Optimal iterative learning control of a PEM fuel cell system during purge processes

This paper deals with an Optimal Iterative Learning Control approach for the anode pressure during the periodic purge processes of a fuel cell system. 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. An Iterative Learning Control is suited for the multiple times executed purge procedure. For this purpose, the nonlinear characteristics of the fuel cell system model are transferred into time-variant, linearized state-space models in discrete-time domain to create learning filters. In order to achieve a fast convergence by a strictly monotonously reduction of the control error, the control problem becomes a minimization problem and therefore an Optimal Iterative Learning Control is applied.