Our Projects

​The context of Julian's research is the urgent global climate challenge of preventing a global mean surface temperature increase of more than 1.5 °C compared to the pre-industrial average, defined as 1850-1900. The IPCC (2023) has warned of serious consequences to human health and societies of such a rise in global temperature. We are already 80% of the way to this threshold: the global mean surface temperature for 2018-2022 the was about 1.2 °C about the pre-industrial average (Met Office 2023).

For his project ​"Integrated Drive System with Modularised Energy Storage for Automotive Applications" Constantinos will attempt to determine and quantify potential merits to the use of modular multilevel converter (MMC) topologies in automotive applications as compared with existing 2-Level Converters or state of the art 3-Level converters.  Future automotive electrified powertrains face severe restrictions on energy consumption and need to meet extremely high real-world benchmarks of efficiency and cost to remain commercially viable but also to offer any real societal benefits in terms of environmental impact.

Three main topologies will be investigated and compared with each other, in order to determine how they might impact the powertrain in terms of efficiency, cost and energy utilisation during various drive cycles.

Preliminary research has shown that it is possible to reduce costs or increase peak efficiency of the main traction inverter’s output stage, but MMCs may offer further benefits in low or partial loads as might be seen in certain drive cycles. 

It is anticipated that the modular nature of this topology may offer, cost benefits by allowing for further system level integration of Power Electronics within the battery pack and functional aggregation as it is able to take on the responsibilities of the On-Board Charger or partially, that of the 12V DC/DC converter while simultaneously outputting a much cleaner AC voltage waveform potentially reducing losses in the Motor.  

While these topologies show promise, their increased complexity or the way the battery is utilised may result in MMCs presenting a technologically or financially low value in certain applications and as such research is being undertaken to evaluate the potential benefits and drawbacks of these topologies in automotive applications.

Edison's project explores integrating Sharing Economy Business models in automotive companies and the strategies for enhancing corporate sustainability. 

While the 20th century represented the era of individual car ownership, the 21st century seems to disrupt this inherited system. The new mobility trend prioritises access over ownership, meaning that users get access to a mobility service instead of having a private vehicle. These practices are called car-sharing and carpooling. The main motivations for car-sharing include cost savings for users, reducing carbon emissions, recirculation of goods, increased utilisation of durable assets, and exchange of services. This incoming mobility system is based on an economic system known as the ‘sharing economy’.  

The sharing economy has social interactions at its core, integrating activities such as renting, trading, swapping, and borrowing. According to Allied Market Research® (2023), the shared economy market size could grow from US$387.1 billion in 2022 to around US$827.1 billion by 2032. This economic model projects such growth as it offers more affordable solutions for consumers than traditional models do, also technological solutions such as digital platforms enhance accessibility to this model which often is aligned with consumers’ sustainability principles.

The automotive companies are undergoing significant transformations driven by the advent of the sharing economy and sustainability goals. The sharing mobility platforms are altering customer behaviour and challenging OEMs' traditional business models. These changes bring uncertainty for the long-term sustainability of these companies and their strength to adapt to new market dynamics. Despite the increasing sharing economy research output, there is a gap in examining its impact on the corporate sustainability of automotive companies. Current literature focuses on operational and consumer implications in the SE but ignores the broader implications for corporate sustainability, including economic viability, social responsibility, and environmental impact. This PhD project aims to fill this gap by investigating how adopting SE business models in OEMs influences their corporate sustainability.

For this purpose, a mixed methods methodology across three interconnected studies will be undertaken. (1) through a systematic literature review, the first study will assess the impact of sharing economy business models on the enhancement of corporate sustainability, (2) the second study will employ structured interviews with OEMs' decision-makers at the corporate level and document analysis of the selected OEMs' annual reports to identify operational and strategic adjustments for the implementation of sharing economy business models. Finally, (3) the third study will evaluate the economic benefits and challenges associated with integrating the sharing economy business models through undertaking financial analysis, surveys and structured interviews.

The aim of Joshua's PhD is to examine how uncertainty is integrated and ultimately reduced when planning transport methods into the future, specifically modelling these within computational Life Cycle Assessment (LCA) techniques.

An LCA refers to the process of systematically analysing the environmental impact of a product throughout its life cycle; this can be from being manufactured to its disposal, known as cradle-to-grave, or manufacture to recycling into another product, that being cradle-to-cradle. It is relatively easy to model impacts in the past and present as for the most part, the data exists already. However, current data is inadequate to use to see into the future.

