Adaptation of an Energy Harvester Working in the Bending Mode to Utilize Dielectric Elastomers

Sikora, Wojciech

doi:10.1109/iccc51557.2021.9454631

Cited by 2 publications

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“…Fig.13dsums up the DEG operation and presents voltage as a function of charge. For an ideal system (e.g., in the case of simulation[20]), it should take a rectangular shape. Its area should be equivalent to the amount of energy generated in a single cycle.…”

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Experimental Investigation of a Uniaxial Dielectric Elastomer Generator

Sikora

2023

Acta Mechanica Et Automatica

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The widespread use of battery-powered electronic devices creates the need to develop methods to extend their maximum operating time. This can be achieved by using ambient energy, which would otherwise be dissipated. The conversion of energy, usually mechanical energy, into electric energy takes place in energy harvesters. Energy harvester systems based on a dielectric elastomer (DE) are a relatively new field that is being constantly developed. Due to their features, dielectric elastomer generators (DEGs) may complement the currently dominant piezoelectric harvesters. The major feature of employing a hyperelastic material is that it allows relatively large displacements to be utilised for generating energy, which is impossible in the case of piezoceramics. This article presents a DEG designed to operate under uniaxial tensile loads and which has a multilayer structure, describes the general operating principles of a DEG, explains the construction and assembly process of the investigated design and shows the electric circuit necessary to properly direct current flow during the DEG operation. The experimental part consists of two series of tests based on a central composite design (CCD). The objective of the first part was to map a capacitance response surface of the DEG in the selected range of the cyclic mechanical load. The second part concerned the amount of generated energy for the specific load case as a function of operating voltages. The result of the work is the formulation of regression models that allow the characteristics of the presented DEG design to be identified.

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confidence: 99%