Base de description des défauts 2D et 3D pour le diagnostic d'une pile à combustible par la mesure de champ magnétique externe

International audienceL’objectif des travaux actuels consiste à parvenir,par la résolution d’un problème inverse magnétostatique àconstruire la distribution de la densité du courant en 3D àl’intérieur d’une pile à combustible par la mesure non-invasivede champ magnétique externe, ce qui va nous amener à concluresur le changement des propriétés électrochimiques de la pile dansle cas d’un défaut quelconque. Une description de ces défautspermet de poser la démarche à suivre afin de les identifier enconstruisant des bases de projections qui regroupent descombinaisons linéaires de défauts possibles

Faults identification on a fuel cell by external magnetic measurements. : towards of determination of local faults

Ce travail a permis de développer une technique non invasive d’identification de la distribution du courant dans une pile à combustible à partir du champ magnétique externe. La mesure du champ s’effectue sur un ensemble de points de mesures choisis spécialement pour détecter les variations du champ par rapport à un fonctionnement optimal de la pile. Les deux composantes du champ magnétique sensibles aux variations sont utilisées. La mesure du champ exploitable est la différence entre un mode considéré sain et un mode quelconque de fonctionnement. Autour de ces mesures de champ magnétique, un problème inverse est modélisé en explorant plusieurs approches de paramétrisation de la distribution du courant. Le caractère mal posé du problème s’est traduit par la non-unicité de la solution et sa sensibilité au bruit. L’affranchissement de ces problèmes est atteint par la régularisation du modèle inverse. L’outil développé permis de reconstruire la distribution du courant indépendamment de la taille du défaut dans la limite de sensibilité. La validation est faite sur un simulateur électrique de pile à combustible et sur une pile de type GENEPAC dans un environnement de laboratoire.A noninvasive technique for identifying the current distribution in a fuel cell from the external magnetic field is developed. The magnetic field measurements are carried out on a set of points chosen in order to detect only the variations of the magnetic field in a case of a faulty fuel cell. Two components of the magnetic field are used because they are sensitive to current heterogeneities. The exploitable measurement of the magnetic field is the difference between a healthy mode and a faulty mode.An ill-posed inverse problem is modeled by investigating several parametrization approaches of the current distribution. The ill-posed property of the problem conducts to a non-uniqueness of the solution and a high sensitivity to the noise. A regularization method is used in order to get a stable solution. The developed tool allows identifying the current distribution independently of the size of the fault within the sensitivity limit. A validation is done on a fuel cell simulator and on a GENEPAC fuel cell in a laboratory conditions

Identification des défauts d'une pile à combustible par la mesure du champ magnétique externe : vers la détermination de défauts locaux

A noninvasive technique for identifying the current distribution in a fuel cell from the external magnetic field is developed. The magnetic field measurements are carried out on a set of points chosen in order to detect only the variations of the magnetic field in a case of a faulty fuel cell. Two components of the magnetic field are used because they are sensitive to current heterogeneities. The exploitable measurement of the magnetic field is the difference between a healthy mode and a faulty mode.An ill-posed inverse problem is modeled by investigating several parametrization approaches of the current distribution. The ill-posed property of the problem conducts to a non-uniqueness of the solution and a high sensitivity to the noise. A regularization method is used in order to get a stable solution. The developed tool allows identifying the current distribution independently of the size of the fault within the sensitivity limit. A validation is done on a fuel cell simulator and on a GENEPAC fuel cell in a laboratory conditions.Ce travail a permis de développer une technique non invasive d’identification de la distribution du courant dans une pile à combustible à partir du champ magnétique externe. La mesure du champ s’effectue sur un ensemble de points de mesures choisis spécialement pour détecter les variations du champ par rapport à un fonctionnement optimal de la pile. Les deux composantes du champ magnétique sensibles aux variations sont utilisées. La mesure du champ exploitable est la différence entre un mode considéré sain et un mode quelconque de fonctionnement. Autour de ces mesures de champ magnétique, un problème inverse est modélisé en explorant plusieurs approches de paramétrisation de la distribution du courant. Le caractère mal posé du problème s’est traduit par la non-unicité de la solution et sa sensibilité au bruit. L’affranchissement de ces problèmes est atteint par la régularisation du modèle inverse. L’outil développé permis de reconstruire la distribution du courant indépendamment de la taille du défaut dans la limite de sensibilité. La validation est faite sur un simulateur électrique de pile à combustible et sur une pile de type GENEPAC dans un environnement de laboratoire

