Polymer Electrolyte Membrane Fuel Cell Modelling and Parameters Estimation for Ageing Consideration

Laffly, Elie; Péra, Marie‐Cécile; Hissel, Daniel

doi:10.1109/isie.2007.4374595

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…There are several ageing models for PEMFCs in the literature. Among the first works about FC ageing, the work of Laffly et al [6], where the authors proposed a static and dynamic model of a PEMFC based on an empirical equations and equivalent electrical impedance modeling. The parameters of the static and dynamic models are provided by the polarization curve (static characterization) and the electrochemical impedance spectroscopy (dynamic characterization).…”

Section: A Short Review On Fuel Cell Prognosismentioning

confidence: 99%

“…The vacuum potential of the cell is a function of the electrochemical potential of the oxidation-reduction reaction, under standard conditions of temperature and pressure (1atm, 25 °C), is equal to 1.23V, in practice the no-load voltage is below 1V [6]. The curve of polarization, also called VI curve, is the characteristic curve of the fuel cell.…”

Section: Static Fuel Cell Modelmentioning

confidence: 99%

“…It corresponds to the energy that must be spent to cross the energy barrier of chemical reactions [14]. This barrier is due to the slowness of the electrochemical reaction on the surface of the electrodes [6]. Part of the voltage produced is lost during the chemical reaction that transfers the electrons to or from the electrode.…”

Section:  Activation Lossesmentioning

confidence: 99%

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Proton Exchange Membrane Fuel Cell Model for Prognosis

Detti

Jemeï

Steiner

2018

2018 IEEE Vehicle Power and Propulsion Conference (VPPC)

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The deployment at a large-scale of fuel cells will only occur if it is sufficiently competitive with other conventional energy generation solutions. Indeed, it is essential that these new technologies can provide acceptable performance and guarantee continuity of service for users. This involves the deployment of a robust, fault tolerant and ageing resilient systems. Fuel cell systems, by their technology, have behaviors that are difficult to apprehend. The nonlinear nature of the phenomena, the reversible or non-reversible character of the degradations, and the interactions between components and parameters make the modeling of aging a difficult task [2]. It is however necessary to develop a complete ageing model of a Fuel Cell system depending on the operating conditions in order to extend the lifetime of the system by setting appropriate control. Fault and degradation tolerant control, or by setting predictive maintenance. In this work, we present a mathematical model that will be used for fuel cell system prognosis, part of a global fault resilient approach. The model developed has been validated on experimental tests. Keywords-proton exchange membrane fuel cell; prognosis; fuel cell ageing, fuel cell model.

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Section: A Short Review On Fuel Cell Prognosismentioning

confidence: 99%

Section: Static Fuel Cell Modelmentioning

confidence: 99%

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Proton Exchange Membrane Fuel Cell Model for Prognosis

Detti

Jemeï

Steiner

2018

2018 IEEE Vehicle Power and Propulsion Conference (VPPC)

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“…As said in this part introduction, a few studies try to estimate the remaining lifespan of a specific component or a single cell. The main idea of these works is to determine in which part of the fuel cell life, the system is operating thanks to different aging tendencies and indicators empirically defined [92,93]. It implies nevertheless to have a sufficient amount of data from multiple fuel cells.…”

Section: Prognosticsmentioning

confidence: 99%

Prognostics and Health Management of PEMFC – State of the art and remaining challenges

Jouin

Gouriveau

Hissel

et al. 2013

International Journal of Hydrogen Energy

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182

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Fuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being competitive, it is not ready for large scale industrial 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.

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“…Therefore, it is important to establish a link between the FC degradation phenomenon and the FC physical characterizations that can be employed throughout ageing time to estimate the FC lifetime, and consequently the remaining duration life during operations. Previous studies have been conducted to evaluate FC stack performances through the time evolution of various parameters [3][4][5]; others have been done to estimate the FC operating time (respectively lifetime) by using Electrochemical Impedance Spectroscopy (EIS) measurements [6].…”

Section: Introductionmentioning

confidence: 99%

Fuel cells static and dynamic characterizations as tools for the estimation of their ageing time

Onanena

Oukhellou

Candusso

et al. 2011

International Journal of Hydrogen Energy

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This paper deals with a pattern-recognition-based diagnosis approach, which aim is to estimate the Fuel Cell (FC) operating time, and consequently its remaining duration life. With the method proposed, both static and dynamic information extracted from the stack (i.e. polarization curve records and Electrochemical Impedance Spectroscopy (EIS) measurements) can be used. The complete diagnosis method consists of several steps. First, features are extracted from EIS measurements and polarization curves independently. This enables us to simplify the extracted information without losing relevant information, and to remove noise. For the polarization curves, an empiric model is exploited to ensure the feature extraction. For the impedance spectra, both expert knowledge and parametric modeling are used to extract features. In particular, a latent regression model is used to split automatically the imaginary part of the spectra into several segments that are approximated by polynomials. The next step of the method consists in selecting the most relevant features from the whole set of extracted features. This helps us to estimate the operating time, while adjusting the complexity of the model. The final step of the approach is a linear regression that uses the selected subset of features to estimate the FC operating time. The performances of the proposed approach are evaluated on a dataset made up of EIS measurements and polarization curves extracted from two FC lifetime tests. A mean error of about 2 h over a global operating duration of 1000 h can be obtained. Moreover, the portability of the method is shown by considering another FC ageing test conducted on a different FC stack type

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Polymer Electrolyte Membrane Fuel Cell Modelling and Parameters Estimation for Ageing Consideration

Cited by 15 publications

References 8 publications

Proton Exchange Membrane Fuel Cell Model for Prognosis

Proton Exchange Membrane Fuel Cell Model for Prognosis

Prognostics and Health Management of PEMFC – State of the art and remaining challenges

Fuel cells static and dynamic characterizations as tools for the estimation of their ageing time

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