In this paper, a polymer electrolyte membrane fuel cell (PEMFC) stack control study is presented. The goal is to track the transient power demand of a real fuel cell (FC) vehicle while ensuring safe and efficient operation. Due to the dynamically changing power demand, fast transients occur in the internal states of the fuel cell (e.g., pressure, humidity, reactant mass) leading to degradation effects (e.g., high/low membrane overpressure, reactants starvation) which are avoided by imposing safety constraints. Efficiency is considered in terms of internal voltage losses minimization as well as minimization of the power of the compressor used to pressurize the cathode. For solving the optimization problem of power demand tracking, adhering to safety constraints, and maximizing efficiency, model predictive control (MPC) has been chosen. Due to the nonlinearity of the FC system, a successive linearization based MPC (SLMPC) is used to control the FC throughout its operating region. Simulation results show that the power demand can be fulfilled while at the same time ensuring safe operation in terms of adhering to constraints and that the minimization of internal voltage losses and compressor power lead to an approximate 9.5% less hydrogen consumption than in the actual reference vehicle.

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.

This paper presents a methodology to design a nonlinear distributed-parameter observer to estimate internal temperature profiles in polymer electrolyte membrane fuel cells. Accurate knowledge of the spatial temperature distributions allows beneficial insight into the cell’s condition since the temperature is strongly coupled to other cell states. The extended Kalman filter-based observer employs a high-fidelity nonlinear non-isothermal distributed-parameter model for predicting internal temperature distributions. A reduced-order model, preserving the major dynamics and coupling effects of the original model, is utilized for real-time capable and feasible state correction. To improve the estimation, the observer scheme is augmented to simultaneously estimate selected material parameters along the temperature profiles online. In a simulation experiment, the observer is compared to an open-loop simulation benchmark. Further, the consistency of the estimation is investigated. The observer methodology is validated using a high-resolution simulated reality since internal temperature distributions are inaccessible in a real system. The validation demonstrates the observer’s accuracy in estimating the unmeasurable internal temperature distributions. The results substantiate the observer’s advantage over open-loop model simulations and outline the importance of temperature profile estimation for meaningful condition monitoring.