Publications

Ryan presented the work from his MSc dissertation on solid-state battery at the Climate Exp0 conference in the Mitigation Solutions theme. With his supervisor Dr Christopher Vagg, he found that by utilising waste heat from the electrical power train, there is enough energy to heat thermally isolated solid-state battery modules in time for them to provide tractive power for a passenger vehicle. This is important as currently available solid-state batteries require an operational temperature of 60°C

Originating from his Master's thesis, Ryan and his supervisor Dr Chris Vagg have published their findings in a new journal article seeking to address the feasibility of a cold start procedure for solid state batteries in automotive applications. The proposed solution involves dividing the battery into sub-packs and heating them sequentially to the required 60°C, primarily using waste heat from the electric powertrain. This could allow high energy density solid state cells to be used despite their temperature constraints.

Accurate estimation of the internal temperatures of electric machines is critical to increasing their power density and reliability since key temperatures, such as magnet temperature, are often difficult to measure. This work presents a new machine learning based modelling approach, incorporating novel physically informed feature engineering, which achieves best-in-class accuracy and reduced training time. The different features introduced are proportional to sources of machine losses and require no prior knowledge of the machine, hence the models are completely data driven. Evaluation using a standard experimental dataset shows that modelling errors can be reduced by up to 82.5%, resulting in the lowest mean squared error recorded in the literature of 2.40 K ². Additionally, models can be trained with less training data and have lower sensitivity to data quality. Specifically, it was possible to train a loss enhanced multilayer perceptron model to a mean squared error <5 K ² with 90 h of training data, and an enhanced ordinary least squares model with just 60 h to the same criteria. The inference time of the model can be 1–2 orders of magnitude faster than competing models and requires no time to optimise hyperparameters, compared to weeks or months for other state-of-the-art prediction methods. These results are highly important for enabling low-cost real-time temperature monitoring of electric machines to improve operational efficiency, safety, reliability, and power density.

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.