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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.

This paper reports the simulation-based analysis of six dynamical structures with respect to their wrist-worn vibration energy harvesting capability. This work approaches the problem of maximizing energy harvesting potential at the wrist by considering multiple mechanical substructures; rotational and linear motion-based architectures are examined. Mathematical models are developed and experimentally corroborated. An optimization routine is applied to the proposed architectures to maximize average power output and allow for comparison. The addition of a linear spring element to the structures has the potential to improve power output; for example, in the case of rotational structures, a 211% improvement in power output was estimated under real walking excitation. The analysis concludes that a sprung rotational harvester architecture outperforms a sprung linear architecture by 66% when real walking data is used as input to the simulations.

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