Controller design for polymer electrolyte membrane fuel cell systems for automotive applications. (1st July 2021) - Gómez, Juan Carlos; Serra, Maria; Husar, Attila
Abstract: Continuous developments in Proton Exchange Membrane Fuel Cells (PEMFC) make them a promising technology to achieve zero emissions in multiple applications including mobility. Incremental advancements in fuel cells materials and manufacture processes make them now suitable for commercialization. However, the complex operation of fuel cell systems in automotive applications has some open issues yet. This work develops and compares three different controllers for PEMFC systems in automotive applications. All the controllers have a cascade control structure, where a generator of setpoints sends references to the subsystems controllers with the objective to maximize operational efficiency. To develop the setpoints generators, two techniques are evaluated: off-line optimization and Model Predictive Control (MPC). With the first technique, the optimal setpoints are given by a map, obtained off-line, of the optimal steady state conditions and corresponding setpoints. With the second technique, the setpoints time profiles that maximize the efficiency in an incoming time horizon are continuously computed. The proposed MPC architecture divides the fast and slow dynamics in order to reduce the computational cost. Two different MPC solutions have been implemented to deal with this fast/slow dynamics separation. After the integration of the setpoints generators with the subsystems controllers, the different control systems are tested and compared using a dynamic detailed model of the automotive system in the INN-BALANCE project running under the New European Driving Cycle. Highlights: The control structure of a PEM fuel cell system in an automotive application is described. An off-line optimal setpoint generator is compared to MPC techniques. Slow and fast dynamics are split in the MPC design for computational burden reduction. Simulations are based on a model tuned using experimental data from a real automotive fuel cell system.