Distributed and Lumped Parameter Models for Fuel Cells

M. Guarnieri, P. Alotto, F. Moro
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引用次数: 1

Abstract

The chapter presents a review of modeling techniques for three types of fuel cells that are gaining industrial importance, namely, polymer electrolyte membrane (PEMFC), direct methanol (DMFC), and solid oxide (SOFC) fuel cells (FCs). The models presented are both multidimensional, suitable for investigating distributions, gradients, and inhomogeneities inside the cells, and zero-dimensional, which allows for fast analyses of overall performance and can be easily interfaced with or embedded in other numerical tools, for example, for studying the interaction with static converters needed to control the electric power flow. Thermal dependence is considered in all models. Some special numerical approaches are presented, which allow facing specific problems. An example is the Proper Generalized Decomposition (PDG) that allows overcoming the challenges arising from the extreme aspect ratio of the thin electrolyte separating anode and cathode. The use of numerical modeling as part of identification techniques, particularly by means of stochastic optimization approaches, for extracting the material parameters from multiple in situ measurements is also discussed and examples are given. Merits and demerits of the different models are discussed.
燃料电池的分布和集总参数模型
本章介绍了三种正在获得工业重要性的燃料电池的建模技术,即聚合物电解质膜(PEMFC),直接甲醇(DMFC)和固体氧化物(SOFC)燃料电池(fc)。所提出的模型是多维的,适用于研究细胞内的分布、梯度和不均匀性,并且是零维的,允许快速分析整体性能,并且可以很容易地与其他数值工具连接或嵌入,例如,用于研究与控制电流所需的静态转换器的相互作用。所有模型都考虑了热依赖性。提出了一些特殊的数值方法,以解决具体问题。适当广义分解(PDG)就是一个例子,它克服了分离阳极和阴极的薄电解质的极端宽高比所带来的挑战。数值模拟作为识别技术的一部分,特别是通过随机优化方法,从多个原位测量中提取材料参数的使用也进行了讨论并给出了例子。讨论了不同模型的优缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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