A global approach for a consistent identification of static and dynamic phenomena in a PEM Fuel Cell

Abstract In this paper, we propose a parameterization process for static and dynamics models dedicated to the analysis of two typical characterizations of a Proton Exchange Membrane Fuel Cell (PEMFC): the polarization curve (V–Icurve) and a set of Electrochemical Impedance Spectroscopies (EIS) carried out for several current densities of this curve. The specificity of the proposed approach is to consider conjointly both characterizations during all the proposed analysis process. This global strategy ensures the separation of the different fuel cell phenomena (activation, diffusion and ohmic) in the static and dynamic domains by imposing, with different ways, an expected consistency between both characterizations. Starting from the measured polarization curve, a first parametric identification is carried out leading to a static model of the Fuel Cell (FC). The EIS data are, at this stage, used to obtain the ohmic resistance of the static model. This is the first consistency forced between both characterizations. By fixing the transfer coefficient to 0.5 (platinum catalysts), it is possible to separate unequivocally the different fuel cell phenomena (activation, diffusion and ohmic) and to identify the different parameters of the laws which describe them in steady state for a given Membrane Electrode Assembly (MEA). In the objective of describing the dynamic of these three phenomena, an identification process for generic models involving RC cells in series and without a priori (number of RC cells not presupposed) is secondly applied to the set of measured EIS. Thanks to time-constant spectra (not classically used in fuel cell world) handled in this approach, the time-constants related to the three phenomena are extracted. The first specificity is to force the consistency between V–I curve and EIS by calculating the activation and diffusion resistances thanks to the derivation of the physical laws used for the static model and parameterized in the first step of our approach. The second specificity consists in forcing the equality between the V–I slope and the “zero-frequency resistor” of the EIS for a given characterized density current. This results in the apparition of a residual part for the impedance involving different suffered phenomena (potentially platinum oxidation, channel pressure oscillations…). The characteristics of this residue are analyzed. To calibrate the proposed method and to demonstrate its sensitivity to changes that may occur in the FC components, experiments concerning a single cell with different sets of components (different membrane thicknesses and different platinum loadings in the Active Layer (AL)) were achieved and analyzed by applying this method. Cyclic voltammetries were carried out in addition of V–I curves and EIS to check the relevancy of the parameters identified through our approach.

[1]  Mark E. Orazem,et al.  Interpretation of Low-Frequency Inductive Loops in PEM Fuel Cells , 2007 .

[2]  Werner Lehnert,et al.  Simulation of a Full Fuel Cell Membrane Electrode Assembly Using Pore Network Modeling , 2016 .

[3]  Xie Changjun,et al.  Drawing impedance spectroscopy for Fuel Cell by EIS , 2011 .

[4]  Suk Woo Nam,et al.  Development of a galvanostatic analysis technique as an in-situ diagnostic tool for PEMFC single cells and stacks , 2012 .

[5]  V. Szekely,et al.  On the representation of infinite-length distributed RC one-ports , 1991 .

[6]  Y. Bultel,et al.  Investigation of the difference between the low frequency limit of the impedance spectrum and the slope of the polarization curve , 2015 .

[7]  Farid Golnaraghi,et al.  Diagnosis of hydrogen crossover and emission in proton exchange membrane fuel cells , 2014 .

[8]  David A. Harrington,et al.  Characterisation of proton exchange membrane fuel cell (PEMFC) failures via electrochemical impedance spectroscopy , 2006 .

[9]  Thomas Génevé Méthodes de diagnostic des piles à combustible , 2016 .

[10]  Amine Jaafar,et al.  Contribution to the modelling of a low temperature PEM fuel cell in aeronautical conditions by design of experiments , 2019, Math. Comput. Simul..

[11]  Vladimir Szekely,et al.  Distributed RC One-Ports: Characteristic Functions and Their Relations , 2016 .

[12]  Jérémi Régnier,et al.  Fuel cell flooding diagnosis based on time-constant spectrum analysis , 2016 .

[13]  Mohamed Machmoum,et al.  Multiphysics DC and AC models of a PEMFC for the detection of degraded cell parameters , 2013 .

[14]  V. Szekely,et al.  Identification of RC networks by deconvolution: chances and limits , 1998 .

[15]  Alexander Wokaun,et al.  Oscillations in gas channels. Part I. The forgotten player in impedance spectroscopy in PEFCs , 2007 .

[16]  V. V. Lopes,et al.  Assessing cell polarity reversal degradation phenomena in PEM fuel cells by electrochemical impedance spectroscopy , 2011 .

[17]  Hubert A. Gasteiger,et al.  Determination of Catalyst Unique Parameters for the Oxygen Reduction Reaction in a PEMFC , 2006 .

[18]  Gareth Hinds,et al.  In situ measurement of active catalyst surface area in fuel cell stacks , 2013 .

[19]  T. Meynard,et al.  Interactions Between Fuel Cells and Power Converters: Influence of Current Harmonics on a Fuel Cell Stack , 2007, IEEE Transactions on Power Electronics.

[20]  Ting Hei Wan,et al.  Influence of the Discretization Methods on the Distribution of Relaxation Times Deconvolution: Implementing Radial Basis Functions with DRTtools , 2015 .

[21]  F. Golnaraghi,et al.  Detecting proton exchange membrane fuel cell hydrogen leak using electrochemical impedance spectroscopy method , 2014 .