A differential steered, 13.6 kg robot was developed as an intelligent tire testing system and was used to investigate the potential of using piezoelectric film sensors in small tube-type pneumatic tires to characterize tire-ground interaction.<br />One focus of recent research in the tire industry has been on instrumenting tires with sensors to monitor the tire, vehicle, or external environment. On small robots, tire sensors that measure the forces and deflections in the contact patch could be used to improve energy efficiency and/or mobility during a mission.<br />The robot was assembled from a SuperDroid Robots kit and instrumented with low-cost piezoelectric film sensors from Measurement Specialties between the inner tube and the tire.  An unlaminated and a laminated sensor were placed circumferentially along the tread and an unlaminated sensor was placed along the sidewall.  A slip ring transferred the signals from the tire to the robot. There, the signal conditioning circuit extended the time constant of the sensors and filtered electromagnetic interference.  The robot was tested with a controlled power sequence carried out on polished cement, ice, and sand at three power levels, two payload levels, and with two tire sizes.<br />The results suggest that the sensors were capable of detecting normal pressure, deflection, and/or longitudinal strain.  Added payload increased the amplitude of the signals for all sensors.  On the smaller tires, sensors generally recorded a smaller, wider signal on sand compared to cement, indicating the potential to detect contact patch pressure and length.  The signals recorded by the unlaminated sensor along the tread of the smaller tire were lower on ice compared to cement, indicating possible sensitivity to tractive force.  Results were less consistent for the larger tires, possibly due to the large tread pattern. / Master of Science

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.

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 / Tires are very crucial components in a vehicle because they are only objects in contact with the road surface on which the vehicle drive. They support the weight of the vehicle and generate forces which make the vehicle drive, stop and turn. Thus, the improvement of vehicle performances such as handling, ride quality and braking can be achieved by understanding and by optimizing tire properties as well as improving the design of the vehicle itself. These days, diverse vehicle control systems such as anti-lock braking and cornering stability control systems have been widely adopted to improve the stability of the vehicle when it is braked or turned. These stability controls usually require information about slippages and forces occurring between the tire and the road surface. These quantities can be indirectly estimated by monitoring vehicle motions, which are measured by sensors installed on the vehicle frame. Although these traditional methods have worked successively, the control algorithms can be improved further by directly sensing the tire behaviors using sensors embedded in the tire. These sensor-embedded tires are often called as ‘intelligent tire’ because tires themselves serve as the monitoring device on driving conditions as well as conduct traditional functions. Also, the measured quantities inside the tire can be effectively used to understand tire characteristics because they have valuable information on tires, especially, mechanism how the tire deforms and generate contact forces when it rolls over the road surface. In this research, strains are measured at the inner surface of the tire during it rolling and cornering on the flat road surface under different loads on the indoor test rig. A strain represents the relative displacement between particles. Based on experimental results, estimation algorithms for test loads, contact lengths, cornering angles and cornering forces are developed. These estimation methods can be incorporated in the vehicle control algorithm in the real driving scenario for improved vehicle controls. A tire is a complex system comprising various composite materials, so their behaviors or characteristics show sever non-linearity which difficult to understand. They have been simplified and modeled in a various way based on diverse physical principles to understand how they are deflected and generate forces and moments during rolling on the road surface under a vertical load. These models are called ‘physical tire model’. To extract and analyze tire physical characteristics, measured strains at the inner surface are combined with these tire models. In this research, tires are modeled as a flexible ring which is supported by viscoelastic materials and this tire model called as a ‘flexible ring model’ which have been utilized to analyze vibration properties and contact phenomena of tires. Strain measurements were fed into the model and crucial tire characteristics are extracted such as tire stiffness, pressure distributions and internal damping. These properties can be used to analyze the tire performance like wear, rolling resistance, ride qualities and the capacity of cornering forces. Since intelligent tire systems are applied for the real driving situation, tire characteristics extracted in this way would have closer links to vehicle performances rather than those measured in the laboratory. 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.

