Our Projects

The automotive industry is under increasing pressure to reduce its environmental impact and contribute to global net-zero goals. Alongside technological changes, such as the shift towards electric vehicles, there is growing attention to Artificial Intelligence (AI) as a potential solution to help reduce emissions. AI is being promoted as a tool for making transport more efficient, reducing waste in production, and supporting greener mobility options. However, while many promises are made about AI’s contribution to net-zero, much less is known about how these claims are communicated to the public and stakeholders, and how this discourse has evolved over time.

This PhD project explores how the automotive industry talks about AI in the context of net-zero and investigates how AI is framed as part of the climate transition. It focuses on how firms, media, and scientific actors construct meaning around AI and sustainability, how they use communication to build legitimacy, and how these narratives shift across time and between actor groups.

The project consists of three academic papers. The first paper explores how automotive firms frame AI within their sustainability narratives, comparing framing strategies across different types of firms (e.g., legacy vs. new entrants, large vs. small, regional differences). The second paper focuses on how media outlets amplify, challenge, or reshape these narratives, looking at differences between general media, industry media, and corporate-affiliated outlets. The third paper looks at the role of scholars, investigating how academic discourse aligns with or diverges from industry and media narratives, and how scientific framing of AI in sustainability contexts evolves over time.

The thesis will conclude with a synthesis chapter that integrates findings across all papers, providing a cross-actor reflection on framing dynamics. It will explore how different actors influence each other’s language over time, which frames become dominant or marginalized, and what this means for the future direction of AI in net-zero transitions. This integrative analysis will offer broader insights into discursive power relations. It will highlight implications for responsible innovation and sustainability governance in the automotive sector.

Berat will investigate a promising solution to increasing emission concerns in air transport is electric urban air mobility (UAM) vehicles, which can provide new mobility models offering zero carbon footprint and be a mainstream mode of air transport in the next decades. However, UAMs cannot offer a ride experience as smooth as that of passenger jets since they will experience complex flight regimes analogous to those of helicopters, such as hover to forward flight transition and high-speed cruise, resulting dynamical loads transferred to the passenger cabin. The dynamical loads are felt by the occupants as oscillations and vibrations, which can lead to an uncomfortable ride. This is deemed to be critical in UAMs since they will be dominantly used in human transport and their market acceptance depends on the passenger comfort perception. Therefore, UAMs are expected to face various comfort related problems, which needs to be addressed using novel tools and methods.

​Lithium-ion batteries (LIB) have become core technology for energy storage and electric vehicle applications due to key advantages like high energy density, long cycle life, and low self-discharge rates. However, they inevitably degrade over time due to irreversible physical and chemical changes, ultimately leading to the end of their usable life. An accurate and comprehensive degradation model would unlock new opportunities for battery use and optimization. This research will apply a physics-based electrochemical-thermal battery degradation model coupled a data-driven neural network model to predict the State of Health (SOH), which refers to the battery’s current capacity or performance relative to its original condition, and the Remaining Useful Life (RUL), which estimates the amount of time the battery can continue to function effectively before it reaches its end of life.

​The recent drive to decarbonise transport is leading to a rapid increase in the number of electric vehicles (EVs). High concentrations of EVs could put excess strain on certain parts of the electrical distribution network, thus requiring expensive grid upgrades. In addition, new public charging infrastructure will need to be installed to meet future demand. However, EV charging demand can be flexible, allowing for the shifting of charging time and power to better align with times of high renewable generation and to perform grid services such as load shifting and frequency response. Understanding where, when and how EVs charge as well as quantifying the flexibility of that demand is imperative in informing the future infrastructure rollout strategy of distribution network operators and local councils to enable cheaper and faster integration of EVs and renewable generation into the grid, thus accelerating the decarbonisation of the automotive and power sectors.

