The Improvement of Simulation Correlation for Formula 1

Abstract

The highly competitive nature of Formula one makes every team eager to find areas of improvement on their cars. In the case of Scuderia Toro Rosso, it was found that there is a gap to improve performance while driving on kerb stones. Analysis of competitors showed that they can take wider driving lines with more speed. In order to improve this performance, the need for a high fidelity dynamic car model arose. This model can be used to understand the car dynamics, which can be used to choose a better suspension setup.

The baseline model used for this study was developed in MSC.Adams. After a discussion with field experts, two areas of model improvement were identified. In the baseline model, the tire is modeled using a MF-Tire model, where the vertical dynamics was modeled as a single spring and damper. In order to simulate the tire dynamics on kerb stones, a more advanced model is necessary. For this research, FTire was chosen as the best option. The other area of improvement is modeling the compliance of the suspension system. In the baseline model,
all suspension members are modeled as rigid links, without any compliance. To model this compliance, an ’equivalent stiffness’ model was proposed, where all stiffnesses are combined into flexible joints.

To use the FTire model, a set of parameters needs to be found for the Formula one tires. To find these parameters, measurements were done to capture the dynamics of the tire. A method is developed to filter out any rig disturbance, and make the data useful for parameter estimation. The estimation was done using Matlab, with MSC.Adams and FTire in-the-loop, and made use of a Genetic Algorithm (GA). Comparing the simulated behavior with the measurement showed good correlation for various conditions.

Since every part in the car contains some compliance, modeling and measuring every single part would be an extensive task. In order to reduce the complexity, all compliances are combined into a simplified model. In this simplified model, all compliances are modeled in the joints. A Pattern-Search algorithm in Matlab is used in combination with MSC.Adams to find the joint stiffnesses in such way the model matches measurements of the overall car compliance.

To measure the overall improvement, both models are combined into a full car model. The baseline and proposed model are validated against measured track data. From this compare, it can be concluded that the proposed model shows a significant better correlation on kerb simulations. This is mainly due to the fact that the baseline tire model is not capable of simulating the harsh road input. The proposed model provides a more powerful tool for engineers to better understand the car dynamics, and to find the best suspension setup for
kerb riding.