Model parameters identification plays an important role in enhancing the currently available diagnosis techniques for fuel cells (e.g. electrochemical impedance spectroscopy). In this work, the dual Kalman filter (DKF) has been used for the parametric identification of a Randles circuit model. The fuel cell has been stimulated with typical EIS input signals, and the results of the identification have been validated by using the impedance spectra produced by the Fouquet impedance model. The obtained results allow to infer a functional relation between the filter settings and the input signal, thus enabling the possibility of detecting faults by inspecting the deviation of model parameters.

Enhanced Kalman Filter-Based Identification of a Fuel Cell Circuit Model in Impedance Spectroscopy Tests

Guarino, Antonio;Petrone, Giovanni;Zamboni, Walter
2020

Abstract

Model parameters identification plays an important role in enhancing the currently available diagnosis techniques for fuel cells (e.g. electrochemical impedance spectroscopy). In this work, the dual Kalman filter (DKF) has been used for the parametric identification of a Randles circuit model. The fuel cell has been stimulated with typical EIS input signals, and the results of the identification have been validated by using the impedance spectra produced by the Fouquet impedance model. The obtained results allow to infer a functional relation between the filter settings and the input signal, thus enabling the possibility of detecting faults by inspecting the deviation of model parameters.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4755670
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