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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.

The linear internal combustion engine-linear generator integrated system (LICELGIS) is a generating unit with high power density, high efficiency and low emission for the range-extended electric vehicle. The LICELGIS starts with the linear generator, which shows the advantages of speed, efficiency and emission reduction, as well as the prerequisite to guarantee the steady operation of the system. This paper focuses on the reversing control method and the energy utilization efficiency in the starting process of the LICELGIS. Pursuant to the starting requirements of the linear internal combustion engine, the fewest driving cycle and the evaluation index are obtained. Meanwhile, the velocity tracking mode and the position tracking mode is proposed for the control of the starting force reversing. The motions of the starting process under two control method are comparatively analyzed, indicating that the former has a high efficiency, while the latter is more likely to achieve.

X-in-the-loop (XiL) framework is a new validation concept for vehicle product development, which integrates different virtual and physical components to improve the development efficiency. With XiL platform the requirements of reproducible test, optimization and validation, in which hardware, equipment and test objects are located in different places, could be realized. In the view of different location and communication form of hardware, equipment and test objects, time delay problem exists in the XiL platform, which could have a negative impact on development and validation process. In this paper, a simulation system of time delay prediction and compensation is founded with the help of BP neural network and RBF neural network. With this simulation system the effect of time delay in a vehicle dynamic model as well as tests of geographically distributed vehicle powertrain system is improved during the validation process.