Supervisor: DI Nikolaus Feith;
Univ.-Prof. Dr Elmar Rückert
Start date: 15th Februar 2023
Theoretical difficulty: mid
Practical difficulty: mid
In the last decade, MotoGP has taken data analytics and telemetrics to a whole new level which has aided in the development and manufacturing of the motorcycles prototypes and their performance on the track.
However, as in other high-end motorsports competitions such as F1, the technological gap keeps getting smaller and, with so, the real potential advantage is gained during testing by trying to find the optimal setup for that specific track and weather conditions. Due to regulations, time dedicated to testing on track is quite scarce, therefore teams and pilots have to find their way around new prototype setups every week to optimize and extract the best performance of the bike.
The main goal of this project is to develop a Bayesian Optimization algorithm that can aid in the fine tuning of ECU parameters of the motorcycle (fuel injection timing and spark ignition timing) while providing a framework and workflow for this methodology.
The work is done in collaboration with the Chair of Cyber-Physical-Systems at Montanuniversitaet Leoben, and Montan Factory Racing, participant of the VII Edition Motostudent Petrol.
Tentative Work Plan
To achieve our objective, the following concrete tasks will be focused on:
- Literature research
- Engine and controller model development in Simulink
- Bayesian optimization algorithm development
- Hardware integration
- Testing in practical applications
- M.Sc. thesis writing and documentation