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.

Potential Impact:
As this proposal addresses the long term issues of the deployment of new types of technology into road vehicles, the whole of the UK road transport sector will benefit from this work. The impacts are wide-ranging, from a step change in the understanding of how key components age through to the development of methodologies that enable the extension of the life of the vehicle while minimising the cost.

There are a number of possible ways of meeting our future CO2 emissions reduction targets in the road transport sector, including advanced technology internal combustion engines, hybrids, and fuel cells. Battery electric propulsion will certainly play a significant role in decarbonising the transport sector (as well as assisting electricity grids with high penetrations of renewable generators) however, with present day technology, electric power is restricted to lightweight, short-range (urban) vehicles. Heavier and/or long-range vehicles will require hybrid low carbon technology. Most of the above scenarios involve the use of power electronics, electrical machines, batteries, capacitors and fuel cells. There is a need for a better fundamental understanding of the science involved in all of these devices, especially as used in vehicles.

This program will advance the understanding and control of these key components, especially the long term effects. This understanding will be captured in a codified set of open models, that will be accessible to all via an open website.

The short term beneficiaries of FUTURE Vehicles research will be other teams working in similar areas of technology; industrial development groups like those at Exide and Rayovac, SAFT and Maxwell, Qinetiq, Morgan, Intelligent Energy, AVL, Ricardo and Zytek. Long term beneficiaries include industrial production groups like those at JLR, Ford, General Motors, Nissan, Honda, Toyota and Lotus, policy makers, such as the DfT and DECC and ultimately the general public who will use vehicles that do not depend on imported petroleum, and do not emit carbon dioxide. In addition, many industrial technology development groups will find the results useful since the proposed work will add important information about new hardware that can be modelled in their simulation work. This will help ensure that the life time cost of the these components becomes more attractive, thus helping to displace the existing high carbon solutions.

Much of policy relies on the ability to predict how technology evolves and diffuses within a society. This relies on having reliable models of the systems. Therefore the work undertaken here will have a direct impact on the understanding of the efficacy and life expectation of advanced low carbon vehicles.

More widely, the results will also be useful to manufacturers of prototype vehicles who are currently restricted by limited understanding of science of these devices. Industrial production groups will benefit in the longer term since they will have the technology to put into the design of their production vehicles.