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

Onanena, Raïssa; Oukhellou, Latifa; Candusso, Denis; Harel, Fabien; Hissel, Daniel; Aknin, Patrice

doi:10.1016/j.ijhydene.2010.10.064

Cited by 61 publications

(31 citation statements)

References 20 publications

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“…11,12 The noninvasive nature of EIS also makes impedance measurements useful in prognostic applications such as fuel cell health estimations 13,14 or prediction of remaining useful lifetime in batteries. 15,16 Qualitative analysis of EIS spectra generally involves assessing the shape of Nyquist plot features to determine the relative importance of different physicochemical processes.…”

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confidence: 99%

Analysis of Li-Ion Battery Electrochemical Impedance Spectroscopy Data: An Easy-to-Implement Approach for Physics-Based Parameter Estimation Using an Open-Source Tool

Murbach

Schwartz

2018

J. Electrochem. Soc.

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The quantitative analysis of electrochemical impedance spectroscopy (EIS) data is important for both characterization and prognostic applications in many electrochemical systems. Here we describe an open-source platform, the ImpedanceAnalyzer, for easy-to-use physics-based analysis of experimental EIS spectra. To demonstrate the use of the platform, we explore the basic capabilities of the pseudo two-dimensional (P2D) battery model to predict publicly available experimental EIS data from a 1500 mAh commercial lithium-ion (LiCoO 2 /graphite) cell. An a priori computed dataset of 38,800 P2D-based impedance spectra simulations, covering a wide range of frequencies (1 mHz to 100 kHz) and model parameters, enables a straightforward least squares matching approach for analyzing experimental spectra. We find an average error of 1.73% between the best-matching computed spectrum from the 38,800 member library and the experimental spectrum being analyzed. Our analysis shows there is significant opportunity to improve the fit between experimental data and physics-based impedance simulations by a combination of a larger computed dataset, local optimization, and further additions to the model physics. Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating a wide variety of electrochemical systems. 1-3 EIS spectra separate individual electrochemical processes by their characteristic timescales, enabling both qualitative and quantitative analysis of electron transport, 4,5 reaction rates and mechanisms, 6,7 intercalation processes, 8 mass transport, 9,10 and electrode structure. 11,12 The noninvasive nature of EIS also makes impedance measurements useful in prognostic applications such as fuel cell health estimations 13,14 or prediction of remaining useful lifetime in batteries. 15,16 Qualitative analysis of EIS spectra generally involves assessing the shape of Nyquist plot features to determine the relative importance of different physicochemical processes. 17,18 In contrast, quantitative analysis relies on fitting a model to the data in order extract values for specific thermodynamic, transport, and/or kinetic parameters. Most experimental datasets are analyzed quantitatively using an equivalent circuit analog. Fitting an equivalent circuit to EIS data is straightforward using standard least squares regression techniques. 19,20 A good fit can often be found with a relatively simple equivalent circuit, particularly if non-ideal elements like the constant phase element are used. Moreover, many simple equivalent circuits, like the Randles' circuit, 21 have physically interpretable parameters based on linearized electrochemical processes. However, as more complex equivalent circuits are derived and utilized, the lumped parameters can lose their direct physical interpretability and the structure of the equivalent circuit analogs themselves can be degenerate. 22 An alternative to equivalent circuits for quantitative analysis of EIS data is to directly fit the data with a physics-based mathematical model ...

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mentioning

confidence: 99%

Analysis of Li-Ion Battery Electrochemical Impedance Spectroscopy Data: An Easy-to-Implement Approach for Physics-Based Parameter Estimation Using an Open-Source Tool

Murbach

Schwartz

2018

J. Electrochem. Soc.

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“…Additional work by [18] used polarisation curve and Electrochemical Impedance Spectroscopy (EIS) techniques to characterise real world fuel cell degradation, then predicting ageing time in the future through analysis of key features in the polarisation and EIS data sets..…”

Section: Fuel Cell Degradation Prognosis and Simulationmentioning

confidence: 99%

Failure Mode and Effect Analysis, and Fault Tree Analysis of Polymer Electrolyte Membrane Fuel Cells

Whiteley

Dunnett

Jackson

2016

International Journal of Hydrogen Energy

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Hydrogen fuel cells have the potential to dramatically reduce emissions from the energy sector, particularly when integrated into an automotive application. However there are three main hurdles to the commercialisation of this promising technology; one of which is reliability. Current standards require an automotive fuel cell to last around 5000 hours of operation (equivalent to around 150,000 miles), which has proven difficult to achieve to date. This hurdle can be overcome through in-depth reliability analysis including techniques such as Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) amongst others. Research has found that the reliability field regarding hydrogen fuel cells is still in its infancy, and needs development, if the current standards are to be achieved. In this work, a detailed reliability study of a Polymer Electrolyte Membrane Fuel Cell (PEMFC) is undertaken. The results of which are a qualitative and quantitative analysis of a PEMFC. The FMEA and FTA are the most up to date assessments of failure in fuel cells made using a comprehensive literature review and expert opinion.

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“…This complies for both PEM fuel cells [22,23] and HTPEM fuel cells [24]. In the work by Hissel et al [25] life time EIS data from two different fuel cell stacks, were 70 used to design a fuzzy-clustering algorithm to determine the type of ageing.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

Jeppesen

Araya

Sahlin

et al. 2017

Journal of Power Sources

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This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode-and cathode stoichiometry is 100 % success rate. Overall global accuracy on the test data is 94.6 %.

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

Cited by 61 publications

References 20 publications

Analysis of Li-Ion Battery Electrochemical Impedance Spectroscopy Data: An Easy-to-Implement Approach for Physics-Based Parameter Estimation Using an Open-Source Tool

Analysis of Li-Ion Battery Electrochemical Impedance Spectroscopy Data: An Easy-to-Implement Approach for Physics-Based Parameter Estimation Using an Open-Source Tool

Failure Mode and Effect Analysis, and Fault Tree Analysis of Polymer Electrolyte Membrane Fuel Cells

Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

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