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.

Le bruit de roulement contribue fortement au bruit perçu à l'intérieur de l'habitacle des automobiles. Ce bruit a pour origine le contact du pneumatique sur une chaussée rugueuse. En basses fréquences (0-400 Hz), il est transmis dans l'habitacle du véhicule essentiellement par la voie solidienne. La méthode actuelle de prévision de ce bruit chez PSA Peugeot Citroën repose sur une approche mixte calcul-mesure longue, coûteuse et pas suffisamment prédictive. Pour contourner ces limitations, une filière purement numérique est envisagée. Elle demande de modéliser le comportement vibro-acoustique du pneumatique en prenant en compte les effets liés à la rotation et de résoudre le problème de contact avec une chaussée rugueuse. Concernant la modélisation d'un pneumatique en rotation, des formulations des effets tournants d'un solide déformable sont établies en utilisant une approche Arbitrairement Lagrangienne Eulérienne (ALE). Ces formulations sont validées par une application sur un nouveau modèle simplifié du pneumatique. Il s'agit d'un modèle d'anneau circulaire incluant les effets de cisaillement soumis localement à une charge représentative de la masse du véhicule. Un modèle plus complexe d'ensemble monté pneu/roue/cavité intégrant l'ensemble des effets liés à la rotation est également validé par une comparaison avec des essais. Ensuite, le contact avec une chaussée réelle est formulé par différentes approches permettant de réduire le temps de calcul pour une utilisation industrielle. En particulier, le calcul du contact est décomposé en un calcul statique non linéaire suivi d'un calcul dynamique linéaire. La validation du modèle de contact est réalisée par une comparaison calcul/essai. Les résultats sont très satisfaisants / The rolling noise contributes significantly to the noise inside cars. This noise comes from the tire/road contact. In low frequencies (0-400 Hz), it is mainly transmitted into the cabin through structural vibration. The current method used at PSA Peugeot Citroen to predict this noise, is a mixed simulation/experimental approach which is long, expensive and not sufficiently predictive. In order to overcome these difficult, a full numerical approach is considered. It requires modeling the tire vibration by taking into account the rotating effects and the contact with the rough surface. Concerning the model of rotating tire, a formulation of a deformable solid is constructed by using an Arbitrary Lagrangian Eulerian approach (ALE). This formulation is validated by an application on a new simplified tire model which is a circular ring including the shear stresses and the non linear effects due to the vehicle weight. A more complex model composed of tire/wheel/cavity including all the rotating effects is also validated by comparison with experiments. Then the contact with a real road is calculated by different approaches to get the acceptable computing time for industrial uses. In particular, the calculation of the contact is divided into a non-linear static analysis followed by a linear dynamic calculation. The validation of this model is successfully achieved by comparison test results

Le développement des systèmes intelligents pour contrôler la stabilité du véhicule et éviter les accidents routier est au cœur de la recherche automobile. L'expansion de ces systèmes intelligents à l'application réelle exige une estimation précise de la dynamique du véhicule dans des environnements diverses (dévers et pente). Cette exigence implique principalement trois problèmes : ⅰ), extraire des informations non mesurées à partir des capteurs faible coût; ⅱ), rester robuste et précis face aux les perturbations incertaines causées par les erreurs de mesure ou de la méconnaissance de l'environnement; ⅲ), estimer l'état du véhicule et prévoir le risque d'accident en temps réel. L’originalité de cette thèse par rapport à l’existant, consiste dans le développement des nouveaux algorithmes, basés sur des nouveaux modèles du véhicule et des différentes techniques d'observation d'état, pour estimer des variables ou des paramètres incertains de la dynamique du véhicule en temps réel. La première étape de notre étude est le développement de nouveaux modèles pour mieux décrire le comportement du véhicule dans des différentes situations. Pour minimiser les erreurs de modèle, un système d'estimation composé de quatre observateurs est proposé pour estimer les forces verticales, longitudinales et latérales par pneu, ainsi que l'angle de dérive. Trois techniques d'observation non linéaires (EKF, UKF et PF) sont appliquées pour tenir compte des non-linéarités du modèle. Pour valider la performance de nos observateurs, nous avons implémenté en C++ des modules temps-réel qui, embarqué sur le véhicule, estiment la dynamique du véhicule pendant le mouvement. / Enhancing road safety by developing active safety system is the general purpose of this thesis. A challenging task in the development of active safety system is to get accurate information about immeasurable vehicle dynamics states. More specifically, we need to estimate the vertical load, the lateral frictional force and longitudinal frictional force at each wheel, and also the sideslip angle at center of gravity. These states are the key parameters that could optimize the control of vehicle's stability. The estimation of vertical load at each tire enables the evaluation of the risk of rollover. Estimation of tire lateral forces could help the control system reduce the lateral slip and prevent the situation like spinning and drift out. Tire longitudinal forces can also greatly influence the performance of vehicle. The sideslip angle is one of the most important parameter to control the lateral dynamics of vehicle. However, in the current market, very few safety systems are based on tire forces, due to the lack of cost-effective method to get these information. For all the above reasons, we would like to develop a perception system to monitor these vehicle dynamics states by using only low-cost sensor. In order to achieve this objective, we propose to develop novel observers to estimate unmeasured states. However, construction of an observer which could provide satisfactory performance at all condition is never simple. It requires : 1, accurate and efficient models; 2, a robust estimation algorithm; 3, considering the parameter variation and sensor errors. As motivated by these requirements, this dissertation is organized to present our contribution in three aspects : vehicle dynamics modelization, observer design and adaptive estimation. In the aspect of modeling, we propose several new models to describe vehicle dynamics. The existent models are obtained by simplifying the vehicle motion as a planar motion. In the proposed models, we described the vehicle motion as a 3D motion and considered the effects of road inclination. Then for the vertical dynamics, we propose to incorporate the suspension deflection to calculate the transfer of vertical load. For the lateral dynamics, we propose the model of transfer of lateral forces to describe the interaction between left wheel and right wheel. With this new model, the lateral force at each tire can be calculated without sideslip angle. Similarly, for longitudinal dynamics, we also propose the model of transfer of longitudinal forces to calculate the longitudinal force at each tire. In the aspect of observer design, we propose a novel observation system, which is consisted of four individual observers connected in a cascaded way. The four observers are developed for the estimation of vertical tire force, lateral tire force and longitudinal tire force and sideslip angle respectively. For the linear system, the Kalman filter is employed. While for the nonlinear system, the EKF, UKF and PF are applied to minimize the estimation errors. In the aspect of adaptive estimation, we propose the algorithms to improve sensor measurement and estimate vehicle parameters in order to stay robust in presence of parameter variation and sensor errors. Furthermore, we also propose to incorporate the digital map to enhance the estimation accuracy. The utilization of digital map could also enable the prediction of vehicle dynamics states and prevent the road accidents. Finally, we implement our algorithm in the experimental vehicle to realize real-time estimation. Experimental data has validated the proposed algorithm.