The growth of the automobile Industry in the past 50 years is radical. The development of chassis control systems have grown drastically due to the demand for safer, faster and more comfortable vehicles. For example, the invention of Anti-lock Braking System (ABS) has resulted in saving more than a million lives since its adaptation while also allowing the vehicles to commute faster. As we move into the autonomous vehicles era, demand for additional information about tire-road interaction to improve the performance of the onboard chassis control systems, is high. This is due to the fact that the interaction between the tire and the road surface determines the stability boundary limits of the vehicles. In this research, a real-time system to classify the road surface into five major categories was developed. The five surfaces include Dry Asphalt, Wet Asphalt, Snow, and Ice and dry Concrete. tri-axial accelerometers were placed on the inner liner of the tires. An advanced signal processing technique was utilized along with a machine learning model to classify the road surfaces. The instrumented Volkswagen Jetta with intelligent tires was retrofitted with new instrumentation for collecting data and evaluating the performance of the developed real-time system. A comprehensive study on road surface classification was performed in order to determine the features of the classification algorithm. Performance of the real-time system is discussed in details and compared with offline results. / Master of Science / The automobile industry has been improving road transportation safety over the past 50 years. While we enter the autonomous vehicles era, the safety of the vehicle is of primary concern. In order to get the autonomous vehicles to production, we will have to improve the on board vehicle control systems to adapt to all surfaces. Gaining more accurate information about the tire and road interaction will help in improving the control systems. Tires have always been considered a passive element of the vehicle. However, more recently, the idea of “tire as a sensor” has surfaced and has become one of the major research thrusts in tire as well as vehicle companies. The intelligent tire research at the Center for Tire Research (CenTiRe) begun in 2010 and has been going strong. In this work, we have developed a classification algorithm to classify the road surfaces in real-time based on acceleration measured inside the tire. The information regarding the road surface would be highly beneficial for the developing new control strategies, automate service vehicles and aid surface prediction in autonomous vehicles.

In recent years, composite materials have been widely used in several applications due to their superior mechanical properties including high strength, high stiffness, and low density. Despite the remarkable advancements in theoretical and computational methods for analyzing composites, investigating the effect of lamina properties and lay-up configurations on the strength of composites still remains an active field of research. Finite Element Method (FEM) and Extended Finite Element Method (XFEM) are powerful tools for solving the boundary value problems. One of the objectives of this work is to employ XFEM as a defect identification tool for predicting the crack initiation and propagation in composites. Another major objective of this study is to investigate the damage development in hyperelastic materials. Two Finite Element models are adopted to study this phenomenon: multiscale modeling of the cord-rubber composites in tires and modeling of intelligent tires for evaluating the feasibility of the proposed defect detection technique. A new three-dimensional finite element approach based on the multiscale progressive failure analysis is employed to provide the theoretical predictions for damage development in the cord-rubber composites in tires. This new three-dimensional model of the cord-rubber composite is proposed to predict the different types of damage including matrix cracking, delamination, and fiber failure based on the micro-scale analysis. This process is iterative and data is shared between the finite element and multiscale progressive failure analysis. It is shown that the proposed cord-rubber composite model solves the problems corresponding to embedding the rebar elements to the solid elements and also increases the fidelity of numerical analysis of composite parts since the laminate characteristic variables are determined from the microscopic parameters. A tire rolling analysis is then conducted to evaluate the effects of different variables corresponding to the cord-rubber composite on the performance of tires. Tires operate on the principle of safe life and are the only parts of the vehicle which are in contact with the road surface. Establishing a computational method for defect detection in tire structures will help manufacturers to fix and develop more reliable tire designs. A Finite Element model of a tire with a tri-axial accelerometer attached to its inner-liner was developed and the effects of changing the normal load, longitudinal velocity and tire-road contact friction on the acceleration signal were investigated. Additionally, using the model, the acceleration signals obtained from several accelerometers placed in different locations around the inner-liner of the intelligent tire were analyzed and the defected areas were successfully identified. Using the new intelligent tire model, the lengths, locations, and the minimum number of accelerometers in damage detection in tires are determined. Comparing the acceleration signals obtained from the damaged and original tire models results in detecting defects in tire structures. / PHD

