Online identification of an electric PEMFC model for power control by NMPC

Of course, fuel cells are also subjects to signs of wear. Over time of usage this is shown by lower power efficiency, the degradation. Degradation correlates with wear due to mechanical stress, high stack temperature, and too fast load changes. Generally it is influenced by operating the fuel cell with wrong conditions regarding to temperature, anode and cathode pressure. Despite this time variant behavior the aim in operation with fuel cell systems is to keep the quality of power control as high as possible. Modeling of long term time variations, especially such as degradation effects, can only hardly be realized. Therefore this paper deals with an online identification of the electrical model of a PEM fuel cell to support the model predictive control (MPC). Due to the amount of constraints to be respected in fuel cell operation, MPC is an appropriate control strategy. Identification by using only the current measured data leads to a very exact model for the actual range of operation, but great model errors outside of this range are occurring. Thus, an appropriate strategy for the storage of measured data in relation to the identification is shown. The aim of the data management is to achieve steady computation times and an accurate model over the entire operation range. Experimental results on a test bench with a 4.4kW PEM fuel cell are presented.