Nonlinear Model Predictive Control of PEM Fuel Cell Systems for Generation of Exhaust Gas with Low Oxygen Content

Polymer electrolyte membrane (PEM) fuel cells are highly efficient energy converters and provide electrical energy, cathode exhaust gas with low oxygen concentration and water. For aircraft applications, PEM fuel cell systems are investigated as replacement for auxiliary power units (APU) that are currently used for electrical power generation. The system investigated is intended for generation of oxygen depleted cathode exhaust air (ODA), which must have a low oxygen concentration. A challenging task is controlling the fuel cell system for this product and simultaneously keeping fuel cell stack degradation, voltage losses and stack damage as low as possible as well as keeping the system within operational limitations such as bounds and gradients on control parameters. This constrained control task is attacked by a nonlinear model predictive control (NMPC) strategy. This paper demonstrates the applicability of NMPC for a fuel cell system. The prediction horizon has a significant influence on control performance and computational cost. Results of Matlab® simulations are shown.