Energy harvesting using ferroelectric/ferroelastic switching: the effect of pre-poling

Abstract

Improved power output and energy density have been achieved in piezoelectric transducers by exploiting ferroelectric/ferroelastic switching. However, a problem is that stable working cycles with polarization switching normally cannot be driven by stress alone. This problem has been addressed by using internal bias fields in a partially poled ferroelectric: the material state is engineered such that compressive stress drives ferroelastic switching during mechanical loading, while residual fields restore the polarized state during unloading. However, although this method has been verified, the devices in engineering material states with the best performance haven’t been explored systematically. In this work, internal bias fields in a partially poled (pre-poled) ferroelectric are used to guide polarization switching, producing an effective energy harvesting cycle. Devices are tested and optimized in the frequency range 1-20Hz, and the influence of the degree of pre-poling in the fabrication process on energy harvesting performance is explored systematically. It is found that pre-poling the ferroelectric ceramic to about 25% of the fully poled state results in a device that can generate a power density up to about 26 mW/cm3 of active material at 20Hz, an improvement on prior work and an order of magnitude advance over conventional piezoelectrics. However, maximizing the power density can result in residual stresses that risk damage to the device during preparation or in service. The relationship between fabrication success rate and pre-poling level is studied, indicating that greater degrees of pre-poling correlate with higher survival rate. This provides a basis for balancing energy conversion with device robustness.

Intelligent Control of SMART Materials for Energy Harvesting and Storage Devices

“…Adaptive control algorithms are a class of computational techniques that combine principles from machine learning with the use of real-time data. The aforementioned algorithms exhibit the capability to adaptively modify energy harvesting and storage parameters in response to varying environmental circumstances [36]. In order to sustain appropriate temperature gradients, a thermoelectric generator might employ a proportional-integral-derivative (PID) controller to regulate the load resistance.…”