronald H., Ferris, John B. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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