Research On Optimization Method Of Heavy Haul Train Driving Strategy

Heavy-haul railway has significant advantages in capacity,operation cost and transportation efficiency,and is an important link in the transportation of bulk goods in China.In recent years,with the strategic adjustment of China,the transportation of coal and other related bulk materials has further changed to the railway leading supply chain,which brings great pressure to heavy-haul railway.This also puts forward higher requirements for the driving ability of heavy-haul train drivers.However,the driving of heavy-haul trains has many difficulties,such as complex line conditions,high driving intensity and complex operation,which brings challenges to the safe and smooth operation of heavy-haul trains.Therefore,this thesis is of great significance to the study of the optimization of driving strategy of heavy-haul trains.This thesis analyzes the characteristics of heavy-haul trains and the historical operation data of excellent drivers,and deals with the data,and studies the target speed curve of heavy-haul trains;then the traction/braking control of the electric locomotive is studied.The main work of this thesis is as follows:(1)Through the longitudinal dynamic analysis of heavy-haul trains,the single particle model and multi particle model for predicting the speed of heavy-haul trains are established.The results are verified by the traction process and braking process in different sections.The verification indicates that the two models reach the unified output results within the error of 0.43km/h.(2)Based on the historical operation data of excellent drivers in different sections of heavy haul line,after data processing,Light-GBM algorithm is used to complete the prediction of target speed curve and the setting of model parameters,and the test is carried out with the help of existing historical operation data.The absolute mean error of target speed curve of test data is 1.44km/h.Considering the requirement of smooth operation of heavy haul train,the artificial bee colony algorithm is used to smooth the target speed curve of heavy haul train.(3)For the train formation using stepless electric locomotive,a model reference adaptive control algorithm is proposed,and a speed tracker for heavy haul train is designed.At the same time,considering the requirements of speed tracking error and stability,the prediction model is established based on the single particle model of heavy haul train;The optimization item of rolling optimization is analyzed and improved in combination with the operation characteristics of heavy haul train.With the help of quadratic programming,the rolling optimization part is solved and the parameters are set.In the rolling optimization,the longitudinal impulse of the train is reduced as much as possible.Finally,the simulation platform of heavy-haul train is built by using the simulation model.The tracking method based on model reference adaptive control algorithm and PID algorithm are applied to the 10000 ton train for comparison.Through comparison,when applying on the heavy haul train which runs on the 30-kilometer undulating slope,the method proposed in this thesis is 33 kilonewton less than the method based on PID in the average maximum coupler force.It is proved that the control strategy based on model reference adaptive control algorithm is feasible.35 figures,15 tables,59 references.