Control-oriented model of fuel processor for hydrogen generation in fuel cell applications

A control-oriented dynamic model of a catalytic partial oxidation-based fuel processor is developed using physics-based principles. The fuel processor system (FPS) converts a hydrocarbon fuel to a hydrogen (H22)-rich mixture that is directly feed to the proton exchange membrane fuel cell stack (PEM-FCS). Cost and performance requirements of the total powerplant typically lead to highly integrated designs and stringent control objectives. Physics-based component models are extremely useful in understanding the system level interactions, implications on system performance and in model-based controller design. The model can be used in a multivariable analysis to determine characteristics of the system that might limit performance of a controller or a control design.In this paper, control theoretic tools such as the relative gain array (RGA) and the observability gramian are employed to guide the control design for a FPS combined with a PEM-FC. For example, this simple multivariable analysis suggests that a decrease in hydro-desulfurizer volume is critical for the H22-starvation control. Moreover, RGA analysis shows different level of coupling between the system dynamics at different power levels. Finally, the observability analysis can help in assessing the relative cost–benefit ratio in adding extra sensors in the system.