On The Optimal Choice Of Electrical Conditioning Circuits For Triboelectric Nanogenerators

The research related to the GRAPHERGIA project’s Work package 4, “Advanced electrical modelling and efficient power management of TENGs for energy harvesting and self-powered sensing IoT applications was presented” was presented at the 2024 IEEE 23rd International Conference on Micro and Miniature Power Systems, Self-Powered Sensors and Energy Autonomous Devices (PowerMEMS) in a conference proceeding. 

This conference took place in November 2024 in Norway, and ESYCOM Lab a research lab from the Université Gustave Eiffel (UGE), a partner in GRAPHERGIA, presented this publication. 

Abstract

This work presents a generic procedure for the optimal choice of an unstable charge pump (UCP) circuit topology for electrostatic kinetic energy harvesters (e-KEH) biased by a triboelectric material, also known as a triboelectric nanogenerators (TENGs). Each UCP can be characterized by the ratio ω that it imposes between the maximum and minimum voltage across the transducer in every cycle of its steady-state operation. The value of ω depends on the detailed topology of each UCP. The triboelectric transducer, on the other hand is characterised by (i) the variation ratio ε between its dynamic capacitance maximum and minimum values under mechanical actuation and (ii) its DC bias VTE which models the triboelectric effect. The analysis done in this paper leads to the optimal ω as a function of the transducer parameters ε, VTE, and as a function of the parasitic capacitance brought by the conditioning circuit or by the setup. As such, this work presents a systematic procedure to select the UCP topology, maximizing the converted power for various triboelectric systems. In particular, this procedure shows which of the low capacitance variation rectifier (LCVR) or of the cascade Bennet doubler rectifier (CBDR), two popular conditioning circuit topologies for e-KEH, is optimal for any given triboelectric transducer.

Authors

Hachem Mortada, Armine Karami, Delaram Haghighi-Talab, Ahmad Delbani (ESYCOM Lab, Univ Gustave Eiffel/CNRS, Marne-la-Vallée, France), Dimitri Galayko (LIP6 Sorbonne Université, Paris, France) and Philippe Basset (ESYCOM Lab, Univ Gustave Eiffel/CNRS, Marne-la-Vallée, France)