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Most of the existing energy management strategies for Extended-Range Electric Vehicles (E-REVs) are heuristic, which restricts coordination between the battery and the Range Extender. This paper presents an instantaneous optimization energy management strategy based on the Minimum Loss Power Algorithm (MLPA) for a fuel cell E-REV. An instantaneous loss power function of power train system is constructed by considering the charge and discharge efficiency of the battery, together with the working efficiency of the fuel cell Range Extender. The battery working mode and operating points of the fuel cell Range Extender are decided by an instantaneous optimization module (an artificial neural network) that aims to minimize the loss power function at each time step.

Hydrogen safety is of great importance in proton exchange membrane fuel cell (PEMFC) systems. Anode pressure control has become a focus point in recent years. The differential pressure between anode and cathode in PEMFC system needs to be carefully controlled under a suitable threshold. In practice, the anode pressure is usually controlled about 20–30kPa higher than the cathode pressure to minimize nitrogen crossover and improve cell stability. High differential pressure could lead to irreversible damage in proton exchange membrane. PID control was the dominant method to control the anode pressure in the past. However, the anode pressure’s fluctuation when hydrogen mass flow suddenly changes is a long-term challenge. As the requirements of control precision are increasingly high, the traditional PID control needs to be improved. Several new control algorithms are presented in recent researches, however, mostly are theoretical and experimental.