Our Projects

The main aim of the research conducted in Vincent's project is to develop an effective system parametrisation process targeting Li-ion battery models and having as a prime example the Equivalent Circuit Model structure referred to as MoBat and proposed by the industrial sponsor of the project, AVL. The first segment of the project targets an automatic extraction process of information from random measurements (test bed, drive cycle) leading to model parameter values and encompasses a predefined set of instructions iteratively improving the result of the parametrisation and quantifying the accuracy of the model. A second research element investigates strategies aiming to reduce system testing efforts by using a prior model of a battery system. In addition to the research conducted using MoBat, the project will also consider the parametrisation of similar models proposed for the virtualisation of the same or different systems, targeting the reconfiguration and transfer of the instructions part of the parametrisation procedure. Finally, the dissemination of project results is set to be achieved by the PhD thesis, while the impact of the research will be amplified through close collaboration with the development team integrating the results of the project into commercial software - ModelFactory.

Lukas's PhD aims to develop a systematic approach for product-description driven system model quality assessment and testcase generation, to enhance Model-based validation and verification (V&V) activities throughout the development process of electrified powertrains. One modelling language capable of describing these aspects is Systems Modelling Language (SysML).  In its most rigorous usage mode, SysML-as-Executable-System-Architecture, SysML can be used to develop an executable system architecture making majority of parametric and behavioural specifications of a System Architecture Model (SAM) simulatable and executable. This allows for partially of fully automated generation of system interfaces and system test cases and other artifacts important for the system verification and validation directly from the SAM across various domains and development phases. 

The objectives to deliver this research project are: 

1. To review current V&V practices used at AVL throughout product development to understand their specific requirements (Phase 1)

   • Different modelling approaches exist to describe and model systems using modelling languages such as SysML. As a first phase of this project, it is therefore important, to clearly identify the business’s needs that would best suit the additional demand of the industrial environment and integrate within existing software and methodologies that are being developed in parallel in the organisation to accomplish the full potential of its application.  

2. To develop a systematic approach to a product-description driven SysML model quality assessment to understand the model’s maturity and identify available artefacts based on requirements obtained from obj. a (Phase 2)

   • In order for the SAM to be sufficiently precise and complete to serve as the truth system architecture blueprint for all engineering disciplines and processes involved in the system it must be correct, complete, clear, concise and consistent (Five ‘C’s). Analysis of the necessary level of information within the system model to allow automated generation of required artifacts such as function lists, interface matrix, FMEA & Safety Analysis inputs, testbed interface and configuration information. The second phase of this project will therefore focus on model quality assessment that will result in static model analysis to identify the level of information available and provide further modelling guidelines.  

3. To develop an automated test generation process for available artefacts (from obj. b) to obtain executable test-program (Phase 3)

   • The interactions between operational, functional, structural, behavioural and communication aspects of the system must be modelled in detail to develop and generate a sufficient test program. The third phase of this research project will therefore focus on capturing the relational aspects of the available SAM to analyse to which extent the test program (including all required information such as test scenario, test case, pre-conditions, post-conditions, test data and expected results) can be generated automatically and develop a software module (addon). This will serve as a proof of concept for automated test case and test artifact generation for system functional verification. 

4. To implement a development-role specific model administration access to present subject matter experts with appropriate information (Phase 4)

   • Achieving the Five ‘C’s quality as described in Phase 2 of the project plane is important to ensure sufficient level of SAM maturity to enable partially or fully automated processes. It is therefore necessary to efficiently manage the human-model interface and allow the appropriate engineer/team to effectively contribute to the model development. The fourth phase of this project will therefore focus on the implementation of a domain/role specific access management for various stakeholders.  

5. To identify system and process boundaries and interfaces of the methods developed in obj. b - obj. d to integrate within existing PLM architecture (Phase 5)  

   • As depicted in the first phase of this project plan, the usage and application of MBSE and SysML varies depending on the specific requirements and demands of the industry and the business’s needs. The fifth phase of this project will therefore focus on identification of system and process boundaries of the project outcome within the existing PLM architecture and systems processes.

Antiwear and reduced friction agents are a class of engine oil additives used to both reduce self-inflicted damage from metal-metal contact inside internal combustion engines, as well as acting as friction modifiers, which serves to improve engine efficiency. Zinc dialkyl dithiophosphates are one of the leading materials used as such agents. However, despite their effectiveness, they are known to contaminate catalytic converters - a problematic issue which has led to significant research into finding replacements. Although the electrification of the transport industry has already started, tribology and the design and formulation of antiwear and antifriction additives play an important role in the optimisation of efficiency of every mechanical device. Extensive use of zinc dialkyldithiophosphates and other materials such as molybdenum disulphide (MoS2) as antiwear and lubricious materials are present across many applications that involve devices with moving mechanical components.

The vision of the EPSRC is to advance the knowledge and technology of scientists to tackle several key areas one of which is climate change. The development of novel lubricious materials aids in the reduction of carbon, not only lowering the effects of climate change but conserving the current environment.

The UK is heavily reliant on private modes of transport. The car offers a type of freedom that other modes of transport don’t: flexibility, privacy, and often an extension of our own identities. The size of the car has increased steadily over the past few decades, meaning they require more resources to build, are more dangerous to pedestrians, and emit more greenhouse gases. The average number of occupants in our car journeys is below two, and most journeys are within cities and towns. Traffic congestion is a norm due to our reliance on the car.

The microcar may offer a solution to many of these issues. These tiny cars are less resource-intensive, easier to drive than average-sized cars, and are also cheaper to buy and run. A city filled with microcars means less congestion and a safer environment for pedestrians. However, the microcar industry is incredibly small, offering few options for consumers. How do we build the demand for microcars, and how can we make them desirable? These are the questions my PhD project will aim to answer.

