Energy management strategy for piezoelectric passive sensing nodes in rail vehicle vibration

SUI Huaishan; JIN Mengke; PENG Lele; ZHENG Shubin; ZHU Ting

doi:10.12299/jsues.24-0053

Volume 39 Issue 2

Jun. 2025

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Article Navigation > Journal of Shanghai University of Engineering Science > 2025 > 39(2): 131-140, 173

SUI Huaishan, JIN Mengke, PENG Lele, ZHENG Shubin, ZHU Ting. Energy management strategy for piezoelectric passive sensing nodes in rail vehicle vibration[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 131-140, 173. doi: 10.12299/jsues.24-0053

Citation:

SUI Huaishan, JIN Mengke, PENG Lele, ZHENG Shubin, ZHU Ting. Energy management strategy for piezoelectric passive sensing nodes in rail vehicle vibration[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 131-140, 173. doi: 10.12299/jsues.24-0053

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Energy management strategy for piezoelectric passive sensing nodes in rail vehicle vibration

doi: 10.12299/jsues.24-0053

SUI Huaishan^{1a
,},
JIN Mengke^1a,
PENG Lele^{1a, 1b
,
,},
ZHENG Shubin^1b,
ZHU Ting²

1a.
School of Urban Railway Transportation, Shanghai University of Engineering Science
1b.
Shanghai Engineering Research Centre of Vibration and Noise Control Technologies for Rail Transit, Shanghai 201620, China
2.
Institute of Science and Technology, Shanghai Railway Bureau, Shanghai 200003, China

Received Date: 2024-03-05
Available Online: 2025-09-30
Publish Date: 2025-06-30

Abstract

Abstract

In response to the system operational anomalies caused by the mismatch between the vibration energy source and power supply requirements in piezoelectric passive sensing nodes for rail vehicle vibration, a node-level energy management strategy was proposed. By analyzing the structural composition, relevant parameters, and operational modes, an energy management strategy model was constructed. The particle swarm optimization (PSO) algorithm was then utilized to optimize and match the design parameters and operational modes, resulting in the design of a system solution and the development of a prototype. The results demonstrate that the node achieves a minimum standby power consumption of 8 mW, accurately collects traction motor vibration data, and extracts bearing fault characteristic frequencies from the data, validating the effectiveness of the proposed energy management strategy.
- rail vehicle,
- passive sensing,
- energy management strategy,
- particle swarm optimization (PSO) algorithm

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References(11)

References

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