The contact patch of the tire is responsible for all of the transmission of a vehicle's motion to the road surface. This small area is responsible for the acceleration, stopping and steering control of the vehicle. Throughout the development of vehicle safety and stability control systems, it is desirable to possess the exact forces and moments at the tire contact patch. The tire is a passive element in the system, supplying no explicit information to vehicle control systems. Current safety and stability algorithms use estimated forces at the tire contact patch to develop these control strategies. An "intelligent" tire that is capable of measuring and transmitting the instantaneous forces and moments at the contact patch to the control algorithms in real-time holds promise to improve vehicle safety and performance. Using the force and friction information measured at the contact patch, an anti-lock braking control strategy is developed using sliding mode control. This strategy is compared to the performance of a current commercial anti-lock braking system that has been optimized by performing a threshold sensitivity analysis. The results show a definite improvement in control system strategy having known information at the tire contact patch. / Master of Science

This thesis aims at estimating vital vehicle states and developing control algorithms for automotive suspensions and vehicle stability. A parametric model of an automotive monotube damper is developed and several control algorithms for semi-active suspensions have been developed. An extensive comparison of different control algorithms has been done. Skyhook, Groundhook, Hybrid, Acceleration-driven, Power-driven, Groundhook-linear, Linear Quadratic Regulator (LQR) optimal, Genetic algorithm optimized Linear Quadratic Regulator optimal, Model-reference adaptive, H∞ robust, µ-synthesis, fuzzy-logic based, and Deep Reinforcement learning based control algorithms have been developed and simulated. A shock dyno is instrumented and skyhook and groundhook control algorithms have been implemented as well. In addition to this, a semi-active suspension switching based control algorithm is developed for reducing the effort of a direct moment yaw rate controller, and improve stability of a vehicle when turning. Accelerometer based intelligent tires have been used to estimate vehicle states like vertical load on tire, velocity of the vehicle, unsprung mass acceleration, and forces on a tire. All these estimations would be helpful in observing various parameters of a vehicle using data from only a tri-axis accelerometer inside the tire. Data was collected in an instrumented Volkswagen Jetta and a Trailer setup as well. The test vehicle was instrumented with a tri-axis accelerometer inside the tire, encoder, Inertial Measurement Unit (IMU), and VBOX Racelogic Global Positioning System (GPS) based velocity measurement unit. For payload estimation, the data collected by the in-tire accelerometer was converted into frequency domain using Welch's method of averaging, followed by feature extraction. The extracted features were fed to a trained bagged trees model. Root mean squared error of 11% was observed on the test dataset. For velocity estimation, the data collected by the accelerometer was fed to a variational mode decomposition process. The extracted mode was converted to time-frequency domain using Hilbert transform and features for machine learning were extracted. A root mean squared error of 1.02kmph was observed on the trained dataset. A Gaussian process model was trained for this application. For unsprung mass acceleration estimation, the test vehicle was instrumented with an accelerometer near the wheel spindle as well. For this estimation problem, Convolutional neural networks (CNN) were used. The time-frequency spectrogram of x, y, and z axis data of the in-tire accelerometer were considered as the three color channels of an image. With this, an image of 224 x 224 x 3 dimensions was generated, which represented the time and frequency variation of data. These images were used for training the CNN and a 96.8% coefficient of correlation was obtained for this regression task. For the last wheel force estimation problem, the concept of training the images generated by overlapping time-frequency matrices was used and an accuracy of 90.1% was achieved. With these estimation of vehicle states, better control algorithms can be developed and deployed for better handling, safety and comfort of vehicles using data from only tri-axis accelerometer in the tire. / Master of Science / The main objective of this thesis is to aid in the development of better control systems for vehicles, using data from accelerometer-based intelligent tire. Payload on the vehicle's tire, vehicle velocity, wheel acceleration, and wheel forces are vital parameters, which if estimated correctly can be instrumental in having better understanding of the vehicle's condition. A tri-axis accelerometer is mounted inside the tire, and is used for estimating these vehicle parameters. Statistical models are developed based on features extracted from the accelerometer data. The main challenge was to use the data collected by only intelligent tire to estimate vehicle states. This makes the developed algorithms independent of other sensors and hence economic. Tires are the only component which serve as a link between the vehicle and road. Hence, these parameter estimations can be accurately observed simultaneously using the in-tire accelerometer data. Testing is done on an instrumented trailer-test setup and a Volkswagen Jetta. The vehicle is instrumented with the intelligent tire, a Global positioning system (GPS) based velocity measuring unit, Inertial measurement unit (IMU), and encoder. Testing is done for different loading conditions, road surfaces, inflation pressures, and vehicle velocities. In this way, it has been attempted to make the developed statistical models robust and expose them to a multitude of test conditions. In addition to this, several suspension semi-active control algorithms have been developed for improving vehicle ride comfort and road holding. A parametric damper model has been developed, and several control algorithms have been simulated. A shock dyno experimental setup has been instrumented and some of the control algorithms have been implemented. With this, several suspension semi-active control algorithms have been developed, and statistical models have been developed for estimation of various vehicle parameters. This research can be helpful for developing accurate control algorithms for active safety systems in a vehicle.

