Transactions on Machine Intelligence

Transactions on Machine Intelligence

A New Approach in Designing Electrostatic Energy Harvesters with Precise Modeling of Capacitors in 3D Interdigitated Electrode Structures Considering Full Edge Capacitance Effects

Document Type : Original Article

Authors
M. Jahangiri 1
J. Yavand Hasani 2
1 MEng Student, Department of Electronics, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
2 Assistant Professor, Department of Electronics, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
10.47176/TMI.2020.136
Abstract
The analysis and mathematical modeling of energy harvesting structures facilitate the optimal design of these generators. The capacitive effect between two metal plates in an energy harvester plays a crucial role in its performance. Therefore, mathematically modeling this capacitive effect is essential for the design, performance analysis, and optimization of these generators. In fixed-voltage energy harvesters, interdigitated electrode structures are used. This paper first presents novel closed-form mathematical relations to calculate the capacitive effect in energy harvesters with interdigitated structures. Then, using the derived formulas, a new approach to optimizing the structure and dimensions of these harvesters is provided. The obtained formula for modeling the capacitor in interdigitated structures is highly accurate, considers the 3D structure, and includes the effect of the number of electrodes. The aim of optimizing the target harvester structure is to maximize the harvested energy for a given dimension. The mathematical relations presented for calculating capacitive effects are initially validated using COMSOL and MATLAB software, demonstrating their high accuracy. Ultimately, the proposed optimization approach yields a 23% increase in output power compared to reported references.
Keywords
Optimization
Electrostatic Energy Harvester
3D Interdigitated Electrodes
Fixed Voltage
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Transactions on Machine Intelligence
Volume 3, Issue 3
Summer 2020
Pages 136-148

  • Receive Date 03 May 2020
  • Revise Date 08 July 2020
  • Accept Date 02 September 2020