Isaac's PhD research aims to develop high-fidelity spatiotemporal models that leverage data-driven, Bayesian, and agent-based techniques to forecast EV adoption, simulate EV charging behaviour, and quantify the potential impacts of EV charging. These models could be used to identify potential vulnerabilities in the distribution network, optimal locations for new public charging infrastructure, and provide estimates for the financial and carbon savings that smart charging can offer.

Classifying road users based on their characteristics allows researchers and policy makers to make general distinctions between different types of road users who may have different needs. The classification vulnerable road user (VRU) is frequently used to describe road users who are not protected by the frame of a vehicle and considered to be high-risk (e.g., motorcyclists, cyclists, or pedestrians). However, there is a body of research challenging this classification, highlighting the emphasis it places on road users as ‘vulnerable’ rather than the vulnerabilities caused by external factors, such as infrastructure design and the behaviour of other road-users. Although this critique is not new, the issues identified in this critical literature are not coherently addressed in empirical work involving these road users that uses the VRU construct and forms assumptions based on it.

This PhD research will explore alternative approaches to categorising road users, particularly those considered vulnerable, and using these categories to understand road-user interactions through modelling and analysis. The aim of the project is to develop a theory to understand road user vulnerability from a complex systems perspective. The project comprises three phases: a theoretical phase to develop the conceptual foundations for the approach, an experimental phase to identify important road user characteristics, and a modelling phase, to verify and iterate the theory developed in phase two and explore relevant metrics and investigate the dynamics of how vulnerability changes as a function of its component parts.

The first phase will examine how the VRU classification is understood across disciplines, explore VRU behaviour and power differentials on the road, and evaluate how the classification is currently operationalised in modelling and analysis. This will involve conducting a scoping literature review, The second phase will employ mixed methods and use secondary census and transport use data, route planning analysis, and photo ethnography to understand the factors that can act as ‘hinge points’, where VRUs change their route or mode of transport. A subsequent study will systematically analyse existing approaches used to interpret VRU behaviours and intentions in transport modelling and predictive systems and how the factors identified in study one are incorporated. This initial work provides a foundation to understand the state of the art, identify important factors that influence VRU behaviour, and identify priorities and metrics to be used in modelling. The aim of this phase is to analyse the processes and power dynamics underlying road user behaviours in a way that can be implemented in empirical modelling methodologies.

The third phase of this research will focus on theory development and simulation. Here, the theoretical processes identified in phase 2 will be formalised into causal models outlining the expected relationships between different elements of vulnerability and their link to behaviour. These models will then be tested and iteratively refined through agent based modelling. The aim of this phase is to explore the validity of the theory proposed and identify potential tipping points or critical factors that can influence road user behaviour based on changes in vulnerability.

As a result of increased electrification within the automotive industry and energy sector, the demands from electric machines have never been greater. Therefore, reducing the cost and size of these machines whilst maximising their power capabilities is crucial. A prime opportunity to achieve these targets is through accurate real-time thermal modelling of key motor components, such as the end windings and permanent magnets. Such a model would enable the measurement of difficult or inaccessible locations within the motor, without the need for expensive sensors. Therefore, the power density of a given machine could be increased as the large safety margin, which exists due to temperature uncertainties within the machine, could be reduced. Additionally, this could enable electric machines to be downsized, whilst still meeting the required power ratings for a given application.

The motivation for this project is broadly to address the aforementioned benefits a real-time thermal model could enable; however, it is more precisely motivated by the current lack of agreement, robustness, and implementation of methods currently proposed within the literature. Comprehensive reviews of the topic outline drawbacks and benefits to many different modelling techniques, including machine learning, reduced order thermal networks, state observers, and hybrid approaches. Although no single method is yet to prevail as a favourite, most methods revolve around lumped capacitance modelling.  

Furthermore, testing found within the literature is based on datasets in strict laboratory conditions, often with simplistic test cycles. This brings into question the models’ robustness and feasibility if implemented onto a real-world system, namely in automotive contexts. This is further reinforced as many publications rely on a pre-recorded experimental dataset for machine learning, parametrisation, and testing. Finally, this experimental dataset, much like many others in the literature, uses a relatively low speed (6000 rpm) liquid cooled motor, hence internal phenomena resulting from high-speed motors may have not been captured and other cooling methods are not understood.

