polito.it
Politecnico di Torino (logo)

Data-Driven Vehicle Performance Optimization for Formula Student Racing

Lal Akin

Data-Driven Vehicle Performance Optimization for Formula Student Racing.

Rel. Andrea Tonoli, Stefano Favelli, Dario Salza, Federico Oldani. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024

[img]
Preview
PDF (Tesi_di_laurea) - Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives.

Download (22MB) | Preview
Abstract:

In the world of motorsports, every driver aims for one thing: to finish with the fastest time. But it's not as simple as hitting the throttle pedal. There are vast numbers of factors involved, from how drivers handle their cars individually to the unpredictable nature of the track and vehicle. Formula Student Racing, where teams are tasked each year to build and race their own cars, is a university level single-seater competition. It's a hands-on learning experience like no other, where students apply classroom knowledge to real-world challenges, pushing the boundaries of automotive technology. Building a fast car is only part of the equation for Squadra Corse PoliTo, the student racing team of Politecnico di Torino. The real challenge lies in integrating and optimizing driver and car performance, which is the main focus of this thesis. Data Science and Machine Learning is implemented to understand how a driver's actions affect the performance of the car and how to perfect it. The goal is to develop a cutting-edge tools for Squadra Corse to offer insights about the hidden relationships between what the drivers put into the car, and how the car responds. By harnessing the power of data and algorithms, the aim is to supply drivers with the competitive edge and deeper understanding they need to outpace themselves and the competition. First, the most critical inputs of driver behaviour on vehicle performance will be identified. Then, with this knowledge, a predictive model that can anticipate how different strategies will impact performance will be built. Final step is to build a framework using both Optimization methods and Imitation Learning techniques to reach the best driver input strategy configuration and create a driver model. With this proposed framework, Squadra Corse can formulate the best strategy for specific configurations and give feedback to their drivers to maximize performance.

Relators: Andrea Tonoli, Stefano Favelli, Dario Salza, Federico Oldani
Academic year: 2024/25
Publication type: Electronic
Number of Pages: 120
Subjects:
Corso di laurea: Corso di laurea magistrale in Data Science And Engineering
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/33043
Modify record (reserved for operators) Modify record (reserved for operators)