scholarly journals The FUZZY CONTROL STRATEGY OF URBAN RAIL ENERGY BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM

2021 ◽  
Vol 51 (2) ◽  
pp. 87-92
Author(s):  
Xiaokan Wang ◽  
Shuang Liang ◽  
Qiong WANG ◽  
Chao Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Control ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Super Capacitor ◽  
Swarm Optimization ◽  
Urban Rail ◽  
Fuzzy Control Strategy ◽  
Composite Energy

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.

Energy Control of HEV Based on Fuzzy Controller Optimized by Particle Swarm Optimization

2014 ◽  
Vol 936 ◽  
pp. 2155-2159 ◽  
Author(s):  
Li Yan Zhao ◽  
Niao Na Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Control ◽  
Optimization Algorithm ◽  
Control Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Energy Control ◽  
Swarm Optimization ◽  
Fuzzy Control Strategy ◽  
Self Learning

Energy control of HEV plays a very important role in the process of HEV design, which is directly related to the safety and feasibility. Considering the drive system of HEV is nonlinear and complex, a fuzzy control strategy which is combined with particle swarm optimization algorithm is designed to realize the energy control of HEV. Fuzzy control strategy does not need to built accuracy mathematics model and has good robustness, but it mostly depends on engineering experience and has poor ability of self-learning. So particle swarm optimization algorithm has been added to solve these disadvantages of fuzzy control strategy. In conclusion, this method can not only keep the advantage of fuzzy control strategy, but also has ability of self-learning and self-adapt because of particle swarm optimization added. And the simulation proves that this method is feasible and effective.

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