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.
The technical know-how generated in this study is expected to provide the tool users with specific guidance such as:
- whether important inputs are missing for specific technologies;
- how to reduce the number of input variables of the problem for faster model training.
- how to run iterations of training to eliminate irrelevant areas of the problem space and instead focus on areas of special interest.
- which statistical models are most suitable for the specific engineering problem.
- whether physically inspired rules should be included in the ML structure to improve the model performance.
A likely deficiency of this approach in highly non-linear systems is that the density for experimental data needed to allow the training of a Machine Learning structure would be impractical. If this proves to be the case, an alternative approach should be considered that seeks to embody the engineer’s understanding of the physical causality that underlies the unit under test. This can be represented in the form of physically inspired ‘rules’ or ‘toy models’ that can then be calibrated to represent the unit using an iterative training approach. Such a model could allow a more sparse dataset to be used without sacrificing predictive power.
The outputs from this PhD would be expected to be integrated into a model factory engineering software tool that supports engineers in the creation of mathematical models.
This project is offered as part of the Centre for Doctoral Training in Advanced Automotive Propulsion Systems (AAPS CDT). The Centre is inspiring and working with the next generation of leaders to pioneer and shape the transition to clean, sustainable, affordable mobility for all.
Prospective students for this project will be applying for the CDT programme which integrates a one-year MRes with a three to four-year PhD
AAPS is a remarkable hybrid think-and-do tank where disciplines connect and collide to explore new ways of moving people. The MRes year is conducted as an interdisciplinary cohort with a focus on systems thinking, team-working and research skills. On successful completion of the MRes, you will progress to the PhD phase where you will establish detailed knowledge in your chosen area of research alongside colleagues working across a broad spectrum of challenges facing the Industry.
The AAPS community is both stretching and supportive, encouraging our students to explore their research in a challenging but highly collaborative way. You will be able to work with peers from a diverse background, academics with real world experience and a broad spectrum of industry partners.
Throughout your time with AAPS you will benefit from our training activities such mentoring future cohorts and participation in centre activities such as masterclasses, research seminars, think tanks and guest lectures.
All new students joining the CDT will be assigned student mentor and a minimum of 2 academic supervisors at the point of starting their PhD.
As a AAPS CDT student sponsored by AVL, you will benefit from the peer support and professional development offered by AVL’s Systems Engineering Lab. In 2014 AVL’s “SE-Lab” was founded as an interdisciplinary communication & collaboration platform for systems engineering. It comprises around 60 students from various studies. A special developed student trainee program provides sustainable basic and advanced trainings to improve systems engineering competencies and prepare young talents for upcoming challenges in a connected world.
Funding is available for four-years (full time equivalent) for Home students.
See our website to apply and find more details about our unique training programme (aaps-cdt.ac.uk)