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Alternative powertrains, in particular electric and plug-in hybrids, create a wide range of unique and challenging NVH (noise, vibration & harshness) issues in today's automotive industry. Among the emerging engineering challenges from these powertrains, their acoustic performances become more complicated, partially due to reduced ambient masking noise level and light weight structure. In addition, the move away from conventional displacement engines to electrical drive units (EDU) has created a new array of NVH concerns and dynamics, which are relatively unknown as compared to the aforementioned traditional setups. In this paper, an NVH optimization study will be presented, focusing on four distinct factors in electric drive unit gear mesh source generation and radiation: EDU housing and bearing dynamics, gear geometry, EDU shafting torsional dynamics, and EDU housing structure. The study involves intensive FEA modeling/analyses jointly with physical validation tests.

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.