An innovative research project led by Jaguar Land Rover, LAtiTuDE investigates new technologies for the Ingenium engine family to improve on its class-leading fuel efficiency whilst maintaining the in-vehicle feel Jaguar and Land Rover customers expect. The collaboration brings together leading expertise from UK engineering organisations Ricardo and GRM, and suppliers Borg Warner and Bosch. The collaborative partnership will research a variable geometry, multi-stage and electronic boosting system integrated with an advanced engine combustion system incorporating leading edge fuel injection equipment and controls. Allied with an optimised engine structure, the research package is targeted to deliver over 10% fuel economy and CO2 improvement compared with current vehicles. The consortium members recognise the importance of collaborative research projects in supporting the UK’s competitiveness and developing skills, innovations and new manufacturing capability throughout the automotive supply chain.

Vehicle energy management (EM) systems currently concentrate on controlling the drivetrain to deliver the requested power to the wheels optimally from one or more energy sources, depending on the level of hybridisation of the drivetrain. Despite the existence of a vast range of such systems, encompassing rule-based to optimisation-based schemes, a number of challenges remain and opportunities exist to realise the next generation of more efficient EM control. The Green Adaptive Control for Future Interconnected Vehicles project aims to directly address these challenges by developing, implementing and testing EM systems that will now be global (simultaneous optimisation of the drivetrain energy, auxiliary systems energy and driving speed rather than only of the drivetrain energy), predictive (optimisation over a 'look ahead' horizon rather than just based on the instantaneous power demand), and newly adaptive (taking into account driver's preferences, traffic and other environmental conditions). The ultimate goal is to reduce by more than 3-5% the fuel consumption of the future fleet of passengers and light duty vehicles for a range of drivetrain architectures (conventional, electric and hybrid electric) and auxiliary systems (cooling systems, and other). To reach this objective this project will design, implement and demonstrate a new generation of EM together with an Adaptive Cruise Control system, which will automatically drive the vehicle at the most appropriate speed. For this to be effective, we also need to make the drivers aware of the benefits and to make small changes in their driving behaviour. Indeed, substantial reductions in energy consumption can be achieved by making small changes to the behaviour of a large number of drivers. Human factors methods will be used in this research to optimise the design of such new EM control systems. The proposed EM systems will have three operating modes: Autonomous, Coaching and Manual, which are all based on the same three layers structure. The first one is the Perception layer, which has the purpose of gathering navigation (e.g. route) information, driving information (e.g. the vehicle position, speed and acceleration), information related to the surrounding vehicles, and finally infrastructure conditions (e.g. the state of the next traffic lights series). We will use this information to feed the Decision layer, which is where the intelligence of the system will lay, and which will also be the core of our project. In the Autonomous mode, the system will manage the car in a much smarter way than a human driver by selecting, case by case, the most appropriate vehicle speed and acceleration taking into account all environmental constraints such as road characteristics, desired time to destination and traffic conditions. Once the EM and speed will be optimised, the Action layer will safely drive the vehicle at the most appropriate speed thanks to the Adaptive Cruise Control system. Even if drivers are not always keen to accept such autonomous systems and want to drive according to their personal style, significant fuel reduction may be achieved by using predictive optimisation, in which the system tries to anticipate the future power demand, which is predicted by the system itself according to the information available. Indeed, by selecting the Manual operating mode, the driver behaviour will be predicted by using a mathematical model that will be appositely developed in this project and eventually we will use such prediction to optimise the EM and reduce fuel consumption. Finally, while using the Coaching operating mode, the most appropriate speed will be calculated by the system and then recommended to the driver by using an appropriate haptic (and possibly visual and acoustic) Human Machine Interface, but the driver will maintain the freedom and the responsibility of keeping the preferred speed.

We propose a new phase of the successful Mathematics for Real-World Systems (MathSys) Centre for Doctoral Training that will address the call priority area "Mathematical and Computational Modelling". Advanced quantitative skills and applied mathematical modelling are critical to address the contemporary challenges arising from biomedicine and health sectors, modern industry and the digital economy. The UK Commission for Employment and Skills as well as Tech City UK have identified that a skills shortage in this domain is one of the key challenges facing the UK technology sector: there is a severe lack of trained researchers with the technical skills and, importantly, the ability to translate these skills into effective solutions in collaboration with end-users. Our proposal addresses this need with a cross-disciplinary, cohort-based training programme that will equip the next generation of researchers with cutting-edge methodological toolkits and the experience of external end-user engagement to address a broad variety of real-world problems in fields ranging from mathematical biology to the high-tech sector. Our MSc training (and continued PhD development) will deliver a core of mathematical techniques relevant to all applied modelling, but will also focus on two cross-cutting methodological themes which we consider key to complex multi-scale systems prediction: modelling across spatial and temporal scales; and hybrid modelling integrating complex data and mechanistic models. These themes pervade many areas of active research and will shape mathematical and computational modelling for the coming decades. A core element of the CDT will be productive and impactful engagement with end-users throughout the teaching and research phases. This has been a distinguishing feature of the MathSys CDT and is further expanded in our new proposal. MSc Research Study Groups provide an ideal opportunity for MSc students to experience working in a collaborative environment and for our end-users to become actively involved. All PhD projects are expected to be co-supervised by an external partner, bringing knowledge, data and experience to the modelling of real-world problems; students will normally be expected to spend 2-4 weeks (or longer) with these end-users to better understand the case-specific challenges and motivate their research. The potential renewal of the MathSys CDT has provided us with the opportunity to expand our portfolio of external partners focusing on research challenges in four application areas: Quantitative biomedical research, (A2) Mathematical epidemiology, (A3) Socio-technical systems and (A4) Advanced modelling and optimization of industrial processes. We will retain the one-year MSc followed by three-year PhD format that has been successfully refined through staff experience and student feedback over more than a decade of previous Warwick doctoral training centres. However, both the training and research components of the programme will be thoroughly updated to reflect the evolving technical landscape of applied research and the changing priorities of end-users. At the same time, we have retained the flexibility that allows co-creation of activities with our end-users and allows us to respond to changes in the national and international research environments on an ongoing yearly basis. Students will share a dedicated space, with a lecture theatre and common area based in one of the UK's leading mathematical departments. The space is physically connected to the new Mathematical Sciences building, at the interface of Mathematics, Statistics and Computer Science, and provides a unique location for our interdisciplinary activities.

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.

The Evoque_e project will design and develop innovative hybrid and electric propulsion systems, integrated structures, power electronics, electric drives and energy optimisation. The project will deliver a technology platform which is scaleable, configurable and compatible. The collaboration of partners is led by Jaguar Land Rover, established large companies and 1st tier suppliers: GKN Driveline (GKN), Zytek Automotive, AVL Powertrain UK Ltd (AVL), TATA Steel and Johnson Matthey (Axeon). Three innovative SMEs: Delta Motorsport, Drive System Design (DSD) and Motor Design Ltd (MDL). Plus three leading Universities: Cranfield University, Bristol University and Newcastle University. For the first time, this unique project develops an integrated approach to system development and optimisation, from design to testing, encompassing three technology vehicle demonstrators: Mild Hybrid Electric Vehicle, Plug-in Hybrid Electric Vehicle and a Battery Electric Vehicle. All vehicles will be based on the Range Rover Evoque platform optimised for high volume production and capable of delivering benchmark performance in terms of cost, weight and sustainable use of materials.