Numerical Optimization of a Nonlinear Nonideal Piezoelectric Energy Harvester Using Deep Learning

Language
en
Document Type
Article
Issue Date
2023-02-06
First published
2023-01-12
Issue Year
2023
Authors
Hegendörfer, Andreas
Steinmann, Paul
Mergheim, Julia
Editor
Publisher
MDPI
Abstract

This contribution addresses the numerical optimization of the harvested energy of a mechanically and electrically nonlinear and nonideal piezoelectric energy harvester (PEH) under triangular shock-like excitation, taking into account a nonlinear stress constraint. In the optimization problem, a bimorph electromechanical structure equipped with the Greinacher circuit or the standard circuit is considered and different electrical and mechanical design variables are introduced. Using a very accurate coupled finite element-electronic circuit simulator method, deep neural network (DNN) training data are generated, allowing for a computationally efficient evaluation of the objective function. Subsequently, a genetic algorithm using the DNNs is applied to find the electrical and mechanical design variables that optimize the harvested energy. It is found that the maximum harvested energy is obtained at the maximum possible mechanical stresses and that the optimum storage capacitor for the Greinacher circuit is much smaller than that for the standard circuit, while the total harvested energy by both configurations is similar.

Journal Title
Journal of Low Power Electronics and Applications
Volume
13
Issue
1
Citation
Journal of Low Power Electronics and Applications 13.1 (2023): 8. <https://www.mdpi.com/2079-9268/13/1/8>
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