The tire and vehicle setup definition, able to optimise grip performance and thermal working conditions, can make the real difference as for motorsport racing teams, used to deal with relevant wear and degradation phenomena, as for tire makers, requesting for design solutions aimed to obtain enduring and stable tread characteristics, as finally for the development of safety systems, conceived in order to maximise road friction, both for worn and unworn tires. The activity discussed in the paper deals with the analysis of the effects that tire wear induces in vehicle performance, in particular as concerns the consequences that tread removal has on thermal and frictional tire behaviour. The physical modelling of complex tire-road interaction phenomena and the employment of specific simulation tools developed by the Vehicle Dynamics UniNa research group allow to predict the tire temperature local distribution by means of TRT model and the adhesive and hysteretic components of friction, thanks to GrETA model. The cooperation between the cited instruments enables the user to study the modifications that a reduced tread thickness, and consequently a decreased SEL (Strain Energy Loss) and dissipative tread volume, cause on the overall vehicle dynamic performance.

Appropriate modelling of the real behavior of viscoelastic materials is of fundamental importance for correct studies and analyses of structures and components where such materials are employed. In this paper, the potential to employ a generalized Maxwell model and the relative fraction derivative model is studied with the aim to reproduce the experimental behavior of viscoelastic materials. For both models, the advantage of using the pole-zero formulation is demonstrated and a specifically constrained identification procedure to obtain the optimum parameters set is illustrated. Particular emphasis is given on the ability of the models to adequately fit the experimental data with a minimum number of parameters, addressing the possible computational issues. The question arises about the minimum number of experimental data necessary to estimate the material behavior in a wide frequency range, demonstrating that accurate results can be obtained by knowing only the data of the upper and low frequency plateaus plus the ones at the loss tangent peak.

Vehicle performances, especially in motorsport, are deeply affected by tire behavior and in particular by tire compound proper working conditions. In this research activity, a series of innovations have been introduced on the Thermo Racing Tire (a physical-analytical tire thermal model, based on Fourier's law of heat transfer applied to a three-dimensional domain) in order to take into account all the main aspects actively involved in the thermal behavior of the tire, as the presence of exhausted gases eventually impacting at the rear axle and the inhomogeneous distribution of local variables (pressure, stress and sliding velocity) within the contact patch, caused in example by the tire camber angle. The new model developed considers the presence of the sidewalls, actively involved in the convective heat exchanges, respectively, with the external airflow and the inner gas fluid, located inside the inflation chamber. The aim of the new version of the tire thermal model is a better physical comprehension of all the phenomena concerning the contact with the asphalt and the prediction of the link between the thermal state and the frictional performance, crucial for the definition of an optimal wheel and vehicle setup.

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In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain).

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The knowledge of key vehicle states is crucial to guarantee adequate safety levels for modern passenger cars, for which active safety control systems are lifesavers. In this regard, vehicle sideslip angle is a pivotal state for the characterization of lateral vehicle behavior. However, measuring sideslip angle is expensive and unpractical, which has led to many years of research on techniques to estimate it instead. This paper presents a novel method to estimate vehicle sideslip angle, with an innovative combination of a kinematic-based approach and a dynamic-based approach: part of the output of the kinematic-based approach is fed as input to the dynamic-based approach, and vice-versa. The dynamic-based approach exploits an Unscented Kalman Filter (UKF) with a double-track vehicle model and a modified Dugoff tire model, that is simple yet ensures accuracy similar to the well-known Magic Formula. The proposed method is successfully assessed on a large amount of experimental data obtained on different race tracks, and compared with a traditional approach presented in the literature. Results show that the sideslip angle is estimated with an average error of 0.5 deg, and that the implemented cross-combination allows to further improve the estimation of the vehicle longitudinal velocity compared to current state-of-the-art techniques, with interesting perspectives for future onboard implementation.