Energy harvesting has become an increasingly important topic thanks to the advantages in renewability and environmental friendliness. In this paper, a comprehensive study on contemporary portable wind energy harvesters has been conducted. The electrical power generation methods of portable wind energy harvesters are surveyed in three major groups, piezoelectric-, electromagnetic-, and electrostatic-based generators. The paper also takes another view of this area by gauging the required mechanisms for trapping wind flow from ambient environment. In this regard, rotational and aeroelastic mechanisms are analyzed for the portable wind energy harvesting devices. The comparison between both mechanisms shows that the aeroelastic mechanism has promising potential in producing an energy harvester in smaller scale although how to maintain the resonator perpendicular to wind flow for collecting the maximum vibration is still a major challenge to overcome for this mechanism. Furthermore, this paper categorizes the previously published portable wind energy harvesters to macro and micro scales in terms of their physical dimensions. The power management systems are also surveyed to explore the possibility of improving energy conversion efficiency. Finally some insights and research trends are pointed out based on an overall analysis of the previously published works along the historical timeline.

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In this study we propose a piezoelectric MEMS vibration energy harvester with the capability of oscillating at low (i.e., less than 200 Hz) resonant frequency. The mechanical structure of the proposed harvester is comprised of a doubly clamped cantilever with a serpentine pattern associated with several discrete masses. In order to obtain the optimal physical aspects of the harvester and speed up the design process, we have utilized a deep neural network, as an artificial intelligence (AI) method. Firstly, the deep neural network was trained with 108 data samples gained from finite element modeling (FEM). Then this trained network was integrated with the genetic algorithm (GA) to optimize geometry of the harvester to enhance its performance in terms of resonant frequency and generated voltage. Our numerical results confirm that the accuracy of the network in prediction is above 90%. Consequently, by taking advantage of this efficient AI-based performance estimator, the GA is able to reduce the device operational frequency from 169 Hz to 110.5 Hz and increase its efficiency on harvested voltage from 2.5 V to 3.4 V under 0.25 g excitation.