Research On Receding Optimization Of Energy-Saving Speed Curve Of Freight Trains

In recent years,with the development of the railway transportation industry,the freight volume has increased,and the number of freight lines and freight trains is increasing.How to effectively reduce the energy consumption of freight trains has become a research hotspot.Firstly,this thesis proposes an adaptive differential evolution algorithm to optimize the train speed curve with the optimization objectives of train punctuality and energy-saving and considering the principle of control condition conversion.To improve the calculation speed of the optimization algorithm,this thesis further considers the length and weight of freight trains and proposes a general gradient equivalent method.Based on the Pontryagin’s Maximum Principle,a three-section receding optimization curve algorithm is designed with train punctuality and energy-saving optimization objectives.Combined with the actual line,the optimal control algorithm for freight trains is studied and tested.The research contents of this thesis are as follows:(1)Firstly,the train dynamic equation is constructed based on the traction and braking characteristics of the train,the protection curve of the train in the speed limit changing section is calculated according to the line speed limit information,and the energy-saving optimization model of the single-mass and multi-mass simulation verification model is established.Secondly,according to Pontryagin’s Maximum Principle,taking train energy saving as the optimization objective,the optimal operation principle of the train is obtained,which provides the optimal operation condition conversion principle for subsequent research.(2)An adaptive differential evolution algorithm is proposed for energy-saving train operation.Considering that the freight line is long and there are many operating conditions changing sections,the number of operating condition sections in the operating section is taken as the dimension of optimizing the individual population.Considering the principle of train control condition conversion,the population individual is initialized,and the adaptive mutation operator is introduced to obtain the optimal train operation strategy through cross selection and other operations.The simulation results of individual dimensions of different populations are compared and analyzed based on the actual line.(3)In order to consider the calculation speed of the optimization algorithm,a rule-based three-section receding optimization algorithm is proposed.According to the equivalent gradient,the line ramp types are defined,and every three adjacent ramp sections form a typical scenarios.Take the train punctuality and energy-saving as the optimization objectives,we optimize the typical scenarios operation strategy,and then select the corresponding three section receding optimization mode according to the train speed on the general ramp.Finally,the simulation is carried out based on the actual data of Dalailong line.Compared with the actual speed curve,the algorithm has achieved better operation and energy-saving objectives.At the same time,the multi-mass model is used to simulate the operation sequence optimized by the single-mass model to verify the feasibility of the improved gradient equivalent algorithm.(4)Based on the research and development project of railway traffic organization and control intelligent system technology,the three-section receding optimization algorithm is tested on the spot.Firstly,the proposed speed curve of the train is calculated by the algorithm program,which is displayed by the industrial tablet computer and provides the recommended speed and control handle of the driving section in front of the driver.After the train stopping,the proposed running speed curve of the train generated based on the three-section receding optimization algorithm is compared with the actual speed curve.The three-section receding optimization algorithm calculation speed is fast,the operation strategy of the typical scene tested is consistent with that of the driver,and achieves punctuality and energy-saving objectives.