NDLI: A recurrent neural approach for modeling non-reproducible behavior of PEM fuel cell stacks

Content Provider	IEEE Xplore Digital Library
Author	da Costa Lopes, F. Watanabe, E.H. Rolim, L.G.B.
Copyright Year	2013
Description	Author affiliation: Dept. of Special Technol., CEPEL-Electr. Energy Res. Center, Rio de Janeiro, Brazil (da Costa Lopes, F.) \|\| Dept. of Electr. Eng., Univ. Fed. do Rio de Janeiro, Rio de Janeiro, Brazil (Watanabe, E.H.; Rolim, L.G.B.)
Abstract	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.
Sponsorship	Ind. Electron. Soc.
Starting Page	661
Ending Page	667
File Size	427522
Page Count	7
File Format	PDF
ISBN	9781467345675
e-ISBN	9781467345699
e-ISBN	9781467345682
DOI	10.1109/ICIT.2013.6505750
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-02-25
Publisher Place	South Africa
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuel cells Voltage measurement Predictive models Temperature measurement Training Load modeling Current measurement voltage prediction PEM fuel cell stack modeling NARX neural network NOE neural network
Content Type	Text
Resource Type	Article

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