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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.

In order to research the vibration and noise reduction in pure electric vehicle power-train, a comprehensive work is to simulate the power-train incentive of a high-speed pure electric vehicle, and indicates significant impact of gear mesh system on the power-train NVH performance. Therefore, it is necessary to further study the impact of meshing gear system on electric vehicle power-train vibration and noise performance and seek reasonable methods to reduce the vibration and noise. In this paper, a typical pure electric vehicle's powertrain was used to conduct vibration and noise dynamic simulation. Firstly, the power train model was established considering the gear meshing stiffness, transmission errors, bearing factors and shell flexible, then the vibration and sound radiation dynamic response of power-train was simulated. Based on the accuracy of prediction model, a gear tooth modification was carried out for vibration and noise optimization.

Faced on transient vibration of EV, considering the characteristics of the electric drive system, active and passive integrated transient vibration control method of power train mounting system was proposed. Models of power train system and mounting system were established, modal characteristics were grasped by simulation and experiment. A feed-forward controller was constructed from the facet of active control, mounting system transient vibration and power train torsion vibration were reduced. Based on this, further optimization of mounting system was conducted from a passive control perspective. Results show that the active and passive integrated control method can effectively reduce the dynamic reaction force of mounting points, improve the vibration conditions of power train and vehicle body as well.

Intelligent and connected vehicle (ICV) and new energy vehicle (NEV) will be two important directions of the automotive technology in the future, and the coordinated development of these two directions reflects relevantly the higher requirements put forward by nowadays society and people. Through the use of intelligent and connected technology (ICT), NEVs can exchange various traffic information data with the outside world (e.g. other running vehicles, road infrastructure, internet, etc.) in real time, which is so-called Vehicle to Everything (V2X). Based on the further analysis of the mutual traffic information, the vehicles can identify the current driving conditions and predict the future driving conditions effectively, which can realize the real time optimization of the energy management strategies (EMSs) of vehicles’ powertrain system, so as to meet the driving requirements of vehicles under different driving conditions.