Fuel Cell Systems (FCS) appear nowadays to be a promising and alternative energy source to face economic and environmental challenges of modern society. However, even if this technology is close to being competitive, it is not yet ready to be considered for large scale industrial deployment: FCS still must be optimized, particularly by increasing their limited lifespan. This involves not only a better understanding but also requires emulating the behavior of the whole system. Additionally, a new area of science and technology emerges: prognostic of FCS is a field of scientific and industrial developments that should be increased. This is the aim of the project: the partners feel a clear motivation for such a research and propose to develop intelligent Prognostics and Health Management (PHM) methods in order to assess the health state of Proton Exchange Membrane Fuel Cell systems (PEMFC), and predict its remaining useful life. To our knowledge, this is an original and still unexplored field: although the research in PHM is continuously increasing, one can note that efforts in the area do not deal with Fuel Cells applications. This may be (partially) explained by the lack of knowledge on the behavior of those systems. Indeed, it is difficult to develop prognostics tools that take into account the inherent uncertainty of not well understood failure mechanisms. In addition, prognostic presents deployment challenges: it is difficult to know how to set a prognostic tool, as well as is there is no systematic way to judge from it without waiting for the irreversible deterioration of the equipment. Following all this, scientific objectives of the project are defined as follows: - develop approaches for reliable prognostics of PEMFC stacks; - facilitate their implementation; in order to move towards a generic approach compatible with industrial constraints. Three related axis of developments are expected. 1. The first axis deals with handling the prognostic process. Here, the aim is not only to be able to estimate the remaining useful life of the fuel cell (provide prognostics estimates), but also to improve the capabilities of the developed prognostics tools by quantifying and controlling their inherent error of estimates. Several approaches will be considered: a model-based prognostic tool based on a Bond Graph approach with parametric uncertainty, and data-oriented prognostics tools by extending "signal" and "connexionist" approaches (like neural networks and neuro-fuzzy systems). The development of an hybrid approach will also be addressed. 2. The second axis scopes to enhance the applicability of prognostics tools. The purpose of this part of works is to look for solutions that enable systematizing the building of a prognostics system while reducing the influence of arbitrary human choices, as well as reducing its learning and/or parameterization times. Several options will be studied: the construction of adaptive and parsimonious systems, the definition of relevant "learning patterns", the identification of singular representatives learning samples, among others. 3. The third axis will focus on the industrial adoption process for fuel cells. The diffusion and transfer of results to industrials cannot be conceived without a precise knowledge of their expectations. It is thus necessary to understand the diffusion process of the Fuel Cell technology in the industrial world, and to identify the corresponding bottlenecks, other than those related to technological aspects. The project consortium has a competence in humanities and social sciences that will help to develop this axis.

The project seeks to make a significant and original contribution to efforts being marshalled by the UK Research Councils - following government level advice - to improve the international profile and national strategic impact of energy modelling in the UK. It will provide valuable insights - both quantitative and qualitative - into questions of key importance fpr policymakers and the power sector as they seek to square the circle of emissions reduction and a viable, secure energy supply by 2050. It will address in the context of the electricity network, perceived general weaknesses in whole energy systems modelling - from the closely related standpoint of complex systems research - in the areas of end-use behaviour, technology dynamics, and energy in industry. According to the Committee on Climate Change, to meet the Government's challenging emissions reduction goals would require almost complete decarbonisation of electricity generation by around 2030. It is highly likely therefore that electricity will become even more significant than its current 37% share of emissions implies, as moves towards the electrification of heat and transport accelerate. This has huge implications for the electricity infrastructure. Another fundamental assumption is that there will be a restructuring of the electricity market as signalled in the Government's recent Draft Energy Bill. These potentially game-changing developments will shape the project. The approach used will be based on De Montfort University's innovative agent-based electricity market modeling Framework known as CASCADE, which so far has been used mainly to explore the relationship between end-users and smart technology; the expected rapid infiltration of distributed generators at low and medium voltage levels; more active participation by demand entities; new communication protocols; and smart energy controllers. The Framework will be developed to improve and expand its models of large-scale generation, the transmission network, the wholesale electricity market and the ability to model technology adoption and diffusion on long time-scales, in order to address the interrelationships and complex effects of such possible developments as Locational Pricing; richer agency models for Distribution Network Operators; congestion management based on economic signals; a restructured Balancing Mechanism. A key principle will be that the modelling methods, assumptions and and limitations will be made clear to stakeholders through accessible data description and highly focussed and structured dissemination activities.

Major challenges in urban governance concern interlinking food, water and energy systems, making these linkages understandable to all stakeholders (government, science, business, and citizens), and facilitating cooperation and knowledge exchange among them. The project titled "Creating Interfaces" will address these challenges by developing and testing innovative approaches for local knowledge co- creation and participation through Urban Living Labs in three mid-size cities on water: Tulcea, Romania, Wilmington, USA and Slupsk, Poland. Complemented by previous research and a citizen science toolbox, these Labs comprise a user-defined co-creative approach where research questions, problems, and solutions are decided and implemented with stakeholders themselves.