Machine Learning Models for Online Prediction of Fuel Quality in Road Vehicles

Description: 

Renewable fuels are becoming more and more used in the transport sector. At the same time the requirements for fuel consumption and near-zero emissions are getting more stringent. As the variability in the fuel is important, the current diesel standard (EN590) allows for a large variation and the engine control can be further optimized if the information of the exact fuel property is available. By using on-line data (available in the ECU, Engine Control Unit) and advanced Machine Learning methods, this project aims at developing such on-line predictors. By a unique combination of design of experiments, technical understanding of the combustion process, data analytics and advanced machine learning methods, there is a potential for improved efficiency and low emissions. The project builds upon ongoing research at Chalmers and could also (in a later stage) be integrated in the research at Chalmers. This project will be a collaboration between CSE and M2 departments at Chalmers.

Requirements

  • Studies computer Science/engineering, physics or mathematics
  • Courses in machine learning and/or AI
  • Good programming skills (preferable in Python)
  • Being motivated, creative, focused and has problem-solving skills
  • Number of students: 1-2 (preferably 2)

Supervisors: Morteza Haghir Chehreghani (CSE): morteza.chehreghani@chalmers.se , Jonas Sjöblom (M2): jonas.sjoblom@chalmers.se

Date range: 
September, 2021 to October, 2024