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The European Physical Journal Special Topics

, Volume 224, Issue 14–15, pp 2909–2918 | Cite as

Energy harvesting in the nonlinear electromagnetic system

  • K. KucabEmail author
  • G. Górski
  • J. Mizia
Open Access
Regular Article Electromagnetic Energy Harvesting
Part of the following topical collections:
  1. Nonlinear and Multiscale Dynamics of Smart Materials in Energy Harvesting

Abstract

We examine the electrical response of electromagnetic device working both in the linear and nonlinear domain. The harvester is consisted of small magnet moving in isolating tube surrounded by the coil attached to the electrical circuit. In the nonlinear case the magnet vibrates in between two fixed magnets attached to the both ends of the tube. Additionally we use two springs which limit the movement of the small magnet. The linear case is when the moving magnet is attached to the repelling springs, and the static magnets have been replaced by the non-magnetic material. The potentials and forces were calculated using both the analytical expressions and the finite elements method. We compare the results for energy harvesting obtained in these two cases. The generated output power in the linear case reaches the peak value 80 mW near the resonance frequency ω0 for maximum base acceleration considered by us, whereas in the non-linear case the corresponding outpot power has the peak value 95 mW and additionally relatively high values in the excitation frequencies range up to ω = 1.2ω0. The numerical results also show that the power efficiency in the nonlinear case exceeds the corresponding efficiency in the linear case at relatively high values of base accelerations greater than 5g. The results show the increase of harvested energy in the broad band of excitation frequencies in the nonlinear case.

Keywords

Static Magnet European Physical Journal Special Topic Excitation Frequency Linear Case Energy Harvest 
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, Springer-Verlag 2015

Authors and Affiliations

  1. 1.Faculty of Mathematics and Natural Sciences, University of RzeszówRzeszówPoland

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