Theses

Thomas' doctoral thesis is a comprehensive study into structural battery electrodes, from transition metal oxide coatings to computational modelling of carbon fibre anodes during charging.

Thomas investigates the role of electrodes in multifunctional carbon fibre structural batteries and their combined ability to store electrical charge with mechanical load-bearing capabilities. By analysing the material properties and mechanisms that occur within these fibres, a deeper understanding can be obtained to lead to the realisation of lightweight energy storage. The research is divided into two distinct sections, each contributing to the advancement of knowledge in this field.

Structural batteries are multifunctional materials that are simultaneously capable of storing energy and transferring mechanical loads. This allows for mass savings on a systems level when they are used as structural components in electrified transport applications. The key to realising the next generation of structural batteries may be in the development of alternative architectures, which use different materials and assembly strategies to those that are used in the current state-of-the-art laminated architecture.

The research presented in Ciarans' thesis aims to identify and investigate the synthesis and detailed characterisation of novel metal oxide precursors that could be utilised as anti-friction and anti-wear additives in automotive lubricants. Initially, this investigation was focused on designing precursors that offer comparable or superior tribological properties to commonly used additives such as MoDTC or ZDDP. These additives function by reducing friction and wear by facilitating the formation of lamellar sulfide materials and phosphate glasses respectively.

However, it is well-documented that the presence of byproducts containing both P and S have been known to impede the efficacy of catalytic converters. Therefore, the primary focus of this research is centred on alleviating the reliance on P and S-based precursor formulations. In response to these challenges, efforts are made to transition towards alternative precursors that mitigate the environmental impact on the catalytic converters and the surrounding ecosystem, with a particular focus on metal oxide-based precursors.

By addressing these concerns, this research contributes to the development of more environmentally friendly and novel precursors, potentially leading to enhanced engine performance and reduced environmental pollution.

Abdu's study, by employing physical one-dimensional and three-dimensional computational fluid dynamics (CFD) tools used in conjunction with supercomputing resources, further illustrates how fully variable valvetrain systems can be fine-tuned to achieve optimal performance across various engine architectures, including both four-stroke and two-stroke configurations. Through the detailed evaluation of scavenging and trapping efficiencies, this research provides valuable insights into the potential for NOx reduction in ICEs equipped with Freevalve technology. The findings highlight how these advanced control strategies can help address the dual challenge of reducing CO2 and NOx emissions through efficiency metrics, ensuring that ICEs remain a viable and environmentally responsible option in the evolving energy landscape.

Elisabetta's Ph.D. thesis explores the integration of Solid Oxide Fuel Cells (SOFCs) into aviation propulsion systems, focusing on their potential to address sustainability challenges in the aviation sector. SOFCs offer high energy efficiency, reduced emissions, and adaptability to various fuels, aligning with aviation’s environmental goals. The lightweight and compact nature of SOFC systems makes them suitable for integration into aircraft.

Joris' thesis advances pLCA methodologies to better represent the current and future environmental impacts of long-lived technologies, focusing on batteries and passenger cars.

Isaacs thesis presents a novel probabilistic framework for forecasting EV adoption and charging demand on the LV network, integrating diverse open datasets with advanced statistical methods to deliver uncertainty-aware forecasts that can support evidence based investment decisions.  

Ryans thesis investigates the application of machine learning techniques to the real-time thermal modelling of electric machines. Traditional methods like high-order lumped parameter models and finite element analysis, while effective, are computationally expensive and often unsuitable for real-time applications. This work seeks to address these limitations by developing data-driven machine learning-based models that are fast, easy to train, generalisable, and accurate.

Lois' thesis provides a nuanced conceptual understanding of evolving Low Emission Zone support, whilst providing policymakers with an efficient, cost-effective, and accessible tool to better understand their community’s policy needs.

Howards thesis covers the work undertaken to investigate the effect that current ripple has on lithiumion cells, specifically with regards to automotive applications. Testing of lithium-ion cells is typically performed using highly filtered signals that are not representative of the noisy signals that exist in real world situations due to the high frequency switching of power electronics.