• Johannes Rohwer

  • Theme:Digital Systems, Optimisation and Integration
  • Project:Towards new statistical modelling techniques combining expert knowledge and experimental data for propulsion systems
  • Supervisor: Nic Zhang
  • Industry Partner: AVL
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Johannes recently completed a pre-doctoral programme at Argonne National Laboratory in the USA. During this time, he was involved in experimental research on advanced, high-efficiency internal combustion engines, with specific focus on pre-chamber systems as as co-optimization of fuels and engines. His other industry experience includes working for an industrial manufacturer of heat exchangers in Germany as well as a robotics start-up in London. Johannes obtained his BEng and MEng at the University of Stellenbosch, South Africa. His research interests at the AAPS are in the field of digital systems, integration and optimisation.


  • I played the trumpet in an 'Oktoberfest band' during my two and a half years stay in Germany.
  • I was the treasurer of a beach sailing club during my MEng.
  • I have lived in three different continents so far and hope to travel to a couple more during my PhD.
  • When I was a kid, I would make my siblings waffles at least once a month.

Towards new statistical modelling techniques combining expert knowledge and experimental data for propulsion systems

Modern automotive powertrain labs create large amounts of data. The data include various key performance metrics, crank angle resolution cycle events and high frequency recordings of all channels in time traces. Historically, the experimental results in the form of lookup tables and scatter plots have not fully exploited the potential of the data and engineers are increasingly focusing on creating statistical models using the available dataset. High quality statistical models can replace some experimental work as the digital twin of physical systems for predictive analysis and can be embedded directly into automotive controllers for model-based control.

With the wide range of modelling tools available, automotive engineers would benefit from a framework of statistical modelling for specific powertrain systems in the form of an automated tool. This PhD will seek to create such a tool with a help of a large commercial database of experimental data. Familiarity with the physical models for individual components (batteries, motors, engines, fuel cells…) should be the starting point of the study. Open source machine learning libraries, such Keras, will then be used to explore the available dataset to investigate the predictive performance of statistical models, such as Neural Networks, compared to the physical models.

© Copyright 2021 AAPS CDT, Centre for Doctoral Training in Advanced Automotive Propulsion Systems at the University of Bath