Diagnostic d’une pile à combustible de type PEM par la mesure de champ magnétique externe

National audienc

Fault Identification on a Fuel Cell by 3D Current Density Reconstruction from External Magnetic Field Measurements

International audienc

Fault detection for polymer electrolyte membrane fuel cell stack by external magnetic field

WOS:000470236500016International audienceAn original non-invasive approach of fuel cell diagnosis is proposed in order to locate different kinds of faults in PEMFC stacks from magnetic field measurements. The method is based on the solving of an inverse linear problem linking the magnetic field signature outside of the fuel cell to the current density distribution inside. The searched solution is a linear combination of conservative current distribution obtained by a set of electrokinetic problems solved by a finite face element method. As the problem is ill-posed, the solution is stabilized using a truncated singular value decomposition. In this work, 30 sensors are used to perform the 2 magnetic tomography of a PEMFC stack consisting of 100 cells with a large active area of 220 cm 2. External magnetic measurement makes possible to identify 2D or 3D changes of current density distribution induced either by a cell flooding or membrane drying as well as by material degradation in a PEMFC stack

Spatial Harmonic Current Density Basis for Faults Identification in Fuel Cell Stack from External Magnetic Field Measurements

International audienc

Fault Identification on a Fuel Cell by 3-D Current Density Reconstruction From External Magnetic Field Measurements

WOS:000468263700001International audienceAn original approach of fuel cell diagnosis is presented. It is based on the solving of an inverse linear problem linking the magn e tic field signature outside of the fuel cell to the current density distribution inside. The searched solution is a linear combination of conservative current distribution obtained by a set of electrokinetic problems solved by a finite face element method. As the problem is ill-posed, the solution is stabilized using a truncated singular value decomposition. The approach is validated to reconstruct a 3D current density distribution in a stack simulator and in a fuel cell stack operating in laboratory conditions. Index Terms-Fuel cell, non-invasive diagnosis, current density identification, magnetic measurements, inverse problem

3D current density reconstruction in a fuel cell stack with external magnetic field measurement

International audienceToday, optimization of the fuel cell lifetime is a challenge for many researchers. Controlling fuel cell aging by the diagnosis allows us to prevent a possible breakdown and ensure its maintenance at the appropriate time. The originality of the presented diagnosis technique consists in being able to reconstruct the distribution of the current density within the fuel cell without disturbing its operation.The diagnosis method is based on the measurement of the magnetic field surrounding a PEMFC stack. It relies on the measurements of the magnetic field signature generated by the current distribution in the PEMFC. The sensors are oriented in such a way to detect only the magnetic signature of a default [1]. From these measurements, an inverse problem is solved to get the 3D current density inside the fuel cell. The current density distribution leads us to conclude on the change of the electrochemical behavior of the stack due to cell ageing or failure.Compared with our previous paper [1], the proposed diagnosis tool is extended to capture 3D default induced by the local properties degradation of one cell or few cells in the stack. Like in [1], a current density basis using 2D Fourier series and completed in 3D with a finite volume method has been built [2]. To properly define 3D current, the proposed approach is reproduced several times along the main current direction. Each vector of this basis is an elementary default which radiates a magnetic field around it. The problem being linear but ill-posed, truncated single value decomposition (SVD) is used to get a stable solution.Current streamlines…Current streamlinesFig. 1: (a): Original 3D current density, (b): Current density basis, (c): Reconstructed 3D current densityIn a first stage, a physics-based model of PEMFC (or direct problem) [2] is used to simulate the 3D distribution of the current density and finally the induced magnetic field around the stack when considering defaults. Second, an inverse problem is solved to build the 3D current density. The two current density distribution obtained from the direct or inverse problem are compared

Fault identification in a fuel cell stack from external magnetic field measurements

International audienc