A comprehensive modeling method for proton exchange membrane electrolyzer development
Graphical abstract
Introduction
Fuel cell systems have been deployed for various applications, primarily in the distributed power generation and transportation sectors [1,2]. The deployment of fuel cell electric vehicles and transition to a hydrogen economy have attracted great attention in obtaining carbon-free hydrogen supplies [[3], [5], [6]]. Hydrogen is an attractive energy carrier for carbon-free power generation and transportation [3], and it enables long driving distances for long-haul trucks or heavy-duty vehicles [1]. Various technologies are available or under development for hydrogen production from different energy sources including conventional hydrocarbon fuels such as coal or natural gas, or carbon-free sources such as electrolysis water splitting by using renewable electricity. Of various water splitting methods, a proton-exchange membrane (PEM) electrolyzer is a promising device that enables a wide range of hydrogen production scales—from centralized generation to distributed supply. However, the cost and performance of PEM electrolyzer limit their broad economic applications, and continuing improvements are needed for cost reduction and performance improvements [7,8].
The PEM electrolyzer technology is on the path for commercialization, and its performance is under sustaining improvements. Carmo et al. [8] have provided a comprehensive review of the PEM electrolysis technologies and the scope of the research status. The review ranges from electrolyzer material, components, and modeling, and it discusses the method and role of the modeling in improving PEM electrolyzer performance. Fig. 1 shows a cross-section of a single channel and details the different species of transport inside an electrolysis cell. In an operating electrolyzer cell, two-phase flow occurs when liquid water enters the cell, and gaseous hydrogen and oxygen are generated and mixed within the liquid- or gas-phase water from vaporization and water crossover. The gas/liquid two-phase flow benefits the thermal management in PEM electrolyzer but causes complexity in modeling species transport and electrochemical processes.
Fig. 1 shows the mass transport and electrochemical processes within a typical PEM electrolysis cell (PEMEC). The overall water-splitting process is H2O(l) → H2(g) + 1/2O2(g), which results in the coexistence of liquid water and gas phases on both sides. PEMEC components include flow distribution channels inside separate plates, porous transport layers, and a membrane-electrode assembly (MEA) comprised of a proton exchange membrane, anode catalyst layer, and cathode catalyst layer. The PEMEC MEA often uses the perfluorosulfonic acid polymer Nafion® membrane as the solid proton-conducting electrolyte. Protons are transported across the membrane from anode to cathode. Water also crosses the membrane via electroosmotic drag or diffusion, which are both modeled in this work.
Oxygen is generated at the anode side and mixes with the liquid water and water vapor. Electrons are derived from the electrochemical oxygen evolution reaction (OER) at the anode, and travel through an external circuit back to the cathode. Electrons and protons recombine at the cathode to produce hydrogen gas. Hydrogen produced at the cathode side will be separated from water crossover through the membrane. Experimentally probing the multi-physics processes within an operating PEMEC, such as species distributions, spatially varying current densities, and thermal fluid flow, is nearly impossible. Therefore, modeling tools in various scales are useful to complement experiments. Modeling results can provide insight and understanding of the component design and material properties on PEMEC performance. Computational modeling can simulate the transport and electrochemical processes to aid the development of PEMECs efficiently and cost effectively. Modeling outcomes in various scopes provide details inside an electrolyzer and help optimize designs and operating conditions through so-called computational experiments.
Section snippets
Overview of PEMEC modeling
PEMEC modeling includes multi-physics and multiscale approaches, spanning from membrane/electrode electrochemical and catalytic processes to cell or stack performance. To analyze the PEMEC designs from a single cell to a large stack, PEMEC models involving thermal-flow distributions can be built upon multi-dimensionality, ranging from a lumped parameter or 0-dimensional (0-D) models, 1-dimensional (1-D), 2-dimensional (2-D), to 3-dimensional (3-D) models. While 1-D and 2-D models can
PEMEC model description
To simulate the PEMEC, the mixture model in Fluent was used to solve the multiphase flow in the electrode components and was coupled to customized UDFs that model the electrochemical processes. UDFs are add-on modules that can integrate with the Fluent solver and are used to define source terms and material properties. Using UDFs leverages the capacity and robustness of the Fluent software, while extending its PEMEC modeling capability.
Fig. 3a shows a schematic of the 25 cm2 flow field and
Results and discussion
The electrochemical parameters used in the CFD model were obtained by fitting experimental polarization curves obtained at 60 °C and 80 °C. Experimental conditions used for the validation studies in this work are summarized in Table 4.
The parameters in the electrochemical model were derived from a least-squares fitting method to fit Eq. (33) to the experimental data with the following parameters as variables: (anode catalyst layer roughness factor), (cathode catalyst layer roughness
Conclusion
Mathematical modeling provides effective insights in PEMEC and product development on various scales and approaches. After comparing various modeling scales and scopes, this paper introduces a computationally efficient modeling framework that encompasses adequate physical understanding of a PEMEC required to study the effects of operating conditions and component design on PEM cell or stack performance. The multi-scale, Multiphysics model incorporates the ANSYS/Fluent CFD solvers and adds
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was authored by the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. The work was partially supported by the NREL H2@scale Laboratory Directed Research and Development (LDRD) led by Dr. Jennifer Kurtz and Dr. Bryan Pivovar. The authors gratefully acknowledge research support from the HydroGEN Consortium, established under the U.S. DOE, Office of Energy Efficiency
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