Application of Improved PSO Algorithms in Train Energy Consumption Optimization

Aiming at the optimization of energy management for energy storage trams, an improved PSO algorithm based on classical PSO algorithm is proposed in this paper. On the premise of determining the operation strategy and operating conditions, the dynamic analysis of tramcar is carried out, the energy-saving model of tramcar is established, its objective function and constraints are analyzed, the model is solved by improved PSO algorithm, and the simulation results are compared with the actual energy consumption. The results show that the PSO algorithm of competition mechanism can improve the convergence of the algorithm, effectively find the turning point of the energy-saving model, reduce the energy consumption of tramcar operation, and improve the safety, precision parking, and comfort of operation.

[1]  J. Van Mierlo,et al.  Analysis and configuration of supercapacitor based energy storage system on-board light rail vehicles , 2008, 2008 13th International Power Electronics and Motion Control Conference.

[2]  François Lacôte Alstom -- future trends in railway transportation , 2005 .

[3]  M. Miyatake,et al.  Optimal speed and charge/discharge control of a train with onboard energy storage devices for minimum energy operation , 2008, 2008 International Symposium on Power Electronics, Electrical Drives, Automation and Motion.

[4]  M. Steiner,et al.  Energy storage on board of railway vehicles , 2005, 2005 European Conference on Power Electronics and Applications.

[5]  I. Szenasy Improvement the energy storage with ultracapacitor in metro railcar by modeling and simulation , 2008, 2008 IEEE Vehicle Power and Propulsion Conference.

[6]  Masamichi Ogasa,et al.  Power Flow Control for Hybrid Electric Vehicles Using Trolley Power and On-board Batteries , 2007 .

[7]  You Xiaojie,et al.  Power distribution control strategy of on-board supercapacitor energy storage system of railway vehicle , 2011, 2011 International Conference on Materials for Renewable Energy & Environment.

[8]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[9]  Yaochu Jin,et al.  A Competitive Swarm Optimizer for Large Scale Optimization , 2015, IEEE Transactions on Cybernetics.