Research Of Trajectory Optimization Of Energy-efficient Freight Train Operation Based On Heuristic Intelligent Algorithm

With the rapid development of the national economy and the increasingly serious environmental pollution problem,energy consumption has has become more and more attractive.Global energy shortages has become a reality,As a huge energy consumer in the economic development,the energy saving problem of the rail transportation industry is widely concerned by the researchers at home and abroad.There are numerous driving strategies for the same train arriving at the established station at the specified time of operation,and the energy consumption of different driving strategies is different.The aim of train energy saving optimization is to find the least energy consumption,the most feasible one among many driving strategies.Firstly,based on Newton's mechanics model and kinematics model of trains,the objective function of train energy consumption is set up,and the speed limit,acceleration,parking precision and running time are used as constraints.Then the real number code of the train handle position is carried out in this paper.The four order energy saving strategy is designed to optimize the train's energy saving traction optimization.Secondly,particle swarm optimization is used to optimize the freight train operation in this paper.The concept,basic principle and algorithm flow of particle swarm optimization are briefly introduced,and the particle swarm optimization algorithm is designed.The algorithm is simulated in the Matlab simulation environment.The results show that PSO algorithm is easy to converge too early and can not guarantee the optimization effect effectively.Finally,aiming at the specific industry characteristics and practical application requirements of the optimization of train energy saving operation,the algorithm of handle bit real coding is applied to expand the algorithm.Based on heuristic genetic algorithm,a four step optimization strategy for train energy saving operation is proposed to screen out initial population and determine population size.The objective function is constructed based on energy consumption index,and time penalty,speed error,displacement error and comfort penalty function are added.The chromosome selection,crossover and mutation schemes based on this optimization problem and the stopping criterion of the algorithm are selected.From the simulation results,we can know that the heuristic genetic algorithm with four order energy saving strategy can improve the convergence speed and reduce the energy consumption of the train by 4.35%.Simulation results show that the effectiveness of heuristicgenetic algorithm is better than that of PSO algorithm.In this paper,a four level optimization strategy for train operation is proposed based on heuristic genetic algorithm to ensure the speed and efficiency of the whole optimization process.