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

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.

APPLICATION OF THE SPECTRAL METHOD TO STOCHASTIC FILTER ANALYSIS

The problem of standing out a signal from an additive mixture of a harmonic signal and white Gaussian noise is considered. The analysis is based on the phenomenon of stochastic resonance (SR), which consists in amplifying a periodic signal under the influence of noise of a certain power. SR is a universal physical phenomenon that is typical of some nonlinear systems, and is came out not only in technical, but also in biological and social systems. When calculating the spectral characteristics of the output signal, Volterra series were used. The problem is solved using the transfer functions of Volterra without the initial definition of kernels. Volterra transfer functions are obtained by the harmonic input signal method. The influence of the input signal parameters, in particular the amplitude and frequency of the harmonic signal and the noise power, on the spectral power density of the output signal is studied. Optimal parameters values are determined. Criteria are formulated for using a stochastic filter to standing out a harmonic signal on the background white Gaussian noise.