Publication
Box-Jenkins autoregressive models for PEMFC operating under dynamical conditions
Conference Article
Conference
Symposium on Modeling and Experimental Validation of Electrochemical Energy Technologies (MODVAL)
Edition
17th
Pages
93
Doc link
https://modval17.epfl.ch/wp-content/uploads/2021/05/BookletMODVAL17.pdf
File
Abstract
The objective of the present work is to explore and validate autoregressive, control oriented models models Proton Exchange Membrane Fuel Cells coperatinf under dynamic condditions. Autoregressive models have several advantages: they are obtained solely from input-output signals, have low computational cost, simple structure and a small number of parameters. Four datasets from experiments in static and dynamic operating conditions are used to estimate an validate the models, each dataset is divided into estimation and prediction subsets. The Box-Jenkins system identification method is used to built the model structure. The models are validated through analysis of the correlation of residuals by the Box-Ljung test and through calculation of the root mean squared error (RMSE).
Categories
control theory, power system control.
Author keywords
PEM fuel cells, modelling, autorregressive models, Box-Jenkins, system identification
Scientific reference
J.A. Aguilar, A.P. Husar and J. Andrade-Cetto. Box-Jenkins autoregressive models for PEMFC operating under dynamical conditions, 17th Symposium on Modeling and Experimental Validation of Electrochemical Energy Technologies, 2021, Sion, Switzerland (Virtual), pp. 93.
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