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An Input Power-Aware Maximum Efficie...

Kawar, Sanad Fares Yousef.

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An Input Power-Aware Maximum Efficiency Tracking Technique for Energy Harvesting in IoT Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Input Power-Aware Maximum Efficiency Tracking Technique for Energy Harvesting in IoT Applications./
作者:	Kawar, Sanad Fares Yousef.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	135 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28151371
ISBN:	9798684685637

An Input Power-Aware Maximum Efficiency Tracking Technique for Energy Harvesting in IoT Applications.
Kawar, Sanad Fares Yousef.

An Input Power-Aware Maximum Efficiency Tracking Technique for Energy Harvesting in IoT Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 135 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--Santa Clara University, 2020.

This item must not be sold to any third party vendors.

The Internet of Things (IoT) enables intelligent monitoring and management in many applications such as industrial and biomedical systems as well as environmental and infrastructure monitoring. As a result, IoT requires billions of wireless sensor network (WSN) nodes equipped with a microcontroller and transceiver. As many of these WSN nodes are off-grid and small-sized, their limited-capacity batteries need periodic replacement. To mitigate the high costs and challenges of these battery replacements, energy harvesting from ambient sources is vital to achieve energy-autonomous operation. Energy harvesting for WSNs is challenging because the available energy varies significantly with ambient conditions and in many applications, energy must be harvested from ultra-low power levels.To tackle these stringent power constraints, this dissertation proposes a discontinuous charging technique for switched-capacitor converters that improves the power conversion efficiency (PCE) at low input power levels and extends the input power harvesting range at which high PCE is achievable. Discontinuous charging delivers current to energy storage only during clock non-overlap time. This enables tuning of the output current to minimize converter losses based on the available input power. Based on this fundamental result, an input power-aware, two-dimensional efficiency tracking technique for WSNs is presented. In addition to conventional switching frequency control, clock non-overlap time control is introduced to adaptively optimize the power conversion efficiency according to the sensed ambient power levels.The proposed technique is designed and simulated in 90nm CMOS with post-layout extraction. Under the same input and output conditions, the proposed system maintains at least 45% PCE at 4μW input power, as opposed to a conventional continuous system which requires at least 18.7μW to maintain the same PCE. In this technique, the input power harvesting range is extended by 1.5x.The technique is applied to a WSN implementation utilizing the IEEE 802.15.4-compatible GreenNet communications protocol for industrial and wearable applications.This allows the node to meet specifications and achieve energy autonomy when deployed in harsher environments where the input power is 49% lower than what is required for conventional operation.

ISBN: 9798684685637Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Clock non-overlap

An Input Power-Aware Maximum Efficiency Tracking Technique for Energy Harvesting in IoT Applications.
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520 $a The Internet of Things (IoT) enables intelligent monitoring and management in many applications such as industrial and biomedical systems as well as environmental and infrastructure monitoring. As a result, IoT requires billions of wireless sensor network (WSN) nodes equipped with a microcontroller and transceiver. As many of these WSN nodes are off-grid and small-sized, their limited-capacity batteries need periodic replacement. To mitigate the high costs and challenges of these battery replacements, energy harvesting from ambient sources is vital to achieve energy-autonomous operation. Energy harvesting for WSNs is challenging because the available energy varies significantly with ambient conditions and in many applications, energy must be harvested from ultra-low power levels.To tackle these stringent power constraints, this dissertation proposes a discontinuous charging technique for switched-capacitor converters that improves the power conversion efficiency (PCE) at low input power levels and extends the input power harvesting range at which high PCE is achievable. Discontinuous charging delivers current to energy storage only during clock non-overlap time. This enables tuning of the output current to minimize converter losses based on the available input power. Based on this fundamental result, an input power-aware, two-dimensional efficiency tracking technique for WSNs is presented. In addition to conventional switching frequency control, clock non-overlap time control is introduced to adaptively optimize the power conversion efficiency according to the sensed ambient power levels.The proposed technique is designed and simulated in 90nm CMOS with post-layout extraction. Under the same input and output conditions, the proposed system maintains at least 45% PCE at 4μW input power, as opposed to a conventional continuous system which requires at least 18.7μW to maintain the same PCE. In this technique, the input power harvesting range is extended by 1.5x.The technique is applied to a WSN implementation utilizing the IEEE 802.15.4-compatible GreenNet communications protocol for industrial and wearable applications.This allows the node to meet specifications and achieve energy autonomy when deployed in harsher environments where the input power is 49% lower than what is required for conventional operation.
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