Abstract
In this paper, a real-time capable reference governor superordinate model predictive controller (RG-MPC) is developed for fuel cell (FC) control suitable for automotive application. The RG-MPC provides reference trajectories for the subordinate proportional-integral (PI) controllers, which act directly on the FC system. Antiwindup and decoupling schemes, which are common problems in multivariable PI control, are unnecessary, given that the RG-MPC can inherently consider constraints and multivariable systems. The PI dynamics are incorporated into the prediction model used for control, ensuring the feasibility of the provided references for the PI controllers. The successive linearization technique is used in the RG-MPC to cope with the model’s nonlinear nature in real-time. The concept has been illustrated in a simulation scenario featuring efficient and safe power control of an FC stack in automotive application using real driving data obtained from an in-house-built FC vehicle. This work is the first step towards upgrading an existing, PI-based control scheme without the necessity of completely rebuilding the interface.
Highlights
In the ever-increasing public awareness of the presence of accelerated environmental pollution processes in the world, researchers from many fields of expertise have focused their efforts into finding energy sources alternative to fossil fuels and new sources altogether [1,2]
The reference governor superordinate model predictive controller (RG-model predictive control (MPC)) fulfills the delivery of the required net power in the vehicle shown in Figure 7, except in the region of high power demand of 60 kW, where the performance limitation is hit, since the current is constrained at 360 A (Figure 8d) and the compressor power (Figure 8a) cannot be further reduced, as it would cause operating in dangerous regions—as it will be demonstrated later on
We proposed using the reference governor model predictive controller approach for controlling fuel cell systems, especially vehicles
Summary
In the ever-increasing public awareness of the presence of accelerated environmental pollution processes in the world, researchers from many fields of expertise have focused their efforts into finding energy sources alternative to fossil fuels and new sources altogether [1,2]. A more sophisticated control algorithm than PI, but still intuitive enough to be understood is model predictive control (MPC) which uses a model to predict future operating conditions of the fuel cell and act on the plant so that all the control goals are fulfilled. The plant model was augmented with the PI dynamics resulting in the prediction model used by the RG-MPC to provide optimized references for the PIs. The control goal was to deliver the power demand from a research fuel cell vehicle presented, while ensuring safe and efficient operating conditions.
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