Publications

This paper reviews electric vehicle (EV) profiling at the distribution substation level in the UK, focusing on eight EV charging trials and projects conducted between 2010 and 2022. It examines current profiling methods and highlights key challenges and opportunities in mapping EV charging demand across distribution substations, including the risk of data drift, the need to consider the interaction between multiple sociodemographic and locational factors, poor visibility of EV adoption, and the inherent uncertainty in the system. The pace of change in EV adoption, technology and policy means that trial datasets quickly become outdated. The multitude of factors influencing EV charging demand requires the collection of rich datasets to quantify explicit relationships between them. While visibility of EV adoption on distribution networks remains prone to inaccuracies, ongoing research is advancing methods, presenting new opportunities to address these gaps through leveraging new insights and address the numerous sources of uncertainty inherent in such a complex and dynamic system. The findings emphasise the need for improved data collection and the development of predictive profiling techniques to support seamless EV integration and inform future network planning.

In this work, we introduce and analyse discontinuous Galerkin (dG) methods for the drift–diffusion model. We explore two dG formulations: a classical interior penalty approach and a nodally bound-preserving method. Whilst the interior penalty method demonstrates well-posedness and convergence, it fails to guarantee non-negativity of the solution.

To address this deficit, which is often important to ensure in applications, we employ a positivity-preserving method based on a convex subset formulation, ensuring the non-negativity of the solution at the Lagrange nodes. 

We validate our findings by summarising extensive numerical experiments, highlighting the novelty and effectiveness of our approach in handling the complexities of charge carrier transport.

The electrification of bus fleets is central to achieving low-carbon transport, yet uncertainties remain around whether electric buses can deliver reliable and cost-effective operations for all types of operators. While previous studies emphasise their lower fuel and maintenance costs, limited attention has been given to the economic implications of vehicle downtime.

This study investigates how electric bus availability influences fleet size requirements and subsidy needs. Using detailed maintenance and downtime data from a UK bus operator, we apply a total cost of ownership (TCO) framework combined with a probabilistic model of bus availability over a 20-year period.

Our analysis shows that although electric buses incur lower maintenance expenditures, their reduced availability substantially increases the number of spare vehicles required, raising capital costs – especially for smaller or rural operators. For example, electric fleet size requirements increase by roughly 18 to 23% per 10-percentage-point shortfall in availability relative to the diesel fleet benchmark.

Policy simulations indicate that that if availability has not improved since 2020, then today’s operators will require procurement grants of between 35.7 and 45.0% depending on the scale of operation – and will continue to require additional financial support beyond 2040. Whereas, if availability has and continues to improve, the procurement grants required by today’s operators fall to between 30.7 and 39.7%, with no procurement grants necessary past 2032.

These results highlight that vehicle availability is a critical but underappreciated factor in fleet electrification and should be explicitly incorporated into both cost analyses and subsidy design.

At a time when global efforts to reduce CO2 and other emissions from transport are gaining momentum, this paper provides a novel, interdisciplinary systematic review of 106 peer-reviewed studies researching zero-car households. The review consolidates international knowledge on the individual and systemic factors influencing zero-car households at the micro (individuals or households), meso (groups or communities), and macro (governmental) scale. In addition to insights into effective policy interventions to reduce dependence on private car ownership, we offer two novel contributions to transport research. 

Firstly, we find that 75% of studies are uninformed by any social science theories which limits the actionable and generalisable insights that can be made on car reduction.

Secondly, this is the first systematic review to draw a distinction between households which are zero-car by constraint (car-less) versus choice (car-free). This differentiation offers important insights into the contrasting needs and priorities of the two groups. A consistent finding across contexts is that low-income, child-free adults are closely associated with zero-car households. There is no evidence that zero-car householders have pro-environmental values but, in some countries, affluent and well-educated city-dwellers are choosing to live car-free. However, there remain practical and emotional barriers to becoming a zero-car household by choice. Effective policy interventions to encourage zero-car households are found to include residential parking controls and car clubs.

The review deepens current understanding of car ownership trajectories and public acceptability of car use and ownership policies. It informs both academics and policymakers in addressing knowledge gaps critical for advancing sustainable urban mobility.

