This is the author's accepted manuscript (preprint). This paper has been accepted for presentation at the 55th International Congress and Exposition on Noise Control Engineering (Inter-Noise 2026), to be held in August 2026. The final published version will be available in the conference proceedings. The proliferation of large-scale Battery Energy Storage System (BESS) stations poses significant environmental noise challenges, necessitating accurate far-field noise prediction in the early design stages. The accuracy of these predictions is critically dependent on precise sound source data of individual BESS containers. This paper presents a novel framework using a mobile robotic arm to perform high-resolution sound intensity scanning (ISO 9614-2) across the container's surfaces. This method enables the precise identification of noise-contributing areas, yielding a high-resolution source map that is used to create a high-fidelity area source model in CadnaA. The model's predictions were validated against on-site measurements at a receiver 71.2 m from the source, achieving a prediction error in overall Sound Pressure Level (SPL) of only 1.5 dB. In contrast, a conventional model based on the ISO 3744 standard yielded a prediction error of 7.5 dB in the same scenario. The proposed framework thus significantly enhances noise prediction accuracy, providing a more reliable tool for designing effective noise control measures for BESS facilities.
Xu et al. (Tue,) studied this question.