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Probabilistic modelling of a rotational energy harvester

Probabilistic modelling of a rotational energy harvester
Probabilistic modelling of a rotational energy harvester
Relatively recently, many researchers in the field of energy harvesting have focused on the concept of harvesting electrical energy from relatively large-amplitude, low-frequency vibrations (such as the movement caused by walking motion or ocean waves). This has led to the development of ‘rotational energy harvesters’ which, through the use of a rackand-pinion or a ball-screw, are able to convert low-frequency translational motion into high-frequency rotational motion. A disadvantage of many rotational energy harvesters is that, as a result of friction effects in the motion transfer mechanism, they can exhibit large parasitic losses. This results in nonlinear behaviour, which can be difficult to predict using physical-law-based models. In the current article a rotational energy harvester is built and, through using experimental data in combination with a Bayesian approach to system identification, is modelled in a probabilistic manner. It is then shown that the model can be used to make predictions which are both accurate and robust against modelling uncertainties.
1045-389X
1-9
Green, Peter L.
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Hendijanizadeh, M.
9631d6d8-f4fb-4088-8d66-950699eba189
Simeone, L.
8a3aae4f-51d5-46fd-ae2f-36df77faf4d3
Elliott, Stephen
721dc55c-8c3e-4895-b9c4-82f62abd3567
Green, Peter L.
79d6999d-5a53-4033-924e-3b2b6b33b3af
Hendijanizadeh, M.
9631d6d8-f4fb-4088-8d66-950699eba189
Simeone, L.
8a3aae4f-51d5-46fd-ae2f-36df77faf4d3
Elliott, Stephen
721dc55c-8c3e-4895-b9c4-82f62abd3567

Green, Peter L., Hendijanizadeh, M., Simeone, L. and Elliott, Stephen (2015) Probabilistic modelling of a rotational energy harvester. Journal of Intelligent Materials Systems and Structures, 1-9. (doi:10.1177/1045389X15573343). (In Press)

Record type: Article

Abstract

Relatively recently, many researchers in the field of energy harvesting have focused on the concept of harvesting electrical energy from relatively large-amplitude, low-frequency vibrations (such as the movement caused by walking motion or ocean waves). This has led to the development of ‘rotational energy harvesters’ which, through the use of a rackand-pinion or a ball-screw, are able to convert low-frequency translational motion into high-frequency rotational motion. A disadvantage of many rotational energy harvesters is that, as a result of friction effects in the motion transfer mechanism, they can exhibit large parasitic losses. This results in nonlinear behaviour, which can be difficult to predict using physical-law-based models. In the current article a rotational energy harvester is built and, through using experimental data in combination with a Bayesian approach to system identification, is modelled in a probabilistic manner. It is then shown that the model can be used to make predictions which are both accurate and robust against modelling uncertainties.

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Accepted/In Press date: 3 March 2015
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 375841
URI: http://eprints.soton.ac.uk/id/eprint/375841
ISSN: 1045-389X
PURE UUID: 983ebc08-88df-4b18-9f13-2ea1fcb83d07

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Date deposited: 17 Apr 2015 10:15
Last modified: 14 Mar 2024 19:33

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Contributors

Author: Peter L. Green
Author: L. Simeone
Author: Stephen Elliott

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