Hierarchical model calibration for designing piezoelectric energy harvester in the presence of variability in material properties and geometry

Piezoelectric energy harvesting which scavenges electric power from ambient vibration energy has received significant attention as an ultimate solution to realize self-powered wireless sensors. For designing a piezoelectric energy harvester, it is of great importance to develop a high-fidelity electromechanical model which predicts the output power under various vibration conditions. To the best of our knowledge, however, there has been no systematic approach to account for variability in the material properties and geometry of a piezoelectric energy harvester. This paper thus presents (1) the hierarchical model calibration to improve the predictive capability of the electromechanical model and (2) the design of energy harvesting (EH) skin to maximize the output power to reliably operate self-powered wireless sensors. In this study, the hierarchical model calibration infers statistical information of unknown model variables (compliance, piezoelectric strain coefficient, and relative permittivity). The calibrated electromechanical model is then used to design EH skin based on the piezoelectric material segmentation to avoid voltage cancellation. The output power predicted by the calibrated electromechanical model is statistically compared with the measured one. Finally, it is concluded from the feasibility demonstration that EH skin can sufficiently generate the output power to realize self-powered wireless sensors without batteries.