To overcome this, we can use ‘prospective’ methods, which assume that certain use and disposal/recycling techniques will change and evolve over many years – in particular, incorporating the anticipated change in renewable energy use. As this kind of uncertainty is lacking in contemporary LCA models in vehicles, this project aims to improve that within newer designs of vehicle design, with a particular emphasis on battery electric vehicles.

The opposed-piston 2-stroke (OP2S) engine has historically been applied to aircraft propulsion as well as engines for power generation and rail traction with great success. More recently, Achates Power have shown the potential of the OP2S engine for automotive applications. The low surface area to volume ratio of the combustion chamber in OP2S engines, combined with its lack of a cylinder head, results in lower heat losses yielding high exhaust gas energy, making it an ideal candidate for turbocharging, as well as increased brake thermal efficiency. However, due to the requirement for a positive delta pressure across the cylinder at all operating points (intake manifold pressure must be higher than exhaust manifold pressure) to ensure the scavenging performance of 2-stroke engines, crankcase scavenging is typically used instead as, unlike a turbocharger-driven charging system, it guarantees a positive delta pressure gradient at all operating points.

Nevertheless, other scavenging systems, such as a supercharger in conjunction with a turbocharger, have been shown to provide effective scavenging performance whilst utilising the otherwise wasted exhaust gas energy. Moreover, the use of a combined supercharger/turbocharger charging system with an OP2S architecture provides greater flexibility in the air-fuel-ratio control and exhaust temperature management, whereas conventional 4-stroke engines are expected to require the use of cylinder deactivation or other thermal management strategies to meet the low emissions standards.

Furthermore, the use of electrically assisted turbochargers not only increases this flexibility but also provides a means of extracting excess work from the turbine by turbocompounding, whilst simplifying the intake air path.

The purpose of this work is to investigate a novel pressure-balanced free-piston engine concept. An OP2S engine model will first be adapted from prior work with a view to understanding the effects of crank phasing and port geometries on gas dynamics. A Libertine free-piston engine will then be used to inform and verify a linear generator engine model constructed using a similar geometrical arrangement as the Libertine engine.

Having completed this work, a verified free-piston OP2S engine model can be developed using learnings from the prior work. This model will yield a greater understanding of the capabilities of the conceptual engine arrangement whilst also providing insight into the intrinsically linked relationships between the mechanical and electrical subsystems.

The model could also be used to assist in the design of a prototype of the concept engine, the manufacture of which would be dependant on time and funding.

Indrek's research project addresses a significant problem in the field of propulsion: the need for cleaner, more efficient liquid fuels. The project focuses on modelling the evaporative behaviour of methanol, a type of alcohol, chosen due to its unique properties such as a high-octane number, auto-ignition temperature, heat of vaporization, embedded oxygen, and excellent lean burn properties. These characteristics can lead to cleaner, more efficient combustion, and thus, reduced emissions while improving performance.

The high evaporative cooling effects of methanol, attributed to its high heat of vaporization, are a key focus.

The project aims to develop and improve overall understanding and low-dimensional models of methanol's evaporative behaviour by conducting experimental data collection on a test engine fuelled with various methanol blends under various operating conditions and numerical experiments for additional, high-fidelity data. By better understanding and predicting the evaporative characteristics of methanol, this research seeks to enhance fuel efficiency and engine performance while simultaneously reducing emissions in marine engines. 

Ultimately, these findings could contribute to helping the UK achieve its net-zero targets by 2050 and be potentially applied to larger marine applications and other transport sectors, such as automotive and aviation, in the future.

Organisations that employ large numbers of people (above 1000 employees) generate and attract trips that, otherwise, would not be made. Commuting generates 5% of the UK’s year total emissions [1] while business air travel accounted for 154 million Mt CO2 globally in 2019 [2]. 

Large employers, aware of the impact of transport in the generation of GHG emissions as well as congestion and pollution, have started to implement policies and interventions to promote sustainable modes of transport among their employees. This is a significant opportunity for public/private collaboration to achieve Net Zero by 2050. But organisational policies do not always translate into changes of behaviours. Previous research suggests that people tend to accept policy if they perceive it as effective and fair, or if they feel like they had been part of the decision-making process[3]. 

