As the aviation industry moves toward higher efficiency electrical power generation, all electric aircraft, or zero emissions and more quiet aircraft, fuel cells are sought as the technology that can deliver on these high expectations. The hybrid solid oxide fuel cell system combines the fuel cell with a microturbine to obtain up to 70% cycle efficiency, and then distributes the electrical power to the loads via a power distribution system. The challenge is to understand the dynamics of this complex multidiscipline system and the design distributed controls that take the system through its operating conditions in a stable and safe manner while maintaining the system performance. This particular system is a power generation and a distribution system, and the fuel cell and microturbine model fidelity should be compatible with the dynamics of the power distribution system in order to allow proper stability and distributed controls design. The novelty in this paper is that, first, the case is made why a high fidelity fuel cell model is needed for systems control and stability designs. Second, a novel modeling approach is proposed for the fuel cell that will allow the fuel cell and the power system to be integrated and designed for stability, distributed controls, and other interface specifications. This investigation shows that for the fuel cell, the voltage characteristic should be modeled, but in addition, conservation equation dynamics, ion diffusion, charge transfer kinetics, and the electron flow inherent impedance should also be included.
A solid oxide fuel cell-based power system is modeled and simulated to investigate both power management and controllability issues experienced while subjecting the system to the typical power requirements of a small aircraft. Initially, the fuel cell stack is assumed to operate along one characteristic I-V curve, thus isolating the power management study to the system’s powertrain components. Electrical converters transfer dc power from the fuel cell to usable ac power for an electric motor-driven propeller. To avoid oversizing, the fuel cell stack is designed to operate near its maximum power limit during aircraft cruising, while a battery is employed as an alternative source to provide additional power beyond the cruising kilowatt requirement (e.g., takeoff or maneuvering).
As fuel cells continue to improve in performance and power densities levels rise, potential applications ensue. System-level performance modeling tools are needed to further the investigation of future applications. One such application is small-scale aircraft propulsion. Both piloted and unmanned fuel cell aircrafts have been successfully demonstrated suggesting the near-term viability of revolutionizing small-scale aviation. Nearly all of the flight demonstrations and modeling efforts are conducted with low temperature fuel cells; however, the solid oxide fuel cell (SOFC) should not be overlooked. Attributing to their durability and popularity in stationary applications, which require continuous operation, SOFCs are attractive options for long endurance flights. This study presents the optimization of an integrated solid oxide fuel cell-fuel processing system model for performance evaluation in aircraft propulsion. System parameters corresponding to maximum steady state thermal efficiencies for various flight phase power levels were obtained through implementation of the particle swarm optimization (PSO) algorithm. Optimal values for fuel utilization, air stoichiometric ratio, air bypass ratio, and burner ratio, a four-dimensional optimization problem, were obtained while constraining the SOFC operating temperature to 650–1000 °C. The PSO swarm size was set to 35 particles, and the number of iterations performed for each case flight power level was set at 40. Results indicate the maximum thermal efficiency of the integrated fuel cell-fuel processing system remains in the range of 44–46% throughout descend, loitering, and cruise conditions. This paper discusses a system-level model of an integrated fuel cell-fuel processing system, and presents a methodology for system optimization through the particle swarm algorithm.
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