Publications

Machine-assisted approaches for free-text analysis are rising in popularity, owing to a growing need to rapidly analyse large volumes of qualitative data. In both research and policy settings, these approaches have promise in providing timely insights into public perceptions and enabling policymakers to understand their community’s needs. However, current approaches still require expert human interpretation – posing a financial and practical barrier for those outside of academia.

For the first time, we propose and validate the Deep Computational Text Analyser (DECOTA) - a novel Machine Learning methodology that automatically analyses large free-text datasets and outputs concise themes. Building on Structural Topic Modelling (STM) approaches, we used two fine-tuned Large Language Models (LLMs) and sentence transformers to automatically derive ‘codes’ and their corresponding ‘themes’, as in Inductive Thematic Analysis.

To automate the process, we designed and validated a novel algorithm to choose the optimal number of ‘topics’ following STM. This approach automatically derives key codes and themes from free-text data, the prevalence of each code, and how prevalence varies with covariates such as age and gender. Each code is accompanied by three representative quotes. Four datasets previously analysed using Thematic Analysis were triangulated with DECOTA’s codes and themes. 

We found that DECOTA is approximately 378 times faster and 1920 times cheaper than human coding, and consistently yields codes in agreement with or complementary to human coding (averaging 91.6% for codes, and 90% for themes). The implications for evidence-based policy development, public engagement with policymaking, and the development of psychometric measures are discussed.

Carbon fibre based electrodes offer the potential to significantly improve the combined electrochemical and mechanical performance of structural batteries in future electrified transport.

This review compares carbon fibre based electrodes to existing structural battery electrodes and identifies how both the electrochemical and mechanical performance can be improved. In terms of electrochemical performance achieved to date, carbon fibre based anodes outperform structural anode materials, whilst carbon fibre based cathodes offer similar performance to structural cathode materials. In addition, while the application of coating materials to carbon fibre based electrodes can lead to improved tensile strength compared to that of uncoated carbon fibres, the available mechanical property data are limited; a key future research avenue is to understand the influence of interfaces in carbon fibre based electrodes, which are critical to overall mechanical integrity.

This review of carbon fibre based electrode materials, and their assembly strategies, highlights that research should focus on sustainable electrode materials and scalable assembly strategies.

Enhancing the performance, safety and reliability of battery management systems is crucial for advancing the state of the art in battery electric vehicles. Current research explores the potential of ultrasound to monitor state of charge (SoC) changes in individual cells. Understanding spatial variations in SoC is essential, as non-uniformities could lead to sub-optimal performance, premature ageing, and possible safety risks.

This study uses ultrasound immersion C-scans to map wave speed and attenuation at different SoC levels during battery cycling. Results indicate non-uniform wave speed and attenuation suggestive of SoC spatial variations within single cells, emphasising the importance of addressing this issue. Acoustic measurements under various C-rates and relaxation periods are discussed, providing insights into lithium-ion rearrangement in graphite particles. Potential causes of structure and manufacturing variations of the cell are discussed, highlighting the need to address these issues to prevent overcharging or overdischarging in specific battery areas.

Equivalent circuit models represent one of the most efficient virtual representations of battery systems, with numerous applications supporting the design of electric vehicles, such as powertrain evaluation, power electronics development, and model-based state estimation. Due to their popularity, their parameter extraction and model parametrization procedures present high interest within the research community, with novel approaches at an elementary level still being identified.

This article introduces and compares in detail two novel parameter extraction methods based on the distinct application of least squares linear regression in relation to the autoregressive exogenous as well as the state-space equations of the double polarization equivalent circuit model in an iterative optimization-type manner. Following their application using experimental data obtained from an NCA Sony VTC6 cell, the results are benchmarked against a method employing differential evolution. 

The results indicate the least squares linear regression applied to the state-space format of the model as the best overall solution, providing excellent accuracy similar to the results of differential evolution, but averaging only 1.32% of the computational cost. In contrast, the same linear solver applied to the autoregressive exogenous format proves complementary characteristics by being the fastest process but presenting a penalty over the accuracy of the results.

This systematic review of existing literature reinforces the need for inclusive design strategies that account for the specific requirements of individuals across the neurodivergent spectrum. It discusses the evolution of inclusive design from earlier movements focused mainly on physical accessibility and extends to embracing the complex experiences of people with different cognitive profiles.

The review identifies fundamental inclusive design principles and their practical applications for neurodivergent populations, concentrating on autism, attention deficit hyperactivity disorder (ADHD), and dyslexia. Its findings are analyzed comparatively, revealing consistencies, differences, and contradictions in design principles across conditions, and highlighting areas that require further examination.

This investigation lays the groundwork for our focused research on co-designing inclusive mobility services with neurodivergent groups.

The automotive sector provides society with the means to move people and goods, however the increasing need for climate change mitigation places responsibility on automotive manufacturers to develop low-carbon technologies to meet these needs.