Are individuals more inclined to abide by restrictive rules when they participate in the process of creating them? The normative idea of public engagement in decision making is well studied in the context of Government-citizen relationship, but not so much in other spheres like the workplace. This research explores the role of deliberation, co-creation and trust in the effectiveness of restrictive travel-related rules in the workplace, providing new insights for policy-makers in public and private organisations alike. 

1. Mobilityways, Zero carbon commuting - the business case, in The CBI, T. CBI, Editor. 2022.
2. Transport & Environment, Travel Smart: Benchmarkin global corporate flyers on leadership towards purposeful travel. 2022: https://www.transportenvironment.org/.
3. CAST, Motivating low-carbon behaviours in the workforce - Insights from Cornwall Council. 2023, Centre for Climate Change and Social Transformations: CAST Briefings.

Fuel cells are devices that use hydrogen and oxygen to produce electricity without burning them. They are clean and efficient sources of energy for many applications, such as cars and buses. One type of fuel cell is called a proton exchange membrane fuel cell (PEMFC). It has a special membrane that allows protons (positive hydrogen atoms) to pass through it, while electrons (negative particles) go around it. This creates an electric current that can power a device.

To understand and improve how PEMFCs work, we need to consider many factors that affect them, such as temperature, humidity, pressure, material thickness, stress, and resistance. These factors can change how well the fuel cell performs and how long it lasts. We want to find the best combination of these factors for different situations.

One way to do this is to create computer models of PEMFCs that can mimic their real behaviour. We can then test different scenarios and see how the fuel cell reacts. This helps us to fine-tune our models and make them more accurate and realistic. This also saves us time and money, as we don’t need to do as many physical experiments.

By doing this, we can learn more about how PEMFCs work and how to make them better. We can also make them more suitable for different uses and environments. This will help us to develop and use PEMFCs more widely and effectively. This is important for the future of fuel cell technology and clean energy.

Electric vehicles (EVs) play a key role in decreasing the carbon footprint of the mobility sector. Their high upfront cost, limited range and slow charging speed are however a barrier to increased EV uptake. Reducing the cost and improving the EV Lithium-ion (Li-ion) battery could reduce these barriers.

There is however limited knowledge in the safe operation and degradation rate of Li-ion batteries. This is largely due to the complex electrochemical mechanisms not being well understood. Furthermore, the large operating envelope (temperature, charging speed etc.) over its lifetime require resource intensive testing to parameterize semi-empirical models. The battery is therefore operated very conservatively, resulting in oversizing the battery and sub-optimal operating conditions resulting in inefficiencies and higher costs.

Johannes's PhD aims to provide optimal testing strategies and accurate modelling (with a focus of degradation) of Li-ion batteries in order to provide information to facilitate more efficient operating strategies (e.g. fast charging). This will be achieved by a combination of advanced design of experiments (DOE), modelling and machine learning.

The initial part of the PhD will focus on building a model structure which is based on a semi-physical neural network. The accuracy of this model will then be assessed using existing battery data in literature and data provided by the industrial partner. An experimental test campaign will then be designed and implemented, in an attempt to efficiently parameterize the battery models. The resultant battery models would then provide important information to improve the safe operation range of the battery.

Most of today’s devices and electric vehicles rely on lithium-ion batteries due to their balanced performance and cost. However, they come with critical safety concerns: lithium-based compounds, which store substantial energy in a compact form, are prone to overheating and even explosion under certain conditions, such as physical impact, rapid temperature change, or exposure to air. Furthermore, lithium mining and production are inefficient, adding environmental challenges.

This has sparked a demand for next-generation batteries using alternative materials to improve safety, efficiency, and sustainability. Although various experimental designs for new batteries exist, research often stops at initial testing, with limited investigation into their underlying chemical behaviours. This lack of insight hampers our ability to predict performance and manage risks effectively.

Eymen's PhD research aims to address these gaps through mathematical modelling, beginning with a thorough review of existing battery models, emerging battery chemistries, and key safety and performance factors. I’ll then develop a mathematical model specifically for next-gen battery cells, embedding it in COMSOL and other tools to simulate their chemical and thermal behaviours. By applying this model to real-world scenarios, such as electric vehicles or drones, Eymen will conduct performance analyses to assess potential risks, such as thermal propagation and overpressure from chemical reactions. The final stage will involve validating these models through experimental data, enabling us to reduce the need for extensive physical testing and propose effective safety measures for future battery designs.