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Maximizing Harvested Energy in Coulomb Force Parametric Generators



Masoud Roudneshin, Kamran Sayrafian, Amir Aghdam


Miniaturized wearable or implantable medical sensors are becoming an important application area for kinetic-based micro energy-harvesters. These harvesters can generate power through the natural human body motion. The architecture based on the Coulomb force parametric generator (CFPG) is one of the most viable solutions for these applications. This paper investigates several methods to adaptively estimate the desirable electrostatic force of a CFPG using the acceleration waveform. These methods include an approximate analytical solution based on the mathematical model of the CFPG as well as the least-squares regression and deep learning using artificial neural networks. Simulation results show promising increase in the harvested power using such adaptive strategies.
Conference Dates
June 8-10, 2022
Conference Location
Atlanta, GA, US
Conference Title
2022 American Control Conference (ACC)


Adaptive optimization, micro energy-harvesters, Coulomb force parametric generator, acceleration waveform


Roudneshin, M. , Sayrafian, K. and Aghdam, A. (2022), Maximizing Harvested Energy in Coulomb Force Parametric Generators, 2022 American Control Conference (ACC), Atlanta, GA, US, [online],, (Accessed July 18, 2024)


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Created September 5, 2022, Updated March 28, 2024