Technical Papers
Apr 6, 2023

Electromagnetic Energy Harvesting via Self-Excited Resonance from Extremely Low-Frequency Vibrations

Publication: Journal of Environmental Engineering
Volume 149, Issue 6

Abstract

This paper presents an electromagnetic energy harvester (EMEH) using gravity-induced self-excited vibrational resonance. The EMEH is capable of harvesting vibration energy below 1 Hz. The structure is a cantilever-mass system, and the mass is comprised of a magnet array and its support. The EMEH is excited by the gravity of the cantilever-mass system intermittently sliding down along a central hub. To explain the output performance of the EMEH visibly, we formulated the motion process and built an eight-stage dynamics motion. The effect of the cantilever thickness on the open-circuit voltage is discussed, and the results agree with the theoretical description well. Additionally, to demonstrate the output performance of the harvester, more experiments were performed, i.e., matching impedance, charging capacitors, and powering light emitting diodes (LEDs). The EMEH delivers a maximum instantaneous output power of 144.1 mW at a beam thickness of 1 mm. For charging experiments, 47 LEDs were successfully lighted up in real-time. The high performance of the gravity-induced resonance scheme indicates tremendous low-frequency application potential.

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Data Availability Statement

All data, models, and codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work was funded and supported by the National Natural Science Foundation of China (No. 62001281), Shuguang Program of Shanghai Education Commission (No. 20SG40), and Shanghai Sailing Program (No. 20YF1412700).

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Go to Journal of Environmental Engineering
Journal of Environmental Engineering
Volume 149Issue 6June 2023

History

Received: Oct 24, 2022
Accepted: Feb 9, 2023
Published online: Apr 6, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 6, 2023

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School of Mechatronic Engineering and Automation, Shanghai Univ., Shanghai 200444, China. Email: [email protected]
Ying Gong, Ph.D. [email protected]
Professor, School of Artificial Intelligence, Shanghai Univ., Shanghai 200444, China. Email: [email protected]
Chuanfu Xin [email protected]
School of Mechatronic Engineering and Automation, Shanghai Univ., Shanghai 200444, China. Email: [email protected]
Xuzhang Peng [email protected]
School of Mechatronic Engineering and Automation, Shanghai Univ., Shanghai 200444, China. Email: [email protected]
Professor, School of Mechatronic Engineering and Automation, Shanghai Univ., Shanghai 200444, China (corresponding author). ORCID: https://orcid.org/0000-0003-3570-3286. Email: [email protected]

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