Critical Parameter Identification of Fuel-Cell Models Using Sensitivity Analysis
| Autor: | Craig Nathan P, Lalit M. Pant, Sarah Stewart, Adam Z. Weber |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Propagation of uncertainty
Energy Renewable Energy Sustainability and the Environment Computer science Membrane electrode assembly Proton exchange membrane fuel cell Materials Engineering PEM Condensed Matter Physics Surfaces Coatings and Films Electronic Optical and Magnetic Materials Macromolecular and Materials Chemistry Identification (information) numerical modeling Materials Chemistry Electrochemistry Range (statistics) parameter sensitivity Fuel Cells Sensitivity (control systems) Saturation (chemistry) Biological system Focus (optics) Physical Chemistry (incl. Structural) |
| Zdroj: | Journal of the Electrochemical Society, vol 168, iss 7 Journal of The Electrochemical Society, vol 168, iss 7 |
| Popis: | Author(s): Pant, LM; Stewart, S; Craig, N; Weber, AZ | Abstract: Numerical modeling has been a vital tool in proton-exchange-membrane fuel-cell (PEMFC) analysis; however, the predictive capabilities of these models depend on the input physical parameters, several of which are either not experimentally measured or have large scatter in measured values. This article presents an uncertainty propagation-based sensitivity analysis to identify the model parameters that impact the model predictions most. A comprehensive 2-D membrane electrode assembly (MEA) model is used to perform local sensitivity analysis at multiple operating conditions, which encompass the range of environments and operating conditions a cell can encounter. While at lower humidities, cathode kinetics and membrane-ohmic-loss related parameters are crucial, gas transport and porous-media saturation behavior are more important at humidified conditions. Several of these findings are different from previous studies presented in literature. Identifying the crucial parameters helps focus future material and cell optimization studies as well as experimental studies to quantify these parameters with higher accuracy. |
| Databáze: | OpenAIRE |
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