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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.

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.

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.

In view of the problem of low-frequency (less than 10Hz, such as 0.5Hz, 1.15Hz, 8Hz in this paper) longitudinal vibration exists in a pure electric vehicle, modeling methods of drive-line torsion vibration system are conducted. Firstly, dynamometer test is performed, signals of motor speed and seat rail acceleration are obtained, the frequency characteristics of flutter is determined using the order analysis and time frequency analysis. Then four types of modeling and analysis are investigated facing the drive-line torsion vibration problem, including single model without electromagnetic stiffness, branch model without electromagnetic stiffness, single model considering electromagnetic stiffness and branch model considering electromagnetic stiffness. The results show that, modeling taking into account the electromagnetic stiffness and branches can reflect more low-frequency characteristics helps to reveal the low-frequency longitudinal flutter of the researched electric vehicle.

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.

Because of the vehicle market competitive and of the raise of customers' demanding, NVH performance became an important job, especially for new energy vehicles. As the electric vehicle moving into the direction of high speed and large torque, electric vehicle vibration and noise problems highlighted gradually. In recent years, CAE has played an increasing role in the design, development and optimization of powertrain NVH at component and system levels. The subject of this paper was the numerical and experimental evaluation of the electromagnetic and vibro-acoustic behavior of an electric powertrain. For this purpose, a coupled and fully flexible dynamics model of the electric powertrain was developed. Then electromagnetic forces including both radial and tangential force and gear mesh excitations including time-varying meshing stiffness, meshing error and meshing impact were computed, which were used to perform forced response analysis on the full FE mesh of the powertrain housing.

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.