The so-called intelligent tires are one of the most promising research fields for automotive engineers. These tires are equipped with sensors which provide information about vehicle dynamics. Up to now, the commercial intelligent tires only provide information about inflation pressure and their contribution to stability control systems is currently very limited. Nowadays one of the major problems for intelligent tire development is how to embed feasible and low cost sensors to obtain reliable information such as inflation pressure, vertical load or rolling speed. These parameters provide key information for vehicle dynamics characterization. In this paper, we propose a novel algorithm based on fuzzy logic to estimate the mentioned parameters by means of a single strain-based system. Experimental tests have been carried out in order to prove the suitability and durability of the proposed on-board strain sensor system, as well as its low cost advantages, and the accuracy of the obtained estimations by means of fuzzy logic.

Vehicles today are equipped with control systems that improve their handling and stability. Knowledge of road bank angle and vehicle parameters is crucial for good behavior in this type of control. This paper develops a new method for estimating different states, such as vehicle roll angle, road bank angle, and vehicle parameters. This method combines a dual Kalman filter with a probability density function truncation method to consider the parameter physical limitations. Experimental results show the effectiveness of the proposed method and demonstrate that the incorporation of parameter constraints improves its estimation accuracy. The proposed method provides an estimation of the parameters and the states' physical meaning and the stable values within the real boundary limits in contrast to other estimation methods.

Tires equipped with sensors, the so-called “intelligent tires”, can provide vital information for control systems, drivers and external users. In this research, tire dynamic strain characteristics in cornering conditions are collected and analysed in relation to the variation of tire working conditions, such as inflation pressure, rolling speed, vertical load and slip angle. An experimental tire strain-based prototype and an indoor tire test rig are used to demonstrate the suitability of strain sensors to establish relations between strain data and lateral force. The results of experiments show that strain values drop sharply when lateral force is decreasing, which can be used to predict tire slip conditions. As a first approach to estimate some tire working conditions, such as the slip angle and vertical load, a fuzzy logic method has been developed. The simulation and test results confirm the feasibility of strain sensors and the proposed computational model to solve the non-linearity characteristics of the tires’ parameters and turn tires into a source of useful information.

Tires are a key sub-system of vehicles that have a big responsibility for comfort, fuel consumption and traffic safety. However, current tires are just passive rubber elements which do not contribute actively to improve the driving experience or vehicle safety. The lack of information from the tire during driving gives cause for developing an intelligent tire. Therefore, the aim of the intelligent tire is to monitor tire working conditions in real-time, providing useful information to other systems and becoming an active system. In this paper, tire tread deformation is measured to provide a strong experimental base with different experiments and test results by means of a tire fitted with sensors. Tests under different working conditions such as vertical load or slip angle have been carried out with an indoor tire test rig. The experimental data analysis shows the strong relation that exists between lateral force and the maximum tensile and compressive strain peaks when the tire is not working at the limit of grip. In the last section, an estimation system from experimental data has been developed and implemented in Simulink to show the potential of strain sensors for developing intelligent tire systems, obtaining as major results a signal to detect tire’s loss of grip and estimations of the lateral friction coefficient.

Tires are essential components of vehicles and are able to transmit traction and braking forces to the contact patch, contribute to directional stability, and also help to absorb shocks. If these components can provide information related to the tire–road interaction, vehicle safety can be increased. This research is focused on developing the tire as an active sensor capable to provide its functional parameters. Therefore, in this work, we studied strain-based measurements on the contact patch to develop an algorithm to compute the wheel velocity at the contact point, the effective rolling radius and the contact length on dynamic situations. These parameters directly influence the dynamics of wheel behavior which nowadays is not clearly defined. Herein, hypotheses have been assumed based on previous studies to develop the algorithm. The results expose to view an experimental test regarding influence of the tire operational condition (slip angle, vertical load, and rolling velocity) onto the computed parameters. This information is used to feed a fuzzy logic system capable of estimating the effective radius and contact length. Furthermore, a verification process has been carried out using CarSim simulation software to get the inputs for the fuzzy logic system at complex maneuvers.