Edison's project explores how digital transformation is reshaping the way car manufacturers do business to make it more sustainable while keeping competitiveness in the market. Reducing waste and making the most of the resources have become priorities in business processes, not only because these practices seem to lower costs but also enable companies to comply with sustainable principles. This economic model is called the “circular economy” and has gained significant presence across industries, however, transformational changes such as digitalisation have altered the traditional business processes making it necessary to holistically analyse their interaction and identify business models that suit this reality.
In simpler terms, Edison's study investigates how the automotive industry is adopting innovative technologies like the Internet of Things, data analytics, and automation to create cars that are more durable and easier to repair. It also looks at how these advancements are enabling manufacturers to recover and recycle materials from old vehicles, contributing to a greener planet.
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-imperical 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' PhD aims to provide optimal testing strategies and accurate modelling 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 Johannes' 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.
Electric machines are becoming more prevalent in the automotive industry as they become the main propulsion system in road vehicles with the industry’s shift towards emissions free mobility. With over 15% of new car sales being electric, being able to accurately characterise electric machines virtually is imperative for maximising their performance and efficiency. A key predictor of a model’s ability to replicate transient behaviour is the accuracy of the parameters used to characterise the motor. 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.
There are many potential use cases for motor models, and many motor architectures of interest. In each combination of use case and motor architecture, the appropriate motor model structure is expected to differ. Typically, the level of spatial and temporal resolution will increase when more insight into detailed motor performance is needed. Once a model structure is defined, the data required to parameterise and validate this model can be defined. Then, the experiments necessary to generate this data, along with the instrumentation required can be defined. Focusing on the model development of electric machines, this project aims to create an end to end workflow between model and data to increase model accuracy and adaptability to new units under test. The work will explore the potential for a general motor model and parameterisation procedure that is compatible with all the likely motor topologies of interest: flux switching, induction, and synchronous motor architectures. It will focus on implementing an autonomous parameter characterisation process, and on streamlining the experimental procedure behind the collection of data required for the parameterisation of an electric machine.
Batteries vary in density as they undergo charging and discharging and recent work demonstrated that ultrasound can identify these material mechanical changes permitting the level of charge to be measured. Our research considers taking this approach and implement it to automotive batteries to maintain continuous in-service charge and structural health monitoring.
Lithium-ion battery cells are regarded as one of the key drivers to sustainability for future transportation solutions. Hence, applying charge monitoring using ultrasound will make battery cells even more interesting. Current techniques used to measure the battery state of charge (SOC) include tracking the battery voltage and currents but the method lacks efficiency and accuracy. In contrary, ultrasound allows the battery SOC to be measured directly at any time and being not charge history dependent means that errors are not carried on successive measurements. This way we will provide more accurate battery SOC readings, improve battery range estimation, and preserve its structural integrity.
Previously, ultrasound charge monitoring was successfully demonstrated on an individual cell under a laboratory environment. The changes in elastic properties and density of the lithium-ion battery vary the wave speed travelling through the test cell. The wave speed is measured by kowing the time taken by the longitudinal wave to traverse the cell and is used to then determine the battery SOC. Automotive batteries contain several cells stacked together, therefore this research explores how various ultrasonic techniques can be applied to multiple cells within a battery module and how this might constrain module design. In a laboratory environment, a built-in test system will be incorporated to gather data ready to be successively analysed using analysis techniques in signal processing and numerical modelling in predictive machine learning. As a starter, the equipment will comprise of an ultrasonic pulse-generator, ultrasonic probes, a built-in custom test bed, and an individual lithium-ion cell prior to moving on with a multiple cells stacked in series.
What is also motivating us to conduct ultrasonic non-destructive evaluation is to provide a more in-depth understanding of the complex electrochemical characteristics of lithium-ion batteries. This means that our ultrasonic techniques will be closely linked with internal mechanical changes that fluid-filled porous media go through under specific events. In this case, we will also advance a logical understanding of the porousmechanics of solids filled with liquid (i.e., lithium-ion battery cell wet in a liquid electrolyte) and investigate the acoustofluidic phenomena with the use of modern, accurate, and cost-effective lenses.
Dwindling fossil fuel supplies and global warming mean there is an urgent need to develop biorenewable replacements for the petrochemical based fuels and lubricants consumed by the automotive industry. Bioethanol from fermentation of lignocellulose biomass and other high energy biofuel replacements derived from hydrogenated vegetable oils have been developed, however many of these biofuels have low densities and volumetric heating values.
Terpenes represent an alternative class of naturally occurring hydrocarbons which have comparatively higher densities that make them ideal-fuel replacements/additives. Nature produces an estimated one billion tonnes of terpene annually, which is a sufficient volume to consider using these hydrocarbons as replacement biofuels. Recent developments in industrial biotechnology have also demonstrated the potential of engineering metabolic pathways into microbes for the industrial production of economically important higher terpenes. The cheapest commercial sources of terpene currently available is Crude Sulfate Turpentine (CST) which is produced as a waste by-product of the Kraft paper pulp process (approx. 240,000 tonnes p/a) and gum turpentine (110,000 tonnes p/a) that is available from sustainable tapping of pine trees. Both turpentine sources are comprised of a mixture of cyclic monoterpenes (-pinene, -pinene, 3-carene and limonene), that are currently used as chemical feedstocks by the flavour/fragrance industries or burnt on-site to provide a cheap energy source for the pulping plant.
We have recently developed a catalytic two-step ring fragmentation/hydrogenation protocol to convert CST into a ‘sulfur free’ p-menthane biofuel containing controllable amounts of aromatic p-cymene. The monocyclic saturated branched ring structure of p-menthane means that it should exhibiy excellent automotive biofuel properties (high energy, branched, resistant to oxidation, low freezing point, non-carcinogenic). Aaron's project will optimise the chemical route from untreated industrial CST (e.g. desulfurisation technology, catalyst recycling) obtained from a Swedish paper mill (Södra) to produce p-menthane/p-cymene blends (ratio dependent on partial hydrogen pressure) whose combustion performance (e.g. melting point, cloud point, cetane level, temperature performance, combustion kinetics/pathways) will then be optimised to enable field tests to be carried out in different types of combustion engine.
To achieve net zero by 2050, the IEA have stated that 50% of the technology required is yet to be developed, thus, rapid testing and development in all sectors is required. Increased intensity of testing could however have a significant impact on energy demand, which conflicts with a transition to a renewable energy system. The automotive industry continues to support, and in many instances grow, an already carbon intensive transport sector; despite lulls during the COVID-19 pandemic and increased electric vehicle sales, road transport still equates to around 28% of global carbon emissions. Therefore one area of focus to support the required decarbonisation of the automotive sector, whilst allowing new low-carbon technology to be developed, is associated with increasing the efficiency and efficacy of the testing phase of vehicle technology development.
Physical testing is time consuming due to real-time constraints and complex and technologically delicate systems, and is thus, highly energy intensive. Additionally, human-errors in set up, faulty or mis-calibrated sensors, or unforeseen mechanical failures are often only identified post-test and result in these tests being redundant and needing to be repeated. Virtual testing environments play a role in minimising these tests, allowing simulations to be run earlier in the development process and for more use-cases to be considered. However, if not provided with robust physical data, these models will not be able to accurately simulate hardware responses - they therefore still rely on physical testing for the test data used to adapt and optimise the virtual models. Some physical testing will also still be required for technology to be suitable for market release to account for product variance, unknown effects and simulation inaccuracies.
As the general change of the powertrain development process makes it harder to compensate poor measurement quality by engineering experience, anomaly detection – a method of finding unexpected patterns in data - presents a possible solution to minimise physical testbed time whilst increasing the reliability of real data to feed into virtual simulation models. If applied to a range of powertrain units, be it internal combustion engine, pure electrical drive, fuel cell or hybrid setup, it has the potential to reduce the energy intensity of vehicle 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 document that lists all of the components and resources needed to build a product, in this case a vehicle. Each car has around 15,000 components. If any of these components are missing or incompatible the factory cannot build the vehicle. The problem is made more complex because vehicle makers offer an almost infinite variety of model variations and customisation options. Each of these needs a complete and accurate BoM if the manufacturing process is to succeed. Therefore, each BoM must be validated to ensure it is correct before the vehicle can be built. Techniques exist to automate this validation process, but there is still a heavy reliance on expert knowledge to ensure that nothing is missed or duplicated.
Using AI techniques, it may be possible to understand the variant configuration of each buildable combination and thus eradicate miss-builds and provide vehicle makers with the good information across the whole product line-up which will allow for more accurate planning in terms of assembly as well as financial control. There is a rich dataset of historical BoMs available which can be used to help with this process, as well as access to human experts whose knowledge may able to be represented in an automated procedure. It is likely that the most effective approach will combine these two approaches.
Lois’s PhD seeks to understand the reasons why certain groups of people may or may not accept climate transport policies, specifically looking at Clean Air Zones and Liveable Neighbourhoods. Her work will adopt a mixed-methods approach, combining quantitative, qualitative and machine-learning methods to determine which groups face barriers to acceptance, why, and finally – how acceptance can be facilitated amongst a diverse population.
Water droplets placed on a surface heated to a sufficient temperature (the Leidenfrost temperature) levitate on a film of their own vapour. A recent Nature article showed that adding a sawtooth pattern to the heated surface causes these levitating droplets to be propelled (even uphill!). Very recent work at Bath has shown that this propulsion can be achieved in mm diameter enclosed pipes. This opens the way to exploitation of Leidenfrost propulsion for active cooling systems that operate without moving parts and use waste heat energy for the thermal pumping effect. Onur's project will seek to extend this work by using AM techniques to created 3D printed cooling systems with internal ratchet microstructures. Work will focus on optimum fluid/surface choices (experiments have previously been confined to water) and ensuring AM quality is sufficient to reliably allow propulsion as surface roughness can cause an increase in the Leidenfrost temperature. The effect of pressure on heat transfer will be studied both experimentally and numerically and set in context against current systems.
