Hybrid electric vehicles (HEV) are far more complex than conventional vehicles. There are numerous challenges facing the engineer to optimise the design and choice of system components as well as their control systems. At the component level there is a need to obtain a better understanding of the basic science/physics of new subsystems together with issues of their interconnectivity and overall performance at the system level. The notion of purpose driven models requires models of differing levels of fidelity, e.g. control, diagnostics and prognostics. Whatever the objective of these models, they will differ from detailed models which will provide a greater insight and understanding at the component level. Thus there is a need to develop a systematic approach resulting in a set of guidelines and tools which will be of immense value to the design engineer in terms of best practice. The Fundamental Understanding of Technologies for Ultra Reduced Emission Vehicles (FUTURE) consortium will address the above need for developing tools and methodologies. A systematic and unified approach towards component level modelling will be developed, underpinned by a better understanding of the fundamental science of the essential components of a FUTURE hybrid electrical vehicle. The essential components will include both energy storage devices (fuel cells, batteries and ultra-capacitors) and energy conversion devices (electrical machine drives and power electronics). Detailed mathematical models will be validated against experimental data over their full range of operation, including the extreme limits of performance. Reduced order lumped parameter models are then to be derived and verified against these validated models, with the level of fidelity being defined by the purpose for which the model is to be employed. The work will be carried out via three inter-linked work packages, each having two sub-work packages. WP1 will address the detailed component modelling for the energy storage devices, WP2 will address the detailed component modelling for the energy conversion devices and WP3 will address reduced order modelling and control optimisation. The tasks will be carried out iteratively from initial component level models from WP1 and WP2 to WP3, subsequent reduced order models developed and verified against initial models, and banks of linear-time invariant models developed for piecewise control optimisation. Additionally, models of higher fidelity are to be obtained for the purpose of on-line diagnosis. The higher fidelity models will be able to capture the transient conditions which may contain information on the known failure modes. In addition to optimising the utility of healthy components in their normal operating ranges, to ensure maximum efficiency and reduced costs, further optimisation, particularly at the limits of performance where component stress applied in a controlled manner is considered to be potentially beneficial, the impact of ageing and degradation is to be assessed. Methodologies for prognostics developed in other industry sectors, e.g. aerospace, nuclear, will be reviewed for potential application and/or tailoring for purpose. Models for continuous component monitoring for the purpose of prognosis will differ from those for control and diagnosis, and it is envisaged that other non-parametric feature-based models and techniques for quantification of component life linked to particular use-case scenarios will be required to be derived. All members of the consortia have specific individual roles as well as cross-discipline roles and interconnected collaborative activities. The multi-disciplinary nature of the proposed team will ensure that the outputs and outcomes of this consortia working in close collaboration with an Industrial Advisory Committee will deliver research solutions to the HEV issues identified.

Energy storage is a great research challenge of our time: the rechargeable Li-ion battery (LiB) has transformed portable electronics; it is the technology of choice for electric and hybrid electric vehicles, and it has a key role to play in grid scale storage applications where it can facilitate more effective and greater use of renewable energy. However, today's consumer electronic Li-ion batteries cannot simply be scaled-up for electric vehicles or grid storage, and new generations of lithium-ion batteries are required that deliver enhanced combinations and improved balances of: cost (<£100/kWh), energy density (>300 Wh/kg), power density (> 2000 W/kg), safety (especially fire resistance), calendar life (> 10 yrs) and lifetime (> 3000 cycles). In the past, efforts to address these challenges have often been based on individual researchers or groups focused on science OR engineering. Our vision is that success requires basic research to tackle these hurdles, but one that employs an integrated programme across a range of science and engineering uniting materials chemists, materials modelling across lengths from the nano-scale to the device-scale, manufacturing engineers, skills in in-situ characterization techniques, in communication with supply chain companies and end-users. Our research spans step-changes in LiBs as well as more radical ideas and technologies beyond LiBs, such as the lithium-air battery. We will - Identify new classes of anode materials to overcome the disadvantages of poor safety and low power inherent to the graphitic anodes currently used in almost all commercial LIBs. - Develop 3D polymer/ceramic interpenetrating networks as protective membranes for lithium metal electrodes, transforming the energy density of the anode. - Develop novel polymer electrolytes and methods to process them, leading to the viable (and much safer) solid-state alternatives to flammable liquid electrolytes in lithium batteries. - Identify and reduce sources of resistance in solid electrolyte-electrode interfaces - Enable the use of higher voltage cathode materials via the use of solid-state electrolytes and coatings. - Address the major hurdles facing the realisation of the game changing lithium-air battery by investigating new redox mediating molecules to reduce charging voltages and electrocatalysts to increase discharge voltages. - Use innovative manufacturing methods to produce 3D and structured composite electrodes to achieve increased energy density, and higher rate performances and lifetime. - Integrate the new materials and electrode structures into lab scale battery devices thus demonstrating the potential of our advances - Engage with all stakeholders in lithium batteries in the UK and abroad - be an advocate for Li batteries, disseminate results. -Train a new cohort of people with experience of working in a team spanning a wide range of science and engineering skills

