Comparative Analysis of Piezo Energy Harvester Optimization Techniques: A Comprehensive Review

Abstract

Various power optimisation strategies were employed to build the customised and cost-effective PZT tile. It is critical for effective power generation to optimise the performance of PZT-based energy harvesting devices. This research compares three well-known PZT power optimisation techniques: Response Surface Methodology (RSM), Taguchi Method, and Machine Learning. (ML) The benefits, constraints, and applicability of each methodology for diverse circumstances are examined to give researchers and engineers with insights into picking the best optimisation method for their unique applications. According to the observations, the RSM and Taguchi analyses limit the input value. In contradiction ML methods can produce an accurate customised and optimised model. The only limitation with ML approaches achieves the accuracy due to data set limit. This paper presented the comparison review of the optimization techniques and suggested the best power optimization. It also includes the future scope of the methods.