The vertical load on a moving tire was estimated by using accelerometers attached to the inner liner of a tire. The acceleration signal was processed to obtain the contact patch length created by the tire on the road surface. Then an appropriate equation relating the patch length to the vertical load is used to calculate the load. In order to obtain the needed data, tests were performed on a flat-track test machine at the Goodyear Innovation Center in Akron, Ohio; tests were also conducted on the road using a trailer setup at the Intelligent Transportation Laboratory in Danville, Virginia. During the tests, a number of different loads were applied; the tire-wheel setup was run at different speeds with the tire inflated to two different pressures. Tests were also conducted with a camber applied to the wheel. An algorithm was developed to estimate load using the collected data. It was then shown how the estimated load could be used in a control algorithm that applies a suitable control input to maintain the yaw stability of a moving vehicle. A two degree of freedom bicycle model was used for developing the control strategy. A linear quadratic regulator (LQR) was designed for the purpose of controlling the yaw rate and maintaining vehicle stability. / Master of Science

No description available.

The dynamic behavior of a vehicle is predominantly controlled by the forces and moments generated at the contact patch between the tire and the road surface. As a result, tire characteristics can dramatically change vehicle response, especially during maneuvers that yields the tires to reach to the limits of its adhesion capacity. To assist the driver in such cases and to prevent other possible instability scenarios, various vehicle control systems e.g. anti-lock brakes (ABS), stability controllers (ESP, ESC) or rollover mitigation schemes are introduced, which are generally known as active vehicle safety systems. Based on the above facts, one can easily come to the conclusion that to improve upon the current control algorithms developed for the technology in use; a vehicle control system design requires accurate knowledge of the tire states. This study proposes the use of a smart tire system that can provide information on momentary variation of tire features through the sensor units attached directly on the tire and develops control algorithms based on this information to assure the match-up between tire and controller dynamics. A prototype smart tire system was developed for field testing and for detailed analysis of its potential. Based on the collected prototype data, novel observer and controller schemes were developed to obtain dynamic tire state information and to improve vehicle handling performance. The proposed algorithms were implemented and evaluated using numerical analysis in Matlab/SimulinkR environment. For a more realistic simulation environment, vehicle models were integrated from Mechanical Simulations CarSimR® software suite. / Ph. D.

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.

Various improvements which have been made to the vehicle (reduced engine noise, reducedaerodynamic related NVH), have resulted in tire road noise as the dominant source of thevehicle interior noise. Generally, vehicle interior noise has two main sources, 1) travellinglow frequency excitation below 800 Hz from road surface through a structure- borne pathand 2) the high frequency (above 800 Hz) air-borne noise that is caused by air- pumpingnoise caused by tread pattern.The structure-borne waves of the circumference of the tire are generated by excitation atthe contact patch due to the road surface texture and characteristics. These vibrations arethen transferred from the sidewalls of the tire to the rim and then are transmitted throughthe spindle-wheel interface, resulting in high frequency vibration of vehicle body panels andwindows.The focus of this study is to develop several statistical-based models for analyzing the roadsurface and using them to predict the tire-road noise structure-borne component. In order todo this, a new methodology for sensing the road characteristics, such as asperities and roadsurface condition, were developed using virtual sensing and intelligent tire technology. In ad-dition, the spindle forces were used as an indicator to the structure-borne noise of the vehicle.Several data mining and multivariate analysis-based methods were developed to extractfeatures and to develop an empirical model to predict the power of structure-borne noiseunder different operational and road conditions. Finally, multiple data driven models-basedmodels were developed to classify the road types, and conditions and use them for the noisefrequency spectrum prediction. / Doctor of Philosophy / Multiple data driven models were developed in this study to use the vibration of the tirecontact patch as an input to sense some characteristics of road such as asperity, surface type,and the surface condition, and use them to predict the structure-borne noise power. Also,instead of measuring the noise using microphones, forces at wheel spindle were measuredas a metric for the noise power. In other words, a statistical model was developed that bysensing the road, and using the data along with other inputs, one can predict forces at thewheel spindle.