Enhancement of vibration based piezoelectric energy harvester using hybrid optimization techniques

Abstract

MEMS unimorph piezoelectric vibration energy harvester is designed and optimized based on Hybrid Optimization techniques [genetic algorithm (GA), bat algorithm (BA), grey wolf optimization (GWO), hybrid bat algorithm tuned genetic algorithm (HBAGA) and hybrid grey wolf optimizer tuned genetic algorithm (HGWOGA)] for performance improvement. In this research work, unimorph piezoelectric vibration energy harvester is modeled using analytical equations, which is derived to determine the amount of voltage and power, harvested from the piezoelectric energy harvester. These derived analytical equations are used as the fitness function of Hybrid optimization techniques, which is a design optimization technique to improve the amount of power and voltage harvested from the unimorph piezoelectric energy harvester, by optimizing the parameters and a comparison is made within these techniques. The effects of geometry optimization on natural frequency and stress are also studied. The un-optimized results are compared with the optimized results (GA, BA, GWO, HBAGA and HGWOGA) and the results obtained, proves that, the optimized results are more efficient in all aspects, which has significantly improved the efficiency of the piezoelectric harvester to a larger extent. Finally, a comparison is made within the optimization techniques and HGWOGA has proved to be more efficient than HBAGA, GWO, BA, GA and this work of MEMS piezoelectric vibration energy harvester is simulated using the MATLAB software.