堆栈(抽象数据类型)
介电谱
阳极
固体氧化物燃料电池
阴极
电阻抗
材料科学
计算机科学
氧化物
电化学
工程类
电气工程
化学
电极
物理化学
冶金
程序设计语言
作者
Giang Tra Le,Leonardo Mastropasqua,Stuart B. Adler,Jack Brouwer
出处
期刊:ECS transactions
[The Electrochemical Society]
日期:2021-07-09
卷期号:103 (1): 1201-1211
被引量:2
标识
DOI:10.1149/10301.1201ecst
摘要
In this work, we apply a machine learning approach to solid oxide fuel cell (SOFC) system diagnostics. Instead of fitting electrochemical impedance spectroscopy (EIS) into a physics based model or equivalent circuit, we train machine learning models to recognize failures from a database of simulated EIS. We use a coarse-grained physics-based model to simulate stack EIS under three different failure modes: fuel maldistribution, delamination, and cathode gas crossover to anode channel. Synthesized machine learning classification models successfully recognize these different degradation mechanisms in simulated data across different operating conditions. We are also able to differentiate these failures from the uniform degradation that tends to occur with SOFC over time. These encouraging results prompt our current effort to implement machine learning diagnostics methods on experimental EIS collected on SOFC short stack.
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