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 majority of 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.
With electrification being one of the biggest potential disruptors in modern transport, there is a growing need for lightweight, reliable and efficient thermal management systems. Conventional solutions will struggle to keep future powertrain and propulsion systems cool without incurring significant mass penalties. In addition, future systems are likely to be highly complex, which may also negate the performance benefits. Recovering waste heat will also be a priority to reach desired overall system efficiencies – affecting both the environment as well as operational economics. Advancements in additive manufacturing technologies combined with mathematical/parametric modelling enable the construction of radically different heat exchangers. These units can be designed to reflect their function and application in particular environments. In this PhD, Edgar will focus on engineering methods development to design and validate high-effectiveness, additively manufactured heat exchangers. This research will consider both analytical and experimental techniques. A modular approach is proposed with a focus on heat transfer through thin-walled components and balancing the trade-off between effectiveness and pressure drop across the unit. Finally, Edgar's project will consider further integration with computational intelligence or optimisation techniques in order to radically accelerate the development of complex heat exchangers.
Batteries based on carbon fibre reinforced plastic (CFRP) have the potential to supply power with an improved overall efficiency (vehicle power to weight rather than battery power to weight) compared to current battery technologies. By integrating batteries into the structure in the form of CFRP, lightweighting is not only achieved from the change in material but also from the removal of the non-structural dead weight of conventional batteries and their casements. For example, in automotive applications, structural batteries achieve a 26% theoretical mass saving over use of separate systems for energy storage and load carrying. The current state-of-the-art in structural batteries is a half-cell based on a structural cathode. Significant work is required before a full cell can be manufactured and expected to sustain loading for multiple discharge and mechanical load cycles. Three projects are suggested which focus on challenges at different length scales this is project A:
Fibre matrix interface scale - Battery concepts and fibre electrolyte electrical connectivity: Rob's PhD will focus on both creation and development of new structural battery concepts and on the dual role of the supporting matrix. The matrix must both mechanically support the fibre, for which complete wetting of the fibre by the matrix is optimal, and allow ion migration, for which partial wetting of the fibre, in some battery constructions, is optimal as it allows liquid electrolyte to be in contact with the electrically conductive carbon fibre electrodes. The interface between the fibre and resin is subject to interface chemistry including sizing/functionalisation of the carbon fibres and processes which can control the wetting of the fibres by the matrix. Exploration of both will be key to Rob's PhD.
Batteries based on carbon fibre reinforced plastic (CFRP) have the potential to supply power with an improved overall efficiency (vehicle power to weight rather than battery power to weight) compared to current battery technologies. By integrating batteries into the structure in the form of CFRP, lightweighting is not only achieved from the change in material but also from the removal of the non-structural dead weight of conventional batteries and their casements. For example, in automotive applications, structural batteries achieve a 26% theoretical mass saving over use of separate systems for energy storage and load carrying. The current state-of-the-art in structural batteries is a half-cell based on a structural cathode. Significant work is required before a full cell can be manufactured and expected to sustain loading for multiple discharge and mechanical load cycles. Three projects are suggested which focus on challenges at different length scales this is project A:
Atom-scale modelling, anode development and charging rates/battery cycling: Thomas' PhD project focuses on optimising the construction of the anode of a Carbon Fibre Reinforced Polymer structural battery (with the cathode and electrolyte interface being investigated at Chalmers University in Sweden) and assessing its performance under load. Thomas' work will be undertaken in atomic modelling of the anode and the change in ion migration pathways as the anode is stretched by intercalation (absorption) of ions and mechanical loading. The work will focus on the electrochemical aspects of anode development and leave mechanical aspects to the other projects.
Alex's research will investigate a new concept of free-piston engine for which a patent has been applied for by the University. This concept (known as “ISOTOPE-X”) is mainly intended to function as a range extender for a battery electric vehicle. The project will investigate the performance of the machine firstly as a combustion engine, including possibilities afforded by the flexibility of the piston motion, to in turn establish the requirements placed on the electrical components, and then secondly model these to gauge the feasibility of the whole device. Control requirements will also be studied, including the potential benefit of using “bounce chambers” for instantaneous energy requirement reduction, and the use of pumping chambers for scavenging air supply the cylinders.
The combustion modelling will include the possibility to vary the compression ratio and so investigate the feasibility of using some form of compression ignition to further improve efficiency and reduce emissions. Heat transfer effects will be quantified as part of establishing the losses and also the magnitude of the thermal challenge passed to the magnets of the electrical machine.
It is anticipated that an initial conceptual design will be modelled and that as the analysis progresses and new insights are gained that updates to the design will be incorporated at various stages.
A significant potential avenue is the investigation of hydrogen as a fuel for the machine, since with advanced combustion modes enabled by variable compression ratio it is possible that this combination could be effectively zero emission and more efficient than a fuel cell for heavy duty applications, while being cheaper too.
Elisabetta's PhD will explore 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.
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. However release of the hydrogen requires a catalytic conversion and ppm levels of ammonia are a poison for PEM fuel cells so 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) and the internal chemistry/catalysers used. Optimisation will be on structural weight reduction, power transfer efficiency, and thermal management of waste heat using AM.
WP1: Exploration of Fuel Cell topology / architecture / fuel: To investigate the most suitable fuel, catalyst and electrolyte performance and topology for aerospace applications. Bath already has equipment required to quantify the conversion efficiencies of the cells while some will be purchased directly as part of the project.
WP2: Integrated thermal management: This will investigate different materials and 3D geometries to highly integrate cells and their thermal management making use of extensive simulations. AM heat exchangers and cell components will be prototyped and tested. This will include consideration of fast start-up capability by application of direct radio-frequency heating.
WP3: Technology demonstrator: a prototype SOFC stack to be produced to demonstrate feasibility.
Our streets are shared by multiple types of user. At the extremes, vulnerable pedestrians might occupy urban spaces with vastly more dangerous heavy goods vehicles. Such interactions introduce many disparities and asymmetries in terms of the ability of one party to harm another, and the extent to which each party is legally and physically regulated. A key issue is how these different classes of road user can effectively communicate with one another. This becomes more pressing as we consider the possibility of future autonomous vehicles, which entirely lack a human component and so might communicate very differently (e.g., they are unlikely to interpret informal signals the way a human driver would). All this takes place within a built environment which is regulated by a legal system and surrounded by cultural influences such as news and mass media. Catherine's PhD will look at how communication between road users currently takes place and how people, policy and engineering might be changed to facilitate and improve this.
Optimum lubrication and low-friction in automotive applications represents a potential for reduced energy consumption and emissions in engines. Moving engine components operate under high-temperature and high-pressure conditions where oil additives activate to form sacrificial protective tribo-films, which in turn reduce friction and wear.
Ciaran’s synthetic tribo-chemistry based PhD will focus on the design, synthesis, characterisation and tribo-testing of new inorganic molecules designed to form wear resistant and low-friction films at points within the internal combustion engine where friction and wear cause significant problems.
Virtualisation of complex automotive propulsions systems represents an indispensable requirement for effectively overcoming engineering challenges encountered in their development cycle. State of the art models enable engineers to build high fidelity virtual prototypes as well as simulations capable of offering a realistic operating environment, leading to effective investigations into system behaviour and reducing the efforts associated with physical experiments.
Depending on the structure and complexity, robust effective models require a significant amount of resources during their development to maximise the amount of information describing the physical system. One vital step during this process is the parametrisation of the model selected which may present itself as costly and time-consuming due to the computational power and expert knowledge required. This type of process is also often devised only for a specific application by incorporating assumptions shaping a readily available dataset or acquisition routine, limiting the repeatability of the process relative to measurement data. These shortcomings increased the demand for automated procedures effective in creating virtual representations.
The main aim of Vicentiu's PhD project is to develop robust parametrisation procedures maximising model accuracy while reducing efforts by efficiently using information specific to the system and model structure employed. The initial use-case targeted is represented by battery systems, while others are set to follow and benefit from techniques that have already been employed by the project. The ideal outcome is represented by a demonstrated automated methodology capable to produce optimally parametrised models for given requirements, recognized by a set of Key Performance Indicators (KPIs) targeting the validity and quality of the parametrisation, while reducing testbed as well as computational effort.
It is expected that Vicentiu will produce a prototype implementation of the final methodology and demonstrate this in the prototype factory at the new Institute for Advanced Propulsion Systems (IAAPS). The outputs of Vicentiu's PhD project are also anticipated for integration as part of a new generation of engineering software tools aimed to assist engineers in the creation of mathematical models used to support further powertrain development tasks.
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.
Addressing climate change requires profound behavioural changes, including within transport. Indeed, reducing car use is one of the most impactful mitigation behaviour changes that individuals can make. Yet, travel behaviours are amongst the most difficult to change. This is partly because they are strongly habitual – unconscious routines triggered by contextual cues (e.g., ‘it’s 8am, time to drive to work’) rather than the product of conscious deliberation of alternatives (e.g., ‘which mode of transport would be best today?’). But since habits are cued by stable contexts, changes in context destabilise habits. Consistent with this, research shows that disruptions – whether concerning a person’s life-course (e.g. moving home) or physical or social context (e.g. infrastructure disruption) – provide opportunities to reshape behaviours in new directions. Interventions targeted to moments of change are thus more effective than at other times.
While much research has explored these ‘windows of opportunity’ during biographical life events, such as moving home, retiring, or becoming a parent, less is understood about how exogenously caused, structural disruptions (e.g., changes to physical environments) might disrupt habits and promote behaviour change. This PhD research will thus explore the impact that physical infrastructure disruptions (e.g., road closures) might have on modal shift and travel demand. Further, the project will evaluate the effectiveness of interventions (e.g., the provision of information, free public transport tickets) promoting active travel and public transport that are implemented during such disruptions.