These rapid technological developments are prone to high costs, skill gaps, and sub-optimal energy-intensive practices, particularly within the testing environments for automotive components. Automated safety thresholds are in place to detect large fluctuations in real-time data, whilst the detection of other, more nuanced faults is manual, but these have the risk of undermining vehicle development. As such, this process is resource intensive, such as the personnel time and energy consumed to rerun tests containing faults, and the financial costs associated with these. Here we show the ability of unsupervised, data-driven machine learning based anomaly detection methods to identify anomalous time-periods within an automotive test, including faults in either the test component or in the test facility equipment, without the need for training data.

We compare the performance of three clustering algorithms –k-means, agglomerative clustering, and DBSCAN – based on their run time and ability to create defined anomalous clusters of the two anomalies. K-means was able to identify the two anomalies with eight total clusters in half the time of agglomerative clustering. DBSCAN clustered the data in half the time ofk-means however was unable to create defined anomalous clusters. 

These results illustrate the potential for unsupervised data-driven anomaly detection to operate within automotive manufacturer testing environments. These methods provide a low-cost digital solution to the resource demands associated with the traditional processes used by automotive manufacturers when developing sustainable transport options.

Advances in noncontact vital sign detection have demonstrated its vast potential to supplant conventional contact sensors, not only within the established healthcare system but also across emerging domains such as smart homes, security systems, and in-cabin sensing. Noncontact cardiorespiratory measurements are among the most common, in part due to their relative ease of measurement using noncontact sensors.

For this application, radar-based sensors have several advantages, including high accuracy measurement that is invariant to ambient lighting conditions, which are especially beneficial in non-clinical settings. Radar-based cardiorespiratory feature extraction architectures consist of two parts: the radar hardware design and the signal processing pipeline. However, the combined complexity of the hardware, the underlying physics and the scene itself, makes the signal processing requirements very challenging.

To address this issue, we first review the recent trends in this domain, including the move towards Frequency Modulated Continuous Wave (FMCW) radar sensors, and then present an empirical investigation using a commercial FMCW radar to illustrate the unsolved real-world signal processing challenges for noncontact cardiorespiratory measurement. Additional in-depth analysis is used to interpret the underlying reasons behind the challenges, together with potential solutions.

The work presented will benefit researchers and industrialists working on radar-based physiological measurement, facilitating a greater understanding of the problem, its benefits and challenges, and potential future research directions.

The main output of Alex's internship with the Institute for Mathematical Innovation was the publication of a research paper titled "An analytical model for wrinkle-free forming of composite laminates". In the article a novel model is developed and validated to rapidly predict the occurrence of wrinkling in the formation of composite laminate materials. Such materials find particular use in aerospace applications, and the novel model aims to act as an initial design tool to help save time and financial costs during the design stage of the manufacturing process.

This paper provides a detailed overview of the current snapshot of available open data for modelling the impacts of electric vehicles (EVs) on the UK distribution network, highlighting opportunities for a digital spine. We are the first to review open data available for UK distribution networks, focusing on spatial data. We also explore data for census small geographies, vehicle ownership, EV charger locations and data on their usage. Several issues are identified, including inconsistencies in dataset availability, file naming conventions, feature definitions and geographic discrepancies.

We specifically analyse EV charger connection data for secondary distribution substations from two UK Distribution Network Operators (DNOs). The validity of the data is assessed by comparing it to known public charger locations from OpenChargeMap. While one DNO provides data coverage for >95% of its substations, it is valid for only 24.1% of substations with at least one public charger. Conversely, the other DNO provides data coverage for 1% of its substations due to privacy-related obfuscation, with data valid for 98.3% of substations with at least one public charger.

Addressing these challenges through standardised data-sharing practices and implementing a digital spine could enhance the accuracy and reliability of EV-grid integration models. These improvements are essential for facilitating the seamless integration of EVs into the grid and supporting the transition to a sustainable energy system.

Diversifying and decarbonising energy production by investing in renewables and clean energy is the UK Government's blueprint to power Britain from Britain.

Technological developments and deployment are progressing rapidly, however, the whole-system approach—bringing together organisations across the traditional boundaries to provide the country with an increasing capability to source affordable, clean and home-grown energy—is still lacking. 

A key barrier to the whole-system approach is lack of a data sharing infrastructure (DSI), which allows standardised and interoperable data to be securely shared between key stakeholders, helping to align giga watt, mega watt and kilo watt renewable and clean energy with end-user demand. Development of a DSI covering the entire problem and organisation space is a complex and costly undertaking. 

This paper advocates for a minimum viable product (MVP) that takes an early, continuous engagement of influencing and impacting stakeholders, facilitates the discovery of desired system functional properties at the earliest possible stage of system development to meet diverse users' needs, mitigate potential risks, and inform the future development. If an MVP offers genuine benefits for early adoptions and the opportunity to address mission critical challenges, it will propel mass collaboration and innovation to accelerate net zero transition and green growth.