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Tyres play a crucial role in determining the ride and handling properties of a vehicle. Multi-body simulation softwares are being used extensively to understand vehicle ride and handling properties using detailed vehicle models. Models need a set of characteristics and parameters to represent the tyre as close as possible to the actual tyre. Tyre properties are generally grouped into vertical, lateral and longitudinal stiffness characteristics. Various test facilities are currently present in educational institutions and commercial organizations. They are prohibitively expensive to use during product development stage where a number of design iterations are carried out before finalizing for mass production. This paper highlights the work done in creating innovative and simplified test facilities for measuring lateral force characteristics, static vertical, lateral and longitudinal stiffness and dynamic vertical stiffness and damping.

The importance of friction between tire and road for the dynamic behavior of road vehicles has been emphasized in many publications. Continuously updated knowledge of the friction potential and the friction demand can help to improve maneuverability and thereby safety of vehicles under slippery road conditions. An on line estimation method, based on combination of side force and self aligning torque, generated by the tire, is theoretically founded on a simple brush type tire model. The system is implemented in the front wheel suspension of a passenger car. To cope with the highly non-linear behavior of the wheel suspension and the actual tire, various static neural networks have been applied in the estimation procedure. Experiments have been carried out both in simulation using a full vehicle multi-body model and with an actual vehicle. Conclusions are drawn regarding the estimation principle, the application of neural networks and the implementation in a test vehicle.