Working with Transport for Wales (TfW), a series of field experiments will be conducted which evaluate the impact of behavioural measures that are introduced alongside physical changes to streets as part of TfW’s South Wales Metro project. Combined impacts of the interventions alongside the structural disruptions on travel mode change will be measured using TfW travel data as well as through the collection of both qualitative and quantitative survey data.
Hydrogen fuel cells are a vehicle power source with several advantages compared to the fossil- fueled incumbents. Fuel cells emit no harmful emissions, producing only electricity and water from hydrogen fuel and oxygen from the air. The electricity generated is used to power electric motors, similar to battery electric vehicles (BEVs), but the use of a consumable fuel instead of batteries alone negates the need for time-costly recharging. Hydrogen fuel can also be produced in a ‘green’ manner, such as by solar-powered electrolysis, which is more environmentally sustainable than fossil fuel use. This combination of benefits make hydrogen fuel cells a pivotal technology for the reduction of carbon emissions in the transport sector.
To feed the chemical reaction in the cell, oxygen is provided to the cathode from the ambient air, but must be compressed and managed in various ways to maximise performance and efficiency. Components added to the system which handle inlet air and improve fuel cell efficiency also induce parasitic losses, reducing overall system efficiency by consuming electrical energy. Air handling consumes 5% of the power provided by the stack in some examples, but could be much more, so is a key area for development to understand and improve system efficiency. There are existing methods to offset parasitic losses, such as using turbines to recover energy from the exhaust flow, as is common in turbocharged diesel engines. This appears to be less lucrative for fuel cells, as the exhaust flow is different in nature. For example, it is comparatively low temperature and is more humid, so contains less energy, and is non-pulsating, presenting different requirements and considerations. These gains and losses from air handling components must be assessed at a system level to ensure optimal air management for fuel cells. There are also further restrictions to consider regarding the practicality of air management solutions, such as avoiding contact of the exhaust water with electrical components.
The aim of Matt's PhD project is to optimise air handling systems for hydrogen fuel cells, particularly for heavy duty applications. Fuel cells are more suited to heavy duty applications than light duty due to the low volumetric density (energy per unit volume) of hydrogen. Heavy duty vehicles tend to have sufficient available space that is needed to store enough hydrogen for an appropriate range. The expected project methodology is as follows:
1. Conduct research on the interaction between the air path and the fuel cell to understand the requirements and constraints of the fuel cell’s air supply. This will help appreciate the role of air handling components later in the project. It is predicted this stage will consist of a literature review and numerical testing in GT Suite to supplement findings from secondary sources.
2. Match the air flow requirements to existing air management solutions. This will consist of further literature review to comprehensively assess a breadth of options, noting the roles, effects, pros, and cons of different components and configurations.
3. Devise an improvement to modelling techniques for fuel cell air management. This might be a humidity model which better represents real life conditions and provides data which is closer to that of physical experiments, for example.
4. Use knowledge gained to propose novel air handling systems and carry out system optimisation. This will provide an understanding of the pros and cons of different configurations and subsequently determine the best applications for each.
Jac’s 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. The value added of Jac’s research will be the development and application of a novel probability of default model which considers the operational complexities of electrified bus fleets, as well as the "real options" available to the operator.
This topic is important because most UK bus operators cannot afford to decarbonise their fleets, especially post-Brexit due to the absence of the EIB lending facility. The UK government’s recent strategy has revolved around centralised public procurement; however, this is both insufficient and unsustainable. It follows that the market needs a green financial instrument to raise private investment for low-carbon bus operations
In response to the climate emergency, transport is transitioning from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs). Despite concerns about toxicity and resource depletion, Life Cycle Assessment (LCA) literature suggests that on average, EVs reduce whole Greenhouse Gas (GHG) emissions by up to 45%. However, EVs have a complex life cycle. The supply chain relies on a diverse range of raw materials produced upstream before manufacture, while post-manufacture, the use stage extends potentially up to 20 years before reaching its end-of-life for recycling. Hence, the environmental impacts are temporally distributed across the life cycle stages. To date, automotive LCA relies on historic data and arbitrary methods that are limited in their ability to capture how future impacts will evolve. Therefore, there is considerable uncertainty about the future consequences of the uptake of new technology such as EVs, adding risk and ambiguity to whether the automotive landscape is moving towards the sustainability agenda.
This PhD project develops prospective methodology that combines the LCA framework with Integrated Assessment Models (IAMs) to anticipate how future environmental impacts of automotive technology will evolve. IAMs consider potential socioeconomic development pathways to optimize outputs such as what future energy mix scenarios may look like. Advanced LCA techniques in Python are developed to incorporate IAMs into LCA inventories, providing the ability to explore future impacts of markets, production processes, and technology. These processes are then used to explore the long-term environmental impacts of automotive that account for changes in upstream production, temporal distribution across life-cycle stages, and potential downstream pathways for end-of-life recycling.
The UK is moving towards zero carbon emissions by 2030. To reach this objective, it is necessary to develop technologies to capture and transform CO2. Artificial photosynthesis, a carbon-negative process that mimics the reaction in plants, is capable of transforming CO2, using energy from the Sun, into different molecules such as methanol which can then be used as liquid fuel. However, poor conversion and selectivity of current artificial photosynthesis mechanisms are the main barriers to the commercialisation of the technology.
Solution: We will investigate the role of continuous microreactors, devices with channel dimensions below 1 mm, which can provide a step-change needed in the technology because of their exceptional mass transfer, offering better conversion and control in the selectivity of the process.
Most of the work to boost the process has focused on the reaction chemistry (e.g. improving the catalyst), but reactor engineering and process intensification can be parallel research lines to improve the technology. Only a few groups have reported the use of microreactors for continuous CO2 reduction and they stated that the better mass transfer in these devices enhanced the selectivity towards liquid hydrocarbons. Mass transfer is essential for reagents to move across the boundary layer adjacent to the surface of the catalyst. It is especially crucial in multi-phase reactions, such as in the case of artificial photosynthesis. Despite the promising results using continuous microreactors, the reactor role in the process has not been deeply understood and further investigation and optimisation of the channel designs could lead to a superior control in the process.
The reactors will be 3D-printed with metallic materials (copper or aluminium) in a laser melting printer at the University of Bath. 3D printing provides flexibility to try different channel designs. The internal surface of the channels will be coated with carbon nitride-based photocatalyst and irradiated with an artificial sunlight lamp. Water saturated with CO2 will be circulated through the reactor by a HPLC pump. At the end of the experiment, the products collected will be analysed with Raman Spectroscopy and Gas Chromatography (GC). The catalyst will be characterised by Scanning Electron Microscopy (SEM), Transmission Electron Microscope (TEM), X-Ray Diffraction (XRD), X-Ray Photoelectron Spectroscopy (XPS) and photoelectrochemical techniques such as transient photocurrent responses. The optimisation will also be augmented by computational studies, which aim to establish a relationship between the products obtained, the flow dynamics, and the mass transfer effects.Enquire now
Batteries based on carbon fibre reinforced plastic (CFRP) have the potential to supply power with an improved overall efficiency (vehicle power to weight rather than battery power to weight) compared to current battery technologies. By integrating batteries into the structure in the form of CFRP, lightweighting is not only achieved from the change in material but also from the removal of the non-structural dead weight of conventional batteries and their casements. For example, in automotive applications, structural batteries achieve a 26% theoretical mass saving over use of separate systems for energy storage and load carrying.
The current state-of-the-art in structural batteries is a half-cell based on a structural cathode. Significant work is required before a full cell can be manufactured and expected to sustain loading for multiple discharge and mechanical load cycles. Three projects are suggested which focus on challenges at different length scales this is project A:
Micromechanical scale - Mechanical resilience: During charging, ions are absorbed into the fibre (intercalation) which causes the anode to swell. Swelling impacts the residual stress state, mechanical properties and microstructure of the composite material, and may result in microscale fracture. Such physical changes will critically influence the ability of the material to hold charge and carry structural load. In this PhD, Paloma will focus on 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. Work will progress to understand similar properties under axial fatigue loading. A proposal for synchrotron time has already been made.
Lithium-ion batteries are prevalent sources of electric energy for a variety of applications, ranging from portable electronic devices like mobile phones, tablets and laptops to Electric Vehicles and Hybrid EVs. Compared to alternative energy storage technologies, Li-ion batteries have excellent energy-to-weight ratio, no memory effect and very low self-discharge rate in idle state. These favourable properties together with the continuously decreasing production costs have established Li-ion batteries as the unique contender for automotive as well as aviation applications.
In the automotive sector, the increasing demand for EVs and HEVs is pushing manufacturers to the limits of contemporary automotive battery technology. These applications form a very challenging task since operating of EVs and HEVs demands large amounts of energy and power to ensure long range and high performance, whilst the battery cells must operate safely, reliably, and durably for a time scale of the order of a decade or more. Typically, a battery pack for an electric vehicle consists of a large number of the battery cells, physical packaging (including bus bars, casing and connectors), and Battery Management System (BMS). A BMS is composed of hardware and software controlling the charging-discharging states, guaranteeing reliable and safe operation. The BMS also handles additional operations, such as cell balancing and thermal management of the pack. The design of a sophisticated BMS is necessary to ensure long life and high performance because battery behaviour varies in time. Additionally, the BMS is crucial for safe usage because Li-ion batteries may explode or ignite if overcharged.
Fundamental physics-based mathematical models allow for highly accurate descriptions of the state of a battery cell; however, their complexity makes obtaining solutions computationally expensive (often prohibitively so). Alex's PhD is focused around using the tools of mathematical analysis to develop efficient numerical methods which by design preserve important structures of the governing model (system of differential equations) at the discrete level. Numerical methods that retain, for example, the correct level of energy dissipation across the system are crucial to accurately reflect the state of the cell. Efficient structure-preserving numerical methods could lead to the more widespread adoption of physics-based models in battery management systems and ultimately improve vehicle lifetime, performance, range, and safety.
