If you are from industry and interested in any of these projects please contact us by emailing aapscdt@bath.ac.uk

If you are student and are interested in any of these projects then please complete an expression of interest and state which project you are interested in.

The current available projects are listed below, followed by full project descriptions

  • Enabling Effective Transdisciplinary Working with the Automotive Sector
  • Automated generation and parameterisation of physics-based propulsion system models, with AVL
  • Towards new statistical modelling techniques combining expert knowledge and experimental data for propulsion - systems, with AVL
  • Intelligent approaches to improve the system reliability of advanced testing methods, with AVL
  • Automated Configuration of Simulation Parameters, with AVL
  • Understanding the influence of battery current ripple, with AVL
  • Non-contact driver attentiveness detection system, with Infineon Technologies AG

Project Descriptions

Enabling Effective Transdisciplinary working with the automotive sector

  • Supervisors: Linda Newnes
  • AAPS Research Theme: Transport Policy and Economics
  • Start: October 2021

Over the past few decades transdisciplinary (TD) has been the subject of increased discourse in the context of large, complex, ill-defined, ‘wicked’ problems. However, there has been less consideration of the potential it offers within the practice of engineering. This research looks to create tools which enable effective TD working within the automotive sector. The Mobility Engineering 2030 FISTA White Paper identifies that changes within the sector mean that interdisciplinary working, involving groups formed from people working in similar disciplines, will not be sufficient. It recognises that in the future there will be a need for transdisciplinary working, which goes beyond the academic disciplines to understand the societal context. For example, legislation, standards, culture. However, achieving effective TD working within organisations is not simple. It requires the creation of tools (e.g. processes and methods) which enable clear communication and knowledge transfer within and beyond an organisation. This PhD will leverage input from the TREND (TRansdisciplinary ENgineering Designers) £1.8m platform grant (Dec 2017 – Dec 2022). The over-arching aim of TREND is to provide tools to assist engineers to work in a transdisciplinary manner and to identify the types of engineers that are transdisciplinary. Identifying what makes engineering teams in the automotive sector transdisciplinary and how to assess their current readiness level to be transdisciplinary is the focus of this PhD activity. The PhD will have a particular focus on ‘common’ characteristics and automotive design team behaviour within and across industry case studies. Mapping findings at various life cycle stages such as designer requirements, use of digital tools etc. for each case study/domain against the manufacturing life-cycle phases. This would be followed by cross case-study analysis. The analysis may use techniques such as input/output system modelling to map the designer requirements at each stage of the manufacturing life cycle, and/or socio-technical analysis could be used to classify and model the designer behaviour. In summary the PhD researcher will be required to create a structured framework to estimate the automotive sectors transdisciplinary readiness level. Specific objectives may include 1. Undertake a literature review to understand the state of TD working within the automotive sector. 2. Engage with stakeholders to gather information which informs the design of the TD readiness tool. 3. Create a TD readiness tool. 4. Validate the proof of concept tool within industry.

Automated generation and parameterisation of physics-based propulsion system models

  • Supervisors: Nic Zhang
  • Industrial Partners: AVL
  • AAPS Research Theme: Digital Systems, Optimisation and Integration
  • Start: October 2021

Accurate simulation models are vital for the implementation of digital development techniques. These models need to be a suitably accurate representation of the system behaviour, whilst also satisfying key requirements in terms of computational speed and scalability. Currently there are two major categories of models:

  1. Those that are physically based which generally have strong scalability but also inherent inaccuracies due to the constraints of the underlying physical model.

  2. those that are data driven which have good accuracy within the range for which data is available, but poor scalability. This PhD will seek to create new algorithms that can automate the creation and parameterisation of physical and semi-physical models that are both scalable and accurate. The starting point for these will always be a known physical model and some limited measurements of the system performance. For physical systems, model parameters of interest are often distributed across a state space, which leads to a large number of parameters to identify, and restricts the usage of state-of-the-art identification procedures. For example, battery resistances will inherently depend on ambient temperature as well as the current state of charge. Your research will be focussing on localisation strategies to identify these distributed parameters independently, such that the identification procedure can be performed in a parallel fashion across the state space, and becomes computationally tractable. Your research will need to consider the future of automotive propulsion to ensure the approach is compatible with relevant technologies. Whilst there will be specialisms associated with the modelling of individual components (batteries, motors, engines, fuel cells…), you should seek to draw out the commonality of the mathematical approach that can be applied more generally. 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

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

  • Supervisors: Nic Zhang
  • Industrial Partners: AVL
  • AAPS Research Theme: Digital Systems, Optimisation and Integration
  • Start: October 2021

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.

Intelligent approaches to improve the system reliability of advanced testing methods

  • Supervisors: Chris Brace
  • Industrial Partners: AVL
  • AAPS Research Theme: Digital Systems, Optimisation and Integration
  • Start: October 2021

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 behavior.

