Power supply availability is a limiting factor in wearable devices whose form factor constrains battery size. Wearable harvesters that collect energy from the environment are a promising technology to achieve the long-life goal for truly wearable devices. However, most of the mature energy harvesting approaches usually imply an outdoor setting, using solar panels, wind turbines or high-frequency vibration as energy sources. In particular, most kinetic harvesters are not effective with human motion frequencies and mobility patterns. In this paper, we evaluate and integrate a highly-efficient kinetic harvester circuit to power autonomous wearable devices, exploiting the energy gathered from human motion. For this purpose, a Kinetron Micro Generator System 26.4 (MSG) has been characterized, conversion circuits have been designed and evaluated using off-the-shelf components. The implemented kinetic harvesting system successfully manages to harvest energy from human movements and stores it in small supercapacitors or batteries. Experimental results show that it is possible to harvest up to 280 μJ from a single small human movement and up to 1.1J per-day using a passive rectifier. This results in up to 180 μJ for single burst of energy and 0.7J per-day stored energy in a battery following a direct current conversion. These are extremely promising results for small wearable kinetic harvesting, and demonstrate that this new generation of kinetic energy harvesters can be instrumental in the design of many self-sustainable wearable devices.

Kinetic energy harvesting: Toward autonomous wearable sensing for Internet of Things / Magno, Michele; Spadaro, Lorenzo; Singh, Jaskirat; Benini, Luca. - STAMPA. - (2016), pp. 7525995.248-7525995.254. (Intervento presentato al convegno International Symposium on Power Electronics, Electrical Drives, Automation, and Motion (SPEEDAM 2016) tenutosi a Anacapri, ITALY nel JUN 22-24, 2016) [10.1109/SPEEDAM.2016.7525995].

Kinetic energy harvesting: Toward autonomous wearable sensing for Internet of Things

MAGNO, MICHELE;SPADARO, LORENZO;BENINI, LUCA
2016

Abstract

Power supply availability is a limiting factor in wearable devices whose form factor constrains battery size. Wearable harvesters that collect energy from the environment are a promising technology to achieve the long-life goal for truly wearable devices. However, most of the mature energy harvesting approaches usually imply an outdoor setting, using solar panels, wind turbines or high-frequency vibration as energy sources. In particular, most kinetic harvesters are not effective with human motion frequencies and mobility patterns. In this paper, we evaluate and integrate a highly-efficient kinetic harvester circuit to power autonomous wearable devices, exploiting the energy gathered from human motion. For this purpose, a Kinetron Micro Generator System 26.4 (MSG) has been characterized, conversion circuits have been designed and evaluated using off-the-shelf components. The implemented kinetic harvesting system successfully manages to harvest energy from human movements and stores it in small supercapacitors or batteries. Experimental results show that it is possible to harvest up to 280 μJ from a single small human movement and up to 1.1J per-day using a passive rectifier. This results in up to 180 μJ for single burst of energy and 0.7J per-day stored energy in a battery following a direct current conversion. These are extremely promising results for small wearable kinetic harvesting, and demonstrate that this new generation of kinetic energy harvesters can be instrumental in the design of many self-sustainable wearable devices.
2016
2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2016
248
254
Kinetic energy harvesting: Toward autonomous wearable sensing for Internet of Things / Magno, Michele; Spadaro, Lorenzo; Singh, Jaskirat; Benini, Luca. - STAMPA. - (2016), pp. 7525995.248-7525995.254. (Intervento presentato al convegno International Symposium on Power Electronics, Electrical Drives, Automation, and Motion (SPEEDAM 2016) tenutosi a Anacapri, ITALY nel JUN 22-24, 2016) [10.1109/SPEEDAM.2016.7525995].
Magno, Michele; Spadaro, Lorenzo; Singh, Jaskirat; Benini, Luca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/588742
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