Model-Predictive-Control-Based Reference Governor for Fuel Cells in Automotive Application Compared with Performance from a Real Vehicle

In this paper, a real-time capable reference governor superordinate model predictive controller (RG-MPC) is developed for fuel cell (FC) control suitable automotive application. The RG-MPC provides trajectories the subordinate proportional-integral (PI) controllers, which act directly on FC system. Antiwindup and decoupling schemes, are common problems in multivariable PI control, unnecessary, given that can inherently consider constraints systems. dynamics incorporated into prediction used ensuring feasibility of provided references controllers. successive linearization technique to cope with model’s nonlinear nature real-time. concept has been illustrated simulation scenario featuring efficient safe power an stack application using real driving data obtained from in-house-built vehicle. This work first step towards upgrading existing, PI-based scheme without necessity completely rebuilding interface.