The scientific impact of this project will be to enable downsizing of electric machines in testing and automotive applications. These machines are currently restricted by large thermal safety margins due to temperature uncertainties within the motor. Additionally, by modelling the temperature within the machine, the cost of test bed systems can be reduced by circumventing the requirement for expensive sensors in poorly accessible locations (e.g., rotor magnets). Finally, Ryan's project seeks to increase the flexibility of modelling machines with differing geometry and cooling systems, reducing the cost and time associated with parameterising the model, whilst delivering reliable and accurate results.

Petrol/diesel is not the only fuel we can combust to propel vehicles. Have you considered grey hydrogen? This is a gas that is generated by the carbon-intensive process of 'steam-methane reformation'. The main problem is that when you make grey hydrogen, it's in a mixture of gases that we need to separate.

Producing blue hydrogen requires the further step of carbon capture, usage and storage (CCUS) - this is something the government is investing £1 billion up to 2025.

There are many porous materials that can separate and store these gases: so many that we would spend all our time testing them to find the few suitable ones. However, we can use computational algorithms to search smartly through huge databases and find the best ones. This will save us time, but how valid is our screening process? In other words, will the computer's selection of the best materials reflect their performance in real life?

UK surface transport emissions have overshot the sixth carbon budget by 224 MtC, requiring emission cuts ten times those saved during the COVID-19 pandemic. Improvements in the quantity, quality, and speed of Travel Demand Management (TDM) implementation is urgently needed if the UK is to meet its target to reduce car miles by 9% before 2035. Employers can act as a bridge between their employees and broader transport policy goals by providing a social context and communicating norms. Employers control the physical and social environments of their employees, shaping their employees’ modal choice. Workplace Travel Plans (WTPs) are long-term TDM strategies developed by organisations to manage their staff’s commutes. They are flexible, cost-effective, (relatively) rapid, and publicly acceptable.

Only 11% of organisations in the private sector currently have a WTP and this must increase to 56%, all else equal, if the UK is to achieve target its Net Zero targets of reducing car miles by 9% before 2035. WTPs can be secured from large employers using the planning process, but not from SMEs, who employ 61% of the UK population. At SMEs the motivations of one or two key decision-makers in SMEs are crucial in the decision to adopt innovations – yet we know very little about what drives their voluntary adoption. Behaviour change interventions could target these key decision-makers to adopt a WTP, catalysing widespread change in travel behaviour.

This thesis focuses on three problem areas in achieving scalable demand mitigation; what is the opportunity for SMEs to contribute to modal shift? What factors can explain voluntary workplace travel plan adoption? Under what conditions could a tipping point in the adoption of workplace travel plans be created? By addressing these questions, this research aims to contribute to achieving a social tipping point in mobility behaviour, ultimately accelerating the transition to sustainable mobility behaviours.

Paloma's PhD will investigate the multifunctional performance of structural batteries. The PhD will focus on the use of synchrotron techniques to measure fibre scale mechanical properties of both anodes and cathodes during charge cycling, accumulation of microscale damage and understanding of ion intercalation patterns within the anode. Paloma will progress to understand similar properties under axial fatigue loading.

Structural batteries combine the load bearing and electrochemical storage capabilities of carbon fibres (CFs), offering significant opportunities for weight saving in aerospace and automotive applications. Recent research has showcased the potential of polyacrylonitrile (PAN) based CFs for structural battery anodes, and LiFePO4 coated carbon fibres as cathodes. Both anode and cathode fibres are embedded in a biphasic Structural Battery Electrolyte (SBE), composed of a liquid electrolyte phase for high ionic conductivity, and a porous stiff polymer matrix for mechanical performance. The resulting carbon fibre-polymer composite structure has the high specific strength/stiffness required for lightweight structural applications, and the high ionic conductivity required for battery functionality.