With advancements in technology, electric vehicles are dominating the world making Internal Combustion engines less relevant, and hence vehicles are becoming quieter than ever before. But noise levels remain a significant concern for both passengers and automotive manufacturers. The vehicle's interior noise can affect the overall driving experience and even the safety of the driver and the passengers. The two main sources of vehicle interior noise are attributed to air-borne noises and structure-borne noises. A modern automobile is a complicated vibration system with several excitation sources like the engine, transmission system, tire/road interface excitation, and wind noise. With electric vehicles on the rise, the engine and transmission noise is practically eliminated, and effective preventive measures and control systems are already in place to reduce the aerodynamic-based noise, vibrations, and harshness (NVH) in modern automobiles making the structure-borne noise the most crucial of the noise sources. Tire/road interaction noise being the most dominant among the structure-borne noise is the main concern of the vehicle interior noise. The two main sources of vehicle interior noise induced by the tire pavement interaction noise are structure-borne noise induced by the low-frequency excitation and air-borne noises produced by the mid and high-frequency excitation. The present study tested an all-season tire over varying operational conditions such as different speeds, normal loads, and inflation pressures on an asphalt surface. Two tri-axial accelerometers attached 1800 apart from each other on the inner liner of the tire of a Volkswagen Jetta were used to measure the circumferential, lateral, and radial acceleration data. An Inertial Measurement Unit (IMU) and velocity box (VBOX) were instrumented in the vehicle to measure the acceleration at the center of gravity (COG) position of the vehicle and the longitudinal velocity of the vehicle respectively. The vehicle was also equipped with a modified hybrid of Close Proximity Testing (CPX) and On-Board Sound Intensity (OBSI) sound measurement systems which were designed and manufactured in-house to measure the tire/road interaction noise at the leading and trailing edges of the tire/road contact patch. Another microphone was instrumented inside the passenger compartment of the vehicle at the passenger's seat right ear position over the tire mounted with the sound measurement system to measure the vehicle interior noise as interpreted by the passengers in the vehicle. Two data acquisition systems coupled with a real-time Simulink model were used to collect all the measured data, one for the noise signals and the other for velocity and acceleration signals. The focus of the current study is to review different generation and amplification mechanisms of the structure-induced tire/road interaction noise and find the relevant dominant frequency ranges of the vehicle interior noise induced by the structure-borne noises using already established physics-based models and correlation techniques. It also aims to find correlations between tire acceleration, vehicle interior noise, and tire pavement interaction noise and their effect on different operational conditions like load, inflation pressure, and velocity. All the signals are studied in the time, frequency, and spectral domain and insights have been drawn on different tire/road noise generation and amplification mechanisms. / Master of Science / Structure-induced vehicle interior noise is one of the main concerns surrounding the automotive NVH industry and tire/road interaction noise being the most dominant source among the structure-borne noises affecting the vehicle interior noise is a major problem to the tire and automotive manufacturers nowadays. It leads to discomfort for the driver and the passengers in the vehicle and can cause fatigue, which in turn can directly affect the vehicle's safety. Several attempts have been made to reduce vehicle interior noise using statistical, physics-based, and hybrid models, but the research is still nowhere near completion. The current study aims to identify the frequency ranges affecting the structure-borne noise-induced vehicle interior noise and uses data-driven approaches in estimating the vehicle interior noise using only the acceleration of the tire. A test setup was designed and developed in-house where a tri-axial accelerometer embedded inside the inner liner of the tire measures the X, Y, and Z acceleration signals. Several microphones are instrumented at the tire/road contact surface and inside the passenger cabin to measure the tire/road interaction noise and the vehicle interior noise. The longitudinal velocity of the vehicle and the accelerations at the center of gravity of the vehicle have also been measured. Multiple data-driven models have been developed to directly predict the vehicle interior noise and tire/road interaction noise using the accelerometer data. This research is directly helpful for the automotive and tire industries by giving them insights on designing and developing quieter tires by using data-driven approaches and further using these with active control systems can mask the vehicle interior noise to acceptable levels in real-time.

