Lack of drivers knowledge about the abrupt changes in pavement friction and poor performance of the vehicle stability, traction and ABS controllers on the low friction surfaces are the most important factors affecting car crashes. Due to its direct relation to vehicle stability, accurate estimation of tire-road characteristics is of interest to all vehicle and tire companies. Many studies have been conducted in this field and researchers have used different tools and have proposed different algorithms. One such concept is the Intelligent Tire. The application of intelligent tire in tire-road characterization is investigated in this study.
Three different test setups were used in this research to study the application of the intelligent tires to improve mobility; first, a wheeled ground robot was designed and built. A Fuzzy Logic algorithm was developed and validated using the robot for classifying different road surfaces such as asphalt, concrete, grass, and soil. The second test setup is a portable tire testing trailer, which is a quarter car test rig installed in a trailer and towed by a truck. The trailer was equipped with different sensors including an accelerometer attached to the center of the tire inner-liner. Using the trailer, acceleration data was collected under varying conditions and a Neural Network (NN) algorithm was developed and trained to estimate the contact patch length, effective tire rolling radius and tire normal load.
The third test setup developed for this study was an instrumented Volkswagen Jetta. Different sensors were installed to measure vehicle dynamic response. Additionally, one front and one rear tire was instrumented with an accelerometer attached to their inner-liner. Two intelligent tire based algorithms, a tire pressure estimation algorithm and a road condition monitoring algorithm, were developed and trained using the experimental data from the instrumented VW Jetta. The two-step pressure monitoring algorithm uses the acceleration signal from the intelligent tire and the wheel angular velocity to monitor the tire pressure. Also, wet and dry surfaces are distinguished using the acceleration signal from the intelligent tire and the wheel angular velocity through the surface monitoring algorithm.
Some of the model based tire-road friction estimation algorithms, which are widely used for tire-road friction estimation, were also introduced in this study and the performance of each algorithm was evaluated in high slip and low slip maneuvers. Finally a new friction estimation algorithm was developed, which is a combination of experiment based and vehicle dynamic based approaches and its performance was also investigated. / PHD / Lack of driver’s knowledge about the abrupt changes in pavement friction and poor performance of the vehicle stability, traction and ABS controllers on the low friction surfaces are the most important factors affecting car crashes. Due to its direct relation to vehicle stability, accurate estimation of tire-road characteristics is of interest to all vehicle and tire companies. Many studies have been conducted in this field and researchers have used different tools and have proposed different algorithms. One such concept is the Intelligent Tire. The application of intelligent tire in tire-road characterization is investigated in this study.
Five main algorithms are developed in this study. First a fuzzy-logic terrain classification algorithm is developed for the small wheeled ground robot that classifies all different surfaces into four known categories; asphalt, concrete, sand and grass. A six-wheel grand robot was designed and built for this study and instrumented with intelligent tire, a tri-axial accelerometer embedded to the tire inner-liner, and other appropriate sensors. The input of the terrain classification algorithm are the intelligent tire signal, the slip ratio at the beginning of the motion and the wheel speed. The second algorithm is an intelligent tire based algorithm to estimate the tire normal load. A portable tire testing trailer, which is a quarter car test rig attached to the back of the trailer and towed by a truck was used for this part of the project. The trailer test setup was instrumented with different sensors and the tire normal load was controlled through a pneumatic force transducer and an air-spring system. A Neural Network algorithm was then trained that estimates the tire normal load using intelligent tire signal, the tire pressure and the wheel speed.
The third and fourth algorithm are intelligent tire based algorithms to monitor the tire pressure and the road surface condition respectively. An instrumented vehicle, which was a Volkswagen Jetta 2003, was prepared and used for this part of the project. The inputs of these algorithms were the intelligent tire signal and the wheel speed and the outputs were the tire pressure condition and road surface condition (dry/ wet) respectively. The last algorithm is a new friction estimation algorithm, which is a combination of experiment based (intelligent tire) and vehicle dynamic based approaches. The algorithm is validated with the experimental data collected using the trailer test setup.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/92883 |
| Date | 13 February 2017 |
| Creators | Khaleghian, Seyedmeysam |
| Contributors | Engineering Mechanics, Taheri, Saied, Ahmadian, Mehdi, Flintsch, Gerardo W., Jung, Sunghwan, Kennedy, Ronald H. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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