Edison's project explores integrating Sharing Economy Business models in automotive companies and the strategies for enhancing corporate sustainability.
While the 20th century represented the era of individual car ownership, the 21st century seems to disrupt this inherited system. The new mobility trend prioritises access over ownership, meaning that users get access to a mobility service instead of having a private vehicle. These practices are called car-sharing and carpooling. The main motivations for car-sharing include cost savings for users, reducing carbon emissions, recirculation of goods, increased utilisation of durable assets, and exchange of services. This incoming mobility system is based on an economic system known as the ‘sharing economy’. The sharing economy has social interactions at its core, integrating activities such as renting, trading, swapping, and borrowing. According to Allied Market Research® (2023), the shared economy market size could grow from US$387.1 billion in 2022 to around US$827.1 billion by 2032. This economic model projects such growth as it offers more affordable solutions for consumers than traditional models do, also technological solutions such as digital platforms enhance accessibility to this model which often is aligned with consumers’ sustainability principles.
The automotive companies are undergoing significant transformations driven by the advent of the sharing economy and sustainability goals. The sharing mobility platforms are altering customer behaviour and challenging OEMs' traditional business models. These changes bring uncertainty for the long-term sustainability of these companies and their strength to adapt to new market dynamics. Despite the increasing sharing economy research output, there is a gap in examining its impact on the corporate sustainability of automotive companies. Current literature focuses on operational and consumer implications in the SE but ignores the broader implications for corporate sustainability, including economic viability, social responsibility, and environmental impact. This PhD project aims to fill this gap by investigating how adopting SE business models in OEMs influences their corporate sustainability.
For this purpose, a mixed methods methodology across three interconnected studies will be undertaken. (1) through a systematic literature review, the first study will assess the impact of sharing economy business models on the enhancement of corporate sustainability, (2) the second study will employ structured interviews with OEMs' decision-makers at the corporate level and document analysis of the selected OEMs' annual reports to identify operational and strategic adjustments for the implementation of sharing economy business models. Finally, (3) the third study will evaluate the economic benefits and challenges associated with integrating the sharing economy business models through undertaking financial analysis, surveys and structured interviews.
Electric vehicles (EVs) play a key role in decreasing the carbon footprint of the mobility sector. Their high upfront cost, limited range and slow charging speed are however a barrier to increased EV uptake. Reducing the cost and improving the EV Lithium-ion (Li-ion) battery could reduce these barriers.
There is however limited knowledge in the safe operation and degradation rate of Li-ion batteries. This is largely due to the complex electrochemical mechanisms not being well understood. Furthermore, the large operating envelope (temperature, charging speed etc.) over its lifetime require resource intensive testing to parameterize semi-empirical models. The battery is therefore operated very conservatively, resulting in oversizing the battery and sub-optimal operating conditions resulting in inefficiencies and higher costs.
Johannes's PhD aims to provide optimal testing strategies and accurate modelling (with a focus of degradation) of Li-ion batteries in order to provide information to facilitate more efficient operating strategies (e.g. fast charging). This will be achieved by a combination of advanced design of experiments (DOE), modelling and machine learning.
The initial part of the PhD will focus on building a model structure which is based on a semi-physical neural network. The accuracy of this model will then be assessed using existing battery data in literature and data provided by the industrial partner. An experimental test campaign will then be designed and implemented, in an attempt to efficiently parameterize the battery models. The resultant battery models would then provide important information to improve the safe operation range of the battery.
Relying solely on the information and specifications provided by the manufacturer to create a robust model is impractical as they often only include information required for the machine’s operation. The overarching aim of this work is to develop a procedure to automate the parameterisation of electric motor models for later use in the vehicle development process.
Lithium-ion batteries are pivotal in powering electric vehicles (EVs), significantly contributing to the electrification of transportation and reducing greenhouse gas emissions. These batteries are favored for their high energy and power density, efficiency, longevity, and durability. A critical aspect of ensuring their safe and reliable operation is the management of the State of Charge (SoC), a key parameter indicating the remaining battery capacity.
There are various methods to measure SoC, including Coulomb counting, Open Circuit Voltage (OCV), and Kalman filtering. Coulomb counting calculates SoC by integrating the current flowing in or out of the battery over time, relative to its total capacity. However, it suffers from cumulative errors, particularly from inaccuracies in current measurement and changes in battery capacity. The OCV method, based on the relationship between SoC and the battery's open-circuit voltage, is simple and accurate but impractical in continuous-use applications due to its need for the battery to reach a steady state. Kalman filtering, a sophisticated approach, combines a battery system model with real-time measurements to estimate SoC, adjusting estimates based on incoming data. While providing dynamic and accurate readings, it requires a detailed battery model and is computationally intensive.
An innovative approach in this realm is Ultrasound Non-Destructive Testing (NDT), particularly for lithium-ion batteries in high-demand applications like EVs. Ultrasound NDT is a non-invasive technique that doesn't require disassembling the battery, crucial for maintaining its integrity in practical applications. This method is safe, reducing the risk of damage or unsafe conditions during testing. It can detect internal structural changes in battery cells correlated with SoC and overall health. For instance, electrode expansion and contraction during charging and discharging cycles can be monitored with ultrasound techniques, potentially identifying internal faults or degradation early. Ultrasound NDT can potentially offer real-time or near-real-time SoC monitoring, integrating into Battery Management Systems (BMS) to enhance battery performance and longevity. This method can complement traditional SoC estimation methods, providing a different data type that can validate or complement electrical measurements.
Mac Geoffrey's PhD project focuses on using ultrasound NDT for lithium-ion battery modules in EVs. The main aim is to apply ultrasound NDT to these battery modules, finding practical solutions to encourage automotive manufacturers to include this technology in their BMS for accurate SoC tracking. Specific objectives include examining SoC non-uniformity across a single cell, comparing traditional SoC estimation methods against acoustic Time of Flight (ToF) measurements, studying ToF behaviour under temperature variations, investigating spatial acoustic changes under different charging/discharging speeds and relaxation periods, assessing the acoustic response of a battery module, and exploring battery module responses during cell balancing with various conditions. This project aims to enhance the accuracy and reliability of SoC estimations in lithium-ion battery modules for EVs, exploring the potential of ultrasound NDT as a practical and efficient method for integration into existing BMS technologies.
Physical testing of new vehicle components is time consuming and complex, and is thus, highly energy intensive. Additionally, many of these tests develop faults which are only discovered after the test-run, and subsequently need to be repeated. Virtual testing can reduce the need for physical tests and reduce energy consumption, however, these models rely on physical testing for high quality test data to adapt and optimise the virtual models. Some physical testing will also still be required prior to market release to ensure customer safety. Ellie's project aims to improve the quality of data from physical testing, using machine learning and human factors analysis, whilst minimising wasted energy consumption.
Anomaly prevention – a term coined to complement anomaly detection - describes the combined methodology of human factors analysis and systems mapping; this was devised after testing procedure and human factors were identified as significant contributors to data quality. Anomaly prevention will identify the processes outside of the test bed that negatively impact data quality and suggest mitigations. Anomaly detection – a method of finding unexpected patterns or points in data via statistical or machine learning techniques - presents a solution to detect anomalies in real time during testing, thus, minimising physical testbed time whilst increasing the reliability of data to feed into virtual models. Together, these methods have the potential to reduce the energy intensity of testing and development, whilst simultaneously increasing the speed at which low-carbon technologies can be released into the public domain to aid large scale decarbonisation of the transport sector.
A Bill of Materials (BoM) is a structured document that contains the information on all components and resources needed to build a product.
The validation of the BoM is an essential process performed to establish the accuracy and completeness of product information. This document acts as a vital source of truth not only to determine the correct product composition, but also for multiple business operations within a manufacturer that rely on this information, such as inventory management and servicing.
The complexity of this task is dependent on the quantity and quality of items and information recorded in the BoM. This can be extensive considering the potential product variations and customisation options available to the customer which determine the extent of unique combinations to be included in the validation.
The validation process requires experts with knowledge of the product design (the constituent components and systems, their procurement and interaction within their respective assemblies) to review each item in the BoM for approval or correction. Computational tools that support this validation process exist, such as rule-based systems, although there is still a heavy reliance on the resource of product knowledge experts to audit the BoM.
One technique which has not be explored extensively is the application of artificial intelligence (AI) to improve the efficiency of the process. AI provides the advantage of being able to assist with, augment or autonomously perform data analysis tasks to support with BoM validation.
The implementation of AI to the validation of a Bill of Materials could provide a means to faster and accurate identification of errors present in a wide range of vehicle BoMs, with less human expert resource required and less need to define and manage specific rules to identify all possible issues for multiple possible combinations. This could more effectively find errors resulting in less chance of miss-builds or undesirable extra strain on resources, and provide manufacturers with more confidence in BoM information when planning, designing, manufacturing and managing products.
Aims and objectives:
The aim of the research is evaluate the capability of AI methods at improving the efficiency of BoM validation. To meet this aim the following objectives will be set:
To understand the range of BoM validation practices, and existing systems that are utilised to support the process in industry
To perform a literature study on the current research surrounding BoM validation and AI methods to validate large datasets
To define the required knowledge and methods to make decisions during validation from the information obtained
To experiment with AI methods to support/ automate an existing validation process to understand potential impact
Potential applications and benefits:
The resulting research can potentially inform the development of more intelligent systems to perform the BoM validation process more efficiently, in terms of reduced resource allocation (e.g. time, human effort, and financial resources). This will provide additional benefits:
Reduced risk of miss builds or stops to production from incorrect part delivery to the production line.
Reduced waste of unnecessary resources and effort for storing, procuring, and scrapping parts which were not required to build the product.
Improved confidence in data driven manufacturing processes and planning, through increased ability to validate the full range of product BoM’s and reduced risks of error
Relevance to the EPSRC research council:
James's topic of study aligns with the EPSRC’s interests and investment in the two research areas of AI and manufacturing technologies. James's work will contribute to the outcome of research towards developing intelligent systems that will address an important challenge in manufacturing.
Lois's PhD aims to understand which groups of individuals may face barriers to accepting environmental transport policies, the reasons for this, and suggest how support may be increased.
Climate change poses an urgent threat to ecosystems, human health and safety, placing it at the top of political agendas across the globe (Biesbroek et al., 2022). Despite international treaties such as the Paris Agreement (2015) seeking to limit global warming, temperatures are projected to surpass the current 1.5°C target within the next few decades if no further action is taken (IPCC, 2022), and air pollution remains a leading environmental health risk, causing ~6.5 million premature deaths per year (European Environment Agency, 2018). It is widely agreed that to mitigate the effects of climate change and air pollution, profound behaviour change is required (Whitmarsh et al., 2021). This behavioural change is often narrowly conceptualised as an individual-level consumer activity, for example recycling or taking the bus. For effective mitigation however, people must also engage in collective actions. Environmental policies are a key way of incentivising transformational collective action. For policies to be successful however, they must be accepted by the public (Steg et al., 2006). Indeed, much research has demonstrated that public resistance creates reluctance amongst politicians to implement ambitious environmental policies, which often results in the termination or re-design of proposed policies (Steg et al., 2005), or withdrawal of existing policies (Fairbrother, 2022). Such terminated plans are costly and not conductive to effective climate change mitigation, making it a priority to understand why certain people may accept or reject a future policy.
Objectives:
Study 1: To understand which predictors are most important in determining the acceptability of a Low Emission Zone, in the general population
Study 2: To understand the reasons for lack of acceptance of Low Emission Zones in the general population, and across particularly low-acceptance groups (identified in [1])
Study 3: To understand if acceptability levels, and reasons for lack of acceptance, are consistent after Low Emission Zone implementation (from [2])
Study 4: To develop a measure of environmental knowledge, suitable for controlling for this covariate in Study 4
Study 5: To understand the predictors of acceptability of a Low Traffic Neighbourhood in general population and minority groups
Study 6: To design an intervention, based on studies 1, 2, 3, and 5, that facilitates the acceptance of climate transport polices
Potential Applications and Impacts:
This research will directly inform the implementation of climate policy at local, nationwide and international levels. Specifically, the outlined theoretical work will inform the types of people who may face barriers in accepting environmental transport policies, as well as the reasons why. Once understood, these challenges can be addressed to facilitate the successful introduction of policies such as Low Emission Zones (also known as Clean Air Zones) and Liveable Neighbourhoods. Methodologically, this PhD will determine if large, readily-available datasets are suitable to understand the support of policies, to enable the efficient and cost-efficient analysis of large sets of public opinion data. Conceptually, it will be understood how we can not only predict, but also encourage acceptance. Drawing upon the student’s awarded public engagement grant, existing links to local councillors, and community connections, this work will be disseminated to various stakeholders.
Relevance to the Research Council
The current project is in line with the ESRPC’s mission to deliver research with genuine economic and societal impact, on a local and globally level. In particular, exploring policy support in both general and minority populations contributes to the ESRPC’s goal of delivering social prosperity, by giving under-researched populations the support they require to engage with climate policy. In this way, the current work also promotes the ESPRC’s diversity and inclusion goals.
