A Recurrent Neural Approach for Modeling Non-Reproducible Behavior of PEM Fuel Cell Stacks

被引：0
|
作者：
Lopes, Francisco da Costa [1 ]
Watanabe, Edson H. [2 ]
Rolim, Luis Guilherme B. [2 ]
机构：
[1] CEPEL, Elect Energy Res Ctr, Dept Special Technol, Rio De Janeiro, Brazil
[2] Univ Fed Rio de Janeiro, Dept Elect Engn, COPPE, Rio De Janeiro, Brazil
来源：
2013 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT) | 2013年
关键词：
PEM fuel cell stack; modeling; NARX neural network; NOE neural network; voltage prediction; NETWORK MODEL; PERFORMANCE; VALIDATION; INTERFACE; SYSTEMS;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This work presents a recurrent neural model for PEM fuel cell stacks based on NARX and NOE neural structures. The practical difficulties to employ electrochemical models and the causes of the odd behavior observed in some PEM stacks are discussed. The model developed predicts the terminal voltage of a stack even in the situation where it presents a non-reproducible behavior, such as unexpected voltage fluctuations. The predictive capacity of the model is evaluated in two different scenarios, showing good agreement with the measured data.
引用
收藏
页码：661 / 667
页数：7
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