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Intelligent approaches to improve the system reliability of advanced testing methods

   Centre for Doctoral Training in Advanced Automotive Propulsion Systems (AAPS)

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  Prof C Brace  Applications accepted all year round  Competition Funded PhD Project (UK Students Only)

About the Project

Automotive propulsion system development processes have advanced greatly over the last years, blending physical experiments and high-fidelity simulation to provide a highly realistic environment in which to study system behaviour.

The system needs to allow the Unit Under Test (UUT) to interact with simulated components just as they would in a vehicle. Take the example of a hybrid vehicle transmission undergoing physical test. The simulation of the remaining parts of the system –engine, vehicle, battery etc. need to cater for conditions such as start, warm up, shut down and error states in a way that is rarely called for in a pure simulation environment.

The preparation of models and test rooms for these blended test scenarios is therefore complex and time consuming. The main areas that lead to errors are errors in the software implantation (bugs) and errors in the simulation behaviour that causes the system to stray into unrealistic operating states.

This project seeks to consider all appropriate techniques that can speed up and improve the setup and verification of such complex test scenarios. It is likely that some measure of expert knowledge or ‘big data’ approaches would be useful, along with some procedures to ensure that all possible test conditions are anticipated and verified before the test program is scheduled on the real test room.

Some likely research objectives are to:

•      Produce all possible errors on a running system

•      Identify and enumerate (cluster) all possible error states

•      Classify error (Error by software or simulation).

•      Identify a recommendation to solve the error.

The successful project will develop techniques that offer new scientific approaches in areas such as:

•      Test Mutation methods to generate scenarios of interest

•      Recommender Systems

•      Property based testing

•      Model based testing

•      Increasing Quality of testing

•      System Transparency for complex hybrid test systems

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. The successful candidate for this project will be working with Engineers from the project partner, AVL, a world class test and simulation techniques developer at their global headquarters in Graz and with teams at the new state of the art IAAPS laboratory complex on the Bristol bath Science Park.

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. 

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 (  

AVL List GmbH is the world's largest independent company for the development, simulation and testing of all types of powertrain systems (hybrid, combustion engine, transmission, electric drive, batteries, fuel cell and control technology), their integration into the vehicle and is increasingly taking on new tasks in the field of assisted and autonomous driving as well as data intelligence.

As a AAPS CDT student sponsored by AVL, you will also benefit from the peer support and professional development offered by AVL’s Systems Engineering Lab, founded in 2014 as an interdisciplinary communication & collaboration platform for systems engineering. It comprises around 60 students from various studies, ranging from computer sciences and engineering to psychology, economics and law.

A specially developed program provides training to improve systems engineering competencies and prepare young talents for upcoming challenges in a connected world. Additional mentoring from qualified AVL experts and constant knowledge exchange is guaranteed throughout your time within AVL SE-Lab.

Funding Notes

AAPS CDT studentships are available on a competition basis for UK students for up to 4 years. Funding will cover UK tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£16,062 per annum for 2022/23 rate) and a training support fee of £1,000 per annum.
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