We consider the model of a networked energy harvester for ambient dispersed vibrations, based on coupled mechanical resonators and a piezoelectric transduction mechanism. The networked harvester is equivalent to a mechanical filter that can be optimized for broadband energy harvesting. Using mechanical-to-electrical analogies, we derive an equivalent circuit model for the energy harvester, and we calculate the transfer function, output voltage, average harvested power and power efficiency in the frequency domain. We discuss the problem of the energy harvester optimization. Because analytical formulas for the objective function and its derivatives are not available, we apply a gradient-free method, based on Particle Swarm Optimization, to find the network parameters that maximize the scavenged energy. We show that, after proper optimization, the networked energy harvester collects significant more power than a single degree-of-freedom energy harvester.
Using Circuit Theory and Swarm Intelligence for the Design and Optimization of Energy Harvesters for Ambient Mechanical Vibrations / Song, Kailing; Bonnin, Michele; Bonani, Fabrizio; Traversa, Fabio L.. - ELETTRONICO. - (2025), pp. 1-6. ( 2025 IEEE 4th Industrial Electronics Society Annual On-Line Conference (ONCON) Kharagpur (Ind) 11-13 December 2025) [10.1109/oncon68412.2025.11384471].
Using Circuit Theory and Swarm Intelligence for the Design and Optimization of Energy Harvesters for Ambient Mechanical Vibrations
Song, Kailing;Bonnin, Michele;Bonani, Fabrizio;Traversa, Fabio L.
2025
Abstract
We consider the model of a networked energy harvester for ambient dispersed vibrations, based on coupled mechanical resonators and a piezoelectric transduction mechanism. The networked harvester is equivalent to a mechanical filter that can be optimized for broadband energy harvesting. Using mechanical-to-electrical analogies, we derive an equivalent circuit model for the energy harvester, and we calculate the transfer function, output voltage, average harvested power and power efficiency in the frequency domain. We discuss the problem of the energy harvester optimization. Because analytical formulas for the objective function and its derivatives are not available, we apply a gradient-free method, based on Particle Swarm Optimization, to find the network parameters that maximize the scavenged energy. We show that, after proper optimization, the networked energy harvester collects significant more power than a single degree-of-freedom energy harvester.| File | Dimensione | Formato | |
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ONCON 2025.pdf
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oncon 2025.pdf
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https://hdl.handle.net/11583/3009198
