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  • Data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator
Title: Data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator
In this study, a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell (PEMFC)stacks is proposed. The primary contributions of this paper are: (1) the design of Koopman-based models for a fuel cell stack, incorporating K-fold cross-validation, varying lifted dimensions, radial basis functions (RBFs), and prediction horizons; and (2) comparison of the performance of Koopman based approach with a more traditional physics-based model. The results demonstrate the high accuracy of the Koopman-based model in predicting fuel cell stack behavior, with an error of less than 3%. The proposed approach offers several advantages, including enhanced computational efficiency, reduced computational burden, and improved interpretability. This study demonstrates the suitability of the Koopman operator for the modeling and control of PEMFCs and provides valuable insights into a novel control-oriented modeling approach that enables accurate and efficient predictions for fuel cell stacks.  more » « less
Award ID(s):
2135735
PAR ID:
10479129
Author(s) / Creator(s):
;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Energy and AI
Volume:
14
Issue:
C
ISSN:
2666-5468
Page Range / eLocation ID:
100289
Subject(s) / Keyword(s):
Proton exchange membrane fuel cell (PEMFC) Data-driven modeling Koopman operator Dynamic modeling Control-oriented modeling Physics-based modeling
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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