Kalman Filter-Based Online Identification of the Electric Power Characteristic of Solid Oxide Fuel Cells Aiming at Maximum Power Point Tracking

Abstract

High-temperature fuel cells are one of the devices currently investigated for an integration into distributed power supply grids. Such distributed grids aim at the simultaneous production of thermal energy and electricity. To maximize the efficiency of fuel cell systems, it is reasonable to track the point of maximum electric power production and to operate the system in close vicinity to this point. However, variations of gas mass flows, especially the concentration of hydrogen contained in the anode gas, as well as variations of the internal temperature distribution in the fuel cell stack module lead to the fact that the maximum power point changes in dependence of the aforementioned phenomena. Therefore, this paper first proposes a real-time capable stochastic filter approach for the local identification of the electric power characteristic of the fuel cell. Second, based on this estimate, a maximum power point tracking procedure is derived. It is based on an iteration procedure under consideration of the estimation accuracy of the stochastic filter and adjusts the fuel cell’s electric current so that optimal operating points are guaranteed. Numerical simulations, based on real measured data gathered at a test rig available at the Chair of Mechatronics at the University of Rostock, Germany, conclude this paper.

Experiments-Based Comparison of Different Power Controllers for a Solid Oxide Fuel Cell Against Model Imperfections and Delay Phenomena

“…Although the electric power maximum of an SOFC is typically specified by adaptations of the electric stack current (partially on the basis of real-time capable parameter identification and optimization procedures such as those recently published by Rauh et al, cf. [4]), short-term adjustments of the produced electric power can effectively by realized by variations of the inlet hydrogen mass flow at the anode side. Note that the prespecification of the average operating point, defined by a triplet of hydrogen mass flow, electric stack current, and resulting electric power, has to be performed in such a way as to guarantee safe and wearless operation.…”

“…This is given by making sure that the average operating point lies within the region of the current-power characteristic that has a positive slope. Overshooting its maximum should be avoided, for example by means of the techniques presented in [4]. Therefore, the control strategies presented in this paper aim at realizing power adaptations by means of the hydrogen mass flow as control input in the neighborhood of suitable operating points that fully lie within the domain of Ohmic polarization [5].…”

Kalman Filter-Based Real-Time Implementable Optimization of the Fuel Efficiency of Solid Oxide Fuel Cells

“…This also holds for the influence of the stack inlet temperature on the electric power characteristic. In previous work, it was therefore proposed to represent the electric power characteristic in a modelfree manner by a current-dependent polynomial [25]. However, the coefficients of this polynomial were not set to constant values but rather estimated during system operation by the application of a Kalman filter [26,27].…”

“…For the online identification of the electric power characteristic of SOFCs, the Kalman filter makes use of the terminal voltage and terminal current of the SOFC stack as the measured parameters. The influence of the thermal operating point, gas mass flow and current dependencies on the electric power of the SOFC stack cannot be described perfectly by the assumed polynomial ansatz in [25]. Therefore, they are dealt with by adapting the polynomial's coefficients in an online, real-time implementable manner by means of the Kalman filter in [25].…”

“…The influence of the thermal operating point, gas mass flow and current dependencies on the electric power of the SOFC stack cannot be described perfectly by the assumed polynomial ansatz in [25]. Therefore, they are dealt with by adapting the polynomial's coefficients in an online, real-time implementable manner by means of the Kalman filter in [25]. The estimation results then provided the required information to derive a maximum power point tracking procedure that uses the electric current as the control variable.…”