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

Abstract

Solid oxide fuel cell systems such as those presented in this paper are not only applicable for a pure supply with electric energy, they can typically also be used in decentralized power stations, i.e., as micro-cogeneration systems for houses, where both electric and thermal energy are required. For that application, obviously, the electric power need is not constant but rather changes over time. In such a way, it essentially depends on the user profiles of said houses which can refer to e.g., private households as well as offices. The power use is furthermore not predefined. For an optimal operation of the fuel cell, we want to adjust the power, to match the need with sufficiently small time constants without the implementation of mid- or long-term electrical storage systems such as battery buffers. To adapt the produced electric power a simple, however, sufficiently robust feedback controller regulating the hydrogen mass flow into the cells is necessary. To achieve this goal, four different controllers, namely, a PI output-feedback controller combined with a feedforward control, an internal model control (IMC) approach, a sliding-mode (SM) controller and a state-feedback controller, are developed and compared in this paper. As the challenge is to find a controller ensuring steady-state accuracy and good tracking behavior despite the nonlinearities and uncertainties of the plant, the comparison was done regarding these requirements. Simulations and experiments show that the IMC outperforms the alternatives with respect to steady-state accuracy and tracking behavior.

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

“…Previous work of the author has shown that the electric power characteristic of SOFCs depends in a non-negligible way on the enthalphy flow of the supplied reaction media at the anode and cathode sides as well as on the internal temperature of the SOFC stack [13,25]. Corresponding system models then make use of a finite-volume discretization of the thermal system behavior according to Figure 1.…”

“…This procedure relies on a principal component analysis that is based on a singular value decomposition approach [34]. In [33], it was shown that the most relevant input parameters, which were acquired in [13,33] with a sampling frequency of 10 Hz at a test rig available at the University of Rostock, Germany, are given by the quantities marked with the symbols in the first row of Table 2 Figure 1. Spatial semi-discretization of the fuel cell stack module (arrangement of finitely large volume elements in up to three space coordinates) [16].…”

“…1.5 hours of the depicted values in Figure 3) but further experiments in which the hydrogen mass flow was adapted for a constant electric current with variable power. These data were acquired in the experiments published in [13].…”

“…All three depicted optimization results as well as the comparison of the roots of the mean square tracking error according to Table 5 show an excellent performance of the proposed methodology. A further smoothening of the input signals and a reduction of remaining tracking errors could be achieved by a combination with the closed-loop controllers presented in [13], for which the method presented in Section 4 could be used as a kind of feedforward control signal generator.…”

“…The extent of the arising wear effects depends on operating strategies which impose constraints on the charging and discharging rates as well as the depth of discharge. For that reason, it is interesting to operate high-temperature fuel cells such as SOFCs not only at a fixed, offline-optimized operating point but with dynamically varying load conditions [13,14]. Such dynamic operating strategies will allow a downsizing of the aforementioned electric energy storage components.…”