Self-tuning stochastic resonance energy harvester for smart tires

2018 ◽  
Author(s):  
Hongjip Kim ◽  
Lei Zuo ◽  
Wei che Tai
Keyword(s):  
Stochastic Resonance ◽  
Energy Harvester ◽  
Resonance Energy ◽  
Self Tuning

Hybrid Vibration Energy Harvester based on Stochastic Resonance

2018 ◽  
Vol 138 (5) ◽  
pp. 185-190
Author(s):  
Meng Su ◽  
Dai Kobayashi ◽  
Nobuyuki Takama ◽  
Beomjoon Kim
Keyword(s):  
Stochastic Resonance ◽  
Energy Harvester ◽  
Vibration Energy ◽  
Vibration Energy Harvester

Piezoelectric passive self-tuning energy harvester based on a beam-slider structure

2020 ◽  
Vol 489 ◽  
pp. 115689
Author(s):  
Liuding Yu ◽  
Lihua Tang ◽  
Tiejun Yang
Keyword(s):  
Energy Harvester ◽  
Self Tuning

A passively self-tuning nonlinear energy harvester in rotational motion: theoretical and experimental investigation

2020 ◽  
Vol 29 (4) ◽  
pp. 045033 ◽  
Author(s):  
Xutao Mei ◽  
Shengxi Zhou ◽  
Zhichun Yang ◽  
Tsutomu Kaizuka ◽  
Kimihiko Nakano
Keyword(s):  
Experimental Investigation ◽  
Rotational Motion ◽  
Energy Harvester ◽  
Self Tuning ◽  
Nonlinear Energy

scholarly journals A Direction Self-Tuning Two-Dimensional Piezoelectric Vibration Energy Harvester

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 77 ◽  
Author(s):  
Haibo Zhao ◽  
Xiaoxiang Wei ◽  
Yiming Zhong ◽  
Peihong Wang
Keyword(s):  
Energy Harvester ◽  
Vibration Energy ◽  
Mass System ◽  
Vibration Direction ◽  
Vibration Energy Harvester ◽  
Tuning Mechanism ◽  
Output Performance ◽  
Self Tuning ◽  
Piezoelectric Vibration Energy Harvester ◽  
Piezoelectric Vibration

Most work from the last decade on the piezoelectric vibration energy harvester (PVEHs) focuses on how to increase its frequency bandwidth but ignores the effect of vibration direction on the output performance of the harvester. However, both the frequency and the direction of the vibration in a real environment are time-variant. Therefore, improving the capability of PVEH to harvest multi-directional vibration energy is also important. This work presents a direction self-tuning two-dimensional (2D) PVEH, which consists of a spring-mass system and a direction self-tuning structure. The spring-mass system is sensitive to external vibration, and the direction self-tuning structure can automatically adjust its plane perpendicular to the direction of the external excitation driven by an external torque. The direction self-tuning mechanism is first theoretically analyzed. The experimental results show that this direction self-tuning PVEH can efficiently scavenge vibration energy in the 2D plane, and its output performance is unaffected by vibration direction and is very stable. Meanwhile, the effect of the initial deflection angle and the vibration acceleration on the direction self-tuning time of the PVEH is investigated. The direction self-tuning mechanism can also be used in other PVEHs with different energy conversion methods for harvesting multi-direction vibration energy.

scholarly journals A Two-Step Cooperative Energy Detection Algorithm Robust to Noise Uncertainty

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Tingting Yang ◽  
Yucheng Wu ◽  
Liang Li ◽  
Weiyang Xu ◽  
Weiqiang Tan
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Resonance Energy ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Second Step ◽  
Final Decision ◽  
Noise Uncertainty ◽  
Robust To Noise ◽  
Significant Increment

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.

Sign in / Sign up

Export Citation Format

Share Document