Design of Piezoelectric Energy Harvester Using Intelligent Optimization Techniques

Abstract

The design and optimization process for enhancement in the performance of MEMS Unimorph Piezoelectric energy harvester based on Genetic Algorithm (GA) and Grey Wolf optimization (GWO) is studied. The analytical equations of Unimorph Piezoelectric Energy harvester have been derived, to determine the amount of voltage and power harvested from the piezoelectric cantilever. These analytical equations are used as the fitness function of GA and GWO. Both of the algorithms are a design optimization technique for optimization problems to enhance power and output voltage, harvested from the unimorph piezoelectric energy harvester. This is achieved by using GA and GWO, to optimize the parameters of the piezoelectric cantilever, in which the fitness function, is taken as the power and voltage derived by analytical equations, and the techniques are compared with each other. Furthermore, the effects of mechanical and electrical properties (natural frequency, stress, load impedance and harvester efficiency) are analyzed. Finally, with the results obtained, un-optimized results are compared with the optimized results (GA and GWO) and the optimized results are proved to be efficient in all aspects, which have significantly improved the efficiency of the piezoelectric energy harvester from 24.9% to 58.9%. Finally, a comparison is made within the optimization techniques and GWO has proved to be more efficient than GA and this performance of MEMS piezoelectric energy harvester is simulated using the MATLAB software.