Parameterization of Nonlinear Model Predictive Control for Automotive Fuel Cell Systems

This paper presents a nonlinear model predictive control strategy for automotive fuel cell system operation. The system-level air-path modeling approach, widely adopted in control-oriented studies, is extended by the critical aspect of membrane hydration. The control problem formulation reflects the task of dynamic power tracking and efficiency-optimized actuation of the peripheral components as well as adherence to system constraints to avoid harmful operation. This is combined with an analysis of the design parameters sampling time, integration scheme, and prediction horizon for efficient transcription of the optimal control problem. Closed-loop simulation results, conducted using a sampling rate of 8ms, the standard fourth-order explicit Runge-Kutta method with one integration step and a horizon length of 25, successfully meet the control objectives.