The development of a modern powertrain system is a complex task that starts with the definition of the system level requirements. Once these requirements have been defined the physical design and manufacture of the system can begin. Today the system is then evaluated experimentally to verify that it meets all the design targets. This is a slow and laborious process, with only a limited capacity to study all the important use cases. In future, verification needs to be much faster and more robust. This PhD is focussed on the development of digital tools to speed up the process, combined with the intelligent use of experiments where these are necessary to give confidence that the system is fit for purpose.
An important focus of the research is the need for a better and faster way to verify our designs early in the development process, building on existing system modelling capability. AVL have significant expertise in the functional representation of the powertrain system in a systems modelling environment – SysML.SysML can be used to capture the capture functional, performance, and interface requirements of the powertrain as a way to evaluate system level interactions before detailed physical models of the components are available.
Lukas' research aims to use this expertise to accelerate the verification phase of the process, performing elements of the system verification in software that today are performed experimentally. Clearly not all of the system behaviours can be verified in this way, experiments will still be essential. Advanced experimental techniques developed by AVL will be used to allow a mix of physical and simulated components to be tested together in real time, bridging the gap between model based and experimental processes in a way that offers a highly robust and rapid verification process.
The methods developed during the PhD will be demonstrated by applying them to a battery electric vehicle concept – incorporating the system simulation, automated generation of test sequences and execution of these sequences on the state of the art experimental platforms with the new IAAPS research facility.
The aim of this research will be to investigate the benefits and trade-offs from the use of predictive, multi-objective control strategies for X-EV connected hybrid vehicles. Charlie will be applying significant rigour to identifying beneficial combinations of mobility system attributes and technologies to carry into more detailed problem definition, simulation and control function development culminating in the practical demonstration of one or more predictive control strategy. The project will be conducted in collaboration with IAAPS partner AVL.
As of the present, a limited but increasing number of automotive companies are bringing to market some form of look ahead, predictive functionality for powertrain management. The scope of these systems is generally somewhat limited, although broader, multi-objective approaches are beginning to emerge in research. There is, at present, an open research question around how the ideas of predictive controls, combined with the emergence of effective high bandwidth communication for vehicles , may be used to best effect in a real-world, multi-objective system.
Vehicle attributes for optimisation may include, without being restricted to: emissions, driving range and energy consumption, performance availability, system lifetime and health or financial costs. It is inevitable that the performance of a system with respect to one attribute will not be independent of others, resulting in a complex optimisation task. In addition, it is possible to assess the performance of a vehicle system over a variety of time horizons, from instantaneous through to whole-lifecycle performance. The advent of advanced connectivity in automotive and mobility as a whole will also increase the potential for impact of an individual vehicle on the wider fleet or infrastructure and vice-versa when control decisions are being made.
The battery cell is probably the most critical component of an EV, and key to the sustainability of future transportation solutions. Currently most battery testing is performed using very “clean” DC test currents whereas in reality, when used in a vehicle, the battery is subjected to current profiles with a lot of high-frequency (AC) components owing to the commutation (switching) of the inverter power electronics. Paramount in understanding how this affects its performance as part of a real powertrain system is the understanding of its electrochemical behaviour and processes.
The goal of this study is to investigate the influence that current ripple has on a Lithium-ion battery cell when it is applied on top of the DC current used to charge/discharge the cell.
Several studies have demonstrated that current ripples applied to cells can impact their performance (capacity, internal resistance, aging, etc) either positively or negatively. This PhD seeks to understand these phenomena in detail through experimentation and thermal-electro-chemical modelling of the cell behaviour, to predict the impact that any profile of current ripple might have on a particular type of battery. The research will have a strong experimental aspect to collect data from a range of battery cells, which in turn will directly support the theoretical investigations.
Howard's outcomes will inform best-practice for powertrain hardware design (inverters and filter capacitors) and software strategies implemented in the Battery Management System, as well as contribute to the understanding of how other techniques, such as battery self-heating using AC, might be applied in the future.
Ryan will develop a reduced order thermal model of a high-speed permanent magnet machine, forming a key part of a future digital twin. The model will allow for a variety of cooling methods such as air, water, and oil to be simulated. The proposed model will enable predictive and real-time estimation of the electric machine’s thermal behavior. Specifically, enabling the temperatures of physically difficult to measure components to be found, such as the rotor or magnets. By better understanding the temperature of key components within the machine in real-time and into the future the machine can be overloaded more often without damage. This will improve the power density of the machines and enable special test cycles to be performed that may otherwise have been thought to cause overheating.
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 project will be undertaken in collaboration with Koenigsegg Automotive AB and Freevalve AB on the novel cam-less engine technology, Freevalve, facilitating major efficiency and power improvements for future powertrains. It will enable the full exploitation of the technical potential of a camless engine and the reduction of harmful gaseous and particulate matter emissions, putting the technology in a market-leading position ready for large-scale implementation.
The supply of low carbon energy to a rapidly growing fleet of electric vehicles (EVs) presents major network constraint and energy supply challenges. From a network perspective, peak load increases resulting from uncontrolled EV charging could surpass the capacity at vulnerable points in the power network, thus requiring expensive grid upgrades. From an energy perspective, variable renewable energy generation does not always align with EV charging demand. As such, any excess renewable energy currently requires storage or curtailment which are both expensive for suppliers.
The smart charging of EVs can help to form a synergistic relationship between the transport and energy sectors, thus accelerating their decarbonisation. Smart charging exploits EV demand-side flexibility by shifting charging time or modulating charging power, subject to grid constraints and the vehicle owner’s needs. Efficient and practical smart charging algorithms require accurate quantification of EV flexibility at different scales to maximise the whole-system benefits.
Current attempts to model and optimise EV charging tend to make large and unrealistic assumptions about consumer travel and charging behaviour. In addition, the modelling of charging at a range of locations is understudied (e.g. domestic vs commercial setting). Studying the relationships between driving behaviour, charging behaviour and the energy system, over a range of spatial and temporal scales can reveal the value of flexibility. Furthermore, understanding how changes in EV charging behaviour and flexibility affect the energy system at local and regional level is critical for energy suppliers and network operators to allow fast and cheap integration of EVs and renewables into the grid.
Isaac's PhD will investigate the definition, quantification, aggregation and optimisation of EV flexibility, and their consequential system values to appropriately reward EV drivers, based on their level of flexibility. Spatiotemporal analysis of charging behaviour will be used to model charging demand in a granular detail with realistic assumptions, identify potential vulnerabilities in the distribution network, and assess the degree of misalignment with renewable energy. To fully realise the potential financial and environmental benefits, EV charging will be optimised over a range of spatial (e.g. single EV, EV cluster, street level, town/city level, regional level and national level) and temporal scales (e.g. hour, day, week, month), and against different weather conditions and local demographics. The outcome of this research will inform how charging optimisation and behaviour should evolve with increasing renewable penetration and changing mobility patterns, and the required upgrading in charging and electrical infrastructure.
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.
When employees pitch their radical business opportunities to resource holders, they are likely to use language in a way that is inconsistent with the current language around strategic priorities. This way of using language may mean that the resource holders imagine the meaning and image of the opportunity in a way that is inconsistent with what the employee intended. This miscommunication leads to inefficiencies in the evaluation and selection process of new venture projects and may lead the organization to miss on the exploration of new opportunities. The aim of this research project is to understand how this type of miscommunication is being or can be prevented through collaborative communication processes between the two parties. In addition, the development of a framework for facilitating internal conversations about radical business opportunities.
A smart and high power density charger is the key power electronics converter to overcome challenges such as range anxiety, slow charging for battery electric vehicles and plug-in hybrid electric vehicles. Wide bandgap (WBG) semiconductor devices, such as SiC and GaN, with fast switching transitions provide a solution to meet stringent automotive requirements for high power on-board chargers, while maintaining a compact size and lightweight design.
Constantinos' PhD will be investigating an innovative multi-level topology tailored for WBG devices. Multi-level topologies offer many advantages such as modularity, scalability, lower losses, limited voltage gradients, and higher AC voltage quality. The modularity of a multi-level topology also lends itself to higher fault tolerance, which is attractive in safety-critical applications. It is widely-accepted as the most promising topology for high voltage and high power applications. In automotive applications, this topology will enable the use of higher voltage DC bus systems and also help facilitate the penetration of low voltage WBG devices in these applications.
In an era where technology and transportation are so interlinked, new mobility concepts arise such as Mobility as a Service (MaaS). MaaS is expected to produce significant improvements in mobility such as the increase in the 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 the strengthening of cooperation and collaboration between public and private entities in order to reinforce the integration of transport modes in one platform accessible to everyone.
Despite being a well-known concept its implementation and subsequent effects have been not been widely explored when it comes to the connection between urban areas or even the linkage between urban and rural areas. Rita's research will be focused on those aspects of MaaS in order to assess its feasibility in these environments and making sure that the concept is design to respond to the citizen's needs while corresponding to the expectations of its implementation.
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. We are already 80% of the way to this threshold (Morice et al 2021, Met Office 2021). In the UK, road transport has reduced its carbon footprint less than other sectors since 1990, and larger vehicles are particularly problematic to decarbonise due to the huge infrastructure requirements for electrification, and the limited range provided by battery traction. Hydrogen fuel cells are a possible solution for powering larger road vehicles cleanly, as outlined in the Hydrogen Strategy of the UK Government (2021). However, about 95% of hydrogen is currently produced by steam methane reforming, 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 technologies or a narrow range of environmental indicators. There is also a need to consider the intersections between decisions made for road transport and competing uses of hydrogen for ammonia production and industrial processes, and domestic heating and cooking. This project is intended to fill these gaps and to assess the potential of hydrogen technologies to sustainably decarbonise large road vehicles. The methodologies will encompass:
consideration and inclusion of a broad range of new hydrogen technologies as they mature;
a wide range of environmental indicators;
real-world performance data rather than simulated or modelled data where possible, including analysis of purification requirements and minimising fugitive greenhouse gas emissions;
consequential Life Cycle Assessment (LCA) with an integrated tool to assist decision makers, such as multi-criteria decision making (MCDM), which considers competing uses of hydrogen in its analysis.