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 two main areas that lead to errors are errors in the software implantation (bugs) and errors in the simulation behavior that causes the system to stray into unrealistic operating states. For example – the engine simulation could simply crash (due to a bug) or it could execute correctly but give dangerously large output predictions which then cause damage to the UUT.

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

The successful candidates will be working with Engineers form 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 (web link).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.

Automated Configuration of Simulation Parameters,

  • Supervisors: Chris Brace
  • Industrial Partners: AVL
  • AAPS Research Theme: Digital Systems, Optimisation and Integration
  • Start: October 2021

Automotive propulsion system development processes have advanced greatly over the last years. State of the art techniques use a blend of physical experiments and high fidelity simulation to provide a highly realistic environment in which to study system behavior.

Practically, this means that the simulated parts of the system need to behave just as if they were physically present in the test room. The test system needs to allow the Unit Under Test (UUT) to interact with the simulated components just as they would in a vehicle. Both requirements lead to significant complexity when compared with traditional scenarios. 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 hybrid test field is a complex combination of physical hardware (UUT) and software simulations of other aspects of the system. Both sides of the system are underpinned by complex computation and test automation platforms. All of these elements need to work together in harmony and make the UUT believe it is operating in a real vehicle in the real world. To achieve this, the simulated aspects of the system must be carefully calibrated to operate in the desired manner. This is a significant task and this PhD aims to develop techniques to speed up and simplify this process for the test engineer. Such a system will need to identify:

  • The best simulation parameters (in terms of Robustness and fewest simulation errors)
  • Identifying the minimum set of simulation parameters that meet this requirement to reduce complexity
  • Generate user friendly information to allow users to understand the system
  • Automatic and guided parameterization with minimum number of experimental runs (Preferably 0, all parameterisation is performed and verified ahead of the physical test programme)
  • Strategies to allow self healing in the case that an error state is encountered.

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

  • Recommender System
  • Co-Simulation
  • Optimization Techniques
  • Usability in Simulation (e.g. Robust System Stability)

The successful candidates will be working with Engineers form 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.

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 CDT AAPS student sponsored by AVL, you will also 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 (part-time, Master/Bachelor/PhD thesis) from various studies, ranging from computer sciences and engineering to psychology, economics and law.

Understanding the influence of battery current ripple

  • Supervisor: Christopher Vagg
  • Industrial Partner: AVL
  • AAPS Research Theme: Propulsion Electrification
  • Start: October 2021

The battery cell is probably the most critical component of an EV, and key to the sustainability of future transportation solutions. Currently most battery testing is performed using very “clean” DC test currents whereas in reality, when used in a vehicle, the battery is subjected to current profiles with a lot of high-frequency (AC) components owing to the commutation (switching) of the inverter power electronics. Paramount in understanding how this affects its performance as part of a real powertrain system is the understanding of its electrochemical behaviour and processes.

The goal of this study is to investigate the influence that current ripple has on a Lithium-ion battery cell when it is applied on top of the DC current used to charge/discharge the cell.

Several studies have demonstrated that current ripples applied to cells can impact their performance (capacity, internal resistance, aging, etc) either positively or negatively. This PhD seeks to understand these phenomena in detail through experimentation and thermal-electro-chemical modelling of the cell behaviour, to predict the impact that any profile of current ripple might have on a particular type of battery. The research will have a strong experimental aspect to collect data from a range of battery cells, which in turn will directly support the theoretical investigations.

The outcomes will inform best-practice for powertrain hardware design (inverters and filter capacitors) and software strategies implemented in the Battery Management System, as well as contribute to the understanding of how other techniques, such as battery self-heating using AC, might be applied in the future.

In undertaking this PhD, you will join a team of researchers within the Institute for Advanced Automotive Propulsion Systems (IAAPS). You will also have direct links with engineers from the project partner, AVL, the world's largest independent company for the development, simulation and testing of all types of powertrain systems.

This project would suit a student with a background in an Engineering discipline (Mechanical/Electrical/Chemical) or in Chemistry.

Non-contact driver attentiveness detection system

  • Supervisors: Ben Metcalfe
  • Industrial Partner: Infineon Technologies AG
  • AAPS Research Theme: Driver and User Behaviour
  • Start: October 2021

The primary objective of this Infineon Technologies AG sponsored project is the development of novel Radar based systems for detecting driver attentiveness via non-contact vital sign monitoring and gaze detection. For level 3 autonomous driving, the human must be attentive in order to take over from the autonomous systems in the vehicle if required. Recent research has shown that it is possible (in a lab setting) to measure vital signs such as heart rate, respiratory rate, blood pressure, and blood oxygenation using non-contact methods such as Radar. These vital signs can be combined (via sensor fusion methods) with movement detection to develop new models of driver attentiveness. For example, low variation in heart rate and a reduction in body temperature are good indicators of drowsiness.

Infineon Technologies AG are specialists in developing automotive Radar systems that are currently used predominantly for collision avoidance and external sensing. This project represents an exciting opportunity to use this same low-cost automotive technology for driver attentiveness monitoring.