Cryogenics is a branch of science studying materials that undergoes phase change between -150 and -273 Degrees Celsius. Most popular examples for cryogenic materials are Oxygen,Nitrogen, Helium and Hydrogen.
Cryogenics have been widely employed as a part of multiple industries such as but not limited to: Aviation, Automotive, Medical and Storage industries. Examples of utilisation cases can be listed as: Rocket fuel and pre-conditioner, Fuel cell propulsion systems, cryosurgery and refrigeration units for cold cargo.
Leidenfrost effect is a physical phenomenon where, a liquid on a hot surface that is above its boiling point produces a vapour layer that acts as an insulation between the liquid and the hot surface. The vapour layer produced acts as an insulation limiting the heat transfer rate between the liquid and the surface. By utilising surface properties, the flow of Leidenfrost Droplets can be sustained which can be described as "Self-propelled Leidenfrost Droplets".
Due to the increasing consciousness around global warming and the request of reducing transportation emissions, propulsion systems are required to be more efficient and less polluting then ever. In support of this, commercial aviation and automotive companies have been seeking alternative propulsion solutions one of which is fuel cells which are powered by cryogenics resulting in clean propulsion without harmful emissions. Because cryogenic systems so far have only been used in specialised and limited life cycle applications in propulsion such as rocket fuels, long term effects and systems level applications in long term must be explored in order to commercialise such systems. Examples of such propulsion systems have been brought to market by multiple OEMs with the price being the largest penalty as well as the complexity, storage and weight challenges surrounding fuel cell systems.
The study will aim to explore the capability of utilising Self-propulsion of Cryogenics inside a Pipe mostly aimed at fuel delivery systems in order to understand the boiling characteristics and physical interactions with the internal pipe surface. The Self-propulsion inside the pipe is aimed to be achieved by the introduction of in-pipe structures to manipulate the flow during the Leidenfrost regime of the Cryogenic liquids to be utilised.
Onur's PhD study is planned to be conducted both in practical experimentation and simulation in order to verify and test varying conditions and flow regimes. Initially, the testing will begin with liquid nitrogen as a working fluid which will then be changed to liquid hydrogen. The reason for the utilisation of liquid nitrogen at the start of the study is to understand the cryogenic working environment and the challenges attached to it. Simulation studies will be employed in order to understand flow regimes that are not possible to replicate using practical means and to verify the results of the practical experimentation.
Immanuel's project investigates a pathway to making water injection for combustion engines mass market proof. Water injection for combustion engines has been implemented a number of times into limited production motor vehicles to enable higher engine performance, mainly with forced induction. In these cases, the technology was used to lift the knock limit by decreasing combustion temperatures. However, water injection enables better thermal efficiency with lower combustion temperatures which can decrease particulate, CO, CO2 and HC emissions together with fuel consumption. Nowadays, a large portion of engines utilise forced induction which at certain times requires extra cooling where the engine is made to run rich. Having lower combustion temperatures removes this need. Furthermore, as mentioned, with lower combustion temperatures, engines could run higher compression ratios which would make engines smaller, hence reducing rotational masses and improving fuel consumption. These potential benefits reflect the current drive toward more efficient and cleaner combustion engines. Electrification is one of the pathways being implemented to fight global warming but combustion engines are set to be part of the drivetrains available in the next few decades. One of the reasons why water injection has not made its way into mass production vehicles is the need for the consumer to refill the water tank after relatively short intervals with distilled water from the grocery store. This is impractical and not acceptable for consumer satisfaction. This project aims to produce a solution where the water vapour in the exhaust gas is condensed to liquid form, cleaned and stored to then be injected back into the engine to result in a closed cycle. There are several issues that need to be addressed when proposing such a solution. Among those are water pH values, moulding, freezing and impurities within the condensed water.
Through the sponsoring company, a natural non-toxic additive is in development and may be used for the purpose of the project to potentially eliminate some of the issues with closed cycle water injection. The proposed solution should then be capable to run several thousand kilometres without a refill of the additive, similar to the AdBlue principle in diesel engines. Depending on the progress of the research, a prototype of the whole system is possible where a side effect of the water injection may be that the currently imposed GPF filter for gasoline engines could be removed due to water injection reducing the particulate emissions. This would be a positive side effect since less aftertreatment would result in a weight and efficiency benefit.
In this PhD, Edgar will be focussing on developing a methodology that enables the user to rapidly and iteratively design a heat exchanger core hat meets a set of heat transfer and pressure drop requirements whilst adhering to spatial constraints.
Metal additive manufacturing (AM) is viewed as a key enabling technology for the next generation of thermal management solutions (e.g. heat exchangers). Heat exchangers, used to transfer heat between two fluids, are essential components in many engineering systems in sectors such as aerospace, automotive and energy. The harmony between AM and heat exchangers arises through the relative ease with which complex and intricate internal geometries (channels) can be produced without the need for costly fabrication stages. As such, AM heat exchangers have already established themselves as highly performant, compact and lightweight alternative to traditional heat exchanger concepts.
However, AM presents significant challenges in terms of development costs and time, particularly where iterative production might be expected. A typical, single machine facility is likely to cost in the range of £1 million, and titanium powder feedstock costs approximately £400 per kg. A heat exchanger with dimensions of 200 by 200 by 200mm would take approximately 10 days to produce. As such, to iteratively develop a new heat exchanger concept using this technology would easily exceed the £100k mark in terms of development cost.
Cellular geometries and, particularly, triply periodic minimal surfaces, have gained a lot of attention in both the literature and industry within the context of heat exchangers. These mathematically-defined geometries present various appealing properties. Most importantly, these are all cellular in nature and split the cell into two equal or unequal volumes, which remain interconnected between adjacent cells. Therefore, two different fluids can travel through the two networks and always be in close proximity (separated by a thin wall) without physically mixing.
Several challenges exist in this field, however. While these are mathematical designs and, hence, can be modified in limitless ways, they do not inherently maximise heat transfer and/or minimise pressure drop, which are the two main challenges in heat exchanger design. The authors believe that there is plenty of scope for at least some of these geometries to be altered to find more optimal designs than the default minimal surfaces as provided by the conventional equations. A part of this project involves comparing different designs of multiple minimal surfaces to provide insight into which of and how these minimal surfaces could be made optimal for specific heat transfer, pressure drop and mass constraints.
Another related but worth-highlighting gap in the literature is the overuse of CFD without sufficient experimental evidence. Most of the work involving additively manufactured heat exchangers is either industry-lead, and therefore fairly vague and opaque, or, in the opinion of the authors, academically-lead but not rigorously validated. The reason for our scepticism lies in the fact that several review papers highlight the difficulty in accurately predicting performance with CFD (which usually underestimates that pressure drop significantly) and the lack of understanding of the effect of surface roughness.
It is the intention of the authors to experimentally investigate the heat transfer and pressure drop in either all or some of the selected minimal surfaces to enhance the knowledge base of flow patterns and behaviours in these intricate and complex geometries, characterised by varying levels of flow mixing, recirculation and turbulence. These depend on the working flow regime, which is expected to be between laminar and transitional for air.
During this campaign, the emphasis will be on surface roughness. As mentioned previously, a lot of resources are invested in the production of heat exchangers with additive manufacturing. Furthermore, two equal designs can vary in performance significantly if they are manufactured with different machines or machine settings. If surface roughness can be isolated successfully from the theory, data obtained from designs with smooth surfaces (like SLA or even within CFD) could be used to predict performance in metal-based prototypes, which take much more effort to produce.
It is expected that this research will play a role in the current trends of significant reduction in emissions in the aforementioned industries, owing to a reduced mass and therefore energy/fuel savings. In addition, enhanced performance will also help to recover and harness wasted heat within these systems. Looking further, it is thought that this could help make future aircraft propulsion and power generation systems viable, such as hydrogen fuel cells and widespread electrification.
The aim of this project is to develop a methodology that enables the user to rapidly and iteratively design a heat exchanger core that meets a set of heat transfer and pressure drop requirements whilst adhering to spatial constraints. The current vision is to combine novel heat transfer modelling with an algorithmic design approach. This will be used to automate the design of the core geometry and therefore reduce the engineering overhead and reduce the time required to reach a new proposition. A heat exchanger test bed will be designed and built to facilitate the thermofluid characterisation of the specimens as well as serve as an integral part of the design methodology. validate the modelling work but also to form an integral part of the design methodology by using it as hardware-in-the-loop.
While lead time and development costs are a major priority in this project, the trade-off with high performance and efficiency must be managed successfully. Due to its unparalleled versatility when producing geometries, AM allows for highly efficient geometries that, while more expensive to produce than traditional heat exchanger designs, could noticeably reduce the running costs of the systems to which they will pertain. Therefore, the ideal version of this methodology would also enable engineers to produce an optimal heat exchanger design for any given application, although this will likely be limited to a parametric optimisation algorithm – an optimal version of a specific heat exchanger concept. A semi-empirical methodology is thought by the researchers to be the most appropriate way of achieving the desired accuracy of thermal modelling and, thus, minimise the probability of expensive and time-consuming alterations to the heat exchanger design (or even a complete design overhaul).
Structural batteries are a class of battery materials that operate in a similar fashion to lithium-ion batteries on a chemistry level, but have the additional functionality of being able to carry large mechanical loads. This allows them to be used as load-bearing components in electrified transport applications, where their bifunctionality in this role allows for considerable mass savings on a systems level. Most structural battery architectures use carbon fibres as the electrode materials which are embedded in a polymer electrolyte matrix. One of the main factors preventing commercialisation of structural batteries is the lack of a suitable material for the polymer electrolyte matrix.
The aim of Rob's project is to improve the performance and safety of polymer electrolyte matrix materials for structural batteries. This aim will be achieved initially by developing a fundamental understanding of the interface between the polymer electrolyte matrix and the carbon fibre electrodes. Understanding the nature of this interface will be key to optimising both the mechanical and electrochemical properties of the composite, but so far this is an area that has been relatively unexplored in the context of structural batteries. This aim will also be achieved by developing new materials for the polymer electrolyte matrix. Current polymer electrolyte matrix materials rely on flammable organic liquids for ion conduction, and this project will look at utilising safer materials for the polymer electrolyte matrix.
Improving the energy density of electrochemical energy storage devices is critical to accelerating the uptake of electrified transport and a subsequent transition away from fossil-based fuels. Structural batteries offer an enticing pathway to achieving increased energy density, due to the aforementioned benefits of their bifunctionality. Developing a mechanically strong, fast ion conducting, and safe polymer electrolyte matrix material is a key milestone towards the commercialisation of structural batteries. This research is also relevant to the Engineering and Physical Sciences Research Council since it fits in with the energy storage research area.
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.
The opposed-piston 2-stroke (OP2S) engine has historically been applied to aircraft propulsion as well as engines for power generation and rail traction with great success. More recently, Achates Power have shown the potential of the OP2S engine for automotive applications. The low surface area to volume ratio of the combustion chamber in OP2S engines, combined with its lack of a cylinder head, results in lower heat losses yielding high exhaust gas energy, making it an ideal candidate for turbocharging, as well as increased brake thermal efficiency. However, due to the requirement for a positive delta pressure across the cylinder at all operating points (intake manifold pressure must be higher than exhaust manifold pressure) to ensure the scavenging performance of 2-stroke engines, crankcase scavenging is typically used instead as, unlike a turbocharger-driven charging system, it guarantees a positive delta pressure gradient at all operating points. Nevertheless, other scavenging systems, such as a supercharger in conjunction with a turbocharger, have been shown to provide effective scavenging performance whilst utilising the otherwise wasted exhaust gas energy. Moreover, the use of a combined supercharger/turbocharger charging system with an OP2S architecture provides greater flexibility in the air-fuel-ratio control and exhaust temperature management, whereas conventional 4-stroke engines are expected to require the use of cylinder deactivation or other thermal management strategies to meet the low emissions standards. Furthermore, the use of electrically assisted turbochargers not only increases this flexibility but also provides a means of extracting excess work from the turbine by turbocompounding, whilst simplifying the intake air path.
The purpose of this work is to investigate a novel pressure-balanced free-piston engine concept. An OP2S engine model will first be adapted from prior work with a view to understanding the effects of crank phasing and port geometries on gas dynamics. A Libertine free-piston engine will then be used to inform and verify a linear generator engine model constructed using a similar geometrical arrangement as the Libertine engine. Having completed this work, a verified free-piston OP2S engine model can be developed using learnings from the prior work. This model will yield a greater understanding of the capabilities of the conceptual engine arrangement whilst also providing insight into the intrinsically linked relationships between the mechanical and electrical subsystems. The model could also be used to assist in the design of a prototype of the concept engine, the manufacture of which would be dependant on time and funding.
Elisabetta's PhD proposes the use of solid oxide fuel cells (SOFCs) for the direct conversion of hydrogen storage vectors such as ammonia to electrical energy. SOFCs have several advantages over PEM, including, multi-fuel capability, resilience to poisoning from fuel impurities and lower use of precious metal catalysts. These systems, however, require a “defects and flaws” control on the structural and functional properties of their ceramic electrolytes because of their high variability of the strength, and their relatively low toughness, which are some of the point of interests of this research project.
