Optimization of MEMS-based Energy Scavengers and output prediction with machine learning and synthetic data approach

Abstract

Micro-electromechanical systems (MEMS) based energy scavengers can serve as perpetual small-scale power sources. In this context, this work presents a novel approach for optimizing MEMS-based energy scavengers by predicting output power and natural frequency of operation through Synthetic Data and Machine Learning (ML) algorithms. For optimization, the work considers a bimorph cantilever-based energy scavenger, which consists of an elastic layer sandwiched between two piezoelectric layers. Various design parameters and their effect on the performance of the energy scavengers were evaluated through different ML models. The exploratory data analysis and preprocessing steps were carried out over the generated data, and subsequently, prediction of frequency and power was performed; 80% of the data was used for training purposes, and 20% of the data was used for testing purposes. Extreme Gradient Boost (XGBoost) was observed to be the best among the ML models for predicting frequency and power - with the R2 score of 97% and 86%, respectively. The optimized scavenger can generate an output power of 5.06 mW with an output voltage of 11.02 V. This approach can help the researchers by predicting the output power and resonant frequency, opening new research opportunities in the optimization of MEMS-based Energy Scavengers.