Nonlinear Model Predictive Control of an Automotive PEMFC System Considering Anode Recirculation Loop Bleeding

This work presents a Nonlinear Model Predictive Controller (NMPC) strategy for the optimal operation of a fuel cell system to maximize its average efficiency and minimize degradation. Because of the different time scales associated with the different controlled variables, the proposed NMPC is divided into three distinct parts, each one customized with its specific prediction horizon and sampling time. The NMPC is based on a comprehensive model of the fuel cell stack, considering manufacturing constraints and incorporating the compressor map. The NMPC outputs are the optimal setpoints for all the subsystems’ local controllers. The studied fuel cell system incorporates the anode recirculation loop with an ejector, a water separator, and a bleed valve, and the proposed NMPC determines the optimal opening percentage of the bleed valve to prevent nitrogen accumulation and efficiency distortion. The proposed strategy yields higher efficiency than offline steady-state setpoints in the evaluated scenario, while full future-current information generally provides further improvement relative to limited preview.