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 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.

In recent years, a lot of research has been carried out in the field of energy management for full HEVs and EVs. Strategies that are based on heuristics can be easily implemented in a real vehicle by using a rule-based strategy or by using fuzzy logic. To find the global optimal solution, control techniques such as linear programming, quadratic programming, optimal control, especially dynamic programming have been studied. A different approach has been proposed in some recent work. In this approach instead of considering one particular driving cycle for calculating an optimal control law, a set of driving cycles is considered, resulting in a stochastic optimization approach. After considering the optimisation based methods described above, the following observation is made. The principal common drawback of all the aforementioned strategies is consideration of drivability as an afterthought. The drivability is considered in an ad hoc fashion as these approaches are not dynamic model based. At best, techniques such as game-theoretic optimisation utilises quasi-static models which are not sufficient to address drivability requirements. Another important drawback of these strategies is robustness. Theses strategies do not include a feedback control block, so the robust performance of the strategy is not guaranteed as the vehicle parameters deviated from their nominal conditions. Ad hoc adaptation of these strategies for drivability does impact their optimality and therefore a negative impact on the emissions and fuel consumption. The robust multivariable control has extensively been used in the aerospace industry, process control and chemical and petrochemical plants. However, the application of the multivariable control in the automotive industry is scarce and when it comes to the energy management development, there is no application. There exists multivariable based method such as Model Predictive Controls, however, these techniques are mainly time domain based, which has its drawbacks and they have the limitations such as on-line implementation is not possible. The multivariable control design has been used by this author for integration of active chassis systems (Active Roll Control and Active Limited Slip Differential). There is a tremendous opportunity for reducing fuel consumption, emissions, and calibration effort to production utilising this methodology. The user fuel economy data obtained from Toyota Prius has shown that there is approximately 10-15% difference between the average real world and driving cycle fuel economy. This difference is mainly due to the drivability impact. Our conservative projection of fuel savings and CO2 reduction of this proposal is 5-7%. An approximate annual savings of 400 Million, due to reduction in calibration effort, based on a volume projection of 50,000 hybrid vehicles after the end of this project. Furthermore, additional possible benefits and savings are foreseeable due to transferability between platform variants. The aim of this work is to design and develop a multivariable feedback controller to replace the ad hoc design currently used to address robustness and vehicle drivability issues as stated above. The robust feedback multivariable is derived based on the dynamic models of the plant so the drivability requirements are addressed as a part of the design and development of the controller and it is not an afterthought. Furthermore, the lack of robustness is not an issue with the Multivariable controls as they are inherently derived from feedback policies.