Publications

Advanced compression ignition (ACI) operation in gasoline direct injection (GDI) engines is a promising concept to reduce fuel consumption and emissions at part load conditions. However, combustion phasing control and the limited operating range in ACI mode are a perennial challenge. In this study the combined impact of fuel properties and engine control strategies in ACI operation are investigated. A DOE was implemented to determine the sensitivity of engine control parameters on the engine load, combustion noise and stability under low load ACI operation for three RON 98 gasoline fuels, each exhibiting disparate chemical composition. Furthermore, the thermodynamic state of the compression histories was studied with the aid of the pressure-temperature framework. Due to the compression ratio constraints imposed by knock limited SI operation, considerable intake temperature heating was required and ACI operation resulted in the intermediate to high temperature autoignition regime. The olefin containing fuel was found to be most sensitive to intake air heating, thereby enhancing its potential suitability for ACI operation, while still enabling high load SI operation. This work was conducted during Johannes’s employment Argonne National Laboratory.

Fan performance characteristic tests of axial flow fans provide information on the global flow field, based on stable inlet flow field distribution. More information is often required on the local flow distribution existing in the vicinity of the fan blades. In this study, a numerical and experimental investigation of a large diameter axial flow fan is conducted to investigate its local pressure distribution and performance. Two specially constructed M-Fan blades with thirty-five pressure taps at five radial locations are manufactured to conduct blade surface pressure measurements on the blades. The experimental M-Fan results are compared against results from a periodic numerical CFD model of a fan blade modelled in an ISO 5801, Type A test facility configuration. The experimental tests and numerical model correlate well with each other and details of the fan blade performance are provided in the paper. This work was conducted during Johannes’s Master of Mechanical Engineering (MEng) at the University of Stellenbosch.

A key chapter from Abdu's Master's thesis at Oxford Brookes University exploring Supervised Neural Network based Machine Learning Algorithms to Predict and Reduce Emissions in Internal Combustion Engines for Racing Applications was accepted for publication by SAE International 

The paper is titled "A Case for Technology - Forcing Transformative Changes in the Formula 1 Power Unit". Following a systematic methodology, it analytically examines area-specific emissions in F1 race events at different track stadia and proposes new targets for F1 power units in conjunction with current World Health Organization (WHO) and European Commission published exposure ug/m³ standards.

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.

For Alex's MRes summer project, he studied the effects of “Turbocompounding the Opposed-Piston 2-Stroke (OP2S) Engine” with a focus on quantifying the expected Brake Specific Fuel Consumption (BSFC) improvement. In order to achieve this, Alex needed to first create a model of an OP2S engine within GT Power, an engine simulation software package. After the MRes year was finished, Alex was able to turn the summer research project report into a conference paper which was published as part of the SAE WCX World Congress Experience Digital Summit in 2021.

Opposed-piston two-stroke engines reveal degrees of freedom that make them excellent candidates for next generation, highly efficient internal combustion engines for hybrid electric vehicles and power systems. The effect of crankshaft phasing and intake and exhaust port height-to-stroke ratios on a range of scavenging performance parameters and other effects are explored in depth in his latest article in Energies: “The Effect of Crankshaft Phasing and Port Timing Asymmetry on Opposed-Piston Engine Thermal Efficiency”, where it has been shown that lean operation favours port timing asymmetry, whereas, under stoichiometric conditions, best efficiencies are found during symmetrical port timing. 

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.

Howard's MRes Summer Project investigated the effect that AC internal heating had on the spatial temperature homogeneity of Lithium-ion cells. With his supervisor Dr Christopher Vagg, he found that the cylindrical MoliCel and DTP Pouch cells both demonstrated significant temperature gradients when heated from sub-zero temperatures. These gradients increased in magnitude with the size of the cell and for the pouch cells were affected further by the current gradients caused by tabs located at the same end of the battery.