The global Connected & Autonomous Vehicles (CAV) industry is estimated to be worth over £50billion (by 2035), with the UK CAV industry being projected over £3billion. Additionally, the UK Government's Industrial Strategy aims to bring fully autonomous cars without a human operator on the UK roads by 2021, one of the first countries in the world to achieve this. However, in order to realise this vision and the market potential, safe introduction of CAV is necessary, requiring significant research to overcome diverse barriers (technological, legislative and societal) associated with public deployment of CAV. While prototype CAV technologies have existed for some time now, ensuring the safety level of these technologies has been proving to be a hindrance to the commercialization of CAV technologies. The vision for CAV is coupled with the challenge of testing and safety analysis as it needs complex solutions to include interactions between a large number of variables and the environment. It is suggested that in order to prove that CAV are safer than human drivers, they will need to be driven for more than 11 billion miles. The vision for this fellowship is to support positioning the UK as the world leader in CAV research and innovation for a long lasting societal and economic benefit. This fellowship will develop pioneering testing methodologies and standards to enable robust and safe use of CAV with a focus on creating both fundamental knowledge and applied research methods and tools. At WMG, University of Warwick, UK, we have created a concept of the "evaluation continuum" for CAV, which involves using various environment like digital world, simulated environment, test track testing and real-world for testing. There are two aspects which are common to each of the evaluation continuum environments and also the focus areas of the fellowship research 1) Test Scenarios (input to the environment) 2) Safety Evidence (output of the environment). On the scenarios theme, while the 11-billion-miles requirement has garnered a lot of publicity, the focus needs to be on what happens in those miles (i.e., smart miles which expose failures in CAV) and not on the number of miles themselves. As a part of this fellowship, three approaches will be explored to identify these smart miles. These include 1) using Machine Learning (ML) based methods including Bayesian Optimisation to create test cases for test scenarios, 2) Safety Of The Intended Functionality (SOTIF) (Innovative safety analysis of CAV) based test scenarios and 3) translating real-world data into executable test scenarios for a simulation tool. All these approaches will together contribute to the creation of a UK's National CAV Test Scenario Database, which will help coordinate the research work in various CAV projects part-funded by the UK Government and will prevent "reinventing of the wheel" in each of the projects with respect to test scenario identification. Industry trends in CAV suggest the widespread adoption of machine learning (ML) in the autonomous control systems. ML-systems by their structure are non-deterministic in nature, making the CAV system highly opaque in nature. Therefore, it is difficult to identify the reason of a failure in such ML-based systems and take the corrective measures. Thus, on the safety strand, fundamental research will be conducted as a part of this fellowship to explore how to make ML-based systems interpretable enabling us to explain the results. This is an essential requirement for safety of CAV due to the critical nature of their deployment and the mitigation of risk. In addition, the fellowship will also benefit from the fellow's first-hand experience as the UK's technical representative on the ISO standards committees, providing further insight and a clear route to deliver impact from the proposed research through the development of international standards, while also ensuring that the UK becomes a world leader in this area.

The emerging development of automated driving demands a mutual understanding and a smooth coordination between human driver and vehicle controller, so as to avoid conflict and mismatch in demands, and instead achieve desirable driving performance, smooth and swift transitions which enhance driving safety during complex operating scenarios. However, such driver-vehicle collaboration during automated driving will impact on the driver's attention and cognition and it is important to consider these effects in order to prevent any negative impact on driving. This project aims to achieve a safe engagement and smooth and swift control-authority shift between the driver and the vehicle controller during adaptive automated driving. To this aim, we will first conduct a comprehensive study of driver attention and cognitive control characteristics when interacting with the vehicle controller. An optimal control authority shifting system which considers driver cognition will then be systematically developed and validated. This cross-disciplinary research challenge will be addressed using a unique combination of researchers from engineering, cognitive neuroscience and human factors. The research will not only contribute to the cutting-edge technology innovations in automated driving, but will also result in a major advance in the science of human attention and cognitive control when interacting with automation.

There are two ongoing revolutions in modern automotive industry. The first is the development of autonomous transportation systems which is leading to greatly improved safety, traffic economy, environment and passenger comfort. The second on is the development of advanced propulsion systems, envisaging reduced fuel consumption and exhaust emissions. The intersection of the autonomous transportation systems and advanced propulsion systems is the future trend, which has been revealed in the strategic partnerships between automakers and IT companies. However, there is a big challenge that the environment information collected by autonomous vehicles is poorly used in propulsion systems, especially when vehicles run in fast changing conditions. Only few examples of using environment data to optimise energy efficiency can be found, although the potential benefits in fuel reduction and mission flexibility are great. This project aims to tackle this challenge by developing a cloud-aided learning framework to merge the two themes as integrity. To establish awareness of the environment, the onboard sensors of autonomous vehicles including cameras, light detecting and ranging (LiDAR), ultrasonic, and radar are used to perceive the environment over short distances. The GPS and intelligent transportation systems are used to perceive the environment at a further distance. The combined information enables the autonomous vehicle to establish a comprehensive model of the external environment. Using advanced machine learning algorithms (e.g., dynamic Bayesian network), the environment information can be used to update the propulsion system model in real time. Combining the real-time updated model with dynamic optimisation methods (e.g., adaptive model predictive control), the optimal actions of the propulsion system can be obtained in s systematic way. Employing high performance computing resources on cloud, the computational intensive modelling and optimisation tasks can be cost-effectively addressed. Aligning with the EPSRC Innovation Fellowship priority area of "Robotics and Artificial Intelligence Systems" through its focus on efficient transport, a cloud-in-the-loop testing platform will be built. This framework focuses on sensing, modelling, control, optimisation and computing of energy efficient autonomous vehicles. In a long term vision, the framework can be generalised from a single vehicle to connected and autonomous for further economic benefits.