This EPSRC First Grant project will concentrate on the use of so-called 'Electrophoretic Deposition (EPD)' to manufacture energy storage electrodes with spatially distributed properties; in order to further advance the performance of electrochemical power devices. The research is aimed at realising a full capacity utilisation while meeting all relevant power extractions. This will be achieved by developing new electrode designs, manufacture them at a meaningful scale, microstructural characterisation and energy storage measurement. Electrodes built in this way will have their energy storage functions met more rationally than conventional monolithic design. Whilst in-depth investigation of materials chemistry is beyond the scope of this manufacturing centred project, the research will perform exemplary experiments involving Nb2O5 and C, in Li-ion battery context. The improved electrodes will be designed, manufactured and validated in the UK's first full battery prototyping lines in a non-commercial environment at the WMG Energy Innovation Centre. Specifically, this project directly challenges the existing manufacturing paradigm in which electrode designs are driven by outdated manufacturing considerations, such as the casting and calendaring of powder-based viscous slurry. The existing technologies, which are clearly scalable and robust, dominate today's electrode manufacturing for batteries and supercapacitors devices. But, the manufacturing approach greatly limit the 'usable' energy density (Wh/kg) and 'usable' capacity (Ah) at device cell level and creates an undesirable viscous circle. This is because calendaring powder-based electrodes for high fraction of active materials results in pore networks with high tortuosity, filled with undesirable quantity of inactive materials such as polymeric binders and electrical conductivity enhancer carbon black particles. In this context, the electrodes must then be thin for high rate. But, thin electrodes result in high fraction of inactive materials; which consequently lowers the maximum achievable 'usable' energy density and 'usable' capacity. A real-world need therefore persists to expand our knowledge about realising high density active material electrodes, whilst having low pore tortuosity and of adequate electrical conductivity, but is less affected by the demanding manufacturing requirements and engineering constraints. The proposed EPD approach is sufficiently generic that it can be applied for any energy storage materials and their chemistries, and the developed tools, processes and methodologies are common across scale can be of direct relevance for systematic optimisation of any existing Li-ion batteries, beyond Li-ion chemistries (e.g., Na-ion, Mg-ion) and higher energy density electrochemical capacitors (based on metal oxides). In short, this project will explore a new direction: the scientific challenges and technological opportunities enabled by the design of 'high density active material electrodes of spatially distributed properties' through modern approaches in electrochemical manufacturing. The project outcomes are expected to impact scientific understandings of how charged materials and electric field interact, and will create improved electrode designs for future energy storage.

Degradation of lithium battery cells is a complex process occurring over multiple temporal and spatial domains. Improved understanding of cell health is a prerequisite for expanded use of Li-ion battery technology in many challenging applications. Early detection of changes in critical parameters would enable performance assessment and degradation forecasting, as well as providing a route to predict the most likely eventual failure modes. Parameter detection requires the ability to measure a diverse set of static and dynamic properties that elucidate the state of a battery system. To enable efficient and safe battery operation, diagnostic schemes need to be fast, accurate, and reliable, work in near real-time, and detect potential faults as early as possible; to enable widespread practical adoption, parameter detection must be achieved with minimal added cost. In tandem, the need to run accurate in-service battery models is critical, and would enable model-based control. Second only to safety monitoring of voltage and temperature, state-of-charge (SOC) estimation is the most important function of a battery management system (BMS). Better BMS SOC could help maximize battery performance and lifetime, but is often accurate to only +/- 10% - and simple methods to improve this accuracy do not currently exist. Models capable of predicting Li-ion performance under modest conditions are highly advanced. But significant progress is still needed to couple operational models suitable for the diagnosis and prognosis of degradation and failure with models of degradation mechanisms. Generally faults and the resulting degradation manifest as capacity or power fade and often state-of-the-art techniques such as X-ray CT, open circuit voltage measurements, and thermal measurements are used to characterise the degradation. This proposal brings together a world-class team to address the critical issue of degradation and health estimation for leading lithium-ion-battery chemistries. We place particular focus on Translational Diagnostics, which we define as diagnostic methods that translate across length scales, across different domains, and across academic research into industry practice. Key outputs from our work will be a suite of new and validated diagnostic tools integrated with battery models for both leading and emerging lithium-ion and sodium- ion battery chemistries. We aim to ensure that these diagnostic tools are capable of cost-effective deployment on both small and large battery systems, and able to run in real time with sufficient accuracy and reliability, such that safer, more durable and lower cost electrochemical energy storage systems can be achieved

