MEMS piezoelectric energy harvester design and optimization based on Genetic Algorithm

Abstract

MEMS piezoelectric energy harvesters, due to their unique features in power density and ease of fabrication, are known as one of the most promising solutions for providing unlimited power sources for low-power electronic applications. In this paper, the analytic equations to estimate generated voltage amount by a piezoelectric cantilever under various vibrations are presented. The comparison between the analytic equations and finite element method (FEM) simulations confirms over 85% accuracy in the estimation of generated voltage amount for the presented analytic model, which can be used as a fitness function of Genetic Algorithm (GA). We have used the GA, which is a design automation technique for optimization problems, to improve the energy harvesting efficiency by optimizing the dimension of the piezoelectric energy harvesters. The observed results from the optimized physical aspects of MEMS piezoelectric energy harvester illustrate an enhancement of energy harvesting efficiency by a factor of 2.13. The proposed method can be considered as a general and efficient technique for enlarging conversion efficiency of piezoelectric energy harvesting devices.