Race car chassis tuning using artificial neural networks

Abstract

Proficient chassis tuning is critical to the overall performance of any race car. Determination of the optimum arrangement for specific track conditions can require a large amount of practical testing and, as such, any tools that reduce this expenditure will be of great value to the racing industry. Traditional computer modeling based on simplified vehicle dynamics has had a growing use in this field, but due to the extremely complex nature of the vehicle/driver/environment entity it has a number of practical limitations. Intelligent models, such as Artificial Neural Networks, on the other hand are not limited in this way and show a number of potential benefits. This study presents a simplified application of ANN to predict the optimum chassis arrangement for a steady state cornering condition for a Formula SAE race car to see if these benefits can be realised. The race car was equipped with a large sensor array, including engine speed, throttle angle, wheel speed, suspension position, steering angle, longitudinal and lateral acceleration and yaw rate, and chassis tuning was accomplished by varying caster, toe and front and rear tyre pressures. Data was collected for a total of six different chassis tuning combinations for the steady state cornering condition and a feed-forward back-propagation ANN model capable of predicting the lateral (centrifugal) acceleration of the vehicle for any given chassis tuning was produced. A numerical investigation was then completed with the ANN model to find the maximum lateral acceleration, and therefore speed, of the vehicle for each different possible chassis tuning combination. Each of the resulting 480 combinations were then ranked and compared against the optimal combination found from extensive practical vehicle testing. Despite a few problems encountered throughout the investigation that deteriorated ANN model accuracy, a high degree of correlation was found.