Publications

This POSTnote by Ellie Smallwood summarises the challenges and options for enabling and encouraging of low-carbon actions by individuals in sectors with the highest emissions, which from the research undertaken as part of her 3 month UKRI Internship with POST. 

Briefing produced for the Centre for Climate Change and Social Transformations (CAST), intended as a resource for decision-makers and other stakeholders who aim to improve the design and implementation of climate policy. The briefing outlines the potential opportunities presented by various types of 'moments of change' in reshaping travel behaviour, and their implications for policy.

In January 2023, Bath & North East Somerset Council (B&NES) implemented the first local planning policies in the UK requiring, first, that all new building developments achieve net zero operational energy, and second, that major developments meet an embodied carbon target. Both go far beyond the existing national building regulations, but they are representative of a growing number of similar policies from local authorities.

This paper describes a collaboration between B&NES and the University of Bath which explored the first months of the new policies’ implementation, to identify the impacts on building designs, the reception by practitioners, and opportunities for policy development and refinement. Thirty-eight eligible planning applications were analysed, the majority for minor residential buildings
eligible only for the operational energy policy. Despite a non-compliance rate of over 50% – primarily caused by a lack of policy awareness – many applications for buildings theoretically achieving net zero operational energy were received, representing efficiencies far beyond current standards. However, scrutiny and monitoring will be required for these ambitions to be met in practice. A
corresponding questionnaire was completed by 65% of applicants. Although the responses were largely negative, with particular concerns over cost and viability, there was broad support for the policies’ aims and an expectation of long-term emissions savings.

A long-term study is now needed to track the evolving industry response, quantify the real emission savings through construction and occupation, and further engage with stakeholders to support the policies’ implementation, development, and wider impact.

This paper studies the single open-circuit failure (OCF) in dual three-phase permanent magnet synchronous motors (DT-PMSM) in transport electrification where wide speed range and torque operation range (TOR) are required. A control scheme is developed to extend the TOR with minimum copper loss based on the well-established fault-tolerant control strategy minimum loss (ML) and maximum torque (MT). The ML strategy allows the demanded torque at the reference speed to be delivered with minimum copper loss. The MT strategy presents wider torque capability in post-fault operation without exceeding the current limit, whilst copper loss within the stator winding is not optimized. However, there is a gap in the permissible TOR of these two strategies. A simple switch of strategy, from ML to MT when the limit of ML's TOR is reached, would result in excessive copper loss. The full-torque-operation-range minimum loss (FTOR-ML), inspired by previous work, is developed to mitigate the excessive copper loss, by analytically analysing the corresponding optimsation problems. The FTOR-ML for the DT-PMSM under OCF for different winding configurations, single (1N) and isolated neutral point (2N), combines the merit of ML and MT where the entire TOR of MT is achieved with minimum copper loss. The novel analytical solution of FTOR-ML derived in this paper contributes to highly simplistic implementation for both winding configurations. Experimental result demonstrates the combined merit and effectiveness of the proposed control scheme.

Automotive OEM introduced Product-Service Systems in the past 20 years, challenging their traditional business model. A qualitative study was developed to characterise the decision-making process across 6 case studies, and similar patterns across different enabled the identification of lessons learned and possible future implications. All PSS initiatives were introduced following an Agile/Lean experimental approach, but the opportunistic nature of trials casts doubts in future validity. New testing methods that generate more robust conclusions need to be developed.

Research on evaluating sustainable transport policies is predominantly focused on their urban effects, often overlooking similar challenges in suburban and rural mobility. Therefore, the development of regionally integrated sustainable transport strategies becomes essential to comprehensively address these concerns. This study aims to bridge this gap by introducing a GIS-supported methodology that combines multiple linear regressions with hazard ratio models to quantify and map the impacts of environmentally driven regional transport policies on air pollution and human health. The main findings of an illustrative case study highlighted the importance of stronger efforts to promote the transition to shared and active transport and address the articulation between urban and rural mobility. This study offers a novel contribution to transport researchers and policymakers by proposing a methodology that (1) forecasts the impacts of regional transport policies using open data and software, ensuring its applicability for diverse regional settings, (2) provides the results in quantitative and visual formats, facilitating output analysis and visualisation and, consequently, decision-making and public consultation on proposed sustainable transport policies, and (3) sets the groundwork for including future transport-related dimensions.

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.