Application of a Sigma-point Kalman-filter for the Online Estimation of Fractional Order Impedance Models for Solid Oxide Fuel Cells

Abstract

To operate a solid oxide fuel cell (SOFC) safely and efficiently, it is necessary to monitor the values of characteristic model parameters of the cell impedance. This paper addresses the problem to compute online estimates of model parameters for SOFCs, whose impedance is represented by a fractional (non-integer) order impedance model. The fractional model of the impedance is approximated through serial connection of a finite number of RC-elements. Assuming that the time-dependent model parameters of the impedance are treated as state-variables of an augmented state vector, the linearity is lost, and the application of the classical linear Kalman filter for state estimation is not longer possible. The so-called sigma-point Kalman filter is therefore used to solve the online estimation problem for nonlinear system dynamics. The feasibility and quality of this online identification approach is outlined using simulated data with additional noise.

Keywords

Fractional-order systems Kalman filter filtering problems system identification fuel cells

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