Julian's research project will produce as its outputs: a review of recent LCAs of hydrogen; a review of the most promising hydrogen technologies; a detailed consequential LCA of hydrogen production, storage and delivery (cradle to station); and an online decision support tool that shows costs and benefits (financial and environmental) for a range of hydrogen pathways under user-selected economic and technological scenarios.
Reaching Net Zero in the timescale required will require enormous behavioural changes. For the UK, the CCC estimates that as much of 62% of emission reductions depend on behaviour change for the bare minimum scenario. Therefore transformative changes are required. Between 20 to 40% of a population needs to adopt a new behaviour before it becomes a social norm, and a tipping point is reached whereby the remaining population rapidly adopts it too (Kaaronen & Strelkovskii, 2020). How can we reach these early adopters? How do we enable them to change their behaviour and speed up this tipping point?
Small and Medium Sized Enterprises (businesses of less than 250 staff) offer a unique opportunity in that they represent 99% of businesses in the EU. Businesses are under increasing security to reduce their scope 3 emissions, which includes their staff commutes. Some already appear to be increasingly open to demand reduction interventions like the Carbon Literacy Program. How can these demand reduction interventions be tailored to the requirements of SMEs? How do these interventions affect the pro-environmental behaviour of employees? Not just in the transport domain but other key areas such as heating choices, material consumption and diets? Do these behavioural changes influence other household members?
Nina's 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. Nina's 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  and formic acid .
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. 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.
Comprehending the intricate workings of a Proton Exchange Membrane Fuel Cell (PEMFC) is a multifaceted task, influenced by numerous internal and external variables. These encompass factors like temperature, humidity, pressure, material thickness, mechanical stress, and resistivity, each playing a significant role in shaping the PEMFC's performance. The importance of delving into these properties lies in our ability to replicate PEMFC behaviour within a virtual environment. By mastering these properties and their interrelationships, we gain the capacity to model PEMFCs in a simulated setting, enabling us to assess their performance across a spectrum of scenarios. This iterative process of simulation and analysis serves to refine our virtual models, closely mirroring real-world conditions. In practical terms, this approach expedites experimentation, reduces both time and financial investments, and accelerates advancements in the realm of PEMFCs. Ultimately, a profound understanding of how internal and external properties impact the lifespan of PEM fuel cells is pivotal. It not only enhances their adaptability for various applications but also streamlines experimental procedures, facilitating rapid progress and seamless integration into diverse practical contexts. This comprehension serves as the linchpin for the continued evolution of PEMFC technology.
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.
Thin ceramic films are hard to manufacture, but very important in energy conversion. Electrospraying (ES) is a versatile technique which has been used to dry, crystallize, and fabricate ultrathin layers of various materials. In ES, a high voltage is applied to a liquid precursor flowing through a nozzle, to create an aerosol of charged monodispersed nanodroplets. Drying air is fed into the drying chamber vaporising the droplets and forming solid particles with crystalline structures. ES allows (i) control over the degree of atomization of the feed, thus increasing the droplet surface area and extent of drying, (ii) control of the direction of the aerosolization jet and particle size deposition; (iii) ultrathin layer formation, controlled by the throughput of the aerosolization jet and voltage; and (iv) film self-healing behaviour when exposed to moisture.
In this project, we will exploit ES to manufacture ultrathin electrolyte and electrode layers for used in solid oxide fuel cells (SOFCs). SOFCs exhibit greater energy efficiency and can tolerate a far wider range of fuel materials compared to PEM. For this reason, they are increasingly being proposed as aviation and marine propulsion devices using zero carbon fuels such as ammonia. SOFCs are presently limited in performance by the ion conductivity of the solid electrolyte, and this would be much improved if a thinner electrolyte could be created. They are also challenging to manufacture due to sequential processing including multiple thermal steps.
Here, we will assess the benefits of electro-confined particle deposition at the fundamental level, as well as explore how the ES process affects the overall performance of SOFCs. At the University of Bath, we have demonstrated the capabilities of ES for polymorph control, and crystal formation in organic molecules; therefore, building on this established framework, this interdisciplinary PhD project - containing aspects of Chemical Engineering, Chemistry and Manufacturing - will further develop this technique to provide insights into the effects of electrical charges and confinement on the formation of ultrathin ceramic layers. We envision, that this PhD project will encompass the following activities:
Explore different ES formulations to generate ultrathin electrolyte/electrode layers.
Benchmark ES against conventional processes such as tape casting and screen printing.
Optimize ES process parameters for film formation
Develop methods for sequential deposition of electrode, electrolyte and support layers to create an SOFC cell.
Assess the performance of the whole fuel cell in SOFC energy converter applications to eliminate fossil fuel technologies.
Nanoporous materials used in adsorption applications play an important role in hydrogen purification and the storage and processing of low carbon fuels. However, numbering in the 100,000s, the enormous range of existing and hypothetical materials to be considered for a specific application makes standard, experimental and simulated screenings prohibitively expensive. Machine learning is emerging in materials screening but often the focus is on traditional machine-learning prediction, where a model is first trained using a very large number of simulations of the application of interest and then used to estimate properties of interest for all structures in the data set to identify the top performing new materials.
We have developed a new approach combining Bayesian optimisation/ active machine learning and molecular simulation, which allows us to identify the top performing materials of a database without having to calculate the performance of 100,000s of individual structures. One of the core attractions of this new methodology is that our model can make recommendations based on limited information, updating itself in-situ from molecular simulation of performance for a given application. We have successfully applied this approach to simple performance targets such as the uptake of hydrogen at a particular storage pressure or the separation of two simple gases. While impressive, this type of screening does not include important process parameters including the presence of impurities or kinetic separation effects which might mean that in practice a promising material identified through computational screening might not be as promising as thought. Another area that is beyond the scope of current screening approaches due to the computational effort required is optimising process conditions such as temperature or pressure ranges.
In this PhD project you will combine active machine learning techniques/Bayesian optimisation with molecular and process simulations to extend our screening approach to more realistic conditions for applications in the areas of hydrogen purification and low carbon fuels, the exact nature of which will be determined in discussions with an industrial partner. As our approach allows identifying promising materials out of a database of 100,000s materials by just conducting a few 100s – 1000s simulations, screening using more expensive simulations such as process simulations becomes more tractable. The overall aim of the PhD project is to integrate multi-scale modelling from the molecular scale to the process scale into the screening of porous materials for processing low carbon fuels, and to develop methods that combine the type of cheap molecular simulation that we already conduct with more complex, targeted simulations (or even experiments) to identify promising porous materials. This will include developing algorithms capable of choosing which simulations / experiments to run in order to discover the best material with the least effort. The project is suitable for engineers and scientists who are interested in modelling and machine learning and have good maths skills.Enquire now
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.
Green hydrogen is a renewable, zero-emissions fuel and in a fuel cell vehicle could replace fossil fuels combustion engines reducing carbon emissions. A major challenge is storage, where refilling a tank is difficult involving extreme pressures and temperatures which can be dangerous and inefficient. One way to solve this challenge is to swap out a fuel tank meaning it can be refilled more efficiently and safely remotely instead of manually. Will's project will look at what needs to be done to see what kind of benefits can be expected from this new approach and how well such a system would work when brought into practice.
As the automotive industry continues to de-carbonise, Fuel Cell vehicles provide a promising alternative to conventional ICE vehicles. Charaterising a fuel cell virtually is fundamental in unlocking performance and efficiency gains in its operation and development. Using data and specifications from the manufacturer to develop a theoretical model is often time consuming given the number of prarmeters and the level of fidelity desired in each use case. Therefore the aim of this work is to paramterise and develop fuel cell models and then to validate using experimental data gathered using efficient experimental methodology.
Depending on the use case, a variety of model types can be used which in turn will utilise varying structure/method to characterise the fuel cell. Definition of the parameters to valdate the model will be chosen and finally upon choice of use case, a methodology and can be selected to produce data in which to validate the developed model.
Alex's work will look into fuel cell model development and parameterisation process along with the expereimental methodology to validate such a model. The experimental procedure will be streamlined based on chosen parameterisation techniques.
The objective is to design a stator winding reconfiguration system which is commercially and technically attractive. The concept of reconfiguring the stator windings depending on the motor operating point is widely known to offer performance advantages in terms of motor efficiency and torque density for a given mass of rare earth magnets. From work carried out in the summer project it is also shown that there is potential to reduce the specification of power electronics.
Reconfiguration of windings between star and delta modes of operation is a common practice in industrial applications for start-up of induction machines, however commercial implementation of such systems for traction applications is almost non-existent owing to cost and packaging constraints. The objective is to develop and demonstrate such a system which could be commercially relevant in automotive traction motor applications, and to evaluate its efficacy.
