Design and optimization of stochastically driven, multi degrees-of-freedom energy harvesters using equivalent circuits and swarm intelligence algorithms

Energy harvesters that extract power from random mechanical vibrations are electromechanical systems designed to convert kinetic energy from ambient vibrations, modeled as white Gaussian noise, into usable electrical energy. Their efficiency is often limited by impedance mismatches between the mechanical and electrical domains, leading to suboptimal energy transfer. Inspired from analogous problems in RF engineering, we reformulate the energy harvester design as a broadband filtering problem, treating mass-spring systems as mechanical filters. We devise an equivalent circuit model for the harvester, and we use frequency domain analysis to derive an integral equation for the harvested power. To optimize performance, we apply a swarm intelligence algorithm – specifically, Flock of Starlings Optimization – resulting in significantly improved efficiency over traditional, single-degree-of-freedom, harvesters.