Multi-objective optimization method for operation of heavy-load train

Abstract

The invention discloses a multi-objective optimization method for operation of a heavy-load train. The method comprises the following steps of S1, acquiring to-be-optimized section data, wherein the to-be-optimized section data includes a locomotive, vehicle information, marshalling information, line data, line speed limit, time table data and the like; S2, initializing a weight set of an objective function, and the relevant data in the step S1; S3, performing numerical solving for train operation optimization of all long and steep slope sections in a to-be-optimized line range to obtain an energy-saving optimization curve; S4, for multi-objective balance adjustment of a non-long and steep slope, comprehensively utilizing a quadratic programming model and a weight self-adaptive adjusting method, thereby obtaining a stable and energy-saving optimization curve meeting operation indexes; S5, for curve results solved in the steps S3 and S4, performing transition connection meeting the operation stability of the train; and S6, outputting a complete train speed optimization curve. The method has the advantages that on the basis of safety and on-time, stable and energy-saving operation global optimization of the heavy-load train in the whole process including the long and steep slope and the non-long and steep slope is realized.

Description

technical field [0001] The invention relates to the technical field of heavy-duty train operation control, in particular to a multi-objective optimization method for heavy-duty train operation. Background technique [0002] Since the 1950s, the transportation of heavy goods by railway has been valued and developed rapidly by countries all over the world because of its huge economic and social benefits. However, from the initial 5,000-ton class to 10,000 tons, 20,000 tons, or even 30,000 tons, the transportation safety problems of heavy-duty trains have gradually become prominent-brake failures, vehicle coupling breakages and other problems continue to occur, which are seriously restricted. With the development of railway transportation, the safe and stable driving of heavy-duty trains needs to be solved urgently. On the other hand, due to the huge economic cost of locomotive traction energy consumption, energy-saving maneuvering has become a hot research topic in this field...

AI Technical Summary

Problems solved by technology

[0012] 1) In the objective function, the stability only considers the traction force, not the braking force;

[0013] 2) The constraint conditions are simple, without considering the characteristics of train traction force / braking force, the constraints of electric phase separation, etc., which is not realistic;

[0014] 3) There is no feedback link of the solution result to the weight, and manual selection is required

[0015] 4) The long downhill is spliced ​​to the original quadratic programming optimization curve in the form of patching, which affects the running time, speed, and smooth transition of working conditions. After patching, the average speed on the long downhill is reduced, resulting in Time loss, requiring a secondary adjustment of the velocity profile

[0020] The algorithm for solving the manipulation of the long downhill slope and the transitional conditions before and after it proposed in this paper is an "indirect method" for energy-saving optimal manipulation, which requires iterative calculation of accompanying variables, and the positive jump of the accompanying variables needs to be checked during the calculation etc., the amount of calculation is large and the model is complex

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