| Summary: | Sensor-embedded tires, known as intelligent tires, have been widely studied because
they are believed to provide reliable and crucial information on tire-road contact
characteristics e.g., slip, forces and deformation of tires. Vehicle control systems such as
ABS and VSP (Vehicle Stability Program) can be enhanced by leveraging this information
since control algorithms can be updated based on directly measured parameters from
intelligent tire rather than estimated parameters based on complex vehicle dynamics and
on-board sensor measurements. Moreover, it is also expected that intelligent tires can be
utilized for the purpose of the analysis of tire characteristics, taking into consideration
that the measurements from the sensors inside the tire would contain considerable
information on tire behavior in the real driving scenarios. In this study, estimation
methods for the tire-road contact features by utilizing intelligent tires are investigated.
Also, it was discussed how to identify key tire parameters based on the fusion technology
of intelligent tire and tire modeling. To achieve goals, extensive literature reviews on the
estimation methods using the intelligent tire system was conducted at first. Strain-based
intelligent tires were introduced and tested in the laboratory for this research.
Based on the literature review and test results, estimation methods for diverse tire-road contact characteristics such as slippages and contact forces have been proposed.
These estimation methods can be grouped into two categories: statistical regressions and
model based methods. For statistical regressions, synthetic regressors were proposed for
the estimation of contact parameters such as contact lengths, rough contact shapes, test
loads and slip angles. In the model-based method, the brush type tire model was
incorporated into the estimation process to predict lateral forces. Estimated parameters
using suggested methods agreed well with measured values in the laboratory
environment.
By utilizing sensor measurements from intelligent tires, the tire physical
characteristics related to in-plane dynamics of the tire, such as stiffness of the belt and
sidewall, contact pressure distribution and internal damping, were identified based on
the combination of strain measurements and a flexible ring tire model. The radial
deformation of the tread band was directly obtained from strain measurements based on
the strain-deformation relationship. Tire parameters were identified by fitting the radial
deformations from the flexible ring model to those derived from strain measurements.
This approach removed the complex and repeated procedure to satisfy the contact3
constraints between the tread and the road surface in the traditional ring model. For tires
with different specifications, identification using the suggested method was conducted
and their results are compared with results from conventional methods and tests, which
shows good agreements. This approach is available for the tire standing still or rolling at
low speeds. For tires rolling at high speeds, advanced tire model was implemented and
associated with strain measurements to estimate dynamic stiffness, internal damping
effects as well as dynamic pressure distributions. Strains were measured for a specific tire
under various test conditions to be used in suggested identification methods. After
estimating key tire parameters step by step, dynamic pressure distributions was finally
estimated and used to update the estimation algorithm for lateral forces. This updated
estimation method predicted lateral forces more accurately than the conventional
method.
Overall, this research will serve as a stepping stone for developing a new generation
of intelligent tire capable of monitoring physical tire characteristics as well as providing
parameters for enhanced vehicle controls. === PHD
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