Smart Railway Transportation: Self‐Powered and Self‐Sensing Vibration Energy Harvester

Safety monitoring sensors in smart railways need a sustainable onboard power supply. This article proposes a counter‐rotating gear energy harvester (CG‐EH) to convert the longitudinal vibration energy of trains into electricity for onboard sensors.CG‐EH consists of a vibration input module, a motion conversion module, and an energy conversion module. The vibration input module converts the longitudinal displacement of the coupler into the rotational motion of the gears. The motion conversion module realizes the conversion of the reciprocating input displacement into the unidirectional rotation based on a counter‐rotating gear set, multi‐stage spur gear sets can effectively mitigate the effects of excitation on CG‐EH. The energy conversion module transforms the kinetic energy of the unidirectional rotation into electrical energy through a generator. Experimental results show that the energy outputs of CG‐EH are improved with longitudinal vibration compared with the usual onboard energy harvester. From the result, the peak output power of CG‐EH is 14.59 W, the peak efficiency reaches 39.2%, enough to power relevant onboard sensors. Moreover, CG‐EH can monitor the running status of trains based on deep learning. From the experiment results and application prospects, CG‐EH is a favorable solution for the power supply problems of onboard sensors in smart railways.

Authors

Institutions

Publication Details

Journal
Energy Technology
Published
2024-09-06
DOI
https://doi.org/10.1002/ente.202401253
Citations
2
Primary Topic
Innovative Energy Harvesting Technologies
Type
article
Field-Weighted Citation Impact
0.43
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart Railway Transportation: Self‐Powered and Self‐Sensing Vibration Energy Harvester

Zutao Zhang, Yongli Hu, Hongjie Tang, J. Zhao et al.
2 citations
Energy Technology
Innovative Energy Harvesting Technologies
0.43
article

Smart Railway Transportation: Self‐Powered and Self‐Sensing Vibration Energy Harvester

Zutao Zhang, Yongli Hu, Hongjie Tang, J. Zhao, Genshuo Liu, Jianhong Zhou, Linyang He, Ang Li, Chengliang Fan
article en
2 citations

Abstract

Safety monitoring sensors in smart railways need a sustainable onboard power supply. This article proposes a counter‐rotating gear energy harvester (CG‐EH) to convert the longitudinal vibration energy of trains into electricity for onboard sensors.CG‐EH consists of a vibration input module, a motion conversion module, and an energy conversion module. The vibration input module converts the longitudinal displacement of the coupler into the rotational motion of the gears. The motion conversion module realizes the conversion of the reciprocating input displacement into the unidirectional rotation based on a counter‐rotating gear set, multi‐stage spur gear sets can effectively mitigate the effects of excitation on CG‐EH. The energy conversion module transforms the kinetic energy of the unidirectional rotation into electrical energy through a generator. Experimental results show that the energy outputs of CG‐EH are improved with longitudinal vibration compared with the usual onboard energy harvester. From the result, the peak output power of CG‐EH is 14.59 W, the peak efficiency reaches 39.2%, enough to power relevant onboard sensors. Moreover, CG‐EH can monitor the running status of trains based on deep learning. From the experiment results and application prospects, CG‐EH is a favorable solution for the power supply problems of onboard sensors in smart railways.

Energy TechnologyVol. 12(12)
Beijing Institute of Technology (CN), Tangshan College (CN), Chengdu Technological University, Qingdao University of Technology (CN), Southwest Jiaotong University (CN)
Openalex Percentile: Top 43%
Innovative Energy Harvesting Technologies
0.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.