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Predicting the vibration of a motor gearbox assembly driven by a PWM inverter in the early stages of development is demanding because the assembly is one of the dominant noise sources of electric vehicles (EVs). In this paper, we propose a simulation model that can predict the transient vibration excited by gear meshing, reaction force from the mount, and electromagnetic forces including the carrier frequency component of the inverter up to 10 kHz. By utilizing the techniques of structural model reduction and state space modeling, the proposed model can predict the vibration of assembly in the operating condition with a system level EV simulator. A verification test was conducted to compare the simulation results with the running test results of the EV.

In response to the growing demand for fuel economy, we are developing a high-efficient variable displacement pump for hydraulic power steering systems. In order to develop a quiet variable displacement pump which generates lower noise for better vehicle interior sound quality, we have been developing a simulation tool which includes hydraulic analysis, vibration analysis, and vehicle interior noise analysis which combines simulation outputs and measured noise transfer functions of the targeted vehicle. This paper provides both validation results of the simulation tool and application examples to design improvement to conclude the effectiveness of the simulation tool developed.

We have developed an excitation source identification system that can distinguish excitation sources on a sub-assembly level (around 30mm) for vehicle components by combining a measurement and a timing analysis. Therefore, noise and vibration problems can be solved at an early stage of development and the development period can be shortened. This system is composed of measurement, control, modeling, and excitation source identification parts. The measurement and the excitation source identification parts are the main topics of this paper. In the measurement part, multiple physical quantities can be measured in multi-channel (noise and vibration: 48ch, general purpose: 64ch), and these time data can be analyzed by using a high-resolution signal analysis (Instantaneous Frequency Analysis (IFA)) that we developed.