Multimodal piezoelectric energy harvesting on a thin plate integrated with SSHI circuit: an analytical and experimental study

Abstract

Multimodal piezoelectric energy harvesting can be achieved by integrating piezo-patch harvesters into plate-like structures available in marine, aerospace, and automotive applications. A synchronized switch harvesting on inductor (SSHI) interface as the harvesting circuit has been well studied for cantilever beams, considering the single vibration mode of the structure. However, integrating a two-dimensional electromechanical structure with a SSHI circuit for multimodal energy harvesting is missing in the literature. This paper evaluates the performance of the SSHI interface integrated with a piezo- patch energy harvester (PEH) on a plate-like host structure. The analytical solution is developed based on an equivalent impedance approach to predict the steady-state electrical response of the harvester as a closed-form solution. The experiments are conducted to validate the analytical solution for the system's first and second vibration modes. The experimental results reveal that integration of SSHI to a plate-like harvester introduces a multi-switching behavior rather than a standard single-switching behavior. Due to the multimodal vibrational characteristics of the plate, the circuit switch is triggered several times at each half period of the vibration, which increases the energy dissipation of the circuit and thus reduces the output voltage. On the other hand, single switching at each half period of the vibration happens for lower piezoelectric voltage levels. This is the desired behavior of the SSHI circuit where the analytical prediction matches with the experimental data. Finally, the energy harvesting performance of the SSHI circuit is compared against the standard rectifier, showing 183% and 134% power output enhancement for the first and second vibration modes, respectively.

Research on the monitoring friction hysteresis for the bolted interface by electromechanical impedance

“…(3) Compensating for signal features under the influence of external loads using deep learning, including factors like temperature [42] and tangential forces [25]. (4) Designing smart sensors tailored to specific application scenarios [43,44].…”