The measurement of hydrogen concentration in fuel cell systems is an important prerequisite for the development of a control strategy to enhance system performance, reduce purge losses and minimize fuel cell aging effects. In this perspective paper, the working principles of hydrogen sensors are analyzed and their requirements for hydrogen control in fuel cell systems are critically discussed. The wide measurement range, absence of oxygen, high humidity and limited space turn out to be most limiting. A perspective on the development of hydrogen sensors based on palladium as a gas-sensitive metal and based on the organic magnetic field effect in organic light-emitting devices is presented. The design of a test chamber, where the sensor response can easily be analyzed under fuel cell-like conditions is proposed. This allows the generation of practical knowledge for further sensor development. The presented sensors could be integrated into the end plate to measure the hydrogen concentration at the anode in- and outlet. Further miniaturization is necessary to integrate them into the flow field of the fuel cell to avoid fuel starvation in each single cell. Compressed sensing methods are used for more efficient data analysis. By using a dynamical sensor model, control algorithms are applied with high frequency to control the hydrogen concentration, the purge process, and the recirculation pump.

Fuel cell systems that utilize anode recirculation generally require a purge process to remove accumulated gaseous impurities from the anode recirculation system. Especially the accumulation of nitrogen leads to a decrease of the cell voltage and therefore a reduced stack efficiency. However, unconsumed hydrogen is lost during the purge process, resulting in a decrease of hydrogen utilization. Therefore, an optimal purge control can help to maximize the overall system efficiency.In order to determine and predict the influence of the purge valve opening on the system efficiency with respect to the hydrogen utilization and the stack efficiency we develop a control-oriented model of a PEMFC anode recirculation system. We then set up a model predictive purge controller and compare its performance to two standard purge strategies using the NEDC vehicle test cycle.

Trains are a corner stone of public transport and play an important role in daily life. A challenging task in train operation is to avoid skidding and sliding, i.e. spinning or blocking of the wheels, during fast changes of traction conditions, which can, for example, occur due to changing weather conditions, crossings, tunnels or forest entries. The latter depends on local track conditions and can be recorded in a map together with other location-dependent information like speed limits and inclination. In this paper, a model predictive control (MPC) approach is developed. Thanks to the knowledge of future changes of traction conditions, the approach is able to avoid short-term skidding and sliding even under fast changes of traction conditions. In a first step, an optimal reference trajectory is determined by a multiple-shooting approach. In a second step, the reference trajectory is tracked by an MPC setup. The developed method is simulated along a track with fast-changing traction conditions for different scenarios, like changing weather conditions and unexpected delays. In all cases, skidding and sliding is avoided.