Vibration energy harvesting has been shown as a promising power source for many small-scale applications
mainly because of the considerable reduction in the energy consumption of the electronics, ease of fabrication and
implementation of smart materials at small scale, and scalability issues of the conventional batteries. However,
conventional energy harvesters are not quite robust to changes in excitation or system parameters, suffer from
narrow bandwidth, and are very inefficient at small scale for low frequency harvesting. In addition, they have
a low power to volume ratio. To remedy the robustness issues, improve their effectiveness, and increase their
power density, we propose to exploit structural instabilities, in particular instabilities in multi-layered composites
which are inherently non-resonant. The induced large strains as a result of the structural instability could be
exploited to give rise to large strains in an attached piezoelectric layer to generate charge and, hence, energy. The
regular high-strain morphological patterns occur throughout the whole composite structure that in turn enable
harvesting at a larger volume compared to conventional harvesters; hence, harvesting via structural instabilities
can significantly improve the harvested power to volume ratio. In this study, we focus on harvesting from
wrinkling type of instabilities.
Vibration energy harvesting has been shown as a promising power source for many small-scale applications mainly because of the considerable reduction in the energy consumption of the electronics and scalability issues of the conventional batteries. However, energy harvesters may not be as robust as the conventional batteries and their performance could drastically deteriorate in the presence of uncertainty in their parameters. Hence, study of uncertainty propagation and optimization under uncertainty is essential for proper and robust performance of harvesters in practice. While all studies have focused on expectation optimization, we propose a new and more practical optimization perspective; optimization for the worst-case (minimum) power. We formulate the problem in a generic fashion and as a simple example apply it to a linear piezoelectric energy harvester. We study the effect of parametric uncertainty in its natural frequency, load resistance, and electromechanical coupling coefficient on its worst-case power and then optimize for it under different confidence levels. The results show that there is a significant improvement in the worst-case power of thus designed harvester compared to that of a naively-optimized (deterministically-optimized) harvester.
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