Gear sound model for an approach of a Mechanical Acoustic Vehicle Alerting System (MAVAS) to increase EV’s detectability

Abstract

Hybrid-electric and electric vehicles significantly reduce noise road emissions. This noise mitigation also causes a reduction in the sound detectability and therefore it increases the potential of causing accidents. A suitable solution arises with the Acoustic Vehicle Alerting Systems (AVAS) emitting a warning sound to alert pedestrians about the presence of a silent vehicle. This paper details an acoustic prediction model capable of simulating the sound produced by a pair of spur dry gears used as a Mechanical Acoustic Vehicle Alerting System (MAVAS). This proposal that tries to reproduce a sound closer to the mechanical sound of a conventional vehicle would be used as an alternative to existing systems. The prediction model developed is validated and consists in two consecutive parts: first, a dynamic model studies the rattle of the gears, then, an analytical model reproduces the sound of each impact of the gear teeth. This sound model makes it possible to characterize a proposed gear combination of the MAVAS, verifying its compliance with the European legislation.