Computationally efficient and stable order reduction methods for a large-scale model of MEMS piezoelectric energy harvester

In this work, we present novel model order reduction approaches for a large-scale multiport model of a MEMS-based piezoelectric energy harvester. These techniques are computationally efficient and generate stable reduced order models. The first method proposed combines model reduction based on Krylov subspaces and a Schur complement transformation of the resulting system. The second method includes structure preserving Krylov subspaces based model order reduction. We demonstrate an excellent match between the full-scale and the reduced order models of the harvester device during harmonic simulation and the co-simulation of the reduced harvester model together with the power management circuitry.