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

Hegendörfer A, Steinmann P, Mergheim J (2023)


Publication Type: Journal article

Publication year: 2023

Journal

Book Volume: 13

Article Number: 8

Journal Issue: 1

DOI: 10.3390/jlpea13010008

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.

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APA:

Hegendörfer, A., Steinmann, P., & Mergheim, J. (2023). Numerical Optimization of a Nonlinear Nonideal Piezoelectric Energy Harvester Using Deep Learning. Journal of Low Power Electronics and Applications, 13(1). https://doi.org/10.3390/jlpea13010008

MLA:

Hegendörfer, Andreas, Paul Steinmann, and Julia Mergheim. "Numerical Optimization of a Nonlinear Nonideal Piezoelectric Energy Harvester Using Deep Learning." Journal of Low Power Electronics and Applications 13.1 (2023).

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