Micro linear generator for harvesting mechanical energy from the human gait
Peng Gui,
Fang Deng,
Zelang Liang,
Yeyun Cai and
Jie Chen
Energy, 2018, vol. 154, issue C, 365-373
Abstract:
A type of multipolar linear permanent magnet generator (MLPMG) was developed in order to harvest human lower-limb motion energy to meet the increasing power supply needs of portable electronic devices. A large acceleration of the foot, particularly at the heel, was noted when analyzing human lower-limb motion during walking, so an energy harvester was placed on the heel. A series of MLPMGs were then designed and the static magnetic induction intensity vector diagram was obtained from each. A key parameter of MLMPG efficiency was found to be the gap between the stator and mover. Another important factor is the thickness of the mover spacers between magnet pieces. Finally, a number of experiments were conducted, which supported the conclusion that the output power of harvesters have negative relation with length of gap and thickness of spacers. It was found that a subject, walking at a speed of 5 km/h with a matched resistor load, can produce an output power of 20 mW.
Keywords: Human gait analysis; Acceleration data; Mechanical energy; Harvester; Magnetic induction vector; Output power density (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218307394
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:154:y:2018:i:c:p:365-373
DOI: 10.1016/j.energy.2018.04.123
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().