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  5. A Combined Wind Harvesting and Speed Sensing System Based on Constant‐Voltage Triboelectric Nanogenerator

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Article
en
2024

A Combined Wind Harvesting and Speed Sensing System Based on Constant‐Voltage Triboelectric Nanogenerator

0 Datasets

0 Files

en
2024
Vol 14 (23)
Vol. 14
DOI: 10.1002/aenm.202400672

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Xiaoru Liu
Lixia He
Baofeng Zhang
+7 more

Abstract

Abstract The triboelectric nanogenerator (TENG) holds significant potential for applications in energy harvesting and environment monitoring. However, a notable challenge lies in developing a combined system capable of efficiently harvesting energy and providing real‐time self‐powered sensing. Here, a combined wind energy harvesting and speed sensing (EHSS) system is constructed based on a noncontact electricity‐induced constant‐voltage TENG (EICV‐TENG) that achieves constant output by coupling two different phases and incorporates a parasitic sensing module in a single phase. Through the optimization of system integration, the EICV‐TENG achieves a current density of 28.7 mA m −2 and an average power density of 0.26 W m −2 Hz −1 . With an optimal power management, the energy output efficiency of the EICV‐TENG reaches up to 96%. And EICV‐TENG can stably power 32 commercial hygrothermographs connected in parallel with the PMC at a wind speed of 7 m s −1 . In addition, the EHSS system demonstrates excellent real‐time sensing performance based on the correlation between the peak number of pulse output in a single phase and wind speeds. This work contributes to the advancement of TENG technology for a combined energy harvesting and self‐powered sensing system in the context of the Internet of Things (IoTs).

How to cite this publication

Xiaoru Liu, Lixia He, Baofeng Zhang, Linglin Zhou, Zhong Lin Wang, Jie Wang, Yuexiao Hu, Xinyuan Li, Yikui Gao, Zhihao Zhao (2024). A Combined Wind Harvesting and Speed Sensing System Based on Constant‐Voltage Triboelectric Nanogenerator. , 14(23), DOI: https://doi.org/10.1002/aenm.202400672.

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Publication Details

Type

Article

Year

2024

Authors

10

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aenm.202400672

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