EGR is a useful means to improve fuel economy in spark-ignited gasoline engines, while also reducing NOx emissions due to the lower peak cylinder temperatures. However, the ability of conventional spark ignition systems to reliably ignite dilute mixtures limits the dilution tolerance due to the onset of combustion instability.  In this study, an active pre-chamber system was simulated and experimentally tested with various EGR rates to provide insight into its operation and performance metrics. This included an assessment of the thermodynamic states and composition over different pre-chamber operating conditions. The active pre-chamber extended the EGR dilution limit from 20% for conventional SI to above 30%, with a higher pre-chamber flow rate (more scavenging) resulting in increased combustion stability. A trade-off between the jet momentum and spark timing was observed when analysing the simulations and the pre-chamber jet momentum was found to decrease with increasing EGR rate. This work was conducted during Johannes’s employment Argonne National Laboratory.

Originating from her PhD research on Mobility as a Service (MaaS), Rita wrote a short article for the City Changers website on balancing the benefits and challenges of implementing MaaS in an urban context, providing a few practical steps towards a MaaS implementation.

Thomas and Rob aided a Synchrotron X-Ray diffraction (SXRD) strain analysis experiment on Carbon Fibre Reinforced Polymers (CFRPs), at Diamond Lightsource. This was the first micro-scale quantification of micro-scale lattice strain in carbon fibre. This work determines the effect of load has on the axial and longitudinal strain of turbostratic atomic structure of the composite material.

With ever stricter legislative requirements for CO2 and other exhaust emissions, significant effort by Original Equipment Manufacturers (OEMs) have launched a number of different technological strategies to meet these challenges such as Battery Electric Vehicles (BEVs). However, a multiple technology approach is needed to deliver a broad portfolio of products since battery costs and supply constraints are considerable concerns hindering mass uptake of BEVs. Therefore, further investment in IC engine technologies to meet these targets are being considered, such as lean burn gasoline technologies and other high efficiency concepts such as dedicated hybrid engines. Hence, it becomes of sound reason to further embrace diversity and develop complementary technologies to assist in rapid conclusions in the transition to the next generation hybrid powertrains. One such approach is to provide increased valvetrain flexibility to afford new degrees of freedom in engine operating strategies. Freevalve is an electronically controlled, pneumatic spring-based, valve actuation system enabling independent control of ICE valves conceptualized by Koenigsegg’s Freevalve AB. Developed primarily in line with increasingly strict emissions legislations, preliminary findings have demonstrated that the cam-less engine technology withholds significant potential, offering up to 10% decreased fuel consumption and 60% less cold start emissions on an average drive cycle. This paper aims to demonstrate the most recent valve operating strategies enabled by the cam-less engine technology using a simplified 3-cylinder hybrid 1D engine model in GT-Suite.

Lois Player, AAPS CDT student, co-authors a study that finds that whilst climate anxiety is low amongst the UK public, it may be an important driver of climate action such as cutting down on waste.

The study published in the Journal of Environmental Psychology coincides with a new briefing paper from the Centre for Climate Change & Social Transformations focused on UK public preferences for low-carbon lifestyles. Its analysis suggests that lifestyle changes (for example, reducing car use or eating less meat), are increasingly seen as both feasible and desirable.

In the paper, the authors emphasise the importance of the media as a motivating force for the lifestyle changes required as we decarbonise. They suggest that the media and public discourse about climate anxiety has the power to create a positive vision for a greener, cleaner future which is significantly less dependent on fossil fuels.

Lois explained: “Our results suggest that the media could play an important role in creating positive pro-environmental behaviour change, but only if they carefully communicate the reality of climate change without inducing a sense of hopelessness.”

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.

Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability. To solve the conflicting interests over the two timescales, this paper: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as &#x2018;active&#x2019; actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.

Environmental knowledge is considered an important pre-cursor to pro-environmental behaviour. Though several tools have been designed to measure environmental knowledge, there remains no concise, psychometrically grounded measure. We validated an existing measure in a British sample, confirming that it had good one- and three-factor structures in line with previous literature. For the first time in this field, we built upon previous Classical Test Theory approaches and used discrimination values derived from Item Response Theory to select the best items, resulting in the 19-Item Environmental Knowledge Test (EKT-19). This measure retained a clear factor structure and had moderate-to-good internal reliability, indicating that it is a parsimonious and psychometrically robust measure for the assessment of overall and specific types of environmental knowledge. The theoretical implications and real-world applications of this measure are discussed.