Automotive vehicles are designed to work in a wide range of conditions, however they may operate in certain conditions better than others.  These vehicles are relatively unintelligent in preparing for variations of driving conditions including changes in the external environment, terrain, and congestion.

This piece of research will enhance industrial understanding of predictive control strategies for automotive applications and provide a demonstrable predictive control strategy for further research.

Faye’s PhD seeks to improve our understanding of how the built environment (such as the transport networks and transport facilities around our homes) influences mental health, and whether this can be explained by active travel behaviour. There are considerable gaps in knowledge that need addressing, such as uncertainties around which factors in the built environment are particularly important for health. By investigating the role of active travel behaviour in these relationships, this PhD aims to shed more light on why we see frequent associations between the built environment and mental health. Faye will also explore these relationships in childhood, and investigate how the built environment (including transport networks and facilities) and travel behaviour shapes child brain development. This is important for understanding why interventions targeted at the built environment and travel choices are not only important for planetary health, but also for population health.

The ever-growing global presence of the electric vehicle is seen as a positive solution to decarbonise the transport industry. As a result, chemists and material scientists are aiming to develop materials that can be used as a backbone for improved electrodes and electrolytes for next-generation batteries and supercapacitors.

Dan's research will focus on the generation of materials that are considered to be part of the next generation of batteries through the use of non-line-of-sight deposition techniques, including chemical vapour deposition (CVD) and atomic layer deposition (ALD). This will provide opportunities to produce current collectors and thin films that are well-defined. Through the methods chosen, the microstructure, morphology and chemistry of the composites can be finely-tuned to overcome potential challenges that battery materials face, such as volume changes during charging and the mechanical, chemical or electrochemical degradation of the electrodes.

Focus will be drawn to potential lithium- or sodium-chalcogenide intercalation or conversion type electrode, or electrolyte materials, such as Lithium sulfides, lithium phosphates and lithium anti-perovskites, and their sodium counterparts.

The initial stages will involve the synthesis of molecules that can be used as precursor material for CVD and ALD, which will then be characterised via a host of methods, including X-ray diffraction, NMR and elemental analysis. The thermal decomposition will be assessed, as will the ability of the precursor to create a thin film. The thin films will be characterised using scanning electron microscopy and will be assessed on its ability as a charge carrier.

The advantages of the chosen techniques (CVD and ALD) will be exploited to improve upon cell performance. These include the ability to deposit uniform layers on a surface which can be used as a protection against chemical degradation, the ability to deposit conformally active materials onto structured backbones, such as nano-tubes, -flakes or -rods. There is also the advantage of high levels of control over stoichiometry of new materials that will be tailored to suit the cell performance by appropriately choosing the precursor materials, changing the deposition parameters and through chemical doping.

Modelling has become critical in recent years for the development of new engine technologies, this is especially important for hydrogen due to its wide range of potential operation. However, modelling hydrogen combustion is particularly challenging due to its wide operating range and complex flame behaviour, including short quenching distances and susceptibility to flame instabilities.

As part of the Prosperity Partnership research group, experimental data will be gathered from a hydrogen engine at IAAPS. Combustion and emissions will be modelled using low-cost 1D simulation tools to generate accurate boundary conditions for more advanced 3D Computational Fluid Dynamics (CFD) simulations. This experimental data will serve to validate both the 1D and 3D models, ensuring their predictive accuracy across a range of operating conditions. The CFD analysis will enhance understanding of hydrogen combustion and emission formation mechanisms, supporting the optimisation of hydrogen engine development.

Thomas’ PhD project is centred on enhancing the construction of structural batteries made from carbon fibre. His research involves examination of the electrochemical performance of individual electrodes subjected under different conditions. Thomas is looking into atomic modelling of the carbon fibre anode to understand the structural changes that occur during charging (in partnership with the University of Virginia). Additionally, he is investigating the extent of lithiation of coated carbon fibre cathode materials (collaboration with Chalmers University).

The primary emphasis of Thomas' research lies in comprehensively understanding the structural changes occurring in each electrode across different conditions and evaluating their respective electrochemical performance. This ground-breaking work promises to contribute valuable insights to the field of carbon fibre-based structural batteries.

Computer vision plays a crucial role in almost all autonomous driving systems and has the potential to be used with traffic management systems of the future. This technology involves the use of cameras and image processing algorithms to interpret and understand the surrounding environment, in the context of automation, allowing the more efficient and accurate management of traffic, automatic crash detection systems, parking management and autonomous driving applications.

In this research project, the primary objective is to enhance the capabilities of computer systems in understanding and predicting the behaviour of vehicles on the road, with the ultimate goal of improving road safety and efficiency. The project will focus on improving the robustness of object tracking by leverage the increased predictability and contextual information of vehicle driving scenarios. Object tracking is the process of observation of vehicles and important information about them, colour, vehicle type, shape, size etc, and the correlation of these properties across video frames in order to associate the same vehicle across an entire video.

The aim is to develop advanced computer algorithms capable of accurately identifying key attributes of vehicles, such as their movements and intentions, in real-time. This understanding of vehicle behaviour will contribute to safer driving scenarios. Additionally, the project seeks to improve existing object tracking algorithms by incorporating contextual information, like lane detection, to enhance trajectory prediction and situational awareness. The involvement of contextual clues specific to automotive situations should allow the algorithms to provide a more robust and reliable result that more generic algorithms.

Sam's project will be completed using a mix of analytical and machine learning algorithms. Where the two different approaches will be compared against each over for speed accuracy and ease of use. In an attempt to find a solution that can both provide usable results in a real-world scenario but also run on systems capable of being deployed.