Optimization Study On EMU Energy-saving Running Speed Curve Of Two-stage Optimization Based On ABC Algorithm And PC Algorithm

Railway has always been the backbone of comprehensive transportation system in china,and has played a critical role in the development comprehensive strength and the expansion of international influence for our country.After more than 50 years of development,the high-speed railway is popular by all countries due to its advantages,such as higher safety,higher speed,and higher energy-saving.However,with the expansion of high-speed railway construction and operation scale,the requirements for high-speed rail system have become more stringent,not only for high performance,fast,safe,punctual,comfortable,and etc,but also the higher requirement on energy-saving of high-speed rail system.Therefore,the investigations on energy-saving operation optimization of high-speed EMU has momentous practical significance for the rapid development of high-speed railway.The numerous of scholars at home and abroad have studied the energy-saving issues of EMU by optimizing the EMU running speed curve,mainly employed the Genetic Algorithm(GA),Differential Evolution(DE)Algorithm,Artificial Neural Network(ANN),and Particle Swarm Optimization(PSO)Algorithm.However,these studies only consider some constraints during the optimization process,such as running time,speed,and distance.It does not take deeply into account the effects of the difference in different slope,slope length,and curve radius in actual line operation on the operating energy consumption of the EMU.Summarizing the above,this thesis proposes a two-stage optimization method of offline global optimization and online line local optimization to optimize the energy-saving running speed curve of the EMU.The specific research contents are as follows:(1)Precise EMU traction calculation model and EMU kinematics model are established to provide the necessary basic theory for studying the energy-saving operation of EMU.Based on this,the composition and main influencing factors of EMU energy consumption are analyzed,the calculation model of EMU operation energy consumption is proposed,and the impact of line conditions on operating energy consumption is emphatically analyzed.(2)The operation control mode of the EMU,the calculation of the operation process of the EMU,and process of control transition are described.According to its operation process,the energy-saving traction strategy of the EMU and based on the control sequence the energy-saving optimization method are proposed.(3)A two-stage optimization method of offline global optimization of Artificial Bee Colony(ABC)Algorithm and online local optimization of Predictive Control(PC)Algorithm is presented.In addition,the ABC Algorithm and PC Algorithm are introduced in detail,and their optimization performance is verified in this thesis.(4)The energy-saving optimization models for single train and tracking train areestablished,and the models are optimized and solved.Firstly,the energy-saving optimization model for single train is established,and the ABC Algorithm is employed to optimize the speed at the operating control transition point corresponding to the lowest energy consumption to obtain the first stage speed curve.Combined with the line situation,the PC algorithm is applied to optimize the second stage speed curve.Secondly,on the basis of the application on single train,the optimized model for tracking train energy-saving operation under the moving block system is established.And the two-stage optimization method is used to re-optimize the tracking train affected by the preceding train,and the speed curve after the two-stage optimization of the tracking train is obtained.Finally,by comparing the performance indexes of single train and tracking train before and after optimization,the feasibility of the two-stage optimization method proposed in this thesis is verified.