Data-driven diagnosis of PEM fuel cell: A comparative study

International audienceThis paper is dedicated to data-driven diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC). More precisely, it deals with water related faults (flooding and membrane drying) by using pattern classification methodologies. Firstly, a method based on physical considerations is defined to label the training data. Secondly, a feature extraction procedure is carried out to pick up the significant features from vectors constructed by individual cell voltages. Finally, a classification is adopted in the feature space to realize the fault diagnosis. Various feature extraction and classification methodologies are employed on a 20-cell PEMFC stack. The performances of these methodologies are compared

Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.Peer ReviewedPostprint (author's final draft

ANOVA Method Applied to PEMFC Ageing Forecasting Using an Echo State Network

International audienceAccording to the International Energy Agency, an increase of the requests of energy of 40% could arise in the next decades, mainly due to the emergence of developing countries. The problem with the nowaday energy system is the use of fossil energy, which is limited and attempt to disappear in the near future. Thus an energy transition has to begin in order to replace the fossil fuels and anticipate their disappearance. Consequently, in recent years, the promotion and development of renewable energy have been realized. One of this renewable energy, the energy vector hydrogen, appears to be a promising solution, mainly due to interesting performance of Fuel Cells (FC) systems and hydrogen abundance on Earth (it is still important to underline that the hydrogen does not exist in natural form). However, this research area is still subject to scientific and technological bottlenecks. One of these major bottlenecks preventing the industrialization of FC systems is it limited useful lifetime. It is therefore important to develop reliable tools for the diagnosis and prognosis of FC system in order to optimize its efficiency. The aim of this article is to present the results of a sensibility analysis applied to a prognosis tools called Echo State Network

Tolérance aux défauts de type court-circuit d'interrupteurs de puissance en SiC utilisés dans un convertisseur DC/DC entrelacé

International audienceL'optimisation de la fiabilité des convertisseurs DC-DC est cruciale pour que la chaine de traction d'un véhicule à pile à combustible puisse fournir, sans interruption, la puissance énergétique demandée par la charge. Pour atteindre cet objectif, un algorithme de détection du défaut est requis afin de l'identifier et le localiser avant que ses effets ne causent l'arrêt du système. Les composants, passifs ou actifs, qui constituent les convertisseurs statiques sont l'une des sources à l'origine de ces défauts. Dans cet article, le défaut de type court-circuit d'interrupteurs de puissance est considéré et un contrôle tolérant aux fautes est proposé. Une architecture modulaire est, par ailleurs, suggérée qui associe plusieurs briques génériques « Pile à Combustible + Convertisseur DC-DC » dans le but d'augmenter l'opérabilité et la disponibilité du système même en mode dégradé. Ainsi, pour améliorer d'avantage les performances du convertisseur, la technologie en carbure de silicium est adoptée

Predicting the Remaining Useful Lifetime of a Proton Exchange Membrane Fuel Cell using an Echo State Network

International audienceOne remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it's important to develop failure diagnostic and prognostic tools enabling the optimization of FC. The Prognostic and Heath Management (PHM) is a discipline involved in the process of industrial maintenance. The objective, in PHM, is to estimate the Remaining Useful Life (RUL) of a system by predicting its future behavior. The RUL enables to predict the moment when a fault could occur on a system. It also allows identifying the relevant part of the system where a fault could happen. Then, a preventive maintenance could be performed to avoid non-reversible degradations. Three main prognosis approaches can be distinguished: model-based, data-based and hybrid methods. Data-based methods such as Artificial Neural Network (ANN), aim to estimate the ageing behavior of the process without specific knowledges related to the physical system phenomenon. Nevertheless, the deployment of such an approach can be a tedious work, mainly due to the trial and error algorithm method, which represents a real problem for industrial applications where real-time complying algorithms must be developed. Among the various methods of this area, the tool chosen here is called Echo State Network (ESN). An ESN consists in the use of a dynamical neurons reservoir where the training step consists in performing a linear regression. The computation time of this algorithm is thus shorter while keeping the same modeling capability of a Recurrent Neural Network (RNN). Created in 2001 by H. Jaeger, an ESN proposes a better human brain paradigm than traditional ANN, and are based on a reservoir of neurons randomly connected to each other. The aim of this paper is to study the application of ESN as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell using an iterative predictive structure, which is the most common approach performing a one-step prediction. This estimation output value is used in the next step as one of the input regressor and these operations can be repeated until the desired prediction horizon. The results obtained thanks to this method exhibits good prediction and they will be detailed in this paper

Development of new test instruments and protocols for the diagnostic of fuel cell stacks

In the area of fuel cell research, most of the experimental techniques and equipments are still devoted to the analysis of single cells or very short stacks. However, the diagnosis of fuel cell stacks providing significant power levels is a critical aspect to be considered for the integration of fuel cell systems into real applications such as vehicles or stationary gensets. In this article, a new instrument developed in-lab is proposed in order to satisfy the requirements of electrochemical impedance studies to be led on large FC generators made of numerous individual cells. Moreover, new voltammetry protocols dedicated to PEMFC stack analysis are described. They enable for instance the study of membrane permeability and loss of platinum activity inside complete PEMFC assemblies. Keywords: PEMFC; Stack; Characterization; Electrochemical Impedance Spectroscopy; Cyclic Voltammetry; Linear Sweep Voltammetry

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

Fuel Cells prognostics using Echo State Network

International audienceOne remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it is important to develop failure diagnostic and prognostic tools enabling the optimization of the FC. Among all the existing prognostics approaches, datamining methods such as artificial neural networks aim at estimating the process' behavior without huge knowledge about the underlying physical phenomena. Nevertheless, this kind of approach needs huge learning dataset. Also, the deployment of such an approach can be long (trial and error method), which represents a real problem for industrial applications where realtime complying algorithms must be developed. According to this, the aim of this paper is to study the application of a reservoir computing tool (the Echo State Network) as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell. Developments emphasize on the prediction of the mean voltage cells of a degrading FC. Accuracy and time consumption of the approach are studied, as well as sensitivity of several parameters of the ESN. Results appear to be very promising

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