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Description
This contribution presents results of the NEẍUS project on noise from battery electric vehicles (BEV) under acceleration in urban settings. The potential of replacing internal combustion engine (ICE) passenger cars with BEVs to reduce noise levels for residents is investigated with scenario calculations using measured traffic data and emission models generated from pass-by measurements. Individual vehicle trajectories and driving states were extracted from aerial drone videos of urban intersections. To create the emission models, a measurement campaign was conducted with various driving profiles, which were enforced by means of real-time feed-back to the driver. Sound exposure levels of nine pairs of comparable BEV and ICE passenger cars were captured and used to derive spectral emission models. The model coefficients were determined with an iterative optimization algorithm, solving the inverse problem considering a variety of speeds and accelerations. Combining these emission models with real-world trajectories enables predicting noise exposure levels for virtual residents near the intersections for either BEV or ICE vehicle fleets. The BEV-only scenario reduces LAeq near investigated intersections by 1.5 dB on average, and up to 3 dB near stop lines at times with high traffic volume. At low frequencies, differences up to 10 dB were observed.