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ABSTRACT
Modeling a proton exchange membrane fuel cell (PEMFC) system is essential for enhancing its performance, particularly by facilitating access to its internal states. Various spatial modeling methods exist, each with unique advantages and disadvantages. Understanding these is crucial for selecting the most appropriate model for the intended purpose. Validating such a model entails utilizing diverse experimental data and involves two crucial steps: calibrating indeterminate parameters and verifying results. To illustrate these concepts, a dynamic, biphasic, and isothermal 1D model is presented.
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Read the articleAUTHORS
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Raphaël GASS: Doctoral student - Ingénieur des Mines de Saint-Étienne - University of Franche-Comté, UTBM, CNRS, FEMTO-ST Institute, FCLAB, Belfort, France - Aix Marseille Univ, CNRS, LIS, Marseille, France
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Zhongliang LI: University Professor - University of Franche-Comté, UTBM, CNRS, FEMTO-ST Institute, FCLAB, Belfort, France
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Rachid OUTBIB: University Professor - Aix Marseille Univ, CNRS, LIS, Marseille, France
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Samir JEMEI: University Professor - University of Franche-Comté, UTBM, CNRS, FEMTO-ST Institute, FCLAB, Belfort, France
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Daniel HISSEL: University Professor - University of Franche-Comté, UTBM, CNRS, FEMTO-ST Institute, FCLAB, Belfort, France - University Institute of France
INTRODUCTION
Fuel cells are devices for converting chemical energy into electricity and heat, and play a crucial role in reducing greenhouse gas emissions and combating global warming. Currently in advanced development, they look promising for powering heavy-duty transport, producing stationary electricity, and providing backup power for data centers. Among the most mature are proton exchange membrane fuel cells (PEMFC), which use the chemical reaction between hydrogen and oxygen to generate electricity, and operate at low pressures and temperatures. Comprising an assembly of cells, they use solid materials such as metals, carbon fiber and polymers to facilitate or prevent the passage of gases (hydrogen, oxygen, nitrogen), liquid water and particles (protons, electrons).
Numerical modelling of stacks enables their design to be optimized by virtually testing new components, at little cost in terms of time and money. The aim is to improve performance and reliability, or reduce costs. Models also enable real-time control of stacks by adjusting their operating conditions, such as pressure, temperature, humidity and gas flow. The aim is to improve performance, avoid faults such as cell flooding and reduce degradation.
Fuel cells come in a variety of designs, each with its own advantages and disadvantages. There is no one-size-fits-all model, and each application must choose the one that is best suited to its needs.
This work describes the various spatial models in the literature, excluding functional block modeling. A procedure for experimental validation of the models is also explained, along with its limitations, followed by a case study of a dynamic, two-phase, isothermal 1D model with no charge transfer.
Key points
Domain: Modeling
Degree of technology diffusion: Growth
Technologies involved : PEM fuel cells
Applications: Transportation, stationary power generation, backup power for data centers.
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KEYWORDS
hydrogen | Proton exchange membrane fuel cell (PEMFC) | Spatial modeling | Experimental validation | Dynamic 1D model
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