Optimisation and design of the multifunctionality of such systems requires an understanding of the coupling of physical phenomena, including thermal, electro-chemical and mechanical processes. In particular, quantifying the mechanical response at different charge states is crucial in the reliable use of these systems in structural applications. In order to achieve this, the project aims are twofold and iterative:

The first aim is the construction of a comprehensive multiphysics model on MSC Marc of a structural battery composite in order to predict the multifunctional performance of structural batteries in various load cases. MSC Marc is an advanced non-linear FEA solver capable of solving multiphysics problems. This will commence with the development of a multiphysics Representative Volume Element (RVE), to couple electrochemical, thermal and mechanical phenomena. Following this, the RVE overall properties may be used to define larger scale modelling. These simulations will supply an enhanced understanding and facilitate the improved design of structural batteries with the ultimate goal of unlocking their significant potential for reducing carbon emissions.

In order to define both the physical parameters and constants present in the multiphysics model, characterisation of material properties is required on a multiscale basis; quantification of individual structural battery components in isolation, and at the full composite level. In order to achieve this, the project will utilise a broad spectrum of experimental methods at different length scales; from the length scale of the fibre (10's of microns) using synchrotron techniques, to the microscale using nanoindentation and other methods.

Additionally, it is key to include assessment of individual component interactions at the multiphysics level. The scope of the project will focus on quantification of the property descriptor coefficients relating charge/electro-chemical load to mechanical response as required to embed this response into multiphysics simulations. In parallel, quantification of the property descriptor coefficients relating mechanical load to electro-chemical response will thereby fully encapsulate the structural battery system.

Prior to joining the AAPS CDT, Alex juggled part-time work at FatFace—pandemic furlough and all—while stepping into the world of independent music. Armed with an MMath in Mathematics from the University of Sheffield, where he focused on abstract algebra, Alex was eager to apply his sharp problem-solving skills to real-world challenges in transport and mobility.

Drawn by the multidisciplinary approach of AAPS, he saw the programme as an ideal platform to explore how mathematical thinking could intersect with engineering and environmental challenges. Along the way, he supported outreach initiatives like ReachSci and the Royal Institution’s masterclasses, advocating for inclusive access to academic spaces. In his downtime, you'd find him experimenting with new recipes, staying active, and creating music under the artist name Eskafell.

PhD Focus

Alex's PhD project, titled ’Structure-Preserving Electrokinetics ‘ zeroes in on how mathematical modelling can revolutionise battery management in electric vehicles. By developing advanced numerical methods—specifically discontinuous Galerkin finite element techniques—his work enhances the accuracy of predicting battery states, from charge levels to internal temperatures.

Focusing on the electrolyte region, Alex works on preserving key physical structures in models that describe ion transport. His project uses extensions of the Poisson-Nernst-Planck system, even branching into fluid dynamics via the Navier-Stokes equations. The long-term goal? Better battery longevity, safer EV operation, and smarter vehicle performance—which could help pave the way toward broader electric adoption and reduced reliance on fossil fuels.

Looking Forward

In the next decade, Alex hopes to carve out a space for himself in academia, aspiring to a permanent position within a university mathematics department. For now, he’s building on the solid foundations laid during his PhD—developing as a researcher and collaborator, and always curious about where his work may lead.

The Reflection on AAPS

For Alex, AAPS has meant more than technical advancement—it’s been about cross-disciplinary connection. The programme helped him learn how to engage with researchers from vastly different academic domains, building skills in communication and collaboration that are vital in both industry and academia.

Beyond the lectures and labs, the CDT experience introduced him to problem-solving approaches from other fields and forged relationships with professionals across academia and industry. These interactions, plus exposure to cohort projects, international training events, and extensive presenting opportunities, have shaped him into a well-rounded researcher ready to take on the multifaceted challenges of the future.