SEHS: Simultaneous Energy Harvesting and Sensing Using Piezoelectric Energy Harvester

Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu
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引用次数: 27

Abstract

Piezoelectric energy harvesting (PEH), which converts ambient motion, stress, and vibrations into usable electricity, may help combat battery issues in a growing number of industrial and wearable Internet of things (IoTs). Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts. These developments suggest that the same PEH hardware could be potentially used for simultaneous energy harvesting and sensing (SEHS), offering a new design space for low cost and low power IoT devices. Unfortunately, realization of SEHS is challenging as the energy harvesting process distorts the sensing signal. To achieve high quality sensing from PEH, the state-of-the-art uses separate PEHs for sensing and energy harvesting, which increases system complexity, form factor, and cost. In this paper, we propose a novel SEHS architecture, which combines energy harvesting and sensing in the same piece of PEH, and minimizes distortion in the sensing signal by applying a special filtering algorithm. We prototype the SEHS concept in the form factor of a shoe, and evaluate its energy harvesting as well as sensing performance with 20 subjects using gait recognition as a case study. We demonstrate that the SEHS prototype harvests up to 127% more energy and detects human gait with 8% higher accuracy while consuming 35% less power compared to the state-of-the-art.
基于压电能量采集器的同步能量采集与传感
压电能量收集(PEH)将环境运动、应力和振动转化为可用的电力,可能有助于解决越来越多的工业和可穿戴物联网(iot)中的电池问题。最近,许多研究令人信服地证明,PEH也可以作为一种自供电传感器,用于检测广泛的机器和人类环境。这些发展表明,相同的PEH硬件可以潜在地用于同步能量收集和传感(SEHS),为低成本和低功耗物联网设备提供了新的设计空间。不幸的是,由于能量收集过程会扭曲传感信号,因此实现SEHS具有挑战性。为了从PEH中获得高质量的传感,最先进的PEH使用单独的PEH进行传感和能量收集,这增加了系统的复杂性、外形因素和成本。在本文中,我们提出了一种新的SEHS架构,该架构将能量收集和传感结合在同一块PEH中,并通过应用特殊的滤波算法来最小化传感信号的失真。我们将SEHS概念原型化在鞋子的外形上,并以20名受试者为例,以步态识别为例,评估其能量收集和传感性能。我们证明,与最先进的产品相比,SEHS原型可获得高达127%的能量,并以8%的准确率检测人类步态,同时消耗的功率减少35%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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