A high-performance conical-neck helmholtz resonator-based piezoelectric self-powered system for urban transportation

Abstract

As urbanization accelerates, the issue of traffic noise escalates. Efficiently harnessing this prevalent acoustic energy and facilitating its collection and conversion has emerged as a notable challenge in contemporary research. This paper introduces a piezoelectric self-powered system anchored on a Conical-Neck Helmholtz Resonator-Based Piezoelectric Self-Powered System (CNHR-PSS) which places the piezoelectric device inside a Conical-Neck Helmholtz resonator. This system amalgamates acoustic energy harvesting, traffic noise abatement, and traffic condition discernment. It combines by two parts, including a Piezoelectric Self-Powered Node (PSN) and a machine learning algorithm. The PSN, employing the Conical Neck Helmholtz Resonator and piezoelectric module, seizes noise and transmutes it into electrical energy, showcasing robust scalability. Multiple PSNs coalesce to form a sound barrier for traffic noise mitigation. Concurrently, the voltage signals emanated by the PSN also encapsulate traffic status information. The algorithm extracts feature from the output signal and employs machine learning to decipher traffic conditions. Simulative and theoretical analyses affirm that the system can efficaciously harvest acoustic energy from urban traffic noise and attenuate noise, with a pinnacle output power of 0.52mW and an average noise reduction of 13.16 %. The recognition accuracy of traffic conditions via SVM attained 100 %. The investigative outcomes underscore the exemplary performance of this self-powered system, rendering it a viable solution for applications in traffic noise mitigation and intelligent transportation.