Chemical molecules such as ammonia have the potential to be excellent hydrogen storage vectors for aviation fuels. They do not require high pressure containment but still achieve very high hydrogen storage densities arising from the hydrogen stored within their chemical structure. Ammonia is also good at conducting and absorbing heat, making it good for energy acquisition while also avoiding the formation of “coke” and other residues that hydrocarbons leave behind under extreme temperatures. This substance is also much easier to store as a liquid because, under this form, it only needs to be kept at -33 degC, which is very close to the temperature at cruising altitude. The release of the hydrogen, however, requires a catalytic conversion and ppm levels of ammonia are a poison for PEM fuel cells, and for this reason, a SOFC is required.
The project proposes the (1) characterisation and (2) optimisation of SOFCs for usage in aerospace electric propulsion applications. Characterisation of the cells will focus on cycle efficiency of different fuels (Ammonia, hydrocarbons, H2), the internal chemistry/catalysers used and their behaviour under certain operating conditions. Optimisation will be on structural integrity of electrolytes and fuel cell weight reduction, power transfer efficiency, and possible thermal management of waste heat.
Classifying road users based on their characteristics allows researchers and policy makers to make general distinctions between different types of road users who may have different needs. The classification vulnerable road user (VRU) is frequently used to describe road users who are not protected by the frame of a vehicle and considered to be high-risk (e.g., motorcyclists, cyclists, or pedestrians). However, there is a body of research challenging this classification, highlighting the emphasis it places on road users as ‘vulnerable’ rather than the vulnerabilities caused by external factors, such as infrastructure design and the behaviour of other road-users. Although this critique is not new, the issues identified in this critical literature are not coherently addressed in empirical work involving these road users that uses the VRU construct and forms assumptions based on it.
This PhD research will explore alternative approaches to categorising road users, particularly those considered vulnerable, and using these categories to understand road-user interactions through modelling and analysis. The aim of the project is to develop a theory to understand road user vulnerability from a complex systems perspective. The project comprises three phases: a theoretical phase to develop the conceptual foundations for the approach, an experimental phase to identify important road user characteristics, and a modelling phase, to verify and iterate the theory developed in phase two and explore relevant metrics and investigate the dynamics of how vulnerability changes as a function of its component parts.
The first phase will examine how the VRU classification is understood across disciplines, explore VRU behaviour and power differentials on the road, and evaluate how the classification is currently operationalised in modelling and analysis. This will involve conducting a scoping literature review, The second phase will employ mixed methods and use secondary census and transport use data, route planning analysis, and photo ethnography to understand the factors that can act as ‘hinge points’, where VRUs change their route or mode of transport. A subsequent study will systematically analyse existing approaches used to interpret VRU behaviours and intentions in transport modelling and predictive systems and how the factors identified in study one are incorporated. This initial work provides a foundation to understand the state of the art, identify important factors that influence VRU behaviour, and identify priorities and metrics to be used in modelling. The aim of this phase is to analyse the processes and power dynamics underlying road user behaviours in a way that can be implemented in empirical modelling methodologies.
The third phase of this research will focus on theory development and simulation. Here, the theoretical processes identified in phase 2 will be formalised into causal models outlining the expected relationships between different elements of vulnerability and their link to behaviour. These models will then be tested and iteratively refined through agent based modelling. The aim of this phase is to explore the validity of the theory proposed and identify potential tipping points or critical factors that can influence road user behaviour based on changes in vulnerability.
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 effefcts of climate change but conserving the current environment.
The main aim of Vincent's research conducted in this 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 uses an evaluation of the accuracy of a prior model of the battery to devise data-effective acquisition strategies aiming to reduce system testing efforts while capturing enough information to improve the parametrisation and validate the model following a specification or targeted application defined by the user. 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
Data from the World Health Organisation (WHO) shows approximately 1.3 million people die annually from road crashes, which are identified as the leading cause of death for children and young adults. In the UK, there were 24,530 people killed or seriously injured in 2021 according to the estimation of the Department for Transport (DfT). Besides concerns on the road safety aspect, road traffic crashes cost most countries 3% of their gross domestic product, leading to considerable financial loss to individuals, their families, and the entire nation.
Meanwhile, various studies prove that human error was the sole factor in more than 50% of road accidents, and was a contributing factor in over 90%. Commonly seen human errors such as drowsy driving, distracted driving, and chemical impairment caused by alcohol or drugs form part of today’s road traffic system, threatening everyone’s life safety. However, the current development in autonomous driving can’t fully mitigate this issue since the takeover by a human driver is still needed before the SAE level 5 is reached, which is decades away. Propelled by societal pressure and legislation, Driver Monitoring System (DMS) was introduced by car manufacturers to tackle this long-existing problem, combining driver behaviour obtained from a camera and driving behaviour from the vehicle itself to determine the driver’s state. Despite the effectiveness of existing commercial systems, the lack of direct measurement remains a challenge to further improve the accuracy. On the other hand, the feasibility of extracting physiological information such as vital signs based on non-contact approaches in the lab environment has been proven.
Therefore, the focus of Gengqian's project is the development of a novel non-contact driver monitoring system for attentiveness detection via radar, camera, or ultrasonic sensors. Firstly, physiological information is obtained by signal processing and then compared with the ground truth from body-attached sensors to develop a robust non-contact vital sign monitoring system. On this basis, extracted features such as heart rate, respiratory rate, skin temperature, and body movements are combined with observations from real-world driving experiments and brain activity measured by EEG to develop a new model of driver attentiveness. For example, a reduction in heart rate, respiratory rate, or blink rate could be good indicators of low attentiveness.
Reducing car use is one of the most impactful decisions a person can make to reduce their carbon footprint. Yet, even with efforts to promote the uptake of walking, cycling, and public transport, the private car remains the most common way of travelling in the UK. This is partly because for many drivers, car use has become something that is habitual and automatic. However, since habits rely on stable contexts (e.g., time, place, social groups) for the same behaviour to be repeated, a change in context may lead to a person’s existing car driving habits to be broken, and for new ones (e.g., walking or cycling) to be formed.
This PhD will explore how changes in the road environment (e.g., road closures, liveable neighbourhoods) impact people’s car driving habits. Through evaluating real-world case studies, it will explore how car drivers respond to different types of road changes and why, in order to develop practical guidance for transport planners to support people towards positive, sustainable behaviour change.
Hydrogen fuel cell vehicles are a potential technology to help alleviate climate change, providing reduced harmful emissions than petrol and diesel cars. In their fuel cell stack, hydrogen gas is reacted with oxygen to produce electricity and water. The electricity produced goes on to power electric motors, as in battery electric vehicles, but hydrogen vehicles have the benefit of being able to be refilled with hydrogen far more quickly than an EV can be charged. The water produced simply exits the exhaust pipe as steam.
The hydrogen fuel is stored in tanks and fed to the fuel cell stack much like petrol or diesel is in an engine, and oxygen is provided by the air. For air to enter the fuel cell stack at high flow rate and pressure, various components are used to condition it. This may include: compressors, which pressurise and accelerate the air; humidifiers, which carefully control the water content in the incoming air, and turbines, which can harness some of the energy from the exhaust which would otherwise be lost to the atmosphere. Many other options for components are possible, in many more configurations. Matt's project investigates these configurations which make up the air handling system as a whole, and aims to determine which are best for various scenarios. It also analyses the effect of the exhaust water on the turbines themselves, as water can condense on the turbine blades and effect the turbine's performance, or even damage it over time.
Jac's PhD research will deliver a green bond designed to finance low-carbon bus operations. This document will provide asset specifications (e.g., issue price, face value, coupon rate, and an expected credit rating), and evidence of alignment to the ICMA green bond principles and UK taxonomy. To achieve this aim, we will investigate the extra cost and default risk associated with fully electrified fleets. By looking at costs from a fleet perspective, we include the monetary impact of electric vehicle range and charging requirements on vehicle scheduling.
Issue price: Price of the green bond
Face value: Payment from the issuer to the bond holder at maturity
Coupon rate: The % of face value received periodically (similar to an interest rate)
Credit rating: Rating that describes the risk to the bond holder that they will not receive the agreed upon payments
ICMA: International Capital Markets Association
Green bond principles: Use of Proceeds, Process for Project Evaluation and Selection, Management of Proceeds, Reporting
UK taxonomy: UK government's categorisation of which activities align to the UK's carbon budget
Probability of default model: Model which estimates the probability that the issuer does not meet their debt obligations
Real options: The ability to change strategy when new information becomes available
Operational complexities: Electric bus vehicle scheduling has to allow time for charging. The moving of buses in and out of service affects crew scheduling.
Joris's PhD projects develops advanced pLCA methodology utilising IAMs for modelling the future environmental impacts of automotive. In collaboration with UCL, this project aims to utilise the TIMES (The Integrated MARKAL-EFOM System) IAM to generate future energy mix scenarios aligning to 2°C and 3°C global warming targets. Computational methods in Python will build on existing Wurst packages to link scenario data to EcoInvent – a worldwide LCA data repository – to modify market and transformation activity data according to scenario timelines (IAM-EcoInvent).
The IAM-EcoInvent model will be applied to a variety of automotive inventory data, using Brightway2 packages to conduct pLCA on the long-term environmental impacts of automotive technology that will: address temporal mismatch between life-cycle stages; incorporate the evolving impacts of upstream production processes; and investigate the influence of future electricity and heat generation mixes on LCA results.
Nina's PhD project will focus on designing, testing, and fabrication of microreactors specifically for photochemical carbon dioxide reduction reactions (CO2RR). The photochemical CO2RR is a chemical process that uses light energy to convert CO2 into fuels or chemicals. A microreactor, also known as a microstructured or microchannel reactor, is a device in which chemical reactions occur within a confined space with lateral dimensions typically smaller than 1 mm.
Most efforts to enhance CO2RRs have focused on improving reaction chemistry, such as catalyst development. However, reactor engineering and process intensification represent parallel research avenues that could also advance the technology. Only a few research groups have explored the use of microreactors for continuous CO2 reduction, with reports indicating that the improved mass transfer in these systems enhances selectivity toward liquid hydrocarbons. Efficient mass transfer, which enables reagents to cross the boundary layer adjacent to the catalyst surface, is critical—particularly in multi-phase reactions like photochemical CO2 reduction. Although continuous microreactors have shown promise, the reactor's role in the process is not yet fully understood. Further investigation and optimisation of microreactor channel designs could lead to better process control and performance.
The design of microreactors plays a crucial role in influencing the outcome of reactions. Factors such as channel design, geometry, and the surface area-to-volume ratio can be adjusted to steer a reaction in a desired direction. The different microreactors designs will be created using computer-aided software and designs will be transformed into the physical model using a 3D printing process. However before the manufacturing of the physical models, computational fluid dynamics will be used to test these designs.
Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses computers to simulate and analyse fluid flow. By employing CFD, the flow and mixing of reactants through the different microreactor designs can be modelled, allowing for an assessment of how the reactants behave and the calculation of various key parameters. The designs with the most efficient mixing and diffusion properties for photochemical reactions will be optimised by making slight modifications and retesting.
Paloma's PhD will investigate the multifunctional performance of structural batteries. The PhD will focus on the use of synchrotron techniques to measure fibre scale mechanical properties of both anodes and cathodes during charge cycling, accumulation of microscale damage and understanding of ion intercalation patterns within the anode. Paloma will progress to understand similar properties under axial fatigue loading.
Structural batteries combine the load bearing and electrochemical storage capabilities of carbon fibres (CFs), offering significant opportunities for weight saving in aerospace and automotive applications. Recent research has showcased the potential of polyacrylonitrile (PAN) based CFs for structural battery anodes, and LiFePO4 coated carbon fibres as cathodes. Both anode and cathode fibres are embedded in a biphasic Structural Battery Electrolyte (SBE), composed of a liquid electrolyte phase for high ionic conductivity, and a porous stiff polymer matrix for mechanical performance. The resulting carbon fibre-polymer composite structure has the high specific strength/stiffness required for lightweight structural applications, and the high ionic conductivity required for battery functionality.
Optimisation and design of the multifunctionality of such systems requires an understanding of the coupling of physical phenomena, including thermal, electro-chemical and mechanical processes. In particular, quantifying the mechanical response at different charge states is crucial in the reliable use of these systems in structural applications. In order to achieve this, the project aims are twofold and iterative:
The first aim is the construction of a comprehensive multiphysics model on MSC Marc of a structural battery composite in order to predict the multifunctional performance of structural batteries in various load cases. MSC Marc is an advanced non-linear FEA solver capable of solving multiphysics problems. This will commence with the development of a multiphysics Representative Volume Element (RVE), to couple electrochemical, thermal and mechanical phenomena. Following this, the RVE overall properties may be used to define larger scale modelling. These simulations will supply an enhanced understanding and facilitate the improved design of structural batteries with the ultimate goal of unlocking their significant potential for reducing carbon emissions.
