Instantaneous Optimization Energy Management for Extended-Range Electric Vehicle Based on Minimum Loss Power Algorithm

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. In order to solve the local optimum problem, a Range Extender output power gain coefficient is introduced, which can automatically adjust the output power of the Range Extender according to the residual amount of on-board hydrogen. Thus battery energy and hydrogen may be extinguished at approximately the same time, allowing global optimal results. To validate the proposed strategy, the energy management strategy presented in this paper is realistically implemented onto a real fuel cell E-REV. Simulation and dynamometer test results prove that the proposed instantaneous optimization energy management strategy can dramatically improve fuel economy performance and adaptability under a broad range of driving conditions compared to rule-based strategies.