Year: 2024
Type: conference-paper
Abstract: This paper presents a framework for parameter estimation of Solid Oxide Electrolyzer Stack (SOES) model. The complexity of multi-physics in SOES models poses a unique challenge for parameter identification due to the presence of nonlinearities, the large number of parameters, and few available measurements. Consequently, this study presents an enhanced method of parameter estimation, based on the Gauss-Newton optimization algorithm, incorporating a truncated Singular Value Decompostion (SVD) of a normalized sensitivity matrix. This modification prioritizes the update of parameters in the directions of high sensitivity while limiting the condition number of the matrix inverted to choose the step size, thus attenuating the adverse effects of noise and model errors unavoidable in the estimation process. This departure from the conventional approaches, allows a more nuanced and effective identification strategy tailored to the intricacies of SOESs. The proposed method is validated using data from an experimental test bench and compared to other identification methods.
Institutions: Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Laboratoire d’Innovation pour les Technologies des Énergies Nouvelles et les nanomatériaux, Laboratoire Ampère, Institut National des Sciences Appliquées de Lyon, Lyon 1 Université, École Centrale de Lyon, Centre National de la Recherche Scientifique, Université Grenoble Alpes
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4401065632