High-frequency whine noise in electric vehicles (EVs) is a significant issue that impacts customer perception and alters their overall view of the vehicle. This undesirable acoustic environment arises from the interaction between motor polar resonance and the resonance of the engine mount rubber. To address this challenge, the proposal introduces an innovative approach to predicting and tuning the frequency response by precisely adjusting the shape of rubber flaps, specifically their length and width.
The approach includes the cumulation of two solutions: a precise adjustment of rubber flap dimensions and the integration of ML. The ML model is trained on historical data, derived from a mixture of physical testing conducted over the years and CAE simulations, to predict the effects of different flap dimensions on frequency response, providing a data-driven basis for optimization. This predictive capability is further enhanced by a Python program that automates the optimization of flap dimensions using a linear combination formula. The automation ensures that the desired frequency response is achieved efficiently and systematically.
By combining the insights from ML with the linear combination formula, the method not only addresses the dynamic peak during frequency sweeps but also mitigates resonance issues through the principles of dual dynamic absorber theory. This comprehensive approach improves the acoustic environment within the vehicle cabin and serves as a preventative measure against potential resonance problems, ultimately contributing to a higher-quality user experience.