Development of methods for measuring the wheel forces during track driving of racing cars and applications for tire characterization

The forces generated at the tires of a vehicle are responsible for its motion and control. In this work, rule-based and machine learning approaches were implemented for measuring the wheel forces during track driving of race vehicles. The proposed approaches were applied on two vehicles developed by Michelotto Engineering SpA to compete in the FIA World Endurance Championship. The two vehicles belong to two different classes, which are called Le Mans Grand Touring Endurance (GTE) and Le Mans Hypercar (LMH). A rule-based approach, the Geometric Matrix method, was applied to double-wishbone and multi-link suspensions enabling the estimation of the wheel forces from the strain gauge channels applied on all the arms. The experimental validation was accomplished by means of specifically designed test rigs, then the instrumented suspensions were equipped on the vehicles to acquire the wheel loads on different circuits with the tires in various operating conditions. On the other hand, to reduce the number of strain gauge channels required for measuring the wheel forces, neural network approaches performing a sensor fusion of a Partially Instrumented Suspension with the Inertial Measurement Unit signals were implemented and validated on different circuits. The main purpose of the wheel force measurement was to perform tire characterization from data acquired on the racetrack using the instrumented vehicles; however, the acquired data can be used also for different purposes, such as structural assessment of the vehicle components, braking performance analysis, development of safety and driver assistance systems. The tires equipped in these vehicles are confidential type and therefore not available to the vehicle manufacturers for characterization purposes outside of the racetrack. On the other hand, the parameters of the Pacejka’s Magic Formula tire model provided by the tire manufacturer lack of accuracy with reality. The main reason is that the tire-road friction coefficient and the cornering stiffness depend on the contact surface, temperature, inflation pressure and wear level, which are not considered in the provided models. In addition, a well-known theory to model the tire temperature variation and its influence on the cited properties is not available in the literature. In this work, a procedure to perform tire characterization from data acquired with the instrumented vehicles was implemented. This characterization algorithm was then adopted to analyze all the cited influences. Quadratic dependencies of the tire-road friction coefficient and linear variation of the cornering stiffness as a function of the tire temperature and inflation pressure were identified. Moreover, the instrumentation equipped on the LMH proved to be reliable for the characterization of the tire-road friction coefficient reduction and cornering stiffness increase with the number of laps travelled. Lastly, part of the research was focused on the development of a preliminary Thermodynamic Tire Model (TTM) for free rolling tires. The tire contact patch area was measured for different levels of the inflation pressure and radial load, then experimental acquisitions on a roller test rig were adopted to perform a preliminary characterization of the heat power exchanged by the tire with the ambient air and at the contact surface, along with the heat generated by the cyclic deflections.