Publications

A transportation system should be designed considering the relevant stakeholders’ needs for a fundamental transformation in travelling behaviour. This research aims to contribute to that by characterising the future network in response to the stakeholders’ requirements, using a systematic literature review paired with a grounded theory approach. Out of 39 reviewed publications, 13 transportation indicators were clustered into six dimensions representing stakeholders’ requirements for the transportation system. These results depict a stakeholder-informed land transportation system as a system of accessible and integrated mode services, which should be supported by policy and infrastructure, economically balanced, socially, and environmentally sustainable and rely on mobility-dedicated assisting features. Further research is proposed on (1) adapting these results to the legal, social, economic, and environmental contexts and (2) the ability of MaaS scenarios to answer the collected dimensions. This research is crucial to determine the areas of focus of a stakeholder-designed transportation system and to frame them in the mobility ecosystem, both individually and interlinked. Furthermore, its originality lies in (1) the application of this methodology to collect, analyse, and define a set of mobility investment priorities, and (2) the recognition of the relevant stakeholders in mobility considering their diverse perspectives and needs.

Catherine Naughtie was part of a team of Early Career Researchers (ECRs) based at the University of Bath who had the opportunity to be guest editors for a special issue The Psychologist, Early career researchers: Our world, our challenges, our future.

This issue is based around the theme: ‘The world we have versus the world we need: What challenges do ECRs currently face, and how could addressing them change our future?’. This edition is split into three separate parts, each of which reflect a different element of the theme.

As part of research completed in his first year of PhD, the published work addresses industrial concerns relating the use of fully variable valvetrain (FVVT) technologies in ICEs for part load and transient performance. Adopting a data-based approach, together with his industry and academic partners Koenigsegg, Freevalve, and KAUST, Abdu concluded optimal FVVT-enabled valve strategies targeting maximum scavenging and optimized EGR rates for maximum fuel conversion efficiency and minimal brake specific fuel consumption. The study then goes on to explore the benefits of integrated FVVT technologies in turbocharged vehicles for transient rise times and how the technology assists in minimization of turbo lag and improvement of drivability. Abdu has been invited to present the published work at WCX SAE World Congress Experience, taking place in Detroit, MI, USA in April of 2023.

In collaboration with our second cohort AAPS CDT student, Joris Simaitis, Abdu authored a Lifecycle Assessment (LCA) paper that has recently been published by SAE International for WCX SAE World Congress Experience 2023 taking place in Detroit, MI, USA. The work presents a comparative global warming potential (GWP) LCA case between a DAC efuel FVVT-equipped hybrid electric vehicle (HEV) and a battery electric vehicle (BEV) for a lifecycle of 150,000 km on two different grid options (a) global average mix and (b) renewable mix. The study uses standardized and peer reviewed LCA database, Ecoinvent, and professional open-source sustainability and LCA footprint modelling software OpenLCA. They found that a net reduction of up to 55% in favour of the DAC efuel FVVT-equipped HEV is evident. The comparison is a first of its kind in the published literature domain, setting a benchmark for DAC efuel FVVT-equipped HEVs in future comparative LCA investigations. Both students have been invited to attend the conference and are due to present the published work.

Following his internship with the Institute for Policy Research (IPR), Jac co-authored a paper which investigated the impact of inclement weather on the service stability, efficiency, and feasibility of mass-transit bus operations delivered by fully electrified fleets. The objective was to provide easy to interpret results from a real-world case study that would reduce some of the uncertainty operators face when decarbonising their fleets. The regression results suggest that higher wind speeds and lower temperatures positively correlate with energy consumption and negatively correlate with the total energy regeneration rate. This effect is especially pronounced at freezing temperatures. The implication of these results is that through their impact on energy consumption and vehicle range, weather effects will influence the profitability of fleet electrification as well as the optimal fleet size, charging infrastructure, and route schedule.

