能量收集
摩擦电效应
电气工程
极限(数学)
电压
电容器
能量(信号处理)
高压
最大功率原理
整流器(神经网络)
功率(物理)
计算机科学
施密特触发器
拓扑(电路)
工程类
物理
数学
量子力学
数学分析
随机神经网络
机器学习
循环神经网络
人工神经网络
作者
Madhav Pathak,Shuo Xie,Cheng Huang,Ratnesh Kumar
出处
期刊:IEEE Transactions on Circuits and Systems Ii-express Briefs
[Institute of Electrical and Electronics Engineers]
日期:2022-03-19
卷期号:69 (5): 2513-2517
被引量:8
标识
DOI:10.1109/tcsii.2022.3160676
摘要
Triboelectric Nanogenerators (TENG) suitable for mechanical energy harvesting typically have ultra-high open-circuit voltage in several hundreds of volts, challenging the energy extraction circuit (EEC) design required for charging load battery/capacitor. Here, we present a novel multi-shot switched EEC that extracts energy in multiple discrete steps to regulate the TENG voltage below the breakdown limit of the technology (70 V in our case), making it suitable for Integrated Circuit (IC) implementation. The proposed strategy maintains high TENG voltage just below the breakdown limit to offer a high electrostatic retardation, enhancing the work done against it by the mechanical source in the form of transduced electrical energy. Mathematical derivation of the circuit's output shows a constant transduction power at all load voltages, fully eliminating Maximum Power Point (MPP) Tracking and saving power for the same. The design and simulation of the proposed EEC in TSMC 0.18 $\mu \text{m}$ BCD process achieve a maximum power conversion efficiency of 63.3% and a 1.91x gain over even an ideal conventional Full Wave Rectifier (FWR) circuit at its optimal MPP load (gain will be higher for a real FWR implementation).
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