Considering the more and more important issues concerning the climate changes and the global warming, the automotive industry is paying more and more attention to vehicle concepts with full electric or partly electric propulsion systems. The introduction of electric power sources allow the designers to implement more advanced motion control systems in vehicle, such as active suspensions. An example of this concept is the Autonomous corner module (ACM), designed by S. Zetterström. The ACM is a modular based suspension system that includes all features of wheel control, such as control of steering, wheel torque and camber individually, using electric actuators. With a good control strategy it is believed that is it possible to reduce the fuel consumption and/or increase the handling properties of the vehicle. In particular, camber angle has a significant effect on vehicle handling. However, very few efforts have been done in order to analyse its effects on tire dissipated energy. The aim of this study is to develop a new tire model, having as starting point the simple Brush Tire model, in order to analyse the tire behaviour, in terms of forces generated and energy dissipated, for different dynamic situations. In order to reach this scope, the characteristic equations of the rubber material are implemented in a 3D Multi-Line brush tire model. In this way the energy dissipated, thus the rolling resistance force, can be studied and analysed, considering also the tire geometry. From the results of this work it is possible to assert that the angular parameters (e.g. camber angle) affect the power losses in rolling tires, as well as the tire geometry influences their rolling resistance. Thus, using a good control strategy, it is possible to reduce the power losses in tires.

Doctor of Philosophy / The control systems installed in modern vehicles lack crucial information regarding the interaction between the tires and the road surface. This knowledge gap significantly impacts the safety and control of the vehicle. Thus, to address this issue, this research introduces a novel fusion approach to estimate friction at the tire-road contact interface. This hybrid fusion friction estimation algorithm employs techniques like signal processing and machine learning, backed up by information from various vehicle and tire dynamics models, to develop algorithms that estimate the level of friction between the tire and the road. This fusion approach enables more precise estimations of the friction coefficient in both normal driving situations and scenarios involving sudden changes in speed or road conditions. Therefore, this research aids in enhancing vehicle safety and control by providing improved information about such tire-road interactions.