On the Way to Purge Volume Control during Purge Processes of a Fuel Cell System by a Predictive Control Strategy

This paper deals with an Optimal Iterative Learning Control approach for the anode pressure during the periodic purge processes of a fuel cell system which is developed to a Predictive Control Strategy under consideration of the correct purge volume. Due to accumulation of diffused nitrogen and water condensate inside the anode volume the chemical reaction is restrained. This adverse effect is avoided through the purge process, by which a short-time opening of the exhaust valve forces the nitrogen and water out of the system. Unfortunately, the opening leads to a pressure drop along the anode volume that causes a force to the membrane. To avoid this mechanical stress the control aim is a constant anode pressure during the purge process by supplying additional hydrogen. Therefore, first an Optimal Iterative Learning Control approach is introduced. Due to an influence of the Optimal Iterative Learning Control on the purge volume, a correction of the purge time interval is applied for a constant purge volume. In the last step a Nonlinear Model Predictive Control approach is presented which allows a correction of the purge volume during load changes as well.