The concept which will be developed within this project is based around a mechanical switching mechanism, as we believe this offers the only realistic route to achieve the cost requirement and make this technology commercially relevant for mainstream automotive applications. Previous work details semiconductor-based switching systems but are difficult to realise for under 200€ for the power electronics alone, not considering control circuitry or cooling. In contrast, the mechanical switching solution uses abundantly available materials and inexpensive manufacturing techniques and so has excellent potential for cost optimisation. To realise the full benefits of this concept, it is desired to be integrated into the motor packaging. This will ensure that the total package is optimised for volume and mass and is preferable to integration within the inverter because it avoids the need for 12 cables connecting the motor and inverter. Despite multiple patents in this area no commercial product has yet reached the market, highlighting the difficulty of achieving a practical solution. We believe one of the key factors in producing a practical design is reducing the number of switches, or more precisely the number of electrical contact faces, since these are ultimately what limits the package volume. A key area in which the proposed solution progresses the state-of-the-art is in reducing the number of electrical contact faces by making maximum use of double-throw switches. A full review of possible switching mechanisms will be conducted as part of the work, including electrical schematics and actuation mechanisms, with a view to minimising cost and volume. This work will ultimately analyse such systems with the aim of establishing a design methodology which can be applied to the general case, to analytically trade off volume with performance, ensuring the project outputs are widely applicable.
The internal combustion engine (ICE) has been the ‘silver bullet’ in powering machinery for the transportation, mining and construction industries. However, with existing and upcoming regulations on CO2 emissions, the industry is exploring the viability of fuelling ICEs with hydrogen as a carbon neutral alternative – notable examples include BMW, Toyota, Yamaha (now also rotary ICEs) and JCB.
Current hydrogen combustion research focuses on achieving high brake thermal efficiency (≥45%) while keeping NOx emissions levels low by utilising direct injection fuelling strategies. This results in increased volumetric efficiency and allows for a more precise control of abnormal combustion events compared to port fuel injection. Nevertheless, topics such as combustion irregularities, turbocharger design for hydrogen-specific operation, heat transfer and injection strategy optimisation remain underresearched.
Current structural batteries research, as part of ongoing AAPS/GKN PhD projects, has identified that current state of the art carbon fibre structural battery architectures need to be revised as using a layered approach places too much physical distance between anode and cathode which slows ion transfer and reduces battery performance. To overcome this issue two novel architectures are being considered. One seeks to intermingle anodes and cathodes (based on carbon fibres coated with battery materials) by decomposing tows of carbon fibres into thinner layers (tens of fibres thick) and the second looks to create ultrathin layers of electrospun batteries to act as veils in manufacture of non-crimp fabrics. Each requires a combined mechanical and chemical manufacturing process to be established together with prototype/proof of principle manufacturing systems. The full PhD will look to prototype the manufacturing process and use a combination of electrochemical and mechanical tests to demonstrate a working product.Enquire now
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.
The promotion and application of electric vehicles (EVs) is a vital strategy for many countries to achieve reduced carbon emissions and realise carbon neutrality by 2050 (Global EV Outlook 2021). As the power source of electric automotive, power batteries play a decisive role in the performance, driving range and lifespan of EVs. At present, lithium-ion (Li-ion) batteries are the most promising candidate to propel usage of EVs due to their high energy/power density, long cycle life, high stability and high energy efficiency. However, Li-ion batteries are sensitive to the operating temperatures. For instance, at temperatures > 35oC, side reactions inside the batteries are intensified, causing capacity fading and battery ageing. More seriously, thermal runaway incidents of EVs due to overheating of batteries are frequently reported, raising questions and attention in EVs’ safety. On the other hand, when the temperature is low, typically < 15oC, the discharge capacity is largely reduced due to the increased internal resistance and depressed reaction kinetics, leading to a much shorter driving range. In addition, the non-uniform temperature distribution will cause inconsistent electrochemical process and further reduce the battery pack capacity and cycle life. Therefore, an efficient battery thermal management system is essential to ensure the safety and performance of Li-ion batteries in EVs.
The main aim of Eymen's research is to design high-performing and safer Li-ion battery designs by using numerical modelling that can fully characterise the interactions between chemical reaction and thermal transport mechanisms of current and next-generation battery designs. The numerical models will be used to provide insights into thermal propagation, possible overpressure due to runaway chemical reaction and other associated risks in the enclosed systems. In addition, he will use the validated models to predict performances of new battery configurations and propose adequate safety measures to prevent disasters, with reduced physical testing. Such an approach is imperative for the design of safer and high capacity Li-ion batteries for EVs.
To achieve our aims, we proposed the following specific objectives:
Develop a series of robust and adaptable numerical models to assess the thermal and chemical stability of current and next-generation Li-ion batteries;
Assess the wide applicability of lumped heat transfer correlations to model thermal propagation of Li-on batteries;
Identify and design new approaches to maintain a stable and uniform temperature of the battery pack;
Design a unified thermal management system (air conditioning and battery) by energy integration;
Design safety measures that can offer prompt and powerful responses to critical thermal issues.
The future of private car use in towns and cities needs a rethink to respond to multiple and interdependent drivers of change including (in the UK): climate change legislation, rising cost of living and health inequalities. These effects, in combination with new technologies to support hybrid working and on-demand mobility, could be leveraged to exert a downward pressure on the incumbent system of private car ownership.
However, research into the psychology of car dependency shows that travel habits are hard to change and that, even when people state a desire to drive less - for example because of environmental concerns - they find it hard to change their behaviour. Many people are, or feel they are, locked-in to system of a car ownership through a complex range of social, economic and built environment/structural factors.
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 the initial research will be used to generate and test a range of scenarios for future car use, ownership and travel demand.
The hypothesis is that Millennials (now age 27-45) have become locked-in to car the system of car ownership (due to societal norms) but that Gen Z’s (now age 13-26) values, attitudes and behaviours towards driving make them more likely to choose not to own a car in the future. The results of researching the hypothesis will be used to develop and test scenarios where young people are supported to move away from individual car ownership. These scenarios could be used by politicians and community groups to design future local and national transport policies aimed at reducing car ownership. The scenarios will help to highlight the positive system change which could build local resilience and sustainability (e.g. to meet the 2030 Sustainable Development Goals). Methodology:
• Literature review focused on car use, ownership, system lock-in across different segments to identify underpinning theories and gaps in research knowledge;
• Statistical analysis of national data sets (NTS, Census, Household Survey etc) and also international longitudinal data e.g. German Socio-Economic panel to develop insights into attitudes and behaviours towards ownership (past/current and future projections);
• Qualitiative data collection through deliverative processes and focus groups targeting 16-26 age group (e.g. students/apprentices/new graduates to develop future scenarios.
• A final stage may be design of a laboratory experiment to test and validate scenarios;
The future scenarios could be used to make (top-down) policy recommendations for local/national governments as well as (bottom-up) community action to support young adults to choose not to own a car
Transportation is in transition, with new technologies such as electric vehicles, fuel cells, and hydrogen. But, for example with battery electric vehicles, the case for their environmental benefits rests on a bet -- that the negative impacts associated with producing the vehicles are outweighed by the benefits of reduced future impacts when the vehicles are driven. The negative impacts are relatively certain since they are happening now or in the near future. On the other hand, there is much greater uncertainty about the future benefits, since they are expected over the coming decade(s). Given the long time-scales involved, how should engineers be making decisions now about what technologies to develop and deploy?
One approach to answering this question is to use Life Cycle Assessment (LCA), but in its basic form this is based on historic data, which can be a poor representation of the future. An improved answer comes through the application of "Prospective LCA", which deals with the fact that, for example, the impacts of end-of-life recycling of batteries may be different in 20 years' time than it would be if it happened today, due to increased renewable energy supply in the future. But this still assumes that the vehicle will be used as intended over its lifetime, and successfully recycled in the expected way. This greater uncertainty in future benefits and impacts is not currently modelled in LCA of vehicles, making it difficult to know how much trust to place in the results during the design and decision-making process.
In this project you will build on current cutting-edge prospective LCA to improve the treatment of future uncertainty within these models, and apply this to design choices within future vehicles. Engagement with an industrial partner would allow the value of different ways of assessing and presenting future uncertainty to be evaluated, and linked to specific engineering decisions. You will gain experience of the theory of industrial ecology and life cycle assessment, and uncertainty and sensitivity analysis, set against the wider context of sustainable transport and the future of battery electric vehicles in particular. Practically, you will work with tools such as Python and Brightway2 to implement LCA calculations, Monte Carlo uncertainty simulations, and sensitivity analysis.
Sealing has been identified as the most cost-effective method of enhancing engine efficiency and performance, critical to reaching net-zero propulsion. Hybrid-electric propulsion systems, ultra-high bypass ratio engines, geared turbofans and NASA N+3 concepts all require new sealing and clearance control solutions. Brush seals are one type of seal typically used in gas turbines and electric motors to prevent parasitic leakages that result in loss of power delivery and an increase in specific fuel consumption. Labyrinth seals are most common in turbomachinery, however, the development limits for this type of seal have been reached. Brush seals typically provide an order of magnitude improvement in leakage reduction compared to a labyrinth seal, while better accommodating radial rotor excursions. However, well-known drawbacks such as excessive wear have prevented widespread application of brush seals to aerospace jet engines in particular.
Generally, gas turbine research has been undertaken using turbine-based rigs running close to engine-operating conditions. The approach at Bath is to conduct more fundamental work to measure, to compute and, most importantly, to understand the flow and heat transfer using generic, fully-instrumented experiments specifically designed for instrumentation access. The understanding obtained from these fundamental investigations assists the interpretation of results obtained for more specific engine conditions and geometries, and this in turn informs their design.
Taif's PhD project will make use of a novel experimental facility designed to produce unique measurements that give unparallel insight into brush seal operation. A fluid dynamically scaled brush seal model will be tested to determine fundamental flow characteristics that are currently lacking from the literature. This increased understanding in brush seal behaviour will inform the design of future industrial hardware to be used in aerospace and electric propulsion applications.
This research project is related to green technology and sustainability, and will be conducted in collaboration with Cross Manufacturing Ltd., a leading supplier of brush seal components to the aerospace and power generation industries. Cross will support the project through their expertise designing, testing and manufacturing brush seals for the past 40 years. Taif will work closely with the company to perform the experiments and ensure the results are implemented into in-house design codes. There is also an expectation that the research is disseminated through publications and presented at the annual ASME Turbo Expo conference.