In order to define both the physical parameters and constants present in the multiphysics model, characterisation of material properties is required on a multiscale basis; quantification of individual structural battery components in isolation, and at the full composite level. In order to achieve this, the project will utilise a broad spectrum of experimental methods at different length scales; from the length scale of the fibre (10's of microns) using synchrotron techniques, to the microscale using nanoindentation and other methods.
Additionally, it is key to include assessment of individual component interactions at the multiphysics level. The scope of the project will focus on quantification of the property descriptor coefficients relating charge/electro-chemical load to mechanical response as required to embed this response into multiphysics simulations. In parallel, quantification of the property descriptor coefficients relating mechanical load to electro-chemical response will thereby fully encapsulate the structural battery system.
Batteries in electric vehicles require managing to maximise vehicle longevity, range, and performance, whilst ensuring safe operation. Battery state estimation involves using real-time data collected from the vehicle to estimate current properties of the battery (for example, remaining charge, internal temperature, etc) and to predict future states, possibly making use of other obtainable data such as driving habits and the environment.
State estimation is performed by the onboard battery management system (BMS), which is also responsible for implementing suitable charging and discharging strategies (including cell-balancing and communication with charging infrastructure) and interfacing with thermal management systems to maintain safe operational conditions. There are various methods for battery state estimation including direct estimation (e.g. lookup tables, which are easy to implement but have relatively low accuracy), data-driven models (which offer increased accuracy but depend on the quality and quantity of available data), and physics-based models (which can be highly accurate but require intensive computational power).
An electrolyte is an electrically neutral medium (most commonly a liquid at present) that facilitates the transport of ions between the two electrodes of a battery cell, and so having an accurate description of behaviour in this region is crucial to understanding the state of a cell.
The aim of this research is to develop efficient novel structure-preserving numerical methods for physics-based mathematical models concerning the electrolyte. By "structure-preserving" we mean that the methods will preserve at the discrete level some important structures possessed by the governing system of differential equations (for example: ionic mass conservation, energy dissipation laws, etc).
The models of interest will feature continuum approximations, where the concentrations of ions are considered (as opposed to molecular dynamics approaches, where the behaviour of individual atoms is modelled). The initial considered model is the Poisson-Nernst-Planck (PNP) system, which has been well studied and can be extended in a variety of ways to describe additional physical processes. For example, the size-modified PNP system takes into account the finite size of ions, and when coupled with the Navier-Stokes equations (yielding the Navier-Stokes-Poisson-Nernst-Planck (NS-PNP) system), the fluid properties of the electrolyte can be described.
The primary numerical methods of interest are discontinuous Galerkin (DG) finite element methods, which are suited to approximating pseudo-discontinuous solutions (boundary layers, known as electric double layers, form close to the electrodes and display steep gradients in the electric potential). There is also motivation to use DG methods in the extension to the compressible NS-PNP system, which allows for non-isothermal modelling (crucial when considering situations such as thermal runaway).
The benefits of this project include increased accuracy in battery state estimation, with a logical model progression (due to the fundamental physics-based nature of the work). This can lead to improved safety for users of the vehicle and longer battery lifetimes and range due to more informed management. These points together could lead to increased uptake of electric vehicles as a result of diminished range anxiety and safety fears, aiding the reduction of fossil-fuel powered vehicles on the road.
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:
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.
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.
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.
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.
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.
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.
Howard's PhD will 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.
Aim:
To understand the influence DC ripple current has on lithium-ion cells in automotive applications, by testing and examining the results of cells applied with various types and frequencies of ripple
Seek to find an explanation of these results with respect to electrochemical theory
This information should aid decisions made in the design of electric vehicles specifically associated with the e-machine and accompanying inverter/power electronics
Objectives:
Conduct a thorough literature review to analyse:
Current and past works and any relevant findings
Any gaps or oversights in these works
Any areas of work that need confirmation
Use electrochemical modelling to select the most relevant parameters associated with DC ripple in Li-ion cells, using this modelling to design an experiment including optimum yet achievable ranges of said parameters
Ripple magnitude, frequency, waveform etc.
Cell chemistry, size, shape etc.
This may include some very preliminary testing to validate any assumptions made about equipment or expected results
Conduct the designed experiment varying the relevant variables whilst recording performance metrics decided in modelling stage
E.g. Capacity fade, power fade, EIS
Beamline experiment for XRD and XAFS for in situ monitoring of the battery with/without ripple to gain insight otherwise unavailable
Process results to see if any additional experimentation is needed
This will likely include a post-mortem analysis of cells by dissection and inspection
Seek to find an explanation for these results through electrochemical theory to truly understand the phenomena observed
Theoretical understanding should explain the empirical data
This will itself be a contribution to knowledge
Potential to exploit these results in the design of Battery Management System (BMS) and even validate these design implications
As a result of increased electrification within the automotive industry and energy sector, the demands from electric machines have never been greater. Therefore, reducing the cost and size of these machines whilst maximising their power capabilities is crucial. A prime opportunity to achieve these targets is through accurate real-time thermal modelling of key motor components, such as the end windings and permanent magnets. Such a model would enable the measurement of difficult or inaccessible locations within the motor, without the need for expensive sensors. Therefore, the power density of a given machine could be increased as the large safety margin, which exists due to temperature uncertainties within the machine, could be reduced. Additionally, this could enable electric machines to be downsized, whilst still meeting the required power ratings for a given application.
The motivation for this project is broadly to address the aforementioned benefits a real-time thermal model could enable; however, it is more precisely motivated by the current lack of agreement, robustness, and implementation of methods currently proposed within the literature. Comprehensive reviews of the topic outline drawbacks and benefits to many different modelling techniques, including machine learning, reduced order thermal networks, state observers, and hybrid approaches. Although no single method is yet to prevail as a favourite, most methods revolve around lumped capacitance modelling.
Furthermore, testing found within the literature is based on datasets in strict laboratory conditions, often with simplistic test cycles. This brings into question the models’ robustness and feasibility if implemented onto a real-world system, namely in automotive contexts. This is further reinforced as many publications rely on a pre-recorded experimental dataset for machine learning, parametrisation, and testing. Finally, this experimental dataset, much like many others in the literature, uses a relatively low speed (6000 rpm) liquid cooled motor, hence internal phenomena resulting from high-speed motors may have not been captured and other cooling methods are not understood.
The scientific impact of this project will be to enable downsizing of electric machines in testing and automotive applications. These machines are currently restricted by large thermal safety margins due to temperature uncertainties within the motor. Additionally, by modelling the temperature within the machine, the cost of test bed systems can be reduced by circumventing the requirement for expensive sensors in poorly accessible locations (e.g., rotor magnets). Finally, Ryan's project seeks to increase the flexibility of modelling machines with differing geometry and cooling systems, reducing the cost and time associated with parameterising the model, whilst delivering reliable and accurate results.
Abdelrahman's PhD will address the shortcomings associated with the conventional map-based controller design and calibration practices used in powertrain development. It will provide novel, futuristic, non-linear physical causality predictive modelling and experimental approaches for system identification to conclude a real-time capable control system.
The context of research;
In machine learning, regression is often considered a black box methodology used commonly for identification of suitable functions from a hypothesis. Its primary aim is to estimate a predicted function which leads to minimal expected error on future data, and not necessarily to benefit the overall understanding of the derived output-input relationship governed by the physical interpretation of the system. Consequently, such modelling applications are often highly constrained, allowing training solely for thinly dispersed linear models. To this day, the task of forming intelligent algorithms capable of nonlinear interpretations through physical system observation has received very little consideration, it nevertheless forms the foundation of the field of system identification. Typically, approaches for nonlinear system identification include autoregressively modelling time evolution or utilising Volterra series multidimensional convolution integrals. This project will address the shortcomings associated with the conventional map-based controller design and calibration practices used in powertrain development. It will provide novel, futuristic, non-linear physical causality predictive modelling and experimental approaches for system identification to conclude a real-time capable control system through supervised neural network-based machine learning algorithms for system optimization. The project will be undertaken in collaboration with Koenigsegg Automotive AB and Freevalve AB on the novel cam-less engine technology, Freevalve, enabling major efficiency and power improvements for future powertrains. It will enable the full utilization of Freevalve’s potential and the reduction of harmful gaseous and particulate matter emissions, putting the technology in a market-leading position ready for large-scale implementation.
Its aims and objectives
The aim of this research is to utilize model-based machine learning algorithms to optimize the real-time multi-variable transient control strategy of a Freevalve-equipped Koenigsegg engine. The project will combine state-of-the-art theoretical modelling approaches, classification and regression predictive algorithms calibrated with dynamic experimental data, to understand and characterise a Freevalve-equipped engine’s cycle-by-cycle behaviour under transient operating conditions to better inform controller design for the purpose of training a deep neural network to produce a real-time capable control model. To achieve the aim of this project, the following objectives have been tentatively drafted:
To review existing data and models of the Freevalve engine to support calibration.
To use existing engine calibration test data, Bath know-how about high output engine combustion, and literature findings to improve and correlate the current combustion model.
To utilise classification and regression machine learning predictive algorithms to assist in physical subsystem (turbocharging, fuel injection, heat transfer, etc.) identification for high fidelity engine performance model.
To build toxic emissions neural network models using existing test data for transient engine emissions prediction.
The validated and calibrated GT model as a result of (b), (c), (d) will serve as a virtual testbed for engine-focused controller optimization work.
To build statistical models to develop optimal control strategies for the aforementioned physical subsystems (Freevalve, injection, EGR, turbocharger, etc)
To validate the control strategies in Hardware in the Loop (HiL) testbed and deploy on vehicle with appropriate real-time hardware and software for clock speed control.
Its potential applications and benefits;
Academic: The outcomes of this research will provide engine researchers and engineers with novel AI-based modelling approaches and experimental methods for real-time engine and controller optimization. Corresponding to forthcoming 4th industrial revolution, mainly focusing on increased connectivity, automation, machine learning, and real-time data processing, groups working on digital systems, optimization, and integration will highly benefit from the analysis and novel interpretation of data outlined as one of the core research aims of this project. Furthermore, groups working on improving cold-start and warm-up behaviours as well as fuel efficiency and lean-burn technologies would have particular interest in the generated modelling and experimental techniques of this project for providing a refined and simplistic methodology for optimization.
Industrial: The outcomes of this project will directly impact the mass roll-out of Freevalve technology to mainstream manufacturers and reduce time and cost outlays currently associated with a largely experimental calibration process. It will serve major assistance to distinguish Freevalve cam-less engine technology as a one-ff product capable of real-time prediction and optimization through offline training. A further prospective iteration once this project is completed could incorporate Freevalve as an online-training capable technology, gathering data from infrastructure and other connected vehicles to perform live adaptation and optimization tasks using cloud computing.
The recent drive to decarbonise transport is leading to a rapid increase in the number of electric vehicles (EVs). High concentrations of EVs could put excess strain on certain parts of the electrical distribution network, thus requiring expensive grid upgrades. In addition, new public charging infrastructure will need to be installed to meet future demand. However, EV charging demand can be flexible, allowing for the shifting of charging time and power to better align with times of high renewable generation and to perform grid services such as load shifting and frequency response. Understanding where, when and how EVs charge as well as quantifying the flexibility of that demand is imperative in informing the future infrastructure rollout strategy of distribution network operators and local councils to enable cheaper and faster integration of EVs and renewable generation into the grid, thus accelerating the decarbonisation of the automotive and power sectors.
Isaac's PhD research aims to develop high-fidelity spatiotemporal models that leverage data-driven, Bayesian, and agent-based techniques to forecast EV adoption, simulate EV charging behaviour, and quantify the potential impacts of EV charging. These models could be used to identify potential vulnerabilities in the distribution network, optimal locations for new public charging infrastructure, and provide estimates for the financial and carbon savings that smart charging can offer.
Kacper’s PhD is intended to advance the field of computational hydrogen combustion modelling in internal combustion engines (ICE), of which the main focus is the modelling of combustion in predictive scenario, in order to accelerate the development of sustainable (people, profit, planet) powertrains by brining new tools and expertise to the industry.
The development of breakthrough innovations is a requirement to address the grand societal challenges we have in a timely manner. To address the need for breakthrough innovations, resource holders and intrapreneurs based in vehicle manufacturers are increasingly collaborating with entrepreneurs from start-ups based in different industries to leverage their expertise and resources. These types of horizontal partnerships are known as Cross-Industry Alliance Entrepreneurship (CIAE).
Given the high degree of technical and market uncertainty associated with breakthrough innovation, the understanding of the entrepreneurial opportunity often changes over the course of the project, requiring a repeat of the opportunity recognition process. Therefore Alliances must have the capability to continuously recognize breakthrough entrepreneurial opportunities throughout a project’s lifespan and to gather the support it requires for its exploitation.
Patrick's PhD research study will provide insights into the routines, experiences, actions, and interactions of entrepreneurs, intrapreneurs, and resource holders to accomplish this task within a Cross-Industry Alliance Entrepreneurship.
