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This paper focuses on a P3 HEV drivetrain for a performance vehicle with a 2-speed gear shift system. The drivetrain NVH performance varies at different gear and different loading conditions, therefore creates a new level of challenges in optimizing the system. This paper presents the methodologies in optimizing the system NVH, including noise sources from both gearbox and eMotor. CAE modeling methods are discussed and illustrated for their usage in optimizing both structural and acoustic responses. Reasonable correlations to test data are achieved and presented.

In the current automotive industry, it is common that the driveline subsystem and components are normally from different automotive suppliers for OEMs. In order to ensure proper system integration and successful development of driveline system NVH performances, collaboration efforts between OEMs and suppliers are very demanding and important. In this paper, a process is presented to achieve successfulness in developing and optimizing vehicle integration through effective teamwork between a driveline supplier and a major OEM. The development process includes multiple critical steps. They include target development and roll down, targets being specific and measurable, comprehension of interactions of driveline and vehicle dynamics, accurate definition of sensitivity, proper deployment of modal mapping strategy, which requires open data sharing; and system dynamics and optimization.

With the trend of electric drive unit gradually replacing ICE powertrain, in additional to gear noise, the motor noise has become a new major NVH challenge. These tonal noises are easier to be detected in the pure electric vehicle that has no masking effect of ICE powertrain. Therefore, how to accurately predict and reduce the motor noise is a key to solve the problem. The accuracy of calculated motor stator modes determines the accuracy of motor noise prediction. This paper presents a simulation method based on the finite element model and defined orthotropic material properties of the stator. The material property parameters of the stator core and hairpin windings are reverse-engineered through iterative correlations to test data. High accuracy FEA model is achieved that can determine the stator mode shapes and frequencies of this hairpin motor accurately, which provides a reliable and effective approach for the motor noise prediction and optimization studies.