Data-Driven Optimization of Piezoelectric Energy Harvesters via Pattern Search Algorithm

Abstract

A data-driven optimization strategy based on a generalized pattern search (GPS) algorithm is proposed to automatically optimize piezoelectric energy harvesters (PEHs). As a direct search method, GPS can iteratively solve the derivative-free optimization problem. Taking the finite element method (FEM) as the solver and the GPS algorithm as the optimizer, the automatic interaction between the solver and optimizer ensures optimization with minimum human efforts, saving designers’ time and performing a more precise exploration in the parameter space to obtain better results. When employing it for the optimization of PEHs, the optimal length and thickness of PZT were 6.0 mm and 4.6 µm, respectively. Compared with reported high-output PEHs, this optimal structure showed an increase of 371% in output power, an improvement by 1000% in normalized power density, and a reduction of 254% in resonant frequency. Furthermore, Spearman’s rank correlation coefficient was calculated for evaluating the correlation among geometric parameters and output performance such as resonant frequency and output power, which provides a data-based perspective on the design and optimization of PEHs.

Structure-Circuit Resistor Integrated Design Optimization of Piezoelectric Energy Harvester Considering Stress Constraints

“…On the other hand, a study with geometry ratio optimization [35] obtained a slightly higher power density than this study. In addition, very high power density was obtained in study that optimized geometry by using power density as the objective function of optimization [36]. The difference between the previous two studies and this study is consideration of discrete variables and stress constraints.…”

“…Through comparison, it can be confirmed that designs derived through optimization give better results than designs based on empirical intuition. Meanwhile, in the study that showed the highest power density [36], power density was used as the objective function. The point to note here is that it is true that an efficient PEH design with high power density was performed, but it cannot be said that the amount of power is maximum in a given space.…”

Special Issue on Energy Harvesters and Self-Powered Sensors for Smart Electronics

“…Thereupon, optimization was performed to boost the total output power which was significantly higher than that of a single piezoelectric energy harvester. Huang et al [5] proposed an automatic data-driven strategy to optimize the design of piezoelectric energy harvesters, based on the algorithm of generalized pattern search (GPS), which can effectively save designers' effort and achieve more precise parameters. Employing the optimal length and thickness, the derived energy harvester showed an increment of 371% and 1000% in output power and normalized power density, respectively.…”