For his project "Integrated Drive System with Modularised Energy Storage for Automotive Applications" Constantinos will attempt to determine and quantify potential merits to the use of modular multilevel converter (MMC) topologies in automotive applications as compared with existing 2-Level Converters or state of the art 3-Level converters. Future automotive electrified powertrains face severe restrictions on energy consumption and need to meet extremely high real-world benchmarks of efficiency and cost to remain commercially viable but also to offer any real societal benefits in terms of environmental impact.
Three main topologies will be investigated and compared with each other, in order to determine how they might impact the powertrain in terms of efficiency, cost and energy utilisation during various drive cycles. Preliminary research has shown that it is possible to reduce costs or increase peak efficiency of the main traction inverter’s output stage, but MMCs may offer further benefits in low or partial loads as might be seen in certain drive cycles. It is anticipated that the modular nature of this topology may offer, cost benefits by allowing for further system level integration of Power Electronics within the battery pack and functional aggregation as it is able to take on the responsibilities of the On-Board Charger or partially, that of the 12V DC/DC converter while simultaneously outputting a much cleaner AC voltage waveform potentially reducing losses in the Motor. While these topologies show promise, their increased complexity or the way the battery is utilised may result in MMCs presenting a technologically or financially low value in certain applications and as such research is being undertaken to evaluate the potential benefits and drawbacks of these topologies in automotive applications.
In an era where technology and transportation are so interlinked, new mobility concepts arise as is the case of Mobility as a Service (MaaS). MaaS is expected to produce significant improvements in mobility such as the increase in modal share of more environmentally friendly and efficient mobility options, the reduction in private car use/ownership, improving accessibility and frequency of the transportation network and strengthening the cooperation and collaboration between public and private entities to reinforce the integration of transport modes in one platform accessible to everyone.
Despite the rapid developments occurring in the transport network, there are still challenges that should be addressed. One example is geographical exclusion which could be associated with a poorer development of the mobility network outside of big urban areas. Another one is the technological and inherent social exclusion, associated with certain types of demographics. There is also the challenge of getting all the necessary stakeholders (policymakers, transport operators, users…) involved and invested in the implementation of MaaS.
Overall MaaS is expected to contribute significantly towards a more inclusive and accessible regional and Nacional transport network, but for that to happen the remaining challenges and barriers need to be considered and mitigated.
Aims
Rita's PhD research aims to assess the potential for Mobility as a Service's implementation in a regional context
Potential applications and benefits
This research will directly inform the transportation sector and its inherent dimensions, from the regulation to the operation stage. Furthermore, it will impact on influencing social and travelling behaviour as well as territorial and transport planning, therefore targeting a wide range of stakeholders involved in the service's implementation.
The potential applications coming out of this project are not only a study of MaaS implementation in the regional context, as opposed to only big urban areas but also guidelines for what should be considered when designing and planning the regional transport network. Additionally, it will benefit stakeholders involved while convincing them to adopt pro-environmental travelling behaviour. This research could be a significant starting point to help reinforce territorial cohesion by setting an example for other areas in similar conditions.
Some of the expected benefits are the improvement in social connection through a more sustainable, equitable and accessible transport network, a decrease in the number of road accidents, encouraging the use of public transportation as opposed to the use of the private vehicle and improving air quality contributing towards a more sustainable transport network.
Relevance to the research council
This project is aligned with the EPSRC’s prosperity outcome of delivering Resilience by exploring ways of strengthening the connection between urban and rural areas through an integrated, multimodal, and accessible transport service. Furthermore, it contributes towards the Council’s goal of accelerating and spreading innovation in transport, benefiting society, the environment, and the economy.
The context of Julian's research is the urgent global climate challenge of preventing a global mean surface temperature increase of more than 1.5 °C compared to the pre-industrial average, defined as 1850-1900. The IPCC (2023) has warned of serious consequences to human health and societies of such a rise in global temperature. We are already 80% of the way to this threshold: the global mean surface temperature for 2018-2022 the was about 1.2 °C about the pre-industrial average (Met Office 2023).
In the UK, road transport has reduced its carbon footprint less than other sectors since 1990, and larger vehicles are particularly challenging to decarbonise due to the huge infrastructure requirements for electrification, and the limited range of battery traction. Hydrogen fuel cells are a possible solution to power larger road vehicles cleanly, as outlined in the Hydrogen Strategy of the UK Government (2021). However, about 95% of hydrogen is currently produced from fossil fuels, which has significant carbon emissions even when carbon capture is implemented (Howarth and Jacobson 2021). Most research on the environmental impacts of hydrogen production, storage and delivery has focused on a narrow subset of hydrogen technology and/or a narrow range of environmental indicators (often just global warming potential and acidification). There is the need for a more comprehensive comparison, and to consider the intersections between decisions made for road transport and competing uses of hydrogen for ammonia production, industrial processes, domestic heating and cooking.
My planned research is intended to fill gaps highlighted by recent studies (Cluzel et al. 2021, Howarth and Jacobson 2021, Ren and Toniolo 2018, Campos-Guzmán et al. 2019, Ji and Wang 2021). In summary, identifying a sustainable decarbonisation pathway will require:
* consideration and inclusion of a broad range of new hydrogen technologies as they mature;
* inclusion of a wide range of environmental indicators;
* real-world performance data rather than simulated or modelled data where possible, with analysis of purification requirements and minimising fugitive greenhouse gas emissions;
* prospective (and perhaps consequential) LCA with an integrated tool to assist decision makers.
Julian's research project will produce as its outputs: a review of recent Life Cycle Assessments (LCAs) of hydrogen; a review of the most promising hydrogen technologies; a detailed LCA of hydrogen production, storage and delivery (cradle to station); and a user-friendly decision support tool that shows costs and benefits (financial and environmental) for a range of hydrogen pathways under user-selected economic and technological scenarios.
Researchers, government officials and other interested parties will have access to a decision support tool that they can customise for their country or industry to find a pathway that provides hydrogen for transport with minimised environmental impacts. Researchers will have full access to all the underlying data, research and methodologies. It will also be possible for researchers to update the support tool with the latest data and calculations for a specific component of the LCA inventory, or a specific locale.
Within Behavioural Science Public Policy, there has been a large focus on changing behaviour at the individual level, within certain contexts. This has not achieved the rapid and transformative change required to hit Net Zero by 2030. New approaches are required that target large swathes of the population; but what does this look like? How might this reasonably be achieved?
Organisations are a good point of intervention for behavioural policymakers. Pragmatically, organisational policy is less politicised than public policy, it is more flexible to the local population and context, and organisations can implement changes faster than the public sector can. They may also be effective at behaviour change for several reasons. Organisations are able to influence the choice environment, or the ‘mid-stream’ in the Behavioural Insight Team’s model. This is very important for modal shift, which is highly influenced by infrastructure and habits. Organisations have the additional benefit of having access to key information about moments of change (such as employees starting a new job, retirement, changing home or paternity leave) which are crucial times to change habits. Organisations also have the power to tackle social change through their roles as influencers of employees, the community and the supply chain.
Why would an organisation implement these interventions (workplace travel plans and corporate travel management)? How do we encourage millions of businesses to implement them? Larger organisations are often required to do so due to planning laws, but the 5.5 million SMEs in the UK have no such incentive. Regulations would be heavy-handed and add additional burden to these businesses, not the least that they are highly unlikely for scope 3 emissions. Existing studies suggest that organisations implement WTP are motivated by personal values and commitment to the community (although this evidence is mixed), yet they are hindered by lack of time, knowledge, and finances. These barriers and invisible nature of workplace travel plans may re-enforce a status-quo or ‘social norm’. This may also be symptomatic of a large pluralistic ignorance (a shared mis-perception of how others’ think or behave) existing among firms. What interventions might break this impasse and lead to a social tipping point? What would good business support look like for the implementation of this intervention? Jess's thesis focuses on the transport domain given it generates 26% of the UK’s total emissions, it is very difficult to change, and scope 3 among SMEs is largely neglected.
This project will harness new topology electrode nanomaterials developed in our laboratory, for applications in fuel cells used in transportation. Their unique nanostructures give enhanced reactivity and stability compared with nanoparticles currently used. The technology is “platform agnostic” in terms of fuel, with properties and reactions common to a range of fuel cells. This project will explore their use in fuel cell reactions and devices, bridging the gap from preliminary data to real world applications and commercialisation.
Electric Vehicles are key to reducing carbon emissions. While rechargeable batteries are likely to be the main technology for cars, there are long-distance applications (boats, planes, lorries, trains) for which the energy density by weight of batteries is too low, and alternatives are required. Fuel cells overcome this problem. In a fuel cell, electricity is generated by an electrochemical reaction between a fuel and oxygen. Powering vehicles in this way uses 50% less fuel than a combustion engine, and the energy density of typical fuels is tens of times greater than that of lithium ion batteries, whether by weight or volume. [1,2] However, wider commercialisation of fuel cells is currently limited by catalyst performance, cost and stability.
Our team has recently developed a route to new nanostructure topologies for high performance electrodes in fuel cells. The process is green, mild, and industrially scalable, and can be used to grow a range of different metals. The electrodes comprise 3D nanowire networks, which give ultra-high surface areas; high stability, avoiding the use of nanoparticles, which present a major limitation on current device lifetimes; and high reactivity. The technology has been adopted widely, and superior reactivity and stability have been demonstrated in the oxidation of alcohols, glycerol [3] and formic acid [4].
Our electrode materials are “platform agnostic” in terms of fuel. There are potential advantages and disadvantages to each of hydrogen, alcohol, and formic acid, and future adoption depends on advances in green methods of production – respectively through water electrolysis, biofuel, and CO2 reduction. Underpinning all of these fuel cell types is the counterpart oxygen reduction reaction, for which superior activity and stability have also been reported for electrode materials similar to ours.[5] Whichever technology wins out, our materials can therefore play a part.
This project will extend the previous work in three directions:
New reactions: characterise our materials’ performance towards the hydrogen oxidation and oxygen reduction reactions
New devices: incorporate our electrode materials into membrane electrode assemblies and evaluate their performance in fuel cells acting under “real” conditions
New metals: our method has so far been applied to platinum and palladium.
[1]https://core.ac.uk/download/pdf/34994335.pdf
[2]https://www.energy.gov/sites/prod/files/2015/11/f27/fcto_fuel_cells_fact_sheet.pdf
[3]https://pubs.acs.org/doi/abs/10.1021/acsami.8b13230
[4]https://onlinelibrary.wiley.com/doi/full/10.1002/ange.201914649
[5]https://pubs.rsc.org/en/content/articlehtml/2021/ta/d1ta01950c
Enquire nowFuel cells are devices that use hydrogen and oxygen to produce electricity without burning them. They are clean and efficient sources of energy for many applications, such as cars and buses. One type of fuel cell is called a proton exchange membrane fuel cell (PEMFC). It has a special membrane that allows protons (positive hydrogen atoms) to pass through it, while electrons (negative particles) go around it. This creates an electric current that can power a device.To understand and improve how PEMFCs work, we need to consider many factors that affect them, such as temperature, humidity, pressure, material thickness, stress, and resistance. These factors can change how well the fuel cell performs and how long it lasts. We want to find the best combination of these factors for different situations.One way to do this is to create computer models of PEMFCs that can mimic their real behaviour. We can then test different scenarios and see how the fuel cell reacts. This helps us to fine-tune our models and make them more accurate and realistic. This also saves us time and money, as we don’t need to do as many physical experiments.By doing this, we can learn more about how PEMFCs work and how to make them better. We can also make them more suitable for different uses and environments. This will help us to develop and use PEMFCs more widely and effectively. This is important for the future of fuel cell technology and clean energy.
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.
Petrol/diesel is not the only fuel we can combust to propel vehicles. Have you considered grey hydrogen? This is a gas that is generated by the carbon-intensive process of 'steam-methane reformation'. The main problem is that when you make grey hydrogen, it's in a mixture of gases that we need to separate. The process can be made low-carbon by carbon capture, usage and storage (CCUS) - this is something the government is investing £1 billion up to 2025. There many porous materials that can separate and store these gases: so many that the scope of the study becomes too large. However, we can use time-saving algorithms to filter through these materials and select the best ones. The next challenge will be to address which algorithms we use. Will they give quality results for a valid screening study? In other words, will the computer's selection of the best materials reflect their performance in real life?
Miles' research will focus on understanding the link between battery degradation and methods of battery thermal management, especially with respect to cells of different sizes.
Exposure to high temperatures and temperature cycling are two of the most significant aggravating factors for battery aging. The trend in automotive applications is for ever increasing cell sizes, with some vehicles now featuring cells of several hundred amp-hours and up to 1m long. As cells sizes increase achieving a uniform temperature across and through the cell is increasingly difficult because only the cell surface is cooled, and because the cooling fluid (air/water/oil) will typically reach some parts of the cell before others. This non-uniform temperature distribution will very likely lead to non-uniform aging of the cells, which Miles' PhD aims to investigate, quantify, understand, and propose mitigation mechanisms against. This is an important topic not only for maximising the lifetime of the cells in the vehicle, but also when considering the potential value of the cells in second life, or how they might be recycled.
