Control of proton exchange membrane fuel cell system breathing based on maximum net power control strategy

In order to achieve the maximum net power, the analysis for the maximum net power characterization of a proton exchange membrane fuel cell (PEMFC) system is carried out. A maximum net power control (MNPC) strategy based on an implicit generalized predictive control (IGPC) and a reference governor is proposed to keep optimal oxygen excess ratio (OER) trajectory. The IGPC based on an effective informed adaptive particle swarm optimization (EIA-PSO) algorithm is developed to solve the predictive control law and reduce the computational complexity in the rolling optimization process. The simulations of three conditional tests are implemented and the results demonstrate that the proposed strategy can track the optimal OER trajectory, reduce the parasitic power and maximize the output net power. The comprehensive comparisons based on three conditional tests verify that the MNPC-IGPC has better robust performance in the presence of large disturbances, time delay and various noises. The experimental comparison with internal control system of Ballard 1.2 kW Nexa Power Module testifies the validity of the MNPC-IGPC for increasing the net power. Hence, this proposed strategy can provide better behavior to guarantee optimal OER trajectory and the maximum net power even though the disturbances and uncertainties occur.