Development of a Toolchain for parametrisation of a simplified double-track model for laptime simulation starting from the multibody model currently in use at the DIL simulator

The objective of this thesis is to develop a toolchain for the parameterisation of a simplified double track vehicle model for lap time simulation. Since the simplified model is implemented in Python, the toolchain is primarily developed in Python, while utilising multibody vehicle models in Dymola to extract the vehicle parameters required to run the simulation. The work is divided over five main areas: static vehicle analysis, acceleration and braking, tyres, aerodynamics and suspension. For each of this areas, a script is developed to extract and process the necessary data. The required inputs are obtained either directly from vehicle parameter files or by setting up experiments using the multibody models of the vehicles developed in Dymola. The static vehicle analysis defines the vehicles mass and geometric characteristics. The acceleration and braking module generates the engine map, computes the torque available at the wheels, and determines the maximum braking torque capability. The tyre module focuses on the optimisation, fitting the 5.2 Pacejka tire model to the simple Pacejka formula currently implemented in the simplified model. In the aerodynamics module, the existing multi-inputs variable linear interpolation of aerodynamic maps is replaced with a neural network approach. Lastly, the suspension module introduces the suspension non-linearities into the model by defining the motion ratio of elastic elements using the principle of virtual work.