Hydrogen PEMFC stack performance analysis: A data-driven approach

Lopes, Vítor V.; Novais, Augusto Q.; Rangel, C. M.

doi:10.1016/j.ijhydene.2010.01.124

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…This section presents the main results from of the two methodologies to the analysis of e collected from an open-air cathode PEMF configuration and under experimental conditi [19]. Polarization curves were run in a ste starting at the open-circuit potential and takin its maximum power.…”

Section: Results and Discussiomentioning

confidence: 99%

“…To address this issue, several researchers suggest that the search for a representative basis should be more confined. Thus, new data factorization methods such as non-negative matrix factorization (NMF) were developed to facilitate a rapid qualitative and quantitative data analysis and interpretation [18,19].…”

Section: A M-nmf Outline: Polarization Curve Data (Steady-state)mentioning

confidence: 99%

“…Specifically, this work proposes a new NMF, M-NMF, to extract base vectors with physical meaning and relevant for the analysis and interpretation of polarization curve data-sets, not requiring the pre-specification of equation models [20].…”

Section: A M-nmf Outline: Polarization Curve Data (Steady-state)mentioning

confidence: 99%

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Novel data-driven methodologies for parameter estimation and interpretation of fuel cells performance

Lopes

Novais

Rangel

2011

11th International Conference on Electrical Power Quality and Utilisation

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Abstract-Fuel cell based power generation systems are expected to become more widespread in the near future. Stationary fuel cells may be used as an uninterruptible or back-up power supply, or to supply micro-grids. In particular, proton exchange membrane fuel cells (PEMFC) are an attractive technology due to its high energy density, rigid and simple structure, low operating temperature and fast start-up characteristics.The power quality assessment of fuel cells as a viable power sources requires a good understanding of the fuel cell performance characteristics. This paper presents two novel datadriven methodologies for the identification of the main steady state (polarization curve) and the dynamic (impedance response) characteristics for fuel-cells allowing the development of rapid, accurate and empirical models based on the experimental data. M-NMF is a modified non-negative matrix factorization technique developed for the analysis of polarization curve data that allows to identify the three main contributions for the fuelcell power degradation, while for impedance spectroscopy data, this paper proposes the use of fractional order transfer functions (FC-FOTC) to describe the main dynamic modes present in the fuel-cell.A brief description of these two approaches is presented, together with the analysis of a real experimental dataset obtained from a 12W open cathode PEMFC stack to illustrate their potential and scope. While the former is instrumental for the deconvolution of the fuel cell polarization curves into its major components, the latter enables the estimation of the parameters related to the inherent transport and kinetic phenomena, thus opening the way, in both cases, for the interpretation of the effect of the operating conditions on the relative dominance and magnitude of these components and phenomena.Fuel-cells performance; Data-driven modeling; Power quality.

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Section: Results and Discussiomentioning

confidence: 99%

Section: A M-nmf Outline: Polarization Curve Data (Steady-state)mentioning

confidence: 99%

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Novel data-driven methodologies for parameter estimation and interpretation of fuel cells performance

Lopes

Novais

Rangel

2011

11th International Conference on Electrical Power Quality and Utilisation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various response surfaces can be used to predict the SV and the DC power in different working conditions and using exclusively input-output data. Multivariate methods of data analysis are now commonly used to characterize biological and environmental systems [22][23][24], but their application to the characterization of fuel cell systems is still relatively rare [25,26]. In particular a recent application of a multivariate data analysis named principal component analysis (PCA) to PEM fuel cells is documented in [27].…”

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confidence: 99%