The nexus of smart transportation: Self-powered and self-sensing node for autonomous rail rapid transit

Abstract

Smart transportation conforms to the developing trend, autonomous rail rapid transit (ART) draws attention as a new form of public transportation. In this paper, a self-powered and self-sensing node (SSN) is proposed to detect the running state of vehicles while providing electrical energy, which can be used as a link in intelligent transportation. The self-powered part stabilizes the system’s response using a zero-pressure angle mechanism and a flywheel. Establish dynamic and kinematic models to study the system response, electrical performance, and neural network model. This paper innovatively studies the enhanced flywheel and its influence on the system, and the benefit of the enhanced flywheel set system proposed is 41.6% higher than traditional flywheel. Experiments show that the energy conversion efficiency of the SSN can reach 75%, and it only takes 60 s to charge one 1.5F supercapacitor fully. In the self-sensing part, the characteristic signals are collected and encoded to generate data sets to train and test the neural network model. The results show that the detection accuracy of the SSN reaches 99.7%, indicates that it can effectively obtain the information we need. This SSN has positive implications for driving the development of ART in smart transportation.