Contribution au pronostic d'une pile à combustible de type PEMFC - approche par filtrage particulaire.

The development of new energy converters, more efficient and environment friendly, such as fuelcells, tends to accelerate. Nevertheless, their large scale diffusion supposes some guaranties in termsof safety and availability. A possible solution to do so is to develop Prognostics and HealthManagement (PHM) on these systems, in order to monitor and anticipate the failures, and torecommend the necessary actions to extend their lifetime. In this spirit, this thesis deals with theproposal of a prognostics approach based on particle filtering dedicated to PEMFCs.The reasoning focuses first on setting a formalization of the working framework and theexpectations. This is pursued by the development of a physic-based modelling enabling a state ofhealth estimation and its evolution in time. The state estimation is made thanks to particle filtering.Different variants of filters are considered on the basis of the literature and new proposals adaptedto PHM are proposed and compared to existing ones. State of health estimates given by the filter areused to predict the future state of the system and its remaining useful life. All the proposals arevalidated on four datasets from PEMFC following different mission profiles. The results show goodperformances for predictions and remaining useful life estimates before failure.Le développement de nouveaux convertisseurs d’énergie, plus efficients et plus respectueux del’environnement, tels que les piles à combustibles, tend à s’accélérer. Leur diffusion à grande échellesuppose cependant des garanties en termes de sécurité et de disponibilité. Une solution possiblepour ce faire est de développer des solutions de Prognostics and Health Management (PHM) de cessystèmes, afin de mieux les surveiller, anticiper les défaillances et recommander les actionsnécessaires à l’allongement de leur durée de vie. Dans cet esprit, cette thèse porte sur la propositiond’une approche de pronostic dédiée aux piles à combustibles de types PEMFC à l’aide de filtrageparticulaire.Le raisonnement s’attache tout d’abord à mettre en place une formalisation du cadre de travailainsi que des exigences de mise en. Ceci se poursuit par le développement d’un modèle basé sur laphysique permettant une estimation d’état de santé et de son évolution temporelle. L’estimationd’état est réalisée grâce à du filtrage particulaire. Différentes variantes de filtres sont considérées surla base d’une de la littérature et de nouvelles propositions adaptées au PHM sont formulées etcomparées à celles existantes. Les estimations d’état de santé fournies par le processus de filtragesont utilisées pour réaliser des prédictions de l’état de santé futur du système, puis de sa durée devie résiduelle. L’ensemble des propositions est validé sur 4 jeux de données obtenus sur des PEMFCsuivant des profils de mission variés. Les résultats montrent de bonnes performances deprédictions et d’estimations de durée de vie résiduelle avant défaillance

Prognostics of proton exchange membrane fuel cell stack in a particle filtering framework including characterization disturbances and voltage recovery.

International audienceIn the perspective of decreasing polluting emissions and developing alternative energies, fuel cells, and more precisely Proton Exchange Membrane Fuel Cells (PEMFC), represent a promising solution. Even if this technology is close to being competitive, it still suffers from too short life duration. As a consequence, prognostic seems to be a great solution to anticipate PEMFC stacks degradation. However, a PEMFC implies multiphysics and multiscale phenomena making the construction of an aging model only based on physics very complex. One solution consists in using a hybrid approach for prognostics combining the use of models and available data. Among these hybrid approaches, particle filtering methods seem to be really appropriate as they offer the possibility to compute models with time varying parameters and to update them all along the prognostics process. But to be efficient, not only should the prognostics system take into account the aging of the stack but also external events influencing this aging. Indeed, some acquisition techniques introduce disturbances in the fuel cell behavior and a voltage recovery can be observed at the end of the characterization process. This paper proposes to tackle this problem. First, PEMFC fuel cells and their complexities are introduced. Then, the impact of characterization of the fuel cell behavior is described. Empirical models are built and introduced in both learning and prediction phases of the prognostics model by combining three particle filters. The new prognostic framework is used to perform remaining useful life estimates and the whole proposition is illustrated with a long term experiment data set of a PEMFC in constant load solicitation and stable operating conditions. Estimates can be given with an error less than 5% for life durations of more than 1000 hours. Finally, the results are compared to a previous work to show that introducing a disturbance modeling can dramatically reduce the uncertainty coming with the predictions

Prognostics of PEM fuel cell in a particle filtering framework.

