A brush-based thermo-physical tyre model and its effectiveness in handling simulation of a Formula SAE vehicle

摘要

The ability to model tyre dynamics precisely is often one of the most critical elements for realistic vehicle dynamics control and handling investigations. The industry-standard empirical models are able to predict the important tyre forces accurately over a short range of vehicle operating conditions, which is often restricted to the operating conditions experienced during the tyre testing process. In this paper, an alternative and practical method to model Formula SAE tyres has been proposed and studied in a series of possible running scenarios. A simple, analytically solved brush-type tyre model is considered for the physical part with the introduction of a novel approach for defining the contact length formulation that incorporates the influence of inflation pressure, camber angle and velocity, while a set of ordinary differential equations are employed to predict the thermal behaviour of the tyre model, which are mostly based on an already-existing method that has not been experimentally validated before. The resulting tyre models provide realistic and informative behaviour of the tyre, which has the ability to consider the majority of the typical operating conditions experienced on a Formula SAE vehicle. The performance of the proposed tyre models is compared against experimental tyre test data, which shows good agreement and indicates that the tyre models have the ability to give realistic predictions of the tyre forces and thermal behaviour in the case of thermal tyre model. Furthermore, the temperature-dependent tyre model has been incorporated into a two-track model of Oxford Brookes Racing's Formula SAE vehicle to study the effectiveness of the tyre model during transient handling simulation. The resulting simulations suggest that the proposed tyre model has the ability to represent realistic operating conditions of tyres, and also that tyre temperatures influence the vehicle dynamic behaviour significantly during on-limit scenarios.