Predictive energy management strategy with optimal stack start/stop control for fuel cell vehicles

Abstract

Energy management strategies (EMSs) for fuel cell vehicles aim at high fuel efficiency but must also consider the lifetimes of the fuel cell system (FCS) and the battery. Regarding both objectives, fuel cell stack shutdowns play a decisive role in real-world driving situations with low or negative power demand. However, each stack start/stop event is associated with degradation, which is why it is important to keep the number of starts/stops low. This work proposes a predictive EMS with optimal stack start/stop control that takes advantage of a route-based prediction of the entire driving mission to minimize both the fuel consumption and the number of start/stop events. Before departure, the prediction of the entire driving mission is processed in a single offline optimization with dynamic programming. This optimization yields maps providing the real-time EMS with optimal control information that continuously adapts depending on the position along the driving mission and the battery state of charge. Considering this predictive information, the real-time EMS optimizes start/stop actions and the stack power such that the cost-to-go, i.e., the fuel consumption for the trip remainder including start/stop penalties, is implicitly minimized in each instant. In this way, the EMS continuously adapts to the actual conditions, making it robust against unpredicted disturbances, e.g., due to traffic. The superior performance of the proposed strategy compared to state-of-the-art start/stop methods is demonstrated in numerical studies based on real-world driving missions for different vehicle classes with single and multi-stack FCSs.