The vehicle control systems domain encounters an increasing number of parameters and calibration targets considering the emerging technologies such as connected vehicles and automated driving. Accordingly, the calibration processes for such systems have become more complex and thus error prone and tedious. Moreover, the derived control policies are not easily transferable between different vehicle configurations, hence, the calibration effort is increasing dramatically with each configuration change. Therefore, the reduction of such efforts needed to setup the control policy is inevitable to further reduce cost of drivetrain development. The proposed methodology therefore is an important means to make BEVs (Battery Electric Vehicles) more attractive for car buyers.

The fast development of the Artificial Intelligence (AI) domain is opening the door to numerous opportunities and applications in the automotive industry. We utilize Reinforcement Learning (RL) techniques to design appropriate control strategies for different vehicle systems, thus improving the conventional approaches and reducing the development effort. Combining the expertise in simulation and big data, we propose a cloud-based solution that runs a high-fidelity simulation to train, test and deploy the thermal management control strategy for a fleet of BEVs. The benefits, among others, are that the RL-based control policies can be designed more rapidly and run more efficient than the traditional rule-based approaches. After deploying the initial model trained against the simulation, we have the capability of collecting data from a fleet of vehicles operating with the latest control strategy. Using collected data, we iteratively train and customize the strategy throughout the operation time.

We have tested the above-mentioned framework on the use case of cabin heating mode selection for BEVs. Our RL agents are trained and evaluated in a model-in-the-loop simulation environment. The policy evaluation is based on the agents’ performance on representative vehicle test measurements (drive cycles). The metrics are selected to quantify the energy efficiency and comfort individually, as well as aggregated to enable a fair comparison. Notably the trained agents achieved better results than the original control policy on most of the individual metrics and significantly better results on the aggregated metric.

At this moment, our framework is tested against simulated vehicle fleet. The first reasonable research question is if the trained control system can be directly transferred to the real vehicle, or whether additional adjustments must be performed to achieve the needed flexibility. Another open question refers to the adequate combination of RL algorithms to achieve even better performance on telemetry data from a connected fleet. Time will tell if the idealized case with continuing on-policy training, or the more complex case using the policy-agnostic offline algorithms, will provide the stronger solution. The main technical contribution of our work is the use case agnostic framework which iteratively improves a conventional rule-based control strategy. Following the automotive V-model, the design-, implementation-, and testing -phase is strictly separated from the in-use phase of a vehicle function. To leverage historic data from the in-use phase, our framework disrupts this classical model and embeds the DevOps and ML-Ops practices into the automotive engineering process.

In this article we suggest a framework which automates the complex and time-consuming creation process of control strategies. The trained control policies provide better results and allow for a continuous improvement after they are finally deployed on a fleet of vehicles.

A pilot study run by the University of Bath in partnership with Bath & North East Somerset Council, Chapter2 Architects and the South West Net Zero Hub

We are grateful to the three commentators on our original article (Donegan et al, 2023), particularly for the diversity of perspectives. Consistent with the global nature of this journal, commentary was derived from: the UK (Professor Sarah Sharples, who wears two UK `hats’, as a Professor of Human Factors at the University of Nottingham and Chief Scientific Adviser for the UK Government’s Department of Transport); Israel (Professor Ben-Elia at Ben-Gurion University of the Negev); and Japan (Professor Fujii at Kyoto University). We are grateful for their engagement and can offer six points of reply to build on their contributions.

