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.
Highlights
Hu et al [18] investigated the optimal length of the piezoelectric layers based on theoretical analysis, finite element method (FEM) simulation and experimental verification, from which they discovered that the optimal length ratio of piezoelectric layers and the beam is approximately 0.2
We proposed a data-driven optimization strategy based on the generalized pattern search (GPS)
PZT layers to validate the effectiveness of the proposed data-driven optimization strategy, and individually optimized the PZT length and the proof mass length intending to Results and Discussion maximize output3. power and compared the optimized result with the previous works
Summary
With the urgent demand of sustainable power supplies for low-power electronic applications such as wireless sensor network systems [1,2,3,4] in Internet of Things, implantable medical devices [5,6] and other devices in some extreme environments [7], energy harvesting from the ambient environments has attracted broad attention and provided potential solutions to the periodical replacement of batteries during the last few decades. To improve the output performance of cantilever PEHs, researchers have studied the effects of different geometric parameters on output performance [20,21] He et al [22] and Jia et al [23] proposed that the optimal mass-beam length ratio is 0.6~0.7 within linear response. The geometric parameters were manually set and tested at a fixed interval based on the researchers’ experience and intuition, which required the researcher to spend more time in trying each possible combination of geometric parameters to obtain the optimal output performance To overcome these shortcomings, several data-driven optimization strategies combining different algorithms to maximize the output performance of PEHs have been proposed recently. Spearman’s rank correlation coefficient was calculated for evaluating the correlation among geometric parameters and output performance, providing a data-based perspective on the design of PEHs
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