ABSTRACT
ABSTRACT: A smart wheel is used to measure the forces at the tyre-ground interface and detect the friction potential (i.e. the available friction, i.e. the maximum horizontal force that the tyre can apply to the ground). The friction potential estimation is based on the Gough-plot and has been performed during simulations and actual tests. Preliminary simulations showed the feasibility of the approach for detecting quickly the change of tyre-ground friction (while passing from dry to wet road surface, or vice versa). Experimental activities have been carried out to test the effectiveness and robustness of the proposed friction potential evaluation method. Indoor tests, performed on a drum machine in a carefully controlled environment, have shown the ability of the algorithm to recognize three different tyre-ground friction levels. Outdoor tests, performed by using a car fitted with smart wheels running on a rough road with alternate sections of dry and wet asphalt, have shown complex dynamic phenomena related to the tyre/suspension deformations. These phenomena deserve more investigation. By resorting to a neural network to process the data, a fairly good estimation of the friction potential has been achieved.
