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Design and experimental validation of a pendulum energy harvester with string-driven single clutch mechanical motion rectifier

James R. Graves, Meiling Zhu

2021Sensors and Actuators A Physical15 citationsDOIOpen Access PDF

Abstract

This work presents a pendulum kinetic energy harvester with a unique mechanical motion rectifier design which uses a string-driven rectifier (SDR) with just a single clutch to convert bidirectional input oscillation of a pendulum to the unidirectional rotation of a DC motor to produce electricity. Unlike typical mechanical rectifiers which use two clutches, this rectification system has no gearing, minimising the complexity and weight of the rectifier for the energy harvester. Through experimentation, the energy harvester was found to have a normalised average power output of 4.39 W/g2 and a normalised average power density of 5.85 W/g2/kg when excitation was applied at the 1.5 Hz resonant frequency with a 0.75 kg pendulum mass. This corresponds to a normalised average voltage production of 55.47 V/g. Time-domain analysis of the transducer showed the successful operation of the SDR. By selectively harvesting kinetic energy during different stages of the pendulum motion, the kinetic energy of the pendulum mass was extracted while the stored potential was preserved and converted to kinetic. This allowed a high pendulum velocity to be maintained, while the rectified input motion generated a single polarity voltage from the DC motor. The construction of this system has advantages over existing designs by reducing the complexity of rectification mechanisms, providing an alternative approach to mechanical motion rectification for pendulum vibration energy harvesters.

Topics & Concepts

Control theory (sociology)Rectifier (neural networks)PendulumMechanical energyRectificationKinetic energyClutchEnergy harvestingOscillation (cell signaling)VoltagePhysicsPower (physics)EngineeringComputer scienceElectrical engineeringMechanical engineeringClassical mechanicsArtificial neural networkStochastic neural networkGeneticsControl (management)Recurrent neural networkBiologyQuantum mechanicsArtificial intelligenceMachine learningInnovative Energy Harvesting TechnologiesEnergy Harvesting in Wireless NetworksWireless Power Transfer Systems