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This paper examines one of the approaches used to identify the root causes of sound quality issues in vehicles, including the direct impact of psychoacoustics on the human experience. Specifically, the absence of masking effects provided by traditional combustion engines has made noise and vibration from electric drives significant factors in decision-making processes, with high-pitched tonal noise from electric motors causing annoyance and sound quality concerns for electrified propulsion systems. During vehicle testing at different speeds, a whining noise was observed, leading to an NVH test to locate the noise source. The noise is traced to the transmission by the dominating order of input reduction along with the contribution from the casing resonance. A multi-physics-based e-NVH analysis was performed, and the test data were correlated.

With increase in demand for quieter product and reduction in masking noise, axle whine management plays a crucial role in the early product development process. Whine is tonal in nature and humans are more sensitive to tonal memory, hence this makes user to experience a very unpleasant ride which in turn results in bad product credibility. Dynamic mesh force excitation is the cause of the axle whine noise. Critical factors in consideration are gear micro geometry variability, misalignments, temperature of operation and resulting bearing pre-load, operating loads, and structural resonances that carry the excitation to the occupant’s ear. The variability associated with gear micro-geometry plays crucial role during optimization in the quest for robust gear design.

In the automotive industry many components face fatigue failure due to prolonged vibrations. This is commonly known as Vibration Induced Fatigue (VIF). There are two approaches to evaluate this; time & frequency domain. A straight forward and widely used method is the rainflow counting technique in the time domain. This counting algorithm is readily available and, apart from the time history, it needs only one variable input (the number of stress ranges). In case of high cycle fatigue, longer time histories are required for a statistically representative fatigue estimate, which makes the time domain approach consume large amounts of time and resources. This shifts our interest towards frequency domain methods. In the frequency domain, Dirlik's method is proven to be robust and gives closer results to the time domain.