Research On Energy-sacing Operation Optimization And Speed Curve Tracking And Control Of Urban Rail Trains Based On FAPSO

Many people have flocked to the urban areas as urbanization is accelerating.As a result,urban traffic congestion has become extremely serious.Therefore,in order to facilitate travel,more and more people are gradually choosing urban rail trains with the characteristics of safety,comfort,environmental protection,and strong transportation capacity as the first choice for travel.As a result,urban traffic congestion has been effectively reduced.Therefore,The advantages of urban rail trains in urban public transportation are also becoming more and more obvious.However,along with the continuous expansion of urban rail transit mainline operation network,urban traffic congestion issues have been largely solved effectively,but also at the cost of massive energy consumption.So,lowering train operation energy consumption contributes to the green,low-carbon,and long-term development of urban rail transit.The research in this paper is aimed at achieving on-time arrival of trains during actual operation and reducing operational energy consumption.Under the conditions of full consideration of the actual running route,a multi-point train running energy consumption model that satisfies the constraints is constructed,and on this basis,the firefly operator is introduced into the standard particle swarm algorithm to build a particle swarm optimization algorithm(FAPSO)containing the firefly operator,and then a two-layer structure integrated optimization control method based on FAPSO algorithm is proposed,and then use this optimal control method to get the target speed profile for energy-efficient,on-time operation of the train between stations.Then,a PID speed controller and a fuzzy adaptive PID speed controller are designed to track the given speed curve obtained by the above optimization,which can improve the comfort and punctuality of running trains and reduce the energy consumption of train operation.The specific study content are as follows:(1)Firstly,the train operation process is analyzed.Then,it is discussed and analyzed which modeling approach is more suitable for the actual train’s operation in terms of singleparticle modeling and multi-particle modeling.Next,the particle swarm algorithm is simply introduced,and the firefly attractivity operator is introduced into the standard particle swarm algorithm to improve the particle swarm algorithm,and a particle swarm optimization algorithm containing the firefly operator,namely FAPSO,is constructed.Finally,the two algorithms are compared and validated by simulation to use the Ackley test function,and the simulation data shows that the FAPSO algorithm outperforms the PSO algorithm in finding the optimum.(2)Secondly,the total train running time of the whole operation line is analyzed,and the FAPSO algorithm-based double-layer structure integration optimization control method is used to optimize the running time and running strategy of trains between stations.In the upper-level optimization,the total redundancy time obtained is allocated by using the FAPSO algorithm,and then the best running time between stations is obtained;On the basis of the optimal running time between stations obtained from the optimization of the upper level,the lower-level optimization compares the operation energy consumption of trains under different operation strategies,and then optimizes the train operation strategy of the whole line.Finally,the optimal scheme of driving for energy-saving and punctual operation between train stations is obtained to guide the train operation.(3)Finally,the control concepts of PID and fuzzy adaptive PID were studied,and PID and fuzzy adaptive PID speed controllers were designed according to the principle of PID and fuzzy adaptive PID.The designed controller is used in the Simulink environment to control the train to track a given speed,so that the train can run comfortably,energy-efficiently and on time.(4)Choose the B2 car produced by CNR Zhu Ji as the simulation model,based on the data of a section of the Guangzhou Metro for simulation experiments.Simulation shows using the double-layer structure integration optimization control method,the total energy consumption of the optimized train operation is reduced by 11.80%.Then,simulations are conducted to compare the effect of the two designed speed controllers in tracking the target speed curve and the simulation results show that the fuzzy adaptive PID speed controller controls the train to track the given speed better,which can make the actual running train safe,comfortable,on-time and energy-efficient operating.