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On the Effectiveness of Vibration-based Energy Harvesting

Roundy, Shad·2005·Journal of Intelligent Material Systems and Structures

Abstract

There has been a significant increase in the research on vibration-based energy harvesting in recent years. Most research is focused on a particular technology, and it is often difficult to compare widely differing designs and approaches to vibration-based energy harvesting. The aim of this study is to provide a general theory that can be used to compare different approaches and designs for vibration-based generators. Estimates of maximum theoretical power density based on a range of commonly occurring vibrations, measured by the author, are presented. Estimates range from 0.5 to 100mW/cm 3 for vibrations in the range of 1–10 m/s 2 at 50–350 Hz. The theory indicates that, in addition to the parameters of the input vibrations, power output depends on the system coupling coefficient, the quality factor of the device, the mass density of the generator, and the degree to which the electrical load maximizes power transmission. An expression for effectiveness that incorporates all of these factors is developed. The general theory is applied to electromagnetic, piezoelectric, magnetostrictive, and electrostatic transducer technologies. Finally, predictions from the general theory are compared to experimental results from two piezoelectric vibration generator designs.

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Metadata

Authors
Roundy, S.
Venue
Journal of Intelligent Material Systems and Structures
Published
2005-10
Volume
16
Issue
10
Pages
809-823
ISSN
  • 1045-389X (ISSN-L)
  • 1530-8138
OpenAlex
W2166477335
Cited by
654
References
0
FWCI
39.36
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: On the Effectiveness of Vibration-based Energy Harvesting. https://app.cochat.ai/papers/10.1177/1045389x05054042
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Roundy, S. (2005). On the Effectiveness of Vibration-based Energy Harvesting. Journal of Intelligent Material Systems and Structures. https://doi.org/10.1177/1045389x05054042

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.