Real World Assessment of an Auto-parametric Electromagnetic Vibration Energy Harvester

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The convention within the eld of vibration energy harvesting (VEH) has revolved around designing resonators with natural frequencies that match single fixed frequency sinusoidal input. However, real world vibrations can be random, multi-frequency, broadband and time-varying in nature. Building upon previous work on auto-parametric resonance, the fundamentally different approach allows multiple axes vibration and has the potential to achieve higher power density as well as wider operational frequency bandwidth. This paper presents the power response of a packaged auto-parametric VEH prototype (practical operational volume 126 cm^3) towards various real world vibration sources including vibration of a bridge, a compressor motor as well as an automobile. At auto-parametric resonance (driven at 23.5 Hz and 1 grms), the prototype can output a peak of 78.9 mW and 4.5 Hz of -3dB bandwidth. Furthermore, up to ~1 mW of average power output was observed from the harvester on the Forth Road Bridge. The harvested electrical energy from various real world sources were used to power up a power conditioning circuit, a wireless sensor mote, a MEMS (micro-electromechanical system) accelerometer and other low power sensors. This demonstrates the concept of self-sustaining vibration-powered wireless sensor systems in real world scenarios, to potentially realise maintenance-free autonomous structural health and condition monitoring.

electromagnetic, parametric resonance, vibration energy harvesting, real vibration data, bridge, compressor, automobile
Journal Title
Journal of Intelligent Material Systems and Structures
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Engineering and Physical Sciences Research Council (EP/L010917/1)
Engineering and Physical Sciences Research Council (EP/N021614/1)
Engineering and Physical Sciences Research Council (EP/K000314/1)
Engineering and Physical Sciences Research Council (EP/I019308/1)
This work was supported by EPSRC (grant EP/L010917/1) and the Cambridge Centre for Smart Infrastructure and Construction.