Parameter Discovery for Optimal Magnetopiezoelastic Energy Harvesters Using Neural Optimization Approach

Piezoelectric transduction of vibrational energy to electric power has attracted interest because of its potential to reduce the dependence on depletable batteries currently used to power micro sensors and devices. Assessment of variations in the output power based on varying the harvester's parameters may not yield an optimal design. For that purpose, we implement a neural optimization approach to optimize the performance of a magnetopiezoelastic energy harvester under specific constraints. The data set used in training the neural networks and optimization approach are generated using simulations of an experimentally validated numerical model. The results demonstrate the usefulness of this approach in the design optimization of piezoelectric energy harvesters.