Work will focus initially on immersive cooling, where battery cells are directly immersed in a dielectric oil. This is because immersive cooling is considered the most advanced and high-performance approach to thermal management and is a current focus for research. Problems with this include the cost and weight added to the system by the fluid. This trade-off will be examined by considering the possibility of partially filling the battery with fluid so that cells are only partially submerged, reducing fluid weight at the expense of some thermal homogeneity.
Opportunities may exist for synergy with the group working on Structural Batteries, depending on the size scale of the batteries which that group have succeeded in producing by this time. These opportunities will be explored as appropriate, as the relevance of this proposed doctoral research is particularly relevant to structural batteries owing to their increased value and added difficulty in recycling them. The work of the existing group to date has focussed primarily on producing working batteries. Degradation has not yet been investigated, and whilst recyclability has been embedded in materials selection no analysis has been performed in this space.
Electrochemical testing of cells will be possible with charging/discharging experiments and electrochemical impedance monitoring. Microstructural characterisation of the impact of degradation will form a key aspect of the doctoral study. This will involve the use of nanoindentation, electron and atomic force microscopy and/or Focused Ion Beam (FIB) to study internal changes to the microstructure through the preparation of microscale cross-sections and lamella. Synchrotron work (microtomography, X-ray diffraction, and/or spectroscopy) with in-situ electrochemical testing will reveal regions of heating/degradation, formation of stresses locally at anode or cathode, and opportunities for retaining battery performance. These insights will be used to generate enhanced models of degradation, providing crucial insights into predicted lifetimes and potential recycling opportunities associated with these systems at end of life.
Carbon emissions from fossil fuelled road vehicles are a major contributor to climate change, internal combustion also has negative effects on local air quality. Green hydrogen is a renewable, zero-emissions fuel which could replace fossil-fuelled combustion engines thereby reducing carbon emissions. A major challenge is storage, refilling a hydrogen car’s fuel tank is difficult due to the high pressures and temperatures requiring specialist equipment to store, compress and dispense fuel in a conventional manner. This equipment represents a barrier due to the high costs of construction and operation refuelling station. One way to solve this challenge is to swap out a fuel tank meaning it can be refilled remotely away from the vehicle in bulk instead of the conventional ‘petrol-pump’ style approach.
William's project will look at developing a concept system and analysing what kind of benefits can be expected from this new approach as well as how well such a system would work when brought into practice. It is expected that a swappable fuel approach could improve well-to-wheel energy efficiency, lower the cost of fuel, remove barriers to station construction and improve customer safety while helping bring the environmental benefits from hydrogen fuel cell vehicles to market.
The aim of the PhD project is to develop, validate and parametrise a fuel cell model virtually using given software in alignment with what AVL is currently using. Initially, a fuel cell stack model will be developed, building on what is available to AVL and in the literature. following this a Balance of Plant model (systems external to the stack) will be developed encompassing the main components in the system, again building on what is already currently available. Particular focus will be paid to the humidifier model as this has been identified as a component which requires further work from AVL. These models will run in real time so they can be used for hardware in the loop and virtual testbed applications.
To improve accuracy of the model in comparison to the hardware, the model will be validated against test data, iteratively improving the accuracy until a satisfactory small value has been achieved.
This then leads to the development of an automated parametrisation process using design of experiment methods, to evaluate individual parameters impacts on the system. This allows for increased efficiency and accessibility in the model as well as a reduction in costs when carrying out experimental testing further in the process.
Almost all electric motors today have a fixed coil of wire in the stator (which maintains stationary) and a fixed magnetic circuit in the rotor (which rotates). When a current is passed through this coil of wire, the stator and rotor magnetically interact with each other, and if controlled correctly, will produce a torque to propell the vehicle. Due to the nature of this fixed winding in the stator, there is a fixed characteristic output of the motor, lets assume a fixed torque as this is mostly true.
Josh's PhD is investigating and improving upon previous work that dynamically 'reconfigures' the winding layout, changing the characteristics of the motor, fundamentally exchanging torque output for rotor speed. In this sense, reconfiguring the windings acts as an electromagnetic gearbox within the motor itself simply by connecting the wires in a different configuration. Currently the maturity of this strategy is restricted by the complexity of implementation and commercial attractiveness, therefore, we are primarily developing cost competitive alternatives that maintains the added performance and efficiencies this technology has shown to deliver.
Hydrogen has emerged as an alternative to traditional fossil fuels for powering internal combustion engines as it is carbon neutral, can be produced in a sustainable way and has high energy density. As such, hydrogen engines offer a direct replacement of fossil fueled engines with additional benefits coming from the intrinsic properties of hydrogen - these include the ability to operate at ultra-lean conditions with high efficiency and low emissions. However, one of the key factors to unlocking further efficiency gains while retaining the safe and reliable operation of the engine remains the understanding of the autoignition process of hydrogen-air mixtures and how the autoignition is affected by the engine's intake air composition.
Therefore, the main goal of this research project is to improve the state-of-the-art modelling methods for hydrogen internal combustion engines and quantify the effects that intake charge composition has on the autoignition in hydrogen engines. This will be achieved by answering the following research questions:
How is the autoignition process of hydrogen modelled? What is the physical interpretation behind that? What is the state-of-the-art on this topic?
How can we improve the modelling process to increase the accuracy against test measurements? What is the implication on the complexity of the modelling technique?
How can we predictively model the combustion process of hydrogen with the necessary accuracy and computational efficiency? How can this be improved?
How does the composition of the intake charge affect the autoignition timing in hydrogen engines?
Can simplified modelling approaches accurately predict the impact of intake charge composition on engine performance metrics?
Automated driving systems (ADS) could revolutionize transportation, increasing safety and sustainability. However, there are still challenges to make ASD accepted by the public. Laura's project focuses on enhancing the user experience for ADS by looking at users' need profiles and assessing the role of customization of user interfaces (UI). Meeting the requirements of diverse individuals and ultimately implementing trustworthy and inclusive ADS technology could promote acceptance and adoption of ADS.
It is crucial to understand which type of information people expect from the vehicle to maintain transparency and to identify the best modality and moment to deliver the information according to different user profiles.
Experts underlined the importance of identifying cultural and individual differences to match users' needs with technical solutions and they underlined the fundamental role of user experience in user acceptance. I aim to inform ADS UI design through a combination of qualitative and quantitative research methods, to understand users' preferences and requirements and assess the effects of UI customization in driving simulations to make sure that the experience of riding ADS is not only safe but also comfortable and inclusive.
Most of today’s devices and electric vehicles rely on lithium-ion batteries due to their balanced performance and cost. However, they come with critical safety concerns: lithium-based compounds, which store substantial energy in a compact form, are prone to overheating and even explosion under certain conditions, such as physical impact, rapid temperature change, or exposure to air. Furthermore, lithium mining and production are inefficient, adding environmental challenges.
This has sparked a demand for next-generation batteries using alternative materials to improve safety, efficiency, and sustainability. Although various experimental designs for new batteries exist, research often stops at initial testing, with limited investigation into their underlying chemical behaviours. This lack of insight hampers our ability to predict performance and manage risks effectively.
Eymen's PhD research aims to address these gaps through mathematical modelling, beginning with a thorough review of existing battery models, emerging battery chemistries, and key safety and performance factors. I’ll then develop a mathematical model specifically for next-gen battery cells, embedding it in COMSOL and other tools to simulate their chemical and thermal behaviours. By applying this model to real-world scenarios, such as electric vehicles or drones, I will conduct performance analyses to assess potential risks, such as thermal propagation and overpressure from chemical reactions. The final stage will involve validating these models through experimental data, enabling us to reduce the need for extensive physical testing and propose effective safety measures for future battery designs.
This aim of Sarah's PhD is to view the system of car dependency through a local, national and international lens to investigate the social, economic and built environment factors that influence car ownership amongst two age cohorts – “Millennials” and “Gen Z” – with a particular focus on gender differences. The insights from initial research will be used to generate and test a range of scenarios for future car use, ownership and travel demand.
To deliver the surface transport carbon reductions needed to achieve climate goals, the incumbent system of (auto)mobility needs to move away from private car ownership being the inevitable social norm or aspiration. This is the key to reducing people’s habit of driving as their default mode choice, even for short trips. If a car (which you have already paid for) is not available outside your front door it makes it easier to choose to walk or cycle for short trips and to use public or shared forms of mobility for longer trips.
A shift away from the current norm of private car ownership will require a socio-technical transition where bold policies on urban sustainable transport, land use planning and place-making are combined with new societal attitudes and norms towards car ownership.
There are weak signals of change in the system that some people are already voluntarily choosing not to own a car, or to "shed" one or more cars from a multi-car household. If these behaviours are supported and amplified, they could result in a tipping point away from private car ownership towards a new transport system where walking, cycling, public transport and (electric) shared forms of mobility are the norm.
The aim of Joshua's doctoral project is to examine how uncertainty is integrated and ultimately reduced when planning transport methods into the future, specifically modelling these within computational Life Cycle Assessment (LCA) techniques. An LCA refers to the process of systematically analysing the environmental impact of a product throughout its life cycle; this can be from being manufactured to its disposal, known as cradle-to-grave, or manufacture to recycling into another product, that being cradle-to-cradle. It is relatively easy to model impacts in the past and present as for the most part, the data exists already. However, current data is inadequate to use to see into the future. To overcome this, we can use ‘prospective’ methods, which assume that certain use and disposal/recycling techniques will change and evolve over many years – in particular, incorporating the anticipated change in renewable energy use. As this kind of uncertainty is lacking in contemporary LCA models in vehicles, this project aims to improve that within newer designs of vehicle design, with a particular emphasis on battery electric vehicles.
Seals are an essential component used in a variety of applications and machinery, from steam engines to electric motors. They are considered a cost-effective method in improving engine performance and efficiency. They are designed to limit parasitic loss, such as hot gases escaping a turbine. Labyrinth seals have been used for some time and are popular in turbomachinery applications. However, brush seals are an alternative (and are considered an improvement) but are prone to excessive wear which prevents their widespread use.
Taif's PhD is concerned with the performance and effectiveness of brush seals used in turbomachinery. Using a dedicated scaled brush seal rig, the research will look at collecting information to understand the fundamental flow behaviour of brush seals. This will give an insight into important brush seal parameters and govern future designs. Ultimately, making brush seals more attractive, effective, and leading to improved engine and motor performance.
This PhD will explore methods of rapidly heating battery cells to operational temperature, including theoretical analysis and a strong element of experimentation.
Thermal management of batteries is an extremely important topic because it directly and significantly impacts many of the batteries’ key performance characteristics, including available energy, efficiency, power availability, and rate of degradation. Much research has been directed towards the effective cooling of batteries, but extremely little has been focussed on rapid heating of batteries. Ideal operating temperatures for batteries are between 10-40°C, however Electric Vehicle batteries are frequently required to operate in colder conditions. Being able to rapidly heat batteries is important for cold-start performance and rapid charging.
Battery heating systems usually use electric heaters to warm the coolant fluid. Heat pumps are an alternative but have poor efficiency at low temperatures. These ‘external heating’ approaches heat the cells from the outside, and so are limited in the temperature uniformity they can achieve across the pack since the working fluid inevitably reaches some cells before others on its path, leading to ‘hot spots’ and ‘cold spots’. The faster the rate of heating, the more exaggerated this effect will be. Both systems are also limited by the power they can draw from their own electrical supplies, incur efficiency losses in DC-DC conversion, and increase the parts count, complexity, and cost of the battery system.
The idea of ‘internal heating’ or ‘self-heating’ of cells has gained traction as a means of avoiding additional heating componentry. The basic principle is to move charge into and out of the battery cells at high frequency using Alternating Current (AC), using the internal resistance of the battery cells to create heat. This project will establish the potential of AC as a means of heating batteries from within. The PhD will compare this internal heating mechanism with common external (surface) heating with respect to achievable heating rate and temperature uniformity across an individual cell and a battery pack. The hypothesis is that AC heating will provide performance benefits in these two areas – rate of heating, and temperature uniformity – which will improve the ability of battery packs to operate in colder climates and facilitate fast charging at short notice. You will explore this through modelling and experimentation, as well as investigating related topics such as effects on battery degradation.
The project is likely to involve aspects of mechanical engineering (thermodynamics), electrical engineering (power control) and chemistry (battery cell modelling). The ideal candidate will have a degree in one of these subjects, and the motivation and initiative to develop in the other two, suitably supported by the supervisory team.
Enquire nowLithium-ion batteries (LIB) have become core technology for energy storage and electric vehicle applications due to key advantages like high energy density, long cycle life, and low self-discharge rates. However, they inevitably degrade over time due to irreversible physical and chemical changes, ultimately leading to the end of their usable life. An accurate and comprehensive degradation model would unlock new opportunities for battery use and optimization.