Recent research suggests biological age, based on epigenetic or neuroimaging measures, may predict health traits in adulthood more accurately than chronological age. However, it is unclear if these findings apply earlier in life.

We aimed to characterise the performance and interdependence between measures of biological age in young people, leveraging a longitudinal subsample from the population-based ALSPAC cohort (n = 386). We derived four epigenetic age measures from blood samples in young people (17–19 years) and a measure of brain age derived from structural neuroimaging data (18–24 years). We examined associations between measures of biological age, and relationships with five measures of physical, cognitive and mental health (8–18 years). 

We found little evidence for an association between brain age and epigenetic age measures, after accounting for age, sex, cell type, array and study (beta range: -0.59 to 0.59, all p > 0.05). Increased smoking _DNAm was associated with advanced epigenetic age (PACE and Zhang clock), and increased BMI _sds with advanced EpiAge _{Horvath(diff)} (all p < 0.05), but not brain age. Depressive symptoms and cognitive ability were unrelated to all measures of biological age.

Our findings highlight the variability of epigenetic- and brain-based age measures in young people, emphasizing the importance of tracking ageing in younger populations.

High-performance superconducting electric motors have taken transportation electrification to a new era and are of great interest for future hydrogen-powered electric propulsion aircraft. Fully superconducting air-cored motors have great potential to achieve high power density and efficiency. This paper introduces a novel equivalent circuit model for a superconducting motor to analytically calculate magnetic field distribution and then estimate its key performance, including motor electromagnetic torque as well as loss in the rotor electromagnetic shield. Layer theory is used in the airgap winding for rapid evaluation in the early design stage to achieve an effective solution. The paper employs an equivalent circuit to decompose and represent the stator, rotor, airgap, and electromagnetic shield. The parameters of equivalent circuit model are calculated based on the physical dimensions. The results from this analytical methodology are compared and validated with simulations of a 2D finite element model in COMSOL.

Accurate state-of-charge estimation is critical to the safe and reliable operation of Li-ion batteries. The accuracy of established state-of-charge estimation methods is limited due to measurement drift over many cycles or models which are challenging to reliably parametrise. Ultrasound sensing techniques have the potential to overcome these issues by providing a direct link between the state-of-charge and physical properties of the cell. Ultrasonic time-of-flight is the predominant measurement used to predict state-of-charge, but this measurement is complicated by changes in cell thickness during cycling, the effect of which has gone underappreciated until now.

Unlike time-of-flight, ultrasonic velocity is unaffected by cell thickness changes, and this paper demonstrates for the first time the advantages of using velocity to predict state-of-charge. Velocity is found to be more consistent than time-of-flight at a range of different C-rates, temperatures, and across multiple cycles. This culminates in a large improvement in the prediction of state-of-charge during a US06 drive cycle, where the mean difference between predicted and actual state-of-charge is improved from 12.3 % to 3.8 % when velocity is used instead of time-of-flight. These results are a significant step towards the realisation of state-of-charge prediction using ultrasound and hence safer and more reliable Li-ion battery operation.

With increasing interest in the recovery of agricultural waste for circular solutions, this study investigates Opuntia ficus-indica agricultural waste as a sustainable, low-cost source of reinforcement fibers for biocomposites. Opuntia ficus-indica cladodes contain a complex three-dimensional hierarchical network of fibers that have biologically evolved to provide mechanical support against bending stresses. Instead of relying on single-fiber performance, these fibrous networks offer planar reinforcement through structural configuration. Fibrous networks were obtained from Opuntia ficus-indica cladodes via two extraction techniques. The comparison between water retting and solid–liquid extraction highlights relevant trade-offs between fiber quality and processing efficiency. Water retting requires significantly longer processing times but produces fibers with higher crystallinity and lower residual parenchyma content. In contrast, solid–liquid extraction substantially reduces extraction time (up to 90%), offering potential advantages in operational efficiency and scalability; however, incomplete removal of non-fibrous tissue can compromise fiber quality and composite compatibility. Older cladodes yield fibrous networks with ideal characteristics for natural fibers intended for biocomposite applications. Further work will explore the mechanical properties of the fibrous networks obtained via water retting of old cladodes and surface treatments to improve the interfacial adhesion between the fibrous networks and polymer matrix.