An energy-based framework for predicting vehicle noise source intensity: From energy consumption to noise.

The vehicle noise source strength prediction model is a crucial component in the field of traffic noise prediction. Despite the establishment of noise source strength localized models in various countries, the theoretical underpinnings of the sound power level models within these frameworks remains unclear. This study addresses this gap by analyzing the correlation between vehicle noise and energy consumption. An energy-based source strength model framework (E-SSIM) is proposed, focusing on developing nonlinear models for basic noise level. E-SSIM is built on acoustical principles and the energy flow of vehicles, integrating noise and energy consumption through the application of multivariate regression theory, characterized by a transient or simplified mathematical framework. Furthermore, sensitivity analysis and road experiments are conducted to validate the proposed framework. The findings reveal that E-SSIM effectively integrates vehicle energy flow and principles of acoustics, thereby providing a theoretical foundation for the logarithmic mathematical structure in classical noise source strength models. The study reveals that in low-speed driving conditions (17-40 km/h), the sensitivity of noise energy to aerodynamic drag energy consumption reaches its peak. Specifically, the sensitivity of E-SSIM, as assessed by the A-weighted sound level, progressively decreases with increasing speed. On the contrary, for the Z-weighted sound level, the sensitivity initially decreases before rising again, reaching its peak stability and robustness at a speed of 23.8 km/h. E-SSIM exhibits superior precision in predicting A/Z-weighted sound pressure levels. Compared to classic logarithmic structural prediction models, the mean absolute percentage error of E-SSIM was reduced by 4.19% and 0.07%. Compared to typical models such as ASJ developed by the Acoustical Society of Japan and CNOSSOS-EU used by the European Commission, E-SSIM yielded a mean absolute percentage error reduction of 68% and 67%. Interestingly, as vehicle internal energy consumption increases, the prediction deviations of E-SSIM, ASJ, and CNOSSOS-EU gradually decrease, possibly because vehicle operating conditions approach stability. E-SSIM can utilize abundant vehicle data to develop generic models, promoting the advancement of noise prediction.

Exploring vehicle-centric strategies for sustainable urban mobility: A theoretical framework for saving energy and reducing noise in transportation.

Adopting energy-saving and noise-reducing technologies in vehicle transportation has the potential to mitigate urban traffic pollution and promote sustainable urban mobility. However, a universal analytical framework for obtaining the combined energy savings and noise reduction patterns in vehicles is still lacking. This study addresses this gap by integrating a fundamental traffic noise model with a vehicle energy conservation equation. A theoretical framework was constructed that establishes the relationship between vehicle noise and energy consumption, with the theoretical origins of this framework explained. By summarizing a substantial body of classical literature, the typical model's properties are analyzed through the principle of optimality, and the noise interval for combined vehicle energy-saving and noise-reducing is determined. Subsequently, a rigorous vehicle experiment was conducted to validate the proposed framework's effectiveness, utilizing synchronized data on energy consumption and noise. The findings indicate that vehicles can achieve unconstrained combined energy-saving and noise-reducing in four driving states and conditional combined energy-saving and noise-reducing in five driving states. The Recall index demonstrates a verification rate exceeding 0.62 for the combined energy-saving and noise-reducing rules. This research provides valuable insights to support energy-saving and noise-reducing measures in urban traffic.