Multi-Objective Design Optimization of Piezoelectric Energy Harvesting System for Unmanned Aerial Vehicles

Abstract

In this work, the optimal design of an unmanned aerial vehicle (UAV) wing spar by multi-objective optimization genetic algorithms is studied. An electromechanical finite element model (FEM) for piezolaminated bimorph cantilever structure with embedded piezoceramics is used combined with multiobjective genetic algorithms. The FEM formulation is based on laminated plate theory combined with the first-order shear deformation theory (FSDT) for which each piezoelectric layer has one additional electrical degree of freedom. Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Generalized Differential Evolution 3 (GDE3) algorithm have been used to optimize the geometric and electric circuit parameters of a UAV generator for maximum power output and minimum mass added by the embedded piezoceramics. It is shown that the proposed algorithms are effective in developing optimal Pareto front curves for maximum electrical power output of the generator spar and minimum mass add by the embedded piezoceramics. Comparison of the generated solutions on the Pareto Front for UAV wing spar design shows that all three algorithms achieve solutions that closely resemble a constructed reference Pareto optimal front.