Tires, being the only component of the vehicle in contact with the road surface, are responsible for generating the forces for maintaining the vehicle pose, orientation and stability of the vehicle. Additionally, the on-board advanced chassis control systems require estimation of these tire-road interaction properties for their operation. Extraction of these properties becomes extremely important in handling limit maneuvers such as Double Lane Change (DLC) and cornering wherein the lateral force transfer is dependent upon these computations. This research focuses on the development of a high-fidelity vehicle-tire model and control algorithm framework for vehicle trajectory tracking for vehicles operating in this limit handling regime. This combined vehicle-tire model places an emphasis on the lateral dynamics of the vehicle by integrating the effects of relaxation length on the contact patch force generation. The vertical dynamics of the vehicle have also been analyzed, and a novel double damper has been mathematically modeled and experimentally validated. Different control algorithms, both classical and machine learning-based, have been developed for optimizing this vertical dynamics model. Experimental data has been collected by instrumenting a vehicle with in-tire accelerometers, IMU, GPS, and encoders for slalom and lane change maneuvers. Different state estimation techniques have been developed to predict the vehicle side slip angle, tire slip angle, and normal load to further assist the developed vehicle-tire model. To make the entire framework more robust, Machine Learning algorithms have been developed to classify between different levels of tire wear. The effect of tire tread wear on the pneumatic trail of the tire has been further evaluated, which affects the aligning moment and lateral force generation. Finally, a Model Predictive Control (MPC) framework has been developed to compare the performance between the conventional vehicle models and the developed vehicle models in tracking a reference trajectory. / Doctor of Philosophy / In our rapidly advancing world, self-driving or autonomous vehicles are no longer a vision of the future but a reality of today. As we grow more reliant on these vehicles, ensuring their safety and reliability becomes increasingly critical. Unlike traditional vehicles, self-driving cars operate without human intervention. Consequently, the onus of passenger and pedestrian safety falls squarely on the vehicle's control systems. The efficiency and effectiveness of these control systems are pivotal in preventing accidents and ensuring a smooth ride. One vital aspect of these control systems lies in understanding the tires' behavior, the only parts of the vehicle that are in contact with the road surface. A tire's interaction with the road surface significantly impacts the vehicle's handling and stability. Information such as how much of the tire is in contact with the road, the forces and moments generated at this contact point, becomes valuable for optimizing the vehicle's performance. This is particularly crucial when a vehicle is turning or cornering, where the forces developed between the tires and the road are key to maintaining control and stability. In this research, a framework has been designed to improve the vehicle performance, primarily by improving the modeling of tire lag dynamics. This refers to the delay or 'lag' between a change in tire conditions (such as pressure, wear, and temperature) and the corresponding change in tire behavior. In addition, in this research a vertical dynamics model of the vehicle has also been developed incorporated with a novel double damper suspension system. To complete the entire framework, the effect of tire wear over time and how this affects its performance and safety characteristics has also been examined. By estimating and understanding this wear, we can predict how it will affect the dynamic properties of the tire, thus improving the reliability and efficiency of our autonomous vehicles. The last piece of this framework comprises the development of an MPC controller to track a reference trajectory and evaluate the performance of the developed model.

Doctor of Philosophy / The control systems installed in modern vehicles lack crucial information regarding the interaction between the tires and the road surface. This knowledge gap significantly impacts the safety and control of the vehicle. Thus, to address this issue, this research introduces a novel fusion approach to estimate friction at the tire-road contact interface. This hybrid fusion friction estimation algorithm employs techniques like signal processing and machine learning, backed up by information from various vehicle and tire dynamics models, to develop algorithms that estimate the level of friction between the tire and the road. This fusion approach enables more precise estimations of the friction coefficient in both normal driving situations and scenarios involving sudden changes in speed or road conditions. Therefore, this research aids in enhancing vehicle safety and control by providing improved information about such tire-road interactions.