This research will apply a physics-based electrochemical-thermal battery degradation model coupled a data-driven neural network model to predict the State of Health (SOH) and Remaining Useful Life (RUL) of LIBs.
In 2030 to reduce carbon emissions, the UK government plans to ban the sale of new petrol and diesel cars followed by a phase out of new diesel trucks with an outright ban in 2040. This will lead to a sudden increase in the number of electric vehicles on the UK road network. To facilitate this the UK needs to rapidly upgrade it’s charging infrastructure. While it could “10 x” the number of “traditional” charging points, this is inefficient at scale and can be easily overwhelmed during busy periods, whilst lying unused much of the time. A further drawback is the need to stop and recharge your vehicle during long journeys introducing unnecessary delays costing time and money. A better course of action might be to electrify key transit corridors. The Centre for Sustainable Road Freight suggests that overhead charging infrastructure is the most cost and energy effective means to decarbonise the road network. Such a solution would enable smaller onboard vehicle batteries as they would only be required for short journeys in between electrified roads. This would also reduce precious battery metal usage (a potential future threat to the green energy transition), vehicle weight, and consequent road damage as well as alleviate the need for mass charging points.
The government is currently funding a £2 million pilot study on a 12.4 mile stretch of the M180 motorway with a £19 billion estimated budget to roll the network out nationwide. While promising, installing overhead electrical catenaries on the road network is not without its challenges. One such challenge is designing optimal modular foundations for the catenary cable supports. We know that similar systems on the rail networks have been heavily overdesigned and as a result are overly carbon and cost intensive. This project would solve this issue by optimising the location and shape of the substructure for a wide variety of different soil types, explicitly considering uncertainty in soil properties. The result of the project would be a family of modular foundations reliably designed for different soil types, that are optimised for greatest utility considering safety, economy, installation, lifecycle, and embodied carbon. System installation will allow for easy integration with existing infrastructure minimising disruption during the transition and facilitating future extension as required.
Enquire nowGas purification gives access to gas feedstocks for uses such as industrial processes, transportation fuels, and cryogenics. Perhaps of greater daily impact is the importance of gas purification in the remediation of waste gases from e.g., internal combustion or semiconductor manufacture. Such approaches are essential to the removal of gases that are inherently toxic (e.g., carbon monoxide), corrosive (e.g., hydrogen chloride), or smog-generating (e.g., oxides of nitrogen). Remediation of many of these are well established, usually via neutralisation, adsorption and/or combustion or by oxidative catalytic processes that provide lower harm products (e.g., toxic carbon monoxide to inert carbon dioxide). In contrast, there are a large number of gases where current remediation methods are unattractive, costly or otherwise limited. One particularly challenging class of compounds are highly fluorinated main group species such carbon tetrafluoride and sulfur hexafluoride. These gases are integral to the semiconductor and battery industries, and their use cannot be obviated at current. They also represent a grave environmental threat; perfluorinated main group compounds are potent greenhouse gases and less fluorinated systems are often ozone depleting. Emission to the atmosphere must be eliminated by gas-purification engineering controls. At current, this is done through high temperature combustion, an energy intensive and industrially unattractive process.
The major challenge in developing alternative remediation methods is the high thermodynamic stability of S-F and C-F bonds which are inert to most conditions. The alkali metals, lithium, sodium, and potassium have been shown to activate C-F and S-F bonds. In the case of sodium, high abundance and low cost also make it an attractive remediation agent. At current, however, the physical properties of sodium, a bulk metal, preclude its application. The Liptrot lab has recently developed a new route to alkali metals which have been deposited onto alkali metal salts. These species have significant potential as gas remediation agents, and can be synthesised in a sufficiently scaleable fashion to allow widespread exploitation.
In this project, Chloe will optimise the generation of Na/NaCl and related systems; tune the loading of alkali metal present; and manipulate the physical properties of these materials to ensure they can react with gas streams. Chloe will then explore their reactivity towards usually unreactive bonds, initially using solution phase model compounds and ultimately towards the identified gas waste streams. In doing so, Chloe will add an important gas abatement solution which will enhance the sustainability of processes that underpin a huge swathe of modern technology in the form of batteries and semiconductors.
Transitioning to a sustainable transport system is going to require substantial changes to infrastructure. Effective delivery of such transport projects is often contingent on gaining and sustaining public support. Evidence from other sectors suggests that the emotions people experience are important to understanding public. However, this area has been neglected in the transport sector, with little research considering how emotions might influence public acceptance. Better understanding these emotions can allow stakeholders to respond to public concerns in a more effective way, ensuring success of sustainable transport projects.
Ruth’s research will be focused on understanding emotional experienced in response to sustainable transport infrastructure projects. She will consider the types of emotions that these projects may elicit, how emotions relate to acceptance of transport projects and whether emotions relate to engagement with the project. Additionally, she will consider individual factors (e.g., trust, values) and project related factors (e.g., novelty, community involvement, transparency) that predict different emotions.
Yue's project aims to improve traffic management by studying how economic incentives, such as tolls and subsidies, can reduce congestion and make traffic systems more efficient. Traffic management can be viewed as a resource allocation problem, where limited road space should be used strategically to reduce congestion and help all drivers reach their destinations efficiently. Typically, drivers will act in their own interests choosing the route they believe will minimise their travel time, which often leads to system inefficiencies. In this project, congestion game models will be used to better understand how individual route choices affect the entire system and how incentives can encourage choices that enhance overall traffic flow and reduce total travel time.
In real-world settings, drivers enter a network at different times; however, current studies have only focused on static congestion games, where drivers enter the network simultaneously. The project will first explore the link between static and dynamic congestion games to identify how their dynamics differ. Based on these insights, we will further examine the effectiveness of tolls and subsidies in dynamic settings. For instance, while marginal cost toll pricing has shown promise in reducing inefficiencies in static scenarios, it remains unknown whether it will perform similarly in time-varying conditions. By developing strategies for applying these incentives in more realistic settings, this research aims to lay the groundwork for future, practical applications. The findings could ultimately help guide policies that improve traffic flow and reduce congestion without requiring extensive infrastructure changes.
Sebastian's PhD will look at Experimental and Theoretical Modelling of Heat Transfer in Aero-engine Compressors.
With increasing compression-ratio demand in gas turbine engines for fuels of the future like SAF and Hydrogen, the tighter tolerances are to be expected. This includes monitoring and predicting expansion of the compressor blades inside the engine, which is dependent on the heat transfer inside the compressor cavity. Since the heat transfer inside these rotating cavities is not static, but depends on the flow structures inside, which also depend on the heat transfer characteristics, it poses conjugate problem that requires further research. By means of experimental investigation the data representative of different operating conditions for gas turbine engines can be obtained and fed into theoretical modelling, and computational fluid dynamics validations.
To achieve variety of operating conditions, not only different non-dimensional parameters of the flow must be investigated, but also numerous modifications to the experimental rig must be added. This includes incorporating pressure sensors, and pre-swirler that would introduce swirl to the upstream flow that is present in all gas turbine engines on aircrafts. This will enable manufacturers to better understand design requirements and limitations of the gas turbine engines, that will contribute towards increasing efficiency of their products and their sustainability.
X in the loop (XiL) methods are an approach of system simulation whereby part of the system is physical hardware, operating on a test bench and the other part is a simulation running on a computer. These configurations are increasingly popular in automotive development because they allow for significant savings in time and money by reducing the need to build full prototypes. The challenge is that there are currently no well-established processes that can be used to correctly prepare the hardware, models and the physical/virtual interface in a way that is not as laborious as creating a full prototype.
Linking hardware and models through a dedicated interface raises questions around all three elements. For the model, what level of accuracy is required, which aspects of reality need to be represented, can such a model be easily (and automatically) created from a broader model? How can accuracy be retained whilst ensuring real time calculation capabilities? For the interface, what delays, lags and uncertainties are introduced through sensors and actuators? How much of the interface needs to be modelled to compensate for its inherent dynamics? Can a generic interface system be create to minimise the amount of integration engineering required?
For the hardware, what level of accuracy is required in applying the boundary conditions to ensure a meaningful result, how can the test instil confidence in the engineering owner that the results are representative of the full system?
This PhD will cover all three elements of the XiL simulation system, as well as seeking to outline a process that can be applied within a large organisation to ensure models, hardware and interfaces are fit for purpose and deliver meaningful data. The vision being that this thesis will create new tools for the design and implementation of XiL configurations, embracing HW selection, modelling and control logic that sits in between the hardware under test and the system model.
Enquire nowAmmonia has a high potential for fast decarbonization of marine transport as well as heavy-duty (HD) and off-road vehicles. These sectors are also commonly referred to as “hard-to-electrify” because they present specific challenges and operation boundary conditions that strongly hinder electrification. Moreover, ammonia is a higher volumetric hydrogen content than liquified hydrogen itself, it allows to be stored and transported easily and therefore, represents a very good candidate renewable fuel for future propulsion systems. For these reasons, the research on ammonia propulsion in combination with internal combustion engines (ICE) for these sectors needs to be pursued with high priority to fulfil the net-zero target for the UK by 2050.
Ammonia’s unusual properties include its gaseous to liquid phase change at elevated pressures, its high ignition temperature and slow laminar flame speed compared to conventional fuels for ICEs. These aspects could present challenges regarding injection, misfire or incomplete fuel combustion which could potentially limit the efficiency and operability of ammonia ICEs. To effectively use ammonia, deeper scientific understanding on the behaviour of ammonia at engine-relevant conditions must be gained. Based on that, simulation models to reproduce these complex phenomena need to be established to allow the effective and timely development of ammonia ICE and their application.
This project aims to investigate the fundamentals of ammonia injection and combustion behaviour, chemistry, and pollutant formation at engine-relevant conditions. Computational fluid dynamic (CFD) simulations will be developed and compared against fundamental experiments and engine measurements to gain an understanding on the underlying physical and chemical phenomena. Experimental data and CFD-validated models will be used then to develop 0D/1D models of key aspects to enable engine simulation in 1D/0D environment, which will consequently unlock the possibility of optimization of engine concept design and operation.
Enquire nowAs the automotive industry moves toward achieving net-zero emissions, companies are exploring a range of sustainability strategies. These might include electrifying vehicles, offsetting carbon emissions, incorporating AI for greener operations, shifting to autonomous vehicles, and creating more sustainable supply chains. While these efforts are important for reducing environmental impact, each may influence public perception in unique ways, which has yet to be fully understood.
This research aims to uncover how the public views these various net-zero strategies by analysing discussions across social media, news, and forums. Using sentiment analysis and topic modelling, the study will identify which types of initiatives resonate most positively with the public. By understanding the sentiment and concerns surrounding each type of initiative, this research hopes to provide insights for automotive companies, helping them shape their sustainability strategies to better align with public expectations and enhance their brand reputation
Electric motors are limited by the current they can carry and experience heat loss. Superconductive material can replace normal copper windings in motors to produce the strong magnetic fields required with high current density and make them more efficient and far more powerful, to be used in heavy vehicles and dynomometers. This PhD focuses on dynomometers for AVL to replace their current setups with just one combined dyno - one that can spin very fast to test EV motors and produce the torque required to act as a brake to test engines. Currently, two different dynomometers are used for these two different use cases. A superconducting motor might be the solution, but a feasibility study using simulations will be required for this to be conclusive.
Indrek's research project addresses a significant problem in the field of propulsion: the need for cleaner, more efficient liquid fuels. The project focuses on modelling the evaporative behaviour of methanol, a type of alcohol, chosen due to its unique properties such as a high-octane number, auto-ignition temperature, heat of vaporization, embedded oxygen, and excellent lean burn properties. These characteristics can lead to cleaner, more efficient combustion, and thus, reduced emissions while improving performance.
The high evaporative cooling effects of methanol, attributed to its high heat of vaporization, are a key focus. The project aims to develop and improve overall understanding and low-dimensional models of methanol's evaporative behaviour by conducting experimental data collection on a test engine fuelled with various methanol blends under various operating conditions and numerical experiments for additional, high-fidelity data. By better understanding and predicting the evaporative characteristics of methanol, this research seeks to enhance fuel efficiency and engine performance while simultaneously reducing emissions in marine engines. Ultimately, these findings could contribute to helping the UK achieve its net-zero targets by 2050 and be potentially applied to larger marine applications and other transport sectors, such as automotive and aviation, in the future.
Object tracking using cameras is a hot research topic with many practical uses, from video surveillance and self-driving cars to analyzing crowd behavior and understanding traffic scenes. The idea is to use one or multiple cameras to follow and identify the location of objects, like people or cars, across several video frames. While this sounds straightforward, it's quite challenging due to factors such as changes in lighting, camera angles, and objects blocking each other.
In recent times, the use of multiple cameras for surveillance has grown due to the availability of affordable, high-quality cameras and powerful computers. Multi-camera systems can offer more comprehensive tracking compared to a single camera, but they also bring additional challenges. For example, ensuring all cameras are in sync, dealing with objects that get blocked from view, and handling changes in how an object looks from different angles.
