Artificial Neural Network Prediction of the Optimal Setup Parameters of a Seven Degrees of Freedom Mathematical Model of a Race Car: IndyCar Case Study

The aim of this paper is the development of a 7-DOF (Degrees Of Freedom) mathematical model of an IndyCar and the implementation of an Artificial Neural Network in order to predict the optimal setup parameters of the car, reducing time and costs for race teams. The mathematical model is created by using MATLABTM and Simulink software starting from a telemetry acquisition at the Houston circuit and is based on Vertical Vehicle Dynamic equations. The optimal setup parameters have been predicted through an Artificial Neural Network (ANN) by using the NFTOOL Toolbox of MATLABTM software. ANN is implemented in a Quarter Car model, firstly, in order to train the network and predict the parameters able to reduce tire deflection and suspension travel in the time domain and the resonance peaks amplitude in the frequency domain. Then, it is implemented in the 7-DOF model in order to predict the best setup parameters able to reduce body movements and the weight transfers of the car.