Theory Of Train Energy-efficient Optimization Operation And Its Application

Train operation is the complex control problem with the result of many factors working together in a complicate and changing environment.In China train's performances mostly depend on the drivers' operational experience and proficiency.Although the unit energy consumption of railway transportation is low,the total energy consumption is huge.Consequently,it is very significant to explore the train energy-efficient optimization operation.This study achieves reducing energy consumption by means of improved train control strategy,and a multi-objective train optimization operation model is founded,and tested in actual operation.The main works are as follows:1.According to train movement the forces are studied during train operation,the main forms of energy consumption are analyzed.It is the key to reduce train energy consumption by keeping the running speed stable and decreasing unnecessary braking from theoretical analysis and experts' experience.Multi-objective optimization model of train movement is established with the goal of energy consumption,running time and parking accuracy.2.Train optimization problems were studied on the basis of genetic algorithms,and optimization algorithm is improved.According to train operation environment and manipulation state,hybrid encoding genetic algorithm is proposed.Meanwhile in order to speed up the convergence of the algorithm,the locomotive driver's experience as a constraint information is integrated into the solution during the update process,in which guiding the optimization process moving toward the optimal solution.Train optimization operation model based on simulated annealing algorithm is established,the simulation result demonstrates that it meet the requirement of train operation control.3.By studying the means of train energy-efficient operation,a new method is proposed with simulated annealing algorithm and genetic algorithms.Comparison between real manipulation and optimization simulation,the result shows that this algorithm has good flexibility,can adapt to different operation conditions,avoid unnecessary brakin and reduce the energy consumption.4.This thesis analyzes and studies train brake process and operation requirements,indicates that the critical factors of train brake are the reasonable selection of braking initial point and releasing point,and reducing kinetic energy losses of train.Discussing the control variables and constraints of train brake,the fuzzy neural network model of train brake is established.The simulation results show that the fuzzy neural network control method is effective to reduce the energy consumption in the condition of ensuring train brake safety,stably and accurately.Based on the theoretical research and practical application of train operation,train optimization operation model is established and verified,the result demonstrates that the model can effectively reduce train energy consumption,it is significant to energy saving and emission reduction in the railway industry.