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This paper presents a simulation model for operation of a power-to-gas plant for the purposes of project feasibility and control strategy analysis. The plant is translated into an optimization problem formulation using a modified unit commitment style technique. A pilot project in France is presented as a case study for simulation model application and results from running the simulation are presented. Using maximizing production and minimizing operational cost as two separate objective functions, the resulting output data are analysed. The results show the influence of wind power, natural gas grid availability and electricity prices on production, highlighting the importance of their inclusion in power-to-gas plant modeling, as well as the consideration of more realistic equipment modeling in feasibility studies.

The deployment of a FC range extender can be asuited solution to overcome the battery autonomy limitations in electrical vehicles and hybridconfigurations. In the framework of the European project Glantleap, a Polymer Electrolyte MembraneFuel Cell (PEMFC) range extender is integrated in an electrical bus to support the batteries' packload demand. The prediction of both the FC ageing and its interactions with the balance of plant(BoP) is considered as a strategical approach to anticipate and consequently reduce the FC systemperformance degradations. A PEMFC stack model has been developed using the Energetic MacroscopicRepresentation formalism. The model integrates the stack ageing laws and is adapted for on-boardprognostic and health management purposes. This paper presents shortly the model and Itsvalidation. For this purpose, the voltage losses increase with the stack ageing is presented. Toprove the model genericity, two different ageing simulationresults are proposed and compared with long-term test results. Data are referred to two differentdata-sets linked to two PEMFC stacks of the same technology, but of different generation.

International audience; A management of multi-stacks fuel cell systems is proposed to extend systems useful life in a Prognostics and Health Management (PHM) framework. The problem consists in selecting at each time which fuel cell stacks have to run and which output power has to be chosen for each of them to satisfy a load demand as long as possible. Multi-stacks fuel cell system useful life depends not only on each stack useful life, but also on both the schedule and the operating conditions settings that define the contribution of each stack over time. As the impact of variable operating conditions on fuel cell lifetime is not well-known, a simplified representation of fuel cell behavior under wear and tear is used to estimate the available outputs over time and their associated Remaining Useful Lives (RUL). This health state prognostics model is configured to suit to Proton-Exchange Membrane Fuel Cells (PEMFC) specific characteristics. The proposed scheduling process makes use of an optimal approach based on a Mixed Integer Linear Program (MILP). Efficiency of the associated commitment strategy is assessed by comparison with basic intuitive strategies, considering constant and piecewise constant load demand profiles.

The presentation illustrates the role of process modelling in the Giantleap project, especially for the application of prognostics, through the application of Energetic Macroscopic Representation (EMR). Modelling of ageing of fuel cells and balance-of-plant components is also presented.

The SAPPHIRE results were presented at the Hannover Fair in April 2016, which is one of the most famous industrial fair in Europe. The SAPPHIRE booth was located in the group exhibit “Fuel Cell and Battery Exhibition”, the Europe's largest exhibition in the hydrogen field. More than 30 contacts were established with visitors coming from 16 different countries. A presentation of the results has been done by the coordinator in the technical forum, the video is available on the fair website as well as on Youtube.

International audience; Proton Exchange Membrane Fuel Cells (PEMFC) are promising energy converters but still suffer from a too short life duration. Applying Prognostics and Health Management seems to be a great solution to overcome that issue. More precisely, developing prognostics to anticipate and try to avoid failures is a critical challenge. To tackle this problem, a hybrid prognostics is proposed. It aims at predicting the power aging of a PEMFC stack working at constant operating condition and constant current solicitation. The main difficulties to overcome are the lack of adapted modeling of the aging for prognostics and the occurrence of disturbances creating recovery phenomena through the aging. Consequently, this work propose a new empirical modeling for power aging that takes into account these recoveries based on different features extracted from the data. These models are used in a joint particle filters framework directly initialized by an automatic parameter estimate process. When sufficient data is available, the prognostics can give very accurate behavior predictions compared to experimentation. Remaining useful life estimates can be given with an error smaller than 5% for a horizon of 500 hours on a life duration of 1750 hours which is clearly long enough for decision making.

The first workshop of the SAPPHIRE Project was held in Coimbra, Portugal, the 26th of October, as an invited session of the international IEEE VPPC (350 attendees). It gathered 30 attendees to listen to 6 presentations and was chaired by two SAPPHIRE partners. The workshop was advertised on the conference website, the booklet and the proceedings USB keys. The full articles related to the 5 scientific presentations will be available on-line on the IEEE-Xplore website.

National audience; Modélisation orientée vers le système Pile à Combustible

International audience; Fuel Cell (FC) systems are promising power-generation sources that are more and more presented as a good alternative to current energy converters such as internal combustion engines. They suffer however from insufficient durability for stationary and transport applications, and lifetime may be improved. A greater understanding of underlying wearing processes is needed in order to improve this technology. However, FCs are in essence multi-physics and multi-scales systems (from the cells to the whole power system), which makes a modeling step of behaviors and degradation very difficult, even impossible. Thereby, data-driven Prognostic and Health Management (PHM) principles (as defined in condition-basedmaintenance scheme CBM) appear to be of great interest to face with the problems of health assessment and life prediction of FCs. According to all this, the aim of this paper is to present the current state of the art on PHM for FCs. Developments emphasize on PHM of the Proton-Exchange Membrane Fuel Cells (PEMFC) stack. The paper is organized so that important aspects like "behavior and losses FCs", "observation techniques", and "advanced PHM techniques" are addressed. Also, a taxonomy of existing works on PHM of PEMFC is given accordingly to the processing layers of CBM. The whole enables PHM practitioners as well as FCs experts to get a better understanding of remaining challenging issues.

International audience

International audience; This paper presents a macroscopic model of solid oxide fuel cell (SOFC) with the aim to perform a simulation of the whole generator. Three sub-models have been developed to take into fluidic, thermal and electrical phenomena. The fluidic sub-model is based on an equivalent circuit based on electrical analogy. Pressure drops in channels are modelled by resistances and the fluid accumulation in the volume is modelled by capacitor. Each electrode compartment (channel+electrode) is represented by two resistances and one capacitor. We have used this model to calculate the pressure at the catalytic sites and gas flows at fuel cell input and output. The electrical response is based on the classical Nernst potential equation, activation, ohmic and concentration overvoltages. The thermal modelling is based on a (2D) nodal network. Two aspects are studied in this article (conduction and the convection heat transfer). Results have been validated on a 5 cell stack.

The presentation illustrates the role of process modelling in the Giantleap project, especially for the application of prognostics, through the application of Energetic Macroscopic Representation (EMR). Modelling of ageing of fuel cells and balance-of-plant components is also presented.

In proton exchange membrane fuel cells (PEMFC) domain, to predict the Remaining Useful Life (RUL) under dynamic operations is currently object of main interest both for research and PEMFC’ makers applications.