The "Internet of Food Things" will create an interdisciplinary network that defragments and expands the UK's food digital economy. Food and drink is the largest manufacturing sector of the UK economy. The food supply chain from farm to consumer generates £108bn GVA per year and employs 3.9m people. In addition, food has highly significant social and environmental impacts. Obesity alone, including downstream health impacts such as diabetes, heart disease etc, costs the UK economy £49bn per annum. There are still c. 1,000,000 cases of food poisoning per year costing £1.5bn p.a.. Food generates up to 30% of the UK's road freight, but 10MT of food, generating 20MTCO2e of GHG emissions, are wasted each year. Digital technology has the potential to transform the food chain, for example, opportunities (that map onto the EPSRC DE Network strategy) include but are not limited to; - New business models via distributed ledger technology (DLT) to underpin the traceability of food. The recent Holmes report identified food as one of the key seven UK industry sectors most likely to benefit from DLTs. - The creation of a "data trust" for the food sector to underpin data sharing, trust and interoperability within complex supply chains. - Wide scale application of the internet of things (IoT) for the service community, for example, the use of IoT by domestic users (refrigerators, cooking devices etc) to improve health outcomes and reduce waste. - The development of new digital labelling protocols that assist with consumer use of food as well as supply chain optimisation, - The use of novel digital technologies (e.g. artificial intelligence) to reduce food waste by optimising whole supply chains from manufacturer to consumer. Hitherto these opportunities have not or are only partially realised. There is an urgent need to defragment the digitally inspired academic community and connect it to food industry practitioners. Although the digital focus is in within EPSRC's remit (IoT, blockchain, data trusts, interoperability issues), we will multiply impact by including interdisciplinary contributions from food science and technology practitioners, policy makers, engineers, management specialists and colleagues in social and behavioural sciences. The network will include academia, industry and consumer interests. The industry interest covers the whole food and digital innovation chain including food manufacturers (e.g. Food and Drink Federation, EPSRC Food CIM), IoT and digital specialists (Siemens and IMS Evolve), the HVM Catapult and regulators such as the Food Standards Agency and GS1 the international agency that sets data standards (bar codes) for retail. Consumers will be represented through out, but the inclusion of food retailers within the consortium provides access to unrivalled data sets demonstrating behaviours. The DE network will facilitate a number of key actions, including a marketing, social media and work shop / conference campaign that yields a large scale (up to 500 persons) network who have mutual interests within the food digital domain. We will host one main conference per year and in addition 3 facilitated workshops p.a. to deep dive key questions within the food domain. We will fund a range of pilot studies (£350K applied) and detailed reviews to underpin horizon scanning. All the research challenges will be co created with industry. We expect that the network will facilitate onward research funding and catalyse interest in the food digital economy. In addition to network activities, we will deliver a comprehensive pathway to impact that engages professional practitioners as well as the general public and schools.

We are witnessing huge global shifts towards cleaner growth and more resource efficient economies. The drive to lower carbon emissions is resulting in dramatic changes in how we travel and the ways we generate and use energy worldwide. Electrical machines are at the heart of the accelerating trends in the electrification of transport and the increased use of renewable energy such as offshore wind. To address the pressing drivers for clean growth and meet the increasing demands of new applications, new electrical machines with improved performance - higher power density, lower weight, improved reliability - are being designed by researchers and industry. However, there are significant manufacturing challenges to be overcome if UK industry is going to be able to manufacture these new machines with the appropriate cost, flexibility and quality. The Hub's vision is to put UK manufacturing at the forefront of the electrification revolution. The Hub will address key manufacturing challenges in the production of high integrity and high value electrical machines for the aerospace, energy, high value automotive and premium consumer sectors. The Hub will work in partnership with industry to address some common and fundamental barriers limiting manufacturing capability and capacity: the need for in-process support to manual operations in electrical machine manufacture - e.g. coil winding, insertions, electrical connections and wiring - to improve productivity and provide quality assurance; the sensitivity of high value and high integrity machines to small changes in tolerance and the requirement for high precision in manufacturing for safety critical applications; the increasing drive to low batch size, flexibility and customisation; and the need to train the next generation of manufacturing scientists and engineers. The Hub's research programme will explore new and emerging manufacturing processes, new materials for enhanced functionality and/or light-weighting, new approaches for process modelling and simulation, and the application of digital approaches with new sensors and Industrial Internet of Things (IoT) technologies.

The manufacturing industry, with the drive towards 'Industrie 4.0', is experiencing a significant shift towards Digital Manufacturing. This increased digitisation and interconnectivity of manufacturing processes is inevitably going to bring substantial change to worker roles and manual tasks by introducing new digital manufacturing technologies (DMT) to shop floor processes. At the same time, the manufacturing workforce is itself also changing - globally and nationally - comprising of an older, more mobile, more culturally diverse and less specialist / skilled labour pool. It may not be enough to simply assume that workers will adopt new roles bestowed upon them; to ensure successful worker acceptance and operational performance of a new system it is important to incorporate user requirements into Digital Manufacturing Technologies design. In the past, Human Factors has shaped the tools used in manufacturing, to make people safe, to make work easy, and to make the workforce more efficient. New approaches to capture and predict the impact of the changes that these new types of technologies, such as robotics, rapidly evolvable workspaces, and data-driven systems are required. These approaches consist of embedded sensor technologies for capture of workplace performance, machine learning and data analytics to synthesise and analyse these data, and new methods of visualisation to support decisions made, potentially in real-time, as to how digital manufacturing workplaces should function. The DigiTOP project will develop the new fundamental knowledge required to reliably and validly capture and predict the performance of a digital manufacturing workplace, integrating the actions and decision of people and technology. It will deliver this knowledge via a Digital Toolkit, which will have three elements: i) Specification of sensor integration and data analytics for performance capture in Digital Manufacturing ii) Quantitative analysis of the impact of four industrial Digital Manufacturing use cases iii) Online interactive tool(s) to support manufacturing decision making for implementation of Digital Manufacturing Technologies The DigiTOP project brings together a team with expertise in manufacturing, human factors, robotics and human computer interaction, to develop new methods to capture and predict the impact of Digital Manufacturing on future work. This project will work closely with a range of industry partners, including Jaguar Landrover, BAE Systems, Babcock International and the High Value Manufacturing Catapult to co-create industry-specified use cases to examine. The overall goal of DigiTOP is to produce a toolkit, derived from new fundamental engineering and science knowledge, that will enable industry to increase productivity, support Digital Manufacturing Technology adoption and de-risk the implementation of future Digital Manufacturing Technologies through the consideration of human requirements and capabilities.