International audienceProton Exchange Membrane Fuel Cells (PEMFC) suffer from a limited lifespan, which impedes their uses at a large scale. From this point of view, prognostics appears to be a promising activity since the estimation of the Remaining Useful Life (RUL) before a failure occurs allows deciding from mitigation actions at the right time when needed. Prognostics is however not a trivial task: 1) underlying degradation mechanisms cannot be easily measured and modeled, 2) health prediction must be performed with a long enough time horizon to allow reaction. The aim of this paper is to face these problems by proposing a prognostics framework that enables avoiding assumptions on the PEMFC behavior, while ensuring good accuracy on RUL estimates. Developments are based on a particle filtering approach that enables including non-observable states (degradation through time) into physical models. RUL estimates are obtained by considering successive probability distributions of degrading states. The method is applied on 2 data sets, where 3 models of the voltage drop are tested to compare predictions. Results are obtained with an accuracy of 90 hours around the real RUL value (for a 1000 hours lifespan), clearly showing the significance of the proposed approach

Prognostics and Health Management of PEMFC - state of the art and remaining challenges.

International audienceFuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being dustrial deployment: FC still must be optimized, particularly by increasing their limited lifespan. This involves a better understanding of wearing processes and requires emulating the behavior of the whole system. Furthermore, a new area of science and technology emerges: Prognostics and Health Management (PHM) appears to be of great interest to face the problems of health assessment and life prediction of FCs. According to this, the aim of this paper is to present the current state of the art on PHM of FCs, more precisely of Proton-Exchange Membrane Fuel Cells (PEMFC) stack. PHM discipline is described in order to depict the processing layers that allow early deviations detection, avoiding faults, deciding mitigation actions, and thereby increasing the useful life of FCs. On this basis, a taxonomy of existing works on PHM of PEMFC is given, highlighting open problems to be addressed. The whole enables getting a better understanding of remaining challenging issues in this area

Remaining useful life estimates of a PEM fuel cell stack by including characterization-induced disturbances in a particle filter model.

International audienceProton Exchange Membrane Fuel Cells (PEMFC) are available for a wide variety of applications such as transportation, micro-cogeneration or powering of portable devices. However, even if this technology becomes close to competitiveness, it still suffers from too short life duration to pretend to a large scale deployment. In a perspective of a longer lifetime, prognostics aims at tracking and anticipating degradation and failure, and thereby enables deciding mitigation actions to increase life duration. Yet, the complexity of degradation phenomena in PEMFC can make prognostic implementation really tough. Indeed, a PEMFC implies multiphysics and multiscale phenomena making the construction of a physics-based aging model very complex. Moreover, prognostics should also take into account external events influencing the aging. Among them, characterization techniques such as electrochemical impedance spectroscopies and polarization curves introduce disturbances in the stack behavior, and a voltage recovery is observed at the end of characterizations process. It means that irreversible degradation and reversible decrease of performances have to be considered. This work proposes to tackle this problem by setting a prognostics system that includes disturbances' effects. We propose a hybrid prognostics approach by combining the use of empirical models and available data. In an evolving system like a fuel stack, a particle filtering framework seems to be really appropriate for life prediction as it offers the possibility to compute models with time varying parameters and to update them all along the prognostics process. Moreover, it offers a great adaptability to include characterization effects and allows giving prediction with a quantified uncertainty. The logic of the work is the following. First, it is shown that simple empirical models only taking into account the aging are very limited in terms of prognostics performances. Then, some features describing the impact of characterization on the stack behavior and aging are extracted and a more complete prognostics model is built. Finally, the new prognostic framework is used to perform remaining useful life estimation and the whole proposition is illustrated with a long term experiment data set in constant current solicitation and stable operating conditions

PHM of Proton-Exchange Membrane Fuel Cells - A review.

International audienceFuel 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

Prognostics of PEM fuel cells under a combined heat and power profile

International audiencePrognostics have started to be applied to Proton Exchange Membrane Fuel Cells (PEMFC). Indeed, it seems an interesting solution to help taking actions that will extend their lifetime. PEMFC are promising solution for combined heat and power generation (µCHP).As power suppliers, they cannot afford running to failure. This work presents a prognostics application on a PEMFC following a µCHP profile. A critical issue with such a mission profile is to be able to model the variation of the power demand. So a key point of this work is the presentation of a model introducing the time dependency of the mission profile as well as the degradations of different inner components of the PEMFC. This model starts from a classical polarization expression transformed based on a detailed understanding of the degradation phenomena and the introduction of time-varying parameters. This model is able to follow accurately the behavior of the PEMFC during its functioning. It is then used to perform prognostics and predict the future behavior of the stack with a particle filter-based framework.The results are very encouraging as the behavior predictions are accurate, with a low uncertainty and an horizon as great as thirty days

contribution to prognostics of fuel cells of PEMFC type : approach based on particle filtering