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 now
Electrification for commercial vehicle (CEV) fleets shows an opportunity to cut emissions, reduce fuel costs, and improve operational metrics. However, infrastructure limitations often inhibit the ability to charge a considerable number of CEVs, particularly all fleets in one designated area.
The use of renewable energy resources such as solar photovoltaic (PV) is a promising energy cost-saving selection for large-scale (commercial) charging stations like depots for commercial electric vehicles (CEVs) to deliver goods and services around the country. However, the intermittency of power output renders it challenging to realise reliable and economical operations. This project proposes a multi-stage hierarchical operation energy management technique of CEVs for depots with a hybrid power generation facility such as on-site PV-grid-tied. In the predictive mode like a day-ahead stage, considering real road networks and battery swapping mode; a coordinated scheduling model of delivery service and charging and discharging for CEVs will be developed to minimise the overall energy and logistics delivery costs considering the predicted PV power generation. Additionally, an optimal solution approach based on digital systems, optimisation, and integration tools like the natural aggregation algorithm can be developed for the software. In the actual operation stage, a model predictive control (MPC)-based rolling horizon operation scenario will be provided to improve CEVs’ charging and discharging decisions with the realisation of real-time PV power output, aiming to minimise the deviation in the actual and day-ahead scheduled interactive power between the depot and the main grid. The project’s main outcome will be a cost-effective software which monitors in real-time and manages the power generation/delivery and charging solutions for CEVs.
In summary, the project’s main tasks are:
1. Developing a hybrid PV-grid-tied microgrid for CEV depots.
2. Equipping the microgrid with prediction algorithms to forecast the PV power generations during the day.
3. Developing an energy management unit to improve the power supply/demand daily.
4. Real-time monitoring of CEVs for daily routes and corresponding charging plans of each CEV.
5. Offering cost-effective software oversees the power network as-a-whole from supply to demand (each CEV) for power reliability and availability assessments.
6. A techno-economic analysis will be carried out to study and better understand the economical benefits for the UK CEVs using the proposed technology.Enquire now
Lithium-ion batteries are deployed in a wide range of applications due to their low and falling costs, high energy densities and long lifetimes. Accurate prediction of lifetime would unlock new opportunities in battery use and optimization. An's research will apply both physics-based electrochemical-thermal battery model and data-driven neural network model to predict battery degradation.
The physics-based model is based on our in-house battery model, LionPower [Yuan et al., Int. J. Heat Mass Transf., 2021], which is developed for accurate and efficient modelling of electrified propulsion and has advantages over existing models including LIONSIMBA, GT-AutoLion, and PyBaMM in terms of accuracy and computational efficiency. Theoretical degradation models involving the loss of lithium-ions and other active materials will be integrated into LionPower to predict battery degradation represented by State of Health (SOH) and Remaining Useful Life (RUL). Meanwhile, advanced discretisation schemes and efficient numerical solvers will be investigated to further improve the performance of the physics-based battery degradation model.
The data-driven neural network model relies on published battery degradation datasets in the literature, which basically uses non-linear functions to emulate the physical processes of battery degradation. Novel structures of neural networks will be proposed to correlate charging parameters and/or curves with SOH and RUL. Besides, neural networks will be applied to predict critical properties, e.g., SEI thickness, electrode particle cracking, lithium plating, etc., during battery degradation to obviate the need for solving coupled PDEs and thus improve computational efficiency, and the prediction can be fed into the physics-based model for accurate estimation of SOH and RUL.
The output of this research will be a comprehensive battery degradation model that can be directly applied to electrified propulsion systems.
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 now
Gas 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.
To what extent do epistemic emotions (e.g., awe, curiosity, surprise, confusion) account for people’s willingness to adopt e-bikes? This psychology project will track people who try a pedal assist e-bike for the first time (e.g., test-ride in a bike shop or a govt. subsidised free e-bike loan scheme). Ruth will measure a range of psychological and environmental variables and quantify which of those variables are most important for successful e-bike adoption.
In the main study for Ruth's project epistemic emotions and lease/purchase intentions will be measured before the test ride, directly after and then 3, 6 and 12 months after the e-bike trial. We anticipate that curiosity measured at the start of the study will be most strongly predictive of intentions to trial the bike. Another emotion, awe (e.g., at how the technology looks and works like a normal bike but allows to climb hills), will increase following trying out the bike, and will predict lease/purchase intentions and behaviour.
In addition to tracking the emotions that people feel while testing an e-bike we will also look at relevant demographic and attitudinal variables such as age, SES, mobility issues, cycling needs, infrastructure, perception of health and environmental benefits and cost saving (e.g., parking fees) as well as perceptions of social attitudes about e-bikes (e.g., e-bike shaming).
Ruth will help evaluate the role that trial schemes play in the adoption of e-bikes and will provide us with insights as to the conditions that need to be fulfilled for such schemes to be successful.
Traffic congestion in the main roads and junctions of our cities is a world-wide problem incurring huge financial losses to the global economy as well as other negative environmental and health effects. We propose to study an algorithmic and economic / game theoretic design of a smart traffic management system to dynamically schedule traffic based on real-time traffic conditions and value of time reports from drivers, aiming to minimize the economic damage of traffic congestion.
Many cities in the UK have various types of static congestion charges where ‘static’ refers to the fee being charge, to the hourly/daily limitations, and to the zone itself. Economically, this could be highly inefficient as various parts of the city could have different congestion patterns that a static fee for example could only partially resolve. A too-low fee will be ineffective while a too-high fee will significantly damage economic and social activities unnecessarily.
Yue will study the possibility of designing a system to dynamically set congestion fees and route traffic based on multiple factors. The proposed system will be composed of three parts: (1) an information module to disseminate knowledge about traffic across the network, enabling each road and junction to obtain a probabilistic forecast about the future arrival of cars; (2) a traffic light scheduling algorithm that, given this information and the information on the value of time of drivers arriving to junction, will decide on the green/red light schedule in order to minimize the aggregate cost of waiting in the junction; and (3) a payment scheme for charging payments from drivers, ensuring that drivers will not exaggerate (nor underestimate) their value of time reports. These payments will effectively create a dynamic toll system that will more efficiently ensure that drivers use roads in an economically efficient way, replacing today’s static toll systems.
Such a dynamic system is advantageous since the resulting tolls depend on the actual route being taken, and drivers’ payments increase when using more congested junctions. Yue's research is to design the algorithmic and economic theory behind such a system, and to evaluate the obtained solutions via computer simulations. The proposed research will rely on a variety of techniques: methods of planning and scheduling from artificial intelligence, algorithmic methods from theoretical computer science, and methods for the design of incentive-compatible (“truthful”) mechanisms from game theory. Such an interdisciplinary interaction will benefit all these disciplines, as it will introduce new questions, new answers, and new tools and techniques, to all of them.
In electric vehicle motors, the most critical temperature-rise occur in the winding (active) of the electrical machines. To improve the thermal capability of the traction electrical machines, additive manufacturing approaches can be used for modularisation of active parts in electrical machines, such as windings (active) and non-active parts, such as shaft, inner rotor core, and frame, to become denser and cooler. The use of additive manufacturing will allow increasing the slot fill factor which can improve the power and torque production significantly. Better thermal capability and an ability to discretely alter power output. From the manufacturing perspective of these newly designed parts, assembly is however more complex and manufacturing tolerances have crucial effect on electromagnetic and mechanical performance through the introduction of sophisticated geometries and designs. Electrical machines with a multi-functional modular structure need more individual components than standard designs, are dependent on the tolerance between components and require more manufacturing steps. In this research, the main aim is to develop new additive manufactured coils where they advantage from embedded cooling ducts via heat exchangers to reduce the steady-state and transient temperature distributions in the machine. In this work, we will offer multi-functional additive manufactured winding and tooth models to increase the power capability of the machine. The outcome of this study will lead into more powerful and less volume and lighter electrical machines. To enable more precise solutions informed by manufacturing processes. A range of electric machine topologies will be considered. Furthermore, we will study hybrid materials in order to develop and classify versatile modular structures which offer improved mechanical integrity, electromagnetic performance and easier assembly. The new designs will be prototyped at Renishaw and will be experimentally tested under different conditions. Note that non-active parts of the electric machine refer to the iron/steel parts where the part electromagnetically does not contribute to the total power and torque in the machine.
The outcome of this work will improve the overall power density of the electric machines via newly developed multi-functional (active) parts. The main objective of this study focuses on innovative designs and manufacturing high performance coils to be used in benchmark traction electric machines. The research team will also investigate the possibility to employ hybrid parts where new materials (e.g. silver) can be modelled for additional functionalities into the active parts of the electric machines.
In summary, the project’s main tasks are:
Objective 1: High performance coil (active) and iron parts (non-active) design state-of-the-art for electric machines. We will also study and select some benchmark electric machine for this project.
Objective 2: The selected benchmark electric machines and their winding designs will be assessed. Investigating the alternative materials and new geometries for the same machines will be done
Objective 3: New coil (active) and iron part (non-active) deigns will be studied using hybrid and potentially new materials. The new multi-functional coils will be simulated using multi-physics high-fidelity methods to analyse electromagnetic, thermal, and mechanical analyses of the developed high performance coils.
Objective 4: The cooling flow through the new coil design will be also investigated using 3D CFD modelling to ensure the duct design and manufacturing feasibility of the novel geometries. This research team will be liaising with Renishaw team to make sure the new geometries are feasible and manufacturable.
Objective 5: Manufacturing the developed novel coil and non-active parts with different alternatives, such as single pure copper powdered and hybrid like copper and silver materials. The new deigns will be prototyped for some experimental tests.Enquire now
New hydrogen storage technology is required to improve volumetric and gravimetric storage density. The USDoE has set an ultimate target of 6.5 wt.% and 50 kgH2/m3 gravimetric and volumetric storage densities respectively.1 At present, only chemical hydrogen storage methods can approach this storage capacity under non-severe conditions: for example, dibenzyl toluene, an organic hydrogen carrier, has a storage capacity of 57 kgH2/m3 when fully hydrogenated. In comparison, pure hydrogen must be either stored at 700 bar or cryogenic condensed liquids which are highly energy consuming.
