STUDY AND DEVLOPMENT OF PERFORMANCE METRICS FOR ANALYSING THE CORNERING BEHAVIOUR OF AN F1 CAR

This thesis investigates about a novel modelling approach of calculating new metrics that determine the yaw behaviour of a Formula 1 car. The focus is to determine the balance of the car during cornering by estimating the Yaw Moment, Control and Stability derivatives in response to driver inputs, car setups and other external conditions capturing the transient operations as well. The engineer does setup modifications on the car with the objective information from the simulation tools. But it is the driver who senses how the car behaves and relays his subjective information to performance engineers. The tire behaviour is the only medium that allows to relate that information from both parties. This thesis attempts to strengthen the feedback loop and benefit the vehicle performance engineers by evaluating the subjective information from the driver and perform setup modifications appropriately. Using simulation software, it is possible to setup the car in a way depending on the track requirements. But when it comes to extracting the best performance, it is necessary to understand the effects of the setup. Current ways include carefully analysing the driver feedback and comparing them with the existing metrics like mechanical balance of the suspension at low-medium speeds and aero loads acting on the car at high speeds. The method used in this thesis, for quantification of balance, is based on the loads acting on the tire using the Milliken Moment Method (MMM) revolving around Milliken Force Moment analysis. The calculation requires certain derived parameters which are normally difficult to extract from a real-life using sensors. To bypass this problem, a driving simulator will be used. The simulator plays a major role in this development since a lot of parameters are available at hand due to the mathematical models incorporated within. Along with that, there is a virtual ECU that computes complex math channels and provides multiple derived parameters required for the calculation. The modelling is done using MATLAB and developed as a standalone tool initially. The main aspect of the metrics depended on the interpretation to use them appropriately. Using the knowledge from setups from previous driving data from the simulator session, the outputs of the new metrics were compared with. The initial results were promising, with the Stability derivative capturing the setup difference well. The control derivative was able to capture the washout of the front axle at certain corners for a particular setup. The yaw moment when compared between two setups, managed to show the yawing ability of the car. But the difference between the two setups could be because of different reasons. An ideal method would be to evaluate the ideal yaw moment required to complete that corner which is not covered in the thesis. Additionally, an attempt to incorporate the compliance in the metrics by modelling them as a lumped element was performed and the results will be discussed in the thesis. Once the Stand-alone model was able to produce decent results, the metrics are modelled in the virtual ECU of the simulator where the metrics can be processed directly from the live data when a simulator session takes place. As a future development, the new metrics can be validated by monitoring a live simulator session and comparing them with the driver feedback post session to ensure correlation and validity of the performance metrics. The scope for this project can be improved modelling the compliance accurately to study the impact of compliance during transient operation. The approach mainly was aimed at bridging the gaps between different languages spoken by the drivers and engineers. And these new metrics are the right step towards that goal.