Research on the Recovery and Reuse Method of Train Regenerative Braking Energy Based on the Decommissioned Equipment of EMU Trains

Lü, Jing-Tao; Zhu, Chengkun; Li, Xin

doi:10.1007/s42835-023-01433-y

J. Electr. Eng. Technol.

2023

DOI: 10.1007/s42835-023-01433-y

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Research on the Recovery and Reuse Method of Train Regenerative Braking Energy Based on the Decommissioned Equipment of EMU Trains

Jing-Tao Lü

Chengkun Zhu

Xin Li

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2024

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Cited by 2 publications

References 11 publications

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

Li,

Fan,

Tang

et al. 2024

Energy Tech

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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.

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

Li,

Fan,

Tang

et al. 2024

Energy Tech

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Building energy management model integrating rule-based control algorithm and genetic algorithm

Gong

2024

Int. J. Renew. Energy Dev.

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Energy is a crucial material foundation for the development of human society. Building energy consumption accounts for a significant proportion of global energy consumption. Optimizing building energy management is of great significance for achieving sustainable development. A building energy management model that integrates rule-based control algorithm and genetic algorithm is proposed, aiming to optimize building energy utilization and reduce operating costs. Mathematical models for different devices in the building energy system are established, and the rule-based control algorithm is used to provide system decision support. Then, the genetic algorithm is integrated to address the complexity and uncertainty of energy optimization problems. The comparative test results showed that the proposed fusion algorithm had higher fitness values and faster convergence speed. The root mean square errors of the algorithm in the training and testing sets were 43.6544 and 43.6844, with the lowest error and highest accuracy among the four algorithms. The simulation experiment results showed that the building energy management model integrating rule-based control algorithm and genetic algorithm had energy expenditures of 788.3 yuan and 967.6 yuan for two types of buildings, respectively. Taking Building 1 as an example, compared with Supervisory Control and Data Acquisition (SCADA), Beetle Antennae Search and Particle Swarm Optimization (BAS-PSO) algorithm, and Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) algorithm, the proposed model reduced the cost of energy consumption optimization by 39.30%, 28.32%, and 20.20%, respectively. Overall, the proposed building energy management model effectively reduces operating costs, utilizes building energy, and contributes to daily building energy management and decision support.

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Research on the Recovery and Reuse Method of Train Regenerative Braking Energy Based on the Decommissioned Equipment of EMU Trains

Cited by 2 publications

References 11 publications

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

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

Building energy management model integrating rule-based control algorithm and genetic algorithm

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