Engineering of today’s complex automotive systems relies on heterogeneous, model-based development toolchains to compute various performance measures to demonstrate product quality and support decision making. Despite advances in System Modelling and simulation, their integration often hits a bottleneck due to cumbersome, expensive and error-prone process of manual transformation of information regarding verification criteria from authoritative System Models into domain specific simulation toolchains. To overcome these obstacles, we introduce a novel methodology that facilitates integration of System Models, built using System Modelling Language (SysML), with simulation, to enable evaluation of quantitative Key Performance Indicators (KPIs) at every system life cycle stage. This is demonstrated in a five-step process using an automotive Adaptive Cruise Control (ACC) application. First, we develop a comprehensive SysML model to analyse the system in its intended context. Second, we extract the KPIs from system requirements written in natural language and formalize them in Signal Temporal Logic (STL) to define constraint parameters. Third, we capture the atomic propositions of STL KPIs to establish traceability between constraint parameters, system properties and interfaces. Fourth, we establish correspondence between the System Architecture and domain-specific models. Fifth, simulation capabilities are leveraged to evaluate temporal, quantitative KPIs, providing insight into the system performance in achieving the desired task. This novel integration eliminates the need for manual transformation of information from System Models to simulation toolchains, reduces opportunity for error, and enhances scalability to streamline the development process of automotive systems using Model Based Systems Engineering (MBSE).

This article considers how theories of social cooperation might be helpful in developing policy levers for changing travel behaviours towards environmentally beneficial outcomes, especially in reducing private car use. ‘Theories of cooperation’ can be described as a shift away from a ‘traditional’ economic focus on selfish individuals to one where individuals care what those around them are doing and even sometimes identify with, and think as, groups. We use a simplified ‘game’ to show how game theory offers a very constrained backdrop to thinking about cooperation in a transport setting: it neglects important social factors, both strategic ones and the general social interactions and ease that may be required as a backdrop to cooperation in real life. We then apply this to ‘use cases’ (lift sharing, on-site travel planning, safe cycle storage and peer-to-peer information sharing) that bridge the gap between the abstractions of theories of cooperation, on the one hand, and the practicalities of policymaking and lived reality, on the other. In doing this, we show how cooperation in travel behaviour can develop in two different ways: as emergent social phenomena (for example, the informal-economy approach to car or bicycle repair) and purposeful policy initiatives (for example, rail-fare discounts for two people travelling together, such as the UK’s ‘two together’ railcard). Somewhat reductively, these could be described as ‘bottom-up’ and ‘top-down’ elements within behaviour-change processes. The article shows that: (1) cooperation exists ‘naturally’ in the ‘travel-behaviour policy space’; (2) there is a wealth of opportunities for policy to help make cooperation happen more and/or work better; and (3) this includes opportunities to create the conditions required for cooperation to exist and flourish.

Mobility as a Service (MaaS) integrates multiple transport modes into a single mobility service accessible on-demand. This research addresses the lack of empirical evidence to substantiate the service’s impacts by conducting a systematic literature review on MaaS trials in urban areas. A total of nine trials were reviewed, with MaaS impacts in diverse areas, from economic to environmental effects. Further research calls for long-term trials focused on environmental and political MaaS impacts. Even so, this review provides a foundation on the mobility-related areas that are important to target in future MaaS trials as well as the challenges that need to be addressed when an implementation of the service is attempted.

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.

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.

In response to current environmental, social and accessibility challenges in the mobility sector, this research focuses on promoting the development of integrated sustainable regional transport policies, supported by a thorough analysis of their distributed economic impacts. This is fulfilled with the development of a new GIS-supported extension of a comprehensive methodology that is currently used for appraising local transport interventions. To illustrate the inputs and outputs of the expanded approach, a regional case study was simulated, highlighting the potential for this methodology to assist in (1) optimising the financial balance between electrification and modal-shift strategies, (2) anticipating and analysing the multiple economic impacts of multimodal transport services (e.g., Mobility as a Service) and (3) understanding how equal the benefits of these policies are across the region. This research will provide novel contributions to the field of transport research and policy development by introducing a comprehensive methodology that quantifies and maps the distributed economic impacts of regional transport policies. This will, consequently, enable the economic outputs of these policies to be easily visualised, analysed and shared with mobility stakeholders, fostering a better understanding of their urban–rural distribution, and promoting the strategic development of sustainable and equitable regional transport systems.

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.

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 occurence 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.