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The European Physical Journal Special Topics

, Volume 222, Issue 7, pp 1553–1562 | Cite as

Nonlinear dynamics of a bistable piezoelectric-composite energy harvester for broadband application

  • D.N. Betts
  • C.R. Bowen
  • H.A. Kim
  • N. Gathercole
  • C.T. Clarke
  • D.J. Inman
Regular Article

Abstract

The continuing need for reduced power requirements for small electronic components, such as wireless sensor networks, has prompted renewed interest in recent years for energy harvesting technologies capable of capturing energy from ambient vibrations. A particular focus has been placed on piezoelectric materials and devices due to the simplicity of the mechanical to electrical energy conversion and their high strain energy densities compared to electrostatic and electromagnetic equivalents. In this paper an arrangement of piezoelectric layers attached to a bistable asymmetric laminate is investigated experimentally to understand the dynamic response of the structure and power generation characteristics. The inherent bistability of the underlying structure is exploited for energy harvesting since a transition from one stable configuration to another, or “snap-through”, is used to repeatedly strain the surface bonded piezoelectric and generate electrical energy. This approach has been shown to exhibit high levels of power extraction over a wide range of vibrational frequencies. Using high speed digital image correlation, a variety of dynamic modes of oscillation are identified in the harvester. The sensitivity of such modes to changes in vibration frequency and amplitude are investigated. Power outputs are measured for repeatable snap-through events of the device and are correlated with the measured modes of oscillation. The typical power generated is approximately 3.2 mW, comparing well with the needs of typical wireless senor node applications.

Keywords

Wireless Sensor Network European Physical Journal Special Topic Digital Image Correlation Piezoelectric Material Energy Harvester 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© EDP Sciences and Springer 2013

Authors and Affiliations

  • D.N. Betts
    • 1
  • C.R. Bowen
    • 1
  • H.A. Kim
    • 1
  • N. Gathercole
    • 1
  • C.T. Clarke
    • 2
  • D.J. Inman
    • 3
  1. 1.Department of Mechanical EngineeringUniversity of BathBathUK
  2. 2.Department of Electronic and Electrical EngineeringUniversity of BathBathUK
  3. 3.Department of Mechanical EngineeringUniversity of BristolBristolUK

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