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

• We present two model order reduction approaches for a piezoelectric energy harvester.
• The methods allow producing stable compact models with low computational cost.
• Excellent match between the full-scale and the reduced models is demonstrated.
• Co-simulation of the reduced model with the power management circuitry is performed.

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.

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