Liquid organic hydrogen carriers (LOHCs) bring a tremendous advantage in that they can be stored at low pressure and ambient temperature, much like conventional hydrocarbon fuels.2 Their drawbacks relate to chemical stability, the energy efficiency through multiple hydrogen storage and release cycles (they must be regenerated off-board), and the use of precious metals in the catalytic conversion. It is these challenges that this project will aim to address.
LOHC systems cycle between a H2 rich state, storing H2 through catalytic hydrogenation, and a H2 lean state releasing H2 via catalytic dehydrogenation. An ideal LOHC would be inherently safe to transport using fossil fuel infrastructure (low viscosity), have low cost and a highly reversible hydrogenation/dehydrogenation. 1st generation LOHCs such as benzene and toluene are petrochemically derived, and their potential was recently realised by establishing a 4,000 km H2 supply chain between Brunei and Japan. 2nd generation LOHCs are based around alcohol and amine mixtures which can be coupled using an appropriate catalyst to release molecular H2 and are sustainably derived from biomass. In this project, you will assess a range of existing and proposed technologies in terms of their potential energy integration into on board propulsion systems directly or as H2 transport vectors to re-fuelling stations. Based on the outcomes of this study you will then drive the development of a prototype delivery/transport system to efficiently store hydrogen at one location in a safe to handle LOHC and release at another location or into a PEM fuel cell at from this H2-rich LOHC to generate electricity.
The project will involve technological assessment of current and emerging technologies, the development and assessment of catalyst materials and integration into a small scale device.
The research is divided into three work packages to address these challenges:
1) Analysis of existing and proposed technologies as part of automotive propulsion systems from and energy integration approach.
2) Development of catalytic technologies to reduce the amounts of precious metals required to cycle these LOHC molecules between H2 rich and lean states.
3) Development of a prototype H2 delivery system.Enquire now
The next generation of aero-engines will be net-zero and compatible with synthetic fuel and hydrogen. This creates a unique problem for the engine designer, as the dimensions of the core architecture will be radically reduced. The resulting reduction in the height of the compressor blades leads to the requirement to control blade tip clearances to significantly tighter tolerances. The blade tip clearance is controlled by the radial growth of the compressor discs, which is strongly affected by the temperature distribution and in turn the heat transfer in rotating cavities. The aircraft operating conditions change for take-off, cruise and landing, and so the prediction of both steady-state and transient heat transfer and disc temperatures is vital if these engines are to operate with future fuels. The aim of this project is therefore to make experimental heat transfer measurements using the Compressor Cavity Rig at the University of Bath, and to use the data generated to validate theoretical models, which in turn will be used in the engine design process.
The direct impact of the work will be to create new thermo-mechanical models at Rolls-Royce. These practical design codes will predict the behaviour of new engine architectures over a range flight cycles, including aborted landing. The models require both empirical data from experiments and information from more theoretical flow physics and fundamental heat transfer. Essential to the success of the impact from the project is the close collaboration between the academic team and engineers at the company. This link is well established and facilitated through the in-kind support offered by Rolls-Royce.
There is a strong academic link to the University of Surrey Rolls-Royce University Technology Centre (UTC) for Thermo-Fluid Systems, which includes world-leading expertise in high fidelity Computational Fluid Dynamics (CFD). This takes place through quarterly review meetings and workshops with the Surrey UTC and Rolls-Royce teams in the UK, USA and Germany. There is also a strong link to the University of Oxford Department of Atmospheric Physics, who explore similar rotating phenomena at a different scale relevant to weather systems. Within Bath we collaborate with the Department of Mathematical Sciences on machine learning and statistical modelling to explore new data methods and analysis, which are also of key interest to Rolls-Royce. This project therefore offers a multidisciplinary opportunity to bring new insight into the design of next generation aircraft.
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 now
Ammonia 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 now
Small and large firms regularly decide to pivot, altering some aspect of their core technology, products, or services. Pivoting implies making a fundamental shift in a company strategy which requires a reorganisation or rearrangement of activities, resources, and attention. Different motivations might drive reconsidering the strategic direction.
Arash's research project studies how different drives (including changes in the market and in the technological or regulatory environment) shape companies’ pivoting. As transportation is one of the most significant sources of emissions, decarbonising mobility is providing growing incentives for firms to pivot towards adjacent markets. While the demand for Net Zero mobility solutions and the availability of new technologies offer new opportunities for firms, they also create technological and business challenges.
As pivoting involves continuity and change, this research project aims to understand how firms leverage existing resources, organisational capabilities and technological competencies and contextually acquire new ones to pivot towards new-zero mobility applications. The project will, among others, explore how firms access funding and acquire new knowledge (e.g., through university or industry collaborations). It aims to contribute to the academic literature on pivoting while providing practical recommendations to companies to meet new market opportunities or regulatory requirements. We also aim to derive policy implications and recommendations.
Aviation industry faces ambitious environmental emissions and noise reduction targets to reduce the environmental impacts of air travel. Electrification of aviation will play a key role in delivering these targets. Liquid nitrogen as the fuel source not only eliminate carbon emissions in flight but also provides cryogenic environment and open new opportunities for superconducting electrical power system. Superconducting electric powertrain has great potential to achieve high power density and high efficiency for aviation applications.
The project aims to explore and design superconducting and cryogenic electric powertrain for hydrogen-powered electric aircraft. This project will focus on investigating the network architecture, control and protection of superconducting electric powertrain. Different network and powertrain architectures will be evaluated and compared. The experimental verification will be carried out at Bath’s Applied Superconductivity Laboratory and IAAPS facilities.
The research project is well aligned with IAAPS’s strategic area of net zero aviation. This project provides key enabling technology for cryogenic and superconducting network for electric aircraft, which has direct impact on aviation industry. For example, Airbus aims to develop the world’s first zero-emission commercial aircraft by 2035 and has initiated the Advanced Superconducting and Cryogenic Experimental powertraiN Demonstrator (ASCEND). GKN Aerospace leads a UK collaboration programme, H2GEAR, which aims to develop a liquid hydrogen propulsion system for sub-regional aircraft that could be scaled up to larger aircraft.Enquire now
Transport electrification plays a key role in achieving emission reduction targets. Dynamometers, as highly dynamic load systems, are critical for testing powertrains and electric motor. There are two common types of dynamometers: Wheel dynamometers are used at low speeds of 0-3000 rpm, but with a high torque of up to 3000 Nm, whilst E-motor dynamometers are used to test e-motors at high speeds, up to 25krpm. Traditionally, a machine that can run at 25krpm will require a rotor too small in diameter to be able to generate the torque required to be used in the wheel dynamometer application.
High temperature superconductors offer high current density and high efficiency, and could potentially be employed to enable the design of a dynamometer that is both a wheel dynamometer and an e-motor dynamometer. A high temperature superconducting dynamometer could also have the advantage of being a very low inertia machine, which would be useful for dynamic testing.
Mark's PhD project aims to explore the feasibility to design a high temperature superconducting dynamometer, capable of functioning as both a wheel and E-motor dynamometer. The project will investigate the feasibility of the rotor using high temperature superconductor. The project will focus on electromagnetic analysis and thermal analysis to evaluate its feasibility. The cooling mechanisms will also need to be considered. Mark's research has a strong application focus, and will involve experimental work to understand dynamometers and superconductor characteristics. This work will take place at the University of Bath Applied Superconductivity Laboratory, IAAPS Ltd. in Bristol, AVL in Graz (Austria), or a combination of these locations as appropriate.
Indrek's research project aims to explore the potential of methanol as an internal combustion engine fuel, capitalizing on its distinct properties to achieve a dual objective of reduced emissions and enhanced efficiency/performance, particularly in the context of marine applications. The project will be undertaken in two stages:
Stage 1: Experimental Conversion and Testing
Taking a 1-cylinder petrol engine, conducting rigorous testing, and subsequently converting it to operate on methanol. This conversion is expected to leverage the specific properties of methanol to boost engine efficiency and concurrently mitigate emissions.
Stage 2: Development and Assessment of Specialized Methanol Combustion Engine
Building upon the insights and expertise garnered during the initial phase, to design of a specialized methanol combustion engine. The ultimate goal is to evaluate whether the additional time and resources invested in developing a new engine, optimized for methanol's unique characteristics, yield superior advantages compared to retrofitting a petrol engine for methanol use.
Indrek's research not only investigates how methanol can work in current engines but also looks into creating entirely new engines tailored for methanol use. The ultimate objective is to understand how methanol’s properties can improve efficiency and cut emissions in marine propulsion, which aligns with the UK's net-zero targets by 2050. Initially focusing on smaller vessels will lay the groundwork before potentially scaling up to larger marine applications. The project also emphasizes creating and testing simulation models to explore different engine concepts and their optimization capabilities based on actual experimental data and detailed computer simulations.
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.
Dmitry'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, Dmitry's 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 has the potential play a crucial role in almost all autonomous driving and 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.
Sam's 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 focuses on two critical aspects: vehicle perception and object tracking. This is the 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 follow the vehicle over time.
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 on 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  while business air travel accounted for 154 million Mt CO2 globally in 2019 .
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.
Lucia is interested in identifying which factors contribute to making a policy to change the behaviour of employees. To do so I 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. I expect 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 wants to study whether there are better ways to engage more people to think 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 will research how these new transport policies have been performing, looking specifically at the West Midlands area. Pete will then use social science methods, like surveys and interviews, to understand who is interested in shifting away from car ownership and why. Finally, he will use insights from social psychology 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 research will make an 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.