Application of Tone-to-Noise Ratio for Early Diagnosis and Control of Electric Drive Whine

High-frequency whine from electric drive systems has become a critical issue restricting the improvement of vehicle sound quality. Traditional evaluation methods struggle to accurately identify masked whine risks in the early research and development (R&D) phase, due to incomplete hardware of prototype vehicles and high interior background noise. This often leads to problems being delayed until the mass production stage, resulting in high rectification costs. To address this issue, this paper proposes and validates an early risk assessment method based on the Tone-to-Noise Ratio (TNR). First, the generation mechanism of Electric Drive (E-Drive) whine is systematically analyzed, identifying the electromagnetic noise of the electric motor and the gear whine of the reducer as the two dominant noise sources. To address this bottleneck, the TNR psychoacoustic metric is introduced to quantify the perceptual salience of tonal noise relative to background noise, which effectively mitigates the masking effect caused by high background noise in early prototype vehicles. Combined with an engineering case of a Plug-in Hybrid Electric Vehicle (PHEV), the study confirms that the TNR method can accurately identify potential high-risk whine orders in the Verification Prototype (VP) phase and enable reliable forward prediction of risks in the Mass Production (MP) stage. On this basis, a multi-dimensional noise reduction strategy covering harmonic current injection, gear microgeometry optimization, and transfer path optimization is developed for the identified risk orders. Full vehicle validation shows that the noise of key whine orders is reduced by 5-10 dB(A) after optimization, while the TNR value decreases significantly, and the vehicle NVH performance reaches industry-leading levels. This research forms a complete technical path from TNR-based early identification to targeted closed-loop control, providing a theoretical basis and practical engineering example for the proactive management and efficient solution of electric drive whine risks in various new energy vehicles.