Le développement de nouveaux convertisseurs d’énergie, plus efficients et plus respectueux de l’environnement, tels que les piles à combustibles, tend à s’accélérer. Leur diffusion à grande échelle suppose cependant des garanties en termes de sécurité et de disponibilité. Une solution possible pour ce faire est de développer des solutions de Prognostics and Health Management (PHM) de ces systèmes, afin de mieux les surveiller, anticiper les défaillances et recommander les actions nécessaires à l’allongement de leur durée de vie. Dans cet esprit, cette thèse porte sur la proposition d’une approche de pronostic dédiée aux piles à combustibles de types PEMFC à l’aide de filtrage particulaire.Le raisonnement s’attache tout d’abord à mettre en place une formalisation du cadre de travail ainsi que des exigences de mise en. Ceci se poursuit par le développement d’un modèle basé sur la physique permettant une estimation d’état de santé et de son évolution temporelle. L’estimation d’état est réalisée grâce à du filtrage particulaire. Différentes variantes de filtres sont considérées sur la base d’une de la littérature et de nouvelles propositions adaptées au PHM sont formulées et comparées à celles existantes. Les estimations d’état de santé fournies par le processus de filtrages ont utilisées pour réaliser des prédictions de l’état de santé futur du système, puis de sa durée devie résiduelle. L’ensemble des propositions est validé sur 4 jeux de données obtenus sur des PEMFC suivant des profils de mission variés. Les résultats montrent de bonnes performances de prédictions et d’estimations de durée de vie résiduelle avant défaillance.The development of new energy converters, more efficient and environment friendly, such as fuelcells, tends to accelerate. Nevertheless, their large scale diffusion supposes some guaranties in termsof safety and availability. A possible solution to do so is to develop Prognostics and HealthManagement (PHM) on these systems, in order to monitor and anticipate the failures, and torecommend the necessary actions to extend their lifetime. In this spirit, this thesis deals with theproposal of a prognostics approach based on particle filtering dedicated to PEMFCs.The reasoning focuses first on setting a formalization of the working framework and theexpectations. This is pursued by the development of a physic-based modelling enabling a state ofhealth estimation and its evolution in time. The state estimation is made thanks to particle filtering.Different variants of filters are considered on the basis of the literature and new proposals adaptedto PHM are proposed and compared to existing ones. State of health estimates given by the filter areused to predict the future state of the system and its remaining useful life. All the proposals arevalidated on four datasets from PEMFC following different mission profiles. The results show goodperformances for predictions and remaining useful life estimates before failure

Combined predictions for prognostics and predictive control of transportation PEMFC

International audienceTo help a transition of prognostics approaches toward industries, it is necessary to show that they can be adapted in every situation. Nowadays, a lot of prognostics applications focus on energy sources, among them Proton Exchange Membrane Fuel Cells (PEMFC) can be cited. Due to their wide range of applications, different prognostics adaptations should be considered. Issues coming with PEMFC used for transportation are considered in this paper. Different time scales are involved, requiring a modification of the existing approaches. This paper proposes a solution to perform short-term and long-term predictions on a PEMFC stack used in a transportation application based on particle filters. After proposing different data reductions, the adapted particle filters configuration for this use case is determined. Accurate State of Health (SoH) estimations and predictions, with high coefficient of determination, are obtained. Behavior predictions are also performed and show promising results

PEMFC aging modeling for prognostics and health assessment.

International audienceWhen a system suffers from a too short lifetime, applying prognostics is a good solution to help taking actions extending its life duration. This solution is applied to Proton Exchange Membrane Fuel Cell (PEMFC) stacks in this paper. An important requirement for prognostics of a PEMFC stack is a well-defined framework as well as a great understanding of the degradation mechanisms and failures occurring within the stack. These requirements are addressed here and allow building an efficient model integrating the different levels (stack – cells - components) as well as the multiple causes leading to degradation. Such a model enables then health assessment and remaining useful life predictions. This work proposes a model built based on a selection of critical degradations and to validate it for both state of health estimationsand prognostics. The results show that the stack's state of health during aging can be followed accurately with coefficients of correlation greater than 0.9. Also, the behavior of the systemcan be assessed with a coefficient of correlation greater than 0.9 showing the great predictive capabilities of the model