Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Adaptive Estimation of Near-Optimal Electrostatic Force in Micro Energy-Harvesters

Published

Author(s)

Kamran Sayrafian, Masoud Roudneshin, Amir G. Aghdam

Abstract

Recent advancements in micro-electronics have led to the development of miniature-sized wearable sensors that can be used for a variety of health monitoring applications. These sensors are typically powered by small batteries which could require frequent recharge. Energy harvesting can reduce the charging frequency of these sensors. Longer operational lifetime can simplify the everyday use of these wearable sensors in many applications. Our objective in this paper is to maximize the output power of a kinetic-based micro energy-harvester. A hybrid machine learning and analytical approach is proposed to adaptively adjust the electrostatic force in a harvester with Coulomb-Force Parametric Generator (CFPG) architecture. The results show considerable improvement in the output power.
Conference Dates
August 24-26, 2020
Conference Location
Montreal, CA
Conference Title
2020 IEEE Conference on Control Technology and Applications (CCTA)

Keywords

wearable sensors, energy harvesting, machine learning, microgenerator, CFPG

Citation

Sayrafian, K. , Roudneshin, M. and Aghdam, A. (2020), Adaptive Estimation of Near-Optimal Electrostatic Force in Micro Energy-Harvesters, 2020 IEEE Conference on Control Technology and Applications (CCTA), Montreal, CA, [online], https://doi.org/10.1109/CCTA41146.2020.9206354 (Accessed April 19, 2024)
Created September 28, 2020, Updated March 28, 2024