Yuqiang's project aims to tackle these challenges by enhancing existing methods and introducing a new framework based on machine learning. The goal is to make tracking objects across multiple cameras more accurate and dependable, ultimately contributing to the betterment of real-world applications such as smarter city management and improved traffic flow.
Dimitry's research focuses on inclusive design for emerging Mobility as a Service (MaaS) systems, targeting neurodivergent populations with Autistic Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Dyslexia, and Dyspraxia. Individuals with these cognitive differences are part of the broader spectrum of neurodiversity and represent an estimated 15% of the global population.
Existing MaaS systems integrate multiple mobility solutions into one platform and involve a complex network of stakeholders. Despite of this complexity, these platforms overlook the unique requirements of users with diverse cognitive profiles. Moreover, these design needs of these demographics are underexplored in academic community across disciplines. This oversight not only impacts user adoption, but also aggravates social inequalities.
In collaboration with multidisciplinary team of experts, and through co-designing with neurodivergent individuals, this research aims to identify unmet needs of target public mobility users, develop system prototypes, conduct empirical testing, and propose tailored design recommendations for inclusive MaaS.
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.
Organisations that employ large numbers of people (above 250 employees) generate and attract trips that, otherwise, would not be made. Commuting generates 5% of the UK’s year total emissions [1] while business air travel accounted for 154 million Mt CO2 globally in 2019 [2].
Large employers, aware of the impact of transport in the generation of GHG emissions as well as congestion and pollution, have started to implement policies and interventions to promote sustainable modes of transport among their employees. This is a significant opportunity for public/private collaboration to achieve Net Zero by 2050. But organisational policies do not always translate into changes of behaviours. Previous research suggests that people tend to accept policy if they perceive it as effective and fair, or if they feel like they had been part of the decision-making process[3].
Lucia is interested in identifying which factors contribute to making a policy to change the behaviour of employees. To do so Lucia will be looking at which strategies are more effective at promoting low-carbon transport behaviours, and how different stakeholders interact to design and implement such policies. Lucia expects the findings from this research can help policymakers, managers, and employees to generate more efficient and better designed policies.
Our UK transport system needs to decarbonise and part of the solution is to enable people to travel differently - to reduce the need for private car ownership and increase the ability to use public transport, walking and cycling. Research finds that for most people, avoiding car use is the single most effective action they can take to reduce their carbon footprint.
National and city are responding with aspirations to reduce car dependence, like “we have a vision for Leeds to be a city where you don't need a car” and “Scotland aims to reduce vehicle distance travelled by 20% by 2030”. They are creating policies that enable households to trade-in their car and receive credit to use alternative local transport services. Lots of these policies are targeted at individuals, rather than engaging streets or communities.
Pete's project studies whether there are better ways to engage more people to think and behave in terms of 'we' not just 'me'.
There is limited research available on which households want to reduce their car ownership, how this differs within neighbourhoods, and whether people's attitudes and values towards their local area, community and environment are a big influence.
Pete's project examines how new transport policies have been performing, looking specifically at the West Midlands area. Social science methods, like surveys and interviews, have proven effective to understand who is interested in shifting away from car ownership and why. Finally, insights from social psychology will be applied to develop a new policy that enables communities to collaborate by trading multiple cars in together and receiving a benefit that improves their local area and transport experience.
Pete's project will impact on local transport operators who are looking for more advanced ways to understand how people want to travel and need more robust and creative methods to design, communicate and test new policy ideas.
This PhD will comprise low order fluid dynamic, heat transfer and thermodynamic modelling of hydrogen fuel cell propulsion with focus on the thermal management system, in particular the radiator that is required for rejection of heat to atmosphere in liquid cooled systems. This research area is topical as high-powered fuel cell stacks (which would be needed in aircraft) that are cooled directly by air would suffer from thermal gradients that lead to unwanted increases in degradation and an associated reduction in voltage (and thereby performance). It follows that liquid cooled systems are likely to feature in future hydrogen fuel cell aircraft propulsion systems. Jet engines – which are the current state-of-the-art aircraft propulsion system and have been for >50 years – reject heat directly to atmosphere via their exhaust stream. Large scale radiators for propulsion system thermal management have therefore not featured in regional passenger aircraft design and so research into low drag and lightweight architectures for air-coolant heat exchange in this context has not been required and is thus lacking. However, with liquid cooled fuel cells for aircraft propulsion becoming an increasingly real prospect, there is an urgent need to develop understanding in this field.
Additional funding has been applied for that, if granted, would also allow the proposed student to design and build a test rig to enable a high-fidelity study of thermal and aerodynamic performance of fuel cell stacks and their radiators. This experimental work would support the validation of the low-order modelling work described above.
The proposed student will overlap for ~18 months with another PhD student who is currently working on a related topic. This current PhD student will offer support to the proposed student in getting up to speed with the fundamental understanding of fuel cells and the associated system architectures, the low order modelling techniques adopted so far, and help with identifying future areas for research in the PhD.
Enquire nowThis research project focuses on addressing a major resource- and energy-efficiency challenge in personal mobility. Systems change is needed in the car industry. Despite cleaner electric drivetrains, cars continue year on year to become heavier, and more resource intensive. The trend towards heavier and more luxurious vehicles is driven by a combination of consumer preferences, regulation, technological advancements, and market competition.
Various authors have been looking at the role that cars play in society (Kinsley and Urry, 2009). Some have begun to explore the technological and economic lock-in to our existing model for personal mobility (Urry, 2013). Much research is being done on the potential impact reduction through new product service systems enabled by autonomous vehicles (e.g. Narayanan et.al., 2020) . However, even in more sustainable future scenarios authors have argued that some personal vehicles will still be needed (Bihouix, 2020).
Micro compact cars also known as bubble cars, microcars or minicars have been developed by main stream manufacturers, entrepreneurs and start-ups since the 1950’s. The latest innovation efforts are from companies such as: Citroen (PSA) , Renault, Squad Mobility, Elio Motors, Electricbrands, Luvly, ElectraMeccanica, Freze Froggy, etc.
In a green industrial future a car industry is needed which delivers resource-efficient personal mobility. Research is needed to investigate micro car innovation today using whole systems and value analysis to determine what the barriers and enablers are to support more rapid growth of this industry.
Candidates from a range of backgrounds are welcome to apply. Candidates might have a background in: innovation, technology or engineering management, industrial strategy, systems engineering, psychology, policy research, product design, industrial design, human factors, sustainable technologies or engineering. The research activities are likely to be qualitative in nature and involve the recruitment and interaction with experts and industry. The multidisciplinary supervisory team are from the Department of Mechanical Engineering and the School of Management.
References: John Urry (2013) Societies beyond oil: Oil dregs and social futures London, UK: (ISBN 978-1-78032-168-4) Kingsley, D. and Urry.J. After the Car. Cambridge: Polity, 2009. Print. Narayanan, S., Chaniotakis, E. and Antoniou, C., 2020. Shared autonomous vehicle services: A comprehensive review. Transportation Research Part C: Emerging Technologies, 111, pp.255- 293. Bihouix, P. (2020). The age of low tech: Towards a technologically sustainable civilization. Policy Press, 2020.
Enquire nowSafran provides the world’s leading airframers with innovative and reliable propulsion systems. Efforts today focus on decreasing fuel consumption and maintenance costs, while designing more eco-responsible systems. Through CFM International (the 50/50 joint company between Safran Aircraft Engines and GE), Safran produce the LEAP® turbofan, powering new-generation single-aisle commercial jets: the Airbus A320neo, Boeing 737 MAX and COMAC C919.
The ultimate goal of the collaborative research programme between Safran and the University of Bath is to make air transport safer and more environmentally friendly in tackling the challenge of climate change and contributing to the transition to carbon-neutral aviation by 2050. Improvements in engine performance are critical to realise this ambition.
One of the most important problems facing gas turbine designers today is the ingestion of hot mainstream gases into the wheel-spaces between turbine discs (rotors) and their adjacent casings (stators). Rim seals are fitted at the periphery of turbine cavities and superposed purge and leakage flows are used to prevent ingress. Through the project ‘Ingress through Gas Turbine Rim Seals 2’ (IRIS2), next generation rim seal technologies are to be developed, seeking to reduce the use of purge flow while minimising aerodynamic loss in the turbine. The problem of hot gas ingestion is exacerbated by the advent of hydrogen as a viable alternative fuel in aerospace. Density ratios between the mainstream gas path and the cooling systems are modified by the presence of hydrogen, rendering significant modifications to the sealing technology of paramount importance in the next generations of engine frames.
This new programme of work will utilise an advanced multi-stage turbine test rig, fully instrumented to assess pressure, velocity, torque and concentration distributions within the turbine stage, to improve the understanding of its flow physics and modelling approaches. State-of-the-art experimentation will be coupled with computational fluid dynamics simulations to develop the modelling capability of the unsteady, three-dimensional flows occurring within the aeroengine stage.
The PhD candidate will join a growing team of turbomachinery researchers at Bath and work closely with academics and senior engineers at Safran Aircraft Engines. It is expected that the work will result in a series of technical publications at international conferences, in addition to frequent visits to the industrial collaborator in Paris.
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Battery electric vehicles (BEVs) are quickly becoming one of the most favoured industry choices for sustainable transport options in our current society due to their zero tailpipe emissions. However, BEVs have a major issue with weight due to the large number of batteries required to provide a suitable range for the vehicle compared to traditional internal combustion engines. Current BEVs have utilised traditional metal structures as casing for the battery packs and modules, adding additional weight leading to reduced travel range and also elevate the fire damage risk due to metal’s superior thermal conductivity.
Composite materials (materials made of two or more component materials) are able to address these issues due to their excellent mechanical performance whilst being lightweight. They are also highly customisable, which enables a variety of different manufacturing methods and starting materials to be used. Matt's project will be looking into using sustainable, bio-based resources to design and develop novel composite materials for EV battery casing using a holistic approach to take into consideration numerical, analytical and experimental approaches with the objective of minimising environmental impact whilst maximising performance
Faye’s PhD investigates 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. 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.
Lithium-ion batteries with solid electrolytes hold great promise for energy storage, offering improved safety and higher energy density. However, their widespread adoption is hindered by the growth of lithium-filled cracks known as dendrites, which can cause short circuits and, in severe cases, thermal runaway. While the mechanisms driving dendrite growth have been studied, a critical factor has been overlooked: at the microscale, lithium is significantly stronger than in its bulk form.
This PhD project aims to tackle this challenge by developing a comprehensive computational model to predict and mitigate dendrite propagation in solid-state batteries. The model will focus on the micromechanics of lithium dendrite growth and be validated against advanced imaging data, characterizing the morphology and crystallography of the dendrites. Additionally, the project will investigate the potential for thermal runaway from dendrite-induced short circuits, incorporating temperature effects on dendrite growth.
This research will advance the understanding of lithium dendrite micromechanics, improve battery failure predictions, and guide the design of safer and more efficient solid-state batteries, critical for the future of electric vehicles and the broader electrification of propulsion systems.
Beyond fundamental science, the successful PhD candidate will gain expertise in computational modelling, battery technology, and advanced imaging techniques. They will join a growing research group focused on lithium-ion battery degradation, positioning them at the forefront of energy storage innovation.
Enquire nowA key element of the UK’s decarbonisation strategy is promoting the electrification of road transport, which promises to significantly reduce national emissions when combined with an expanded share of renewable generation in the energy mix. This presents challenges from an energy system perspective, as charging demand will put pressure on network and charging infrastructure, potentially requiring costly upgrades. However, it also offers exciting opportunities; EV charging could be controlled to optimally align with renewable energy supply and network constraints, and vehicles could even return power using vehicle-to-grid (V2G) technology, helping facilitate increased uptake of renewables and offsetting requirement for investment in other types of storage.
Oliver’s PhD will investigate the implications of future EV adoption for the grid both from a demand (charging) and generation (V2G) perspective. He will simulate the effects of vehicle charging and V2G storage on network flexibility in a range of future grid and EV adoption scenarios, incorporating realistic assumptions on charging behaviour and taking a bottom-up approach to forecasting EV uptake, building upon the research of fellow CDT student Isaac Flower. This research will help guide optimal planning and investment in system upgrades to support co-decarbonisation of transport and energy, and will contribute towards the realistic whole-system understanding of the UK energy network being developed by the power group at the university.
Enquire nowBerat will investigate a promising solution to increasing emission concerns in air transport is electric urban air mobility (UAM) vehicles, which can provide new mobility models offering zero carbon footprint and be a mainstream mode of air transport in the next decades. However, UAMs cannot offer a ride experience as smooth as that of passenger jets since they will experience complex flight regimes analogous to those of helicopters, such as hover to forward flight transition and high-speed cruise, resulting dynamical loads transferred to the passenger cabin. The dynamical loads are felt by the occupants as oscillations and vibrations, which can lead to an uncomfortable ride. This is deemed to be critical in UAMs since they will be dominantly used in human transport and their market acceptance depends on the passenger comfort perception. Therefore, UAMs are expected to face various comfort related problems, which needs to be addressed using novel tools and methods.
This research aims following to tackle the problem:
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