Sudden torque changes and torque fluctuations caused by a traction motor in HEV (hybrid electric vehicle) can result in a driveline oscillation. This paper focuses on the HEV launch vibration in pure electric mode while only the electric motor is generating output torque. A relevant mathematical model has been built based on the working principle. According to the model, the torsional vibration characteristics of the electric propulsion system have been analyzed. Two active control methods, feed-forward control (FFC) and pole placement (PP) have been applied to suppress the HEV launch vibration. However, such longitudinal vibration may still be perceived by passengers, though they can attenuate the vibration to some extent. Therefore another method, wave superposition control strategy (WSCS), has been proposed to suppress the vibration more effectively. In order to prove the control effectiveness of WSCS, all these three active control methods have been applied in simulation for a comprehensive comparison of the vibration suppression effect. Simulation results demonstrate that compared with FFC and PP, WSCS minimizes the overshoot of wheel angular acceleration (WAA) most and doesn't have much effect on the system response quickness, which can be regarded as the most effective control method.

One of the key challenges with the development of range extended electric vehicles is the noise, vibration and harshness behavior, specifically the uncomfortable ride experience during the range extender starting and stopping. This article focuses on providing an active control method to address this critical noise, vibration and harshness issue. A control-oriented nonlinear model for the start-stop vibration analysis, including range extender mount system, engine-clutch-motor shaft system, engine inertia torque and force, engine friction torque, engine gas torque, engine manifold pressure, electric motor torque and range extender controller, is thus built. In the developed model, a new estimation method for gas torque is proposed, where the initial crank angle is considered as well as the relevant equations are simplified. The method is proved to predict gas torque accurately without the expense of complex calculation process. Based on the model, the mount system vibration response has been analyzed that the excitation of range extender block on vehicle body in x-direction is the most prominent. The concept of the active control method, gas torque compensation control, is thus introduced in detail to reduce such a system vibration during start-stop phase. According to the control principle, the torque compensation coefficient for gas torque compensation control is determined and the compensation speed range is analyzed. In order to evaluate the control effectiveness, gas torque compensation control is implemented in the set-up model. The simulation outcomes demonstrate that gas torque compensation control is effective in reducing the vibration generated during start-stop transitions.

A two-stage series-parallel vibration isolation system is already widely used in various industrial fields. However, when the researchers analyze the vibration characteristics of a mechanical system, the system is usually regarded as a singlestage one composed of two substructures. The dynamic modeling of a two-stage series-parallel vibration isolation system using frequency response function-based substructuring method has not been studied. Therefore, this article presents the source-path-receiver model and the substructure property identification model of such a system. These two models make up the transfer path model of the system. And the model is programmed by MATLAB. To verify the proposed transfer path model, a finite element model simulating a vehicle system, which is a typical two-stage series-parallel vibration isolation system, is developed. The substructure frequency response functions and system level frequency response functions can be obtained by MSC Patran/Nastran and LMS Virtual.lab based on the finite element model. Next, the system level frequency response functions are substituted into the transfer path model to predict the substructural frequency response functions and the system response of the coupled structure can then be further calculated. By comparing the predicted results and exact value, the model proves to be correct. Finally, the random noise is introduced into several relevant system level frequency response functions for error sensitivity analysis. The system level frequency response functions that are most sensitive to the random error are found. Since a two-stage series-parallel system has not been well studied, the proposed transfer path model improves the dynamic theory of the multi-stage vibration isolation system. Moreover, the validation process of the model here actually provides an example for acoustic and vibration transfer path analysis based on the proposed model. And it is worth noting that the proposed model can be widely applicable in mechanical systems.

The mechanical system applied in industry and manufacturing fields is generally a complex multi-stage isolation system, which contains a lot of connection parts. In order to better analyze the effect of the connection parts, the transfer path model of a two-stage serial system is developed in this article using frequency response function based substructuring method. To verify the proposed transfer path model, a finite element model that simulates a two-stage serial vibration isolation system is built. By comparing the predicted results and exact values, the model proves to be correct. Furthermore, the random noise is introduced into the system-level frequency response functions for error sensitivity analysis and the influence on the substructure frequency response functions is quantified through comparison. Since the possible errors are unknown in the experimental measurement, 5%, 25%, and 50% random errors are introduced into all the input frequency response functions individually to analyze the influence of different noise levels on the prediction accuracy. In order to solve the ill-conditioned inverse problem involved in the model, the truncated singular value decomposition is also applied. The simulation results show that compared with the direct-inverse method, truncated singular value decomposition can decrease the prediction error caused by the introduced noise more effectively.

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