Power Control for Efficient Operation of a PEM Fuel Cell System by Nonlinear Model Predictive Control

Efficiency of polymer electrolyte membrane (PEM) fuel cells depends directly on anode and cathode gas pressures, and stack temperature. For an efficient chemical reaction and a safe operation for both stationary and dynamic loads all values must fall within specific ranges. This paper deals with realtime nonlinear model predictive control (NMPC) of electrical power for a PEM fuel cell system. The NMPC is split into three nonlinear optimization problems to calculate the optimal stack current and the supply of anode and cathode with hydrogen and oxygen. For real-time application the predictive control algorithm is matched to the sampling time of the system. This NMPC handles all kinds of dynamic load changes, while maximizing power efficiency without a steady state error. Modeling of relevant anode and cathode site fuel cell peripherals is shown. Experimental results on a test bench with a 4.4kW PEM fuel cell are presented.