Open AccessThe FUZZY CONTROL STRATEGY OF URBAN RAIL ENERGY BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM
Author(s) -
Xiao-Kan Wang,
Shuang Liang,
Qiong Wang,
Chao Chen
Publication year - 2021
Publication title -
latin american applied research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.123
H-Index - 23
eISSN - 1851-8796
pISSN - 0327-0793
DOI - 10.52292/j.laar.2021.536
Subject(s) - particle swarm optimization , train , computer science , multi swarm optimization , energy management , reliability (semiconductor) , fuzzy logic , fuzzy control system , energy (signal processing) , process (computing) , controller (irrigation) , energy consumption , engineering , stability (learning theory) , automotive engineering , control theory (sociology) , mathematical optimization , control engineering , control (management) , algorithm , mathematics , artificial intelligence , electrical engineering , biology , operating system , power (physics) , quantum mechanics , machine learning , agronomy , statistics , physics , cartography , geography
The city rail train starts and brakes frequently in the process of operation, and the existing braking technology can not make full use of this part of energy. In this study, a lithium battery super capacitor composite energy storage system is proposed, which uses the fuzzy control of particle swarm optimization algorithm for energy optimization management. The fuzzy energy controller is established to optimize the system parameters by using particle swarm optimization (PSO) algorithm. Simulation results show that the strategy can not only optimize the energy management of urban rail trains, but also improve the stability, reliability and economic performance of train operation and reduce fuel consumption.