The vision for the AlCoVes (Alumotor for Commercial Vehicles) is to demonstrate a highly-innovative traction motor that meets vehicle and package requirements for BEV LCVs, at lower cost than current technology, with reduced life-cycle impact and a domestic supply chain The motor technology being developed presents a disruptive approach, enabling high speed and power density, while also removing rare earth or other magnetic materials. The increased power density enables volume and mass reduction, improving vehicle efficiency. By eliminating non-conventional materials, our motor is suitable for manufacture within the UK without reliance on materials that are subject to price volatility, or subject to manipulation or restrictions from foreign governments. This drives the price of motors down, whilst also reducing the lifecycle impact of the motors.

HeatWave will explore the use of waste exhaust heat and microwaves, to increase the calorific value of fuel for IC engines. This feasibility study is preparatory to industrial research. It brings together two large organisations looking to share knowledge and prove an early stage technology, currently being developed for the aerospace sector, in the automotive market. The project is designed to test the basic technical and commercial viability of the core technology, when enhanced by use of engine exhaust heat in the automotive application. This is an innovative, alternative use of waste exhaust heat. The initial commercial application is in Heavy Duty Vehicles, which contribute an increasing proportion of UK road transport CO2 (currently approximately 24%), and where significant carbon savings can be made. Operator Return on investment is expected to be fast and attractive.

The Seebeck effect is a thermoelectric effect whereby a temperature gradient across a material is converted to a voltage, which can be exploited for power generation. The growing concern over fossil fuels and carbon emissions has led to detailed reviews of all aspects of energy generation and routes to reduce consumption. Thermoelectric (TE) technology, utilising the direct conversion of waste heat into electric power, has emerged as a serious contender, particular for automotive and engine related applications. Thermoelectric power modules employ multiple pairs of n-type and p-type TE materials. Traditional metallic TE materials (such as Bi2Te3 and PbTe), available for 50 years, are not well suited to high temperature applications since they are prone to vaporization, surface oxidation, and decomposition. In addition many are toxic. Si-Ge alloys are also well established, with good TE performance at temperatures up to 1200K but the cost per watt can be up to 10x that of conventional materials. In the last decade oxide thermoelectrics have emerged as promising TE candidates, particularly perovskites (such as n-type CaMnO3) and layered cobaltites (e.g. p-type Ca3Co4O9) because of their flexible structure, high temperature stability and encouraging ZT values, but they are not yet commercially viable. Thus this investigation is concerned with understanding and improving the thermoelectric properties of oxide materials based on CaMnO3 and ZnO. Furthermore, not only do they represent very promising n-type materials in their own right but by using them as model materials with different and well-characterised structures we aim to use them to identify quantitatively how different factors control thermoelectric properties. The conversion efficiency of thermoelectric materials is characterised by the figure of merit ZT (where T is temperature); ZT should be as high as possible. To maximise the Z value requires a high Seebeck coefficient (S), coupled with small thermal conductivity and high electrical conductivity. In principle electrical conductivity can be adjusted by changes in cation/anion composition. The greater challenge is to concurrently reduce thermal conductivity. However in oxide ceramics the lattice conductivity dominates thermal transport since phonons are the main carriers of heat. This affords the basis for a range of strategies for reducing heat conduction; essentially microstructural engineering at the nanoscale to increase phonon scattering. The nanostructuring approaches will be: (i) introduction of foreign ions into the lattice, (ii) development of superlattice structures, (iii) nanocompositing by introducing texture or nm size features (iv) development of controlled porosity of different size and architecture, all providing additional scattering centres. Independently, TE enhancement can also be achieved by substitution of dopants to adjust the electrical conductivity. By systematically investigating the effect of nanostructuring in CaMnO3 and ZnO ceramics, plus the development of self-assembly nanostructures we will be able to define the relative importance of the factors and understand the mechanisms controlling thermal and electron transport in thermoelectric oxides. A key feature of the work is that we will adopt an integrated approach, combining advanced experimental and modelling techniques to investigate the effect of nanostructured features on the properties of important thermoelectric oxide. The modelling studies will both guide the experimentalists and provide quantitative insight into the controlling mechanisms and processes occurring at the atom level to the grain level, while the experiments will provide a rigorous test of the calculation of the different thermoelectric properties. We will assess the mechanical performance of optimised n-type and p-type materials, and then construct thermoelectric modules which will be evaluated in automobile test environments.

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

Computational fluid dynamics (CFD) is fundamental to modern engineering design, from aircraft and cars to household appliances. It allows the behaviour of fluids to be computationally simulated and new designs to be evaluated. Finding the best design is nonetheless very challenging because of the vast number of designs that might be explored. Computational optimisation is a crucial technique for modern science, commerce and industry. It allows the parameters of a computational model to be automatically adjusted to maximise some benefit and can reveal truly innovative solutions. For example, the shape of an aircraft might be optimised to maximise the computed lift/drag ratio. A very successful suite of methods to tackle optimisation problems are known as evolutionary algorithms, so-called because they are inspired by the way evolutionary mechanisms in nature optimise the fitness of organisms. These algorithms work by iteratively proposing new solutions (shapes of the aircraft) for evaluation based upon recombinations and/or variations of previously evaluated solutions and, by retaining good solutions and discarding poorly performing solutions, a population of optimised solutions is evolved. An obstacle to the use of evolutionary algorithms on very complex problems with many parameters arises if each evaluation of a new solution takes a long time, possibly hours or days as is often the case with complex CFD simulations. The great number of solutions (typically several thousands) that must be evaluated in the course of an evolutionary optimisation renders the whole optimisation infeasible. This research aims to accelerate the optimisation process by substituting computationally simpler, dynamically generated "surrogate" models in place of full CFD evaluation. The challenge is to automatically learn appropriate surrogates from a relatively few well-chosen full evaluations. Our work aims to bridge the gap between the surrogate models that work well when there are only a few design parameters to be optimised, but which fail for large industry-sized problems. Our approach has several inter-related aspects. An attractive, but challenging, avenue is to speed up the computational model. The key here is that many of these models are iterative, repeating the same process over and over again until an accurate result is obtained. We will investigate exploiting partial information in the early iterations to predict the accurate result and also the use of rough early results in place of the accurate one for the evolutionary search. The other main thrust of this research is to use advanced machine learning methods to learn from the full evaluations how the design parameters relate to the objectives being evaluated. Here we will tackle the computational difficulties associated with many design parameters by investigating new machine learning methods to discover which of the many parameters are the relevant at any stage of the optimisation. Related to this is the development of "active learning" methods in which the surrogate model itself chooses which are the most informative solutions for full evaluation. A synergistic approach to integrate the use of partial information, advanced machine learning and active learning will be created to tackle large-scale optimisations. An important component of the work is our close collaboration with partners engaged in real-world CFD. We will work with the UK Aerospace Technology Institute and QinetiQ on complex aerodynamic optimisation, with Hydro International on cyclone separation and with Ricardo on diesel particle tracking. This diverse range of collaborations will ensure research is driven by realistic industrial problems and builds on existing industrial experience. The successful outcome of this work will be new surrogate-assisted evolutionary algorithms which are proven to speed up the optimisation of full-scale industrial CFD problems.