Characterization of Real-world Vibration Sources and Application to Nonlinear Vibration Energy Harvesters

Abstract

A tremendous amount of research has been performed on the design and analysis of vibration energy harvester architectures with the goal of optimizing power output. Often, little attention is given to the actual characteristics of common vibrations from which energy is harvested. In order to shed light on the characteristics of common ambient vibration, data representing 333 vibration signals were downloaded from the NiPS Laboratory “Real Vibration” database, processed, and categorized according to the source of the signal (e. g. vehicle, machine, etc.), the number of dominant frequencies, the nature of the dominant frequencies (e. g. stationary, band-limited noise, etc.), and other metrics. By categorizing signals in this way, the set of idealized vibration inputs (i. e. single stationary frequency, Gaussian white noise, etc.) commonly assumed for harvester input can be corroborated and refined. Furthermore, some heretofore overlooked vibration input types are given motivation for investigation. The classification determined that, of the set of signals used in the study, 64 % of the animal source signals are best described with nonstationary dominant frequencies, 58 % of machine source signals are best described with stationary frequencies, and vehicle source signals are poorly described by any one signal type used in the classification. Nonlinear harvesters with a cubic stiffness term have received extensive attention in the scholarly literature; a numerical simulation and optimization procedure were performed using several representative signals as vibration inputs to determine the prevalence with which such a nonlinear harvester architecture might provide improvement to power output. The analysis indicated that a nonlinear harvester architecture may prove beneficial in increasing power output over a linear counterpart if the signal contains a single, dominant frequency that is not stationary in time, as evidenced by a 14 % increase in harvester power output when employing an architecture with a nonlinear cubic stiffness function. Other studies have indicated that nonlinear architectures may be beneficial for signals with nonstationary frequencies or filtered noise. 53 % of the all characterized signals fall into categories that could potentially benefit from a nonlinear oscillator architecture.

Feasibility of Wireless Horse Monitoring Using a Kinetic Energy Harvester Model

“…The damping in the energy harvester is modeled by two dampers. First, a mechanical damping factor (b m ) representing mechanical parasitic losses in the system such as air resistance, structural losses, internal friction losses, hysteresis losses, etc., [37,43,44] and a second electromechanical damping, resulting in the usefull conversion of energy (b e ) in the energy harvester, with the total damping factor [45][46][47] b = b m + b e . The harvester quality factor Q determines the spectral width of the energy harvester and is calculated as follows:…”

“…The 33 collected datasets are first evaluated with the standard parameters for the energy harvester from Section 4.1, based on the research from [38,45] The resonance frequency ( f r = 2.08 Hz) is not matched with the most dominant motions in the dataset. Therefore, it will only be possible to give a general insight on the order of the harvested power, not on the maximal achievable power after tuning.…”

Adaptive and Robust Operation with Active Fuzzy Harvester under Nonstationary and Random Disturbance Conditions

“…This study focuses on piezoelectric vibration energy harvesting (PVEH). Important objectives of PVEH studies are to design a method that can output voltage efficiently from disturbances at identical scales [ 19 ] and a method that achieves effective harvesting under the vibration in real environments such as aerodynamic excited vibrations [ 20 , 21 ]. Introducing switch elements to a harvesting circuit and actively switching the circuit connections can improve the harvesting performance [ 22 ].…”

Performance of An Electromagnetic Energy Harvester with Linear and Nonlinear Springs under Real Vibrations

“…However, most of the works have been done with the assumption that the input excitations are sinusoidal, colored noise, or Gaussian white noise [ 20 , 21 , 22 ]. Only a limited number of studies have focused on the harvester performance under real-world ambient vibrations [ 23 , 24 , 25 ]. Beeby et al [ 23 ] presented the comparison of output power from linear and nonlinear harvesters under vibration data taken from measurements of a diesel ferry engine, heat and power pump, car engine, and white noise vibration.…”

“…It was concluded in their paper that the potential benefits of nonlinear energy harvester solutions are sensitive to the nature of ambient vibration sources. Rantz and Roundy [ 25 ] considered a broad range of real vibrations and provided a comparative analysis of the theoretical maximum output power that linear and nonlinear harvester architectures can reach under these inputs. These optimal values may not be obtained in the real devices with design restrictions.…”