Hydrogen (H₂) is increasingly valued as a carbon-free energy carrier, however, the environmental impact of the different methods for hydrogen production are sometimes overlooked. This article provides a comprehensive overview of the environmental impacts and costs of a diverse range of methods for producing hydrogen. Ninety-nine life cycle assessments (LCAs) published between 2015 and 2022 are categorised by geography, production method, energy source, goal and scope, and compared by data sources and methodology. A meta-analysis of methodological choices is used to identify a subset of mutually comparable studies whose results are then compared, initially by global warming potential (GWP), then low-GWP scenarios are compared by other indicators. The results show that the lowest GWP is achieved by methods that are currently more expensive (∼US $4–9/kg H₂) compared to the dominant methods of producing hydrogen from fossil fuels (∼US $1–2/kg H₂). The research finds that data are currently limited for comparing environmental indicators other than GWP, such as terrestrial acidification or freshwater eutrophication. Recommendations are made for future LCAs of hydrogen production.

As ambient air pollution increases, governments are imposing traffic management strategies to improve air quality. A common strategy is the implementation of Low Emission Zones (LEZs), which have generated considerable public debate. Nonetheless, little research has explored which factors determine their public acceptability. Previous empirical studies have also typically lacked power for regression analyses and have not determined the relative importance of different predictors. After conducting a large online survey in a UK city, well-powered multiple regression and dominance analyses demonstrated that psychological factors, such as environmental moral obligation, were the most important predictors of LEZ acceptability. However, travel-related and socio-demographic factors, such as distance lived from the LEZ and having dependent children, were also unique and important predictors. Overall, we argue that, whilst psychological factors are important, travel-related and socio-demographic barriers must not be overlooked during LEZ implementation.

Life cycle assessment (LCA) quantifies the whole-life environmental impacts of products and is essential for helping policymakers and manufacturers transition toward sustainable practices. However, typical LCA estimates future recycling benefits as if it happens today. For long-lived products such as lithium-ion batteries, this may be misleading since there is a considerable time gap between production and recycling. To explore this temporal mismatch problem, we apply future electricity scenarios from an integrated assessment model—IMAGE—using “premise” in Brightway2 to conduct a prospective LCA (pLCA) on the global warming potential of six battery chemistries and four recycling routes. We find that by 2050, electricity decarbonization under an RCP2.6 scenario mitigates production impacts by 57%, so to reach zero-carbon batteries it is important to decarbonize upstream heat, fuels, and direct emissions. For the best battery recycling case, data for 2020 gives a net recycling benefit of −22 kg CO2e kWh−1 which reduces the net impact of production and recycling from 71 to 49 kg CO2e kWh−1. However, for recycling in 2040 with decarbonized electricity, net recycling benefits would be nearly 75% lower (−6 kg CO2e kWh−1), giving a net impact of 65 kg CO2e kWh−1. This is because materials recycled in the future substitute lower-impact processes due to expected electricity decarbonization. Hence, more focus should be placed on mitigating production impacts today instead of relying on future recycling. These findings demonstrate the importance of pLCA in tackling problems such as temporal mismatch that are difficult to capture in typical LCA.

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.

The Heart Rate (HR) is a vital sign that is used to assess the physical and mental state of an individual. There is a growing interest in incorporating HR measurement into Driver Monitoring Systems (DMS), providing physiological measurements to help address long-existing road safety issues by minimising human error. In real-world driving scenarios, the HR must be measured using non-contact approaches that avoid distracting or restricting the driver. The most common approaches to non-contact HR measurement use either computer vision (CV) or mm-wave radar, both showing acceptable performances in controlled studies. However, the relative merits of different sensor modalities for real-world scenarios remain unclear, and the potential benefits of a combined approach are unquantified. To address these questions, this paper first proposes and implements non-contact HR measurement architectures for both CV and mm-wave radar systems and characterises their HR estimation performance, using electrocardiography (ECG) to provide ground truth measurements. The effects of distance to sensors and of illumination variations on HR estimation are also studied, showing the relative errors for both modalities to be less than 0.5% for the distances found in practical DMS. These results also highlight the distinctive characteristics of each modality and the benefits of a multi-modality approach for DMS.