Energy consumption optimization method for heavy haul trains based on gray wolf optimization algorithm

Abstract

The invention relates to a method for optimizing the energy consumption of heavy-duty trains based on the gray wolf optimization algorithm. The ideal running curve of the train is determined according to the train traction force curve, the train braking force curve and the train running resistance curve; the total energy consumption model of the train is constructed according to the ideal running curve of the train And set the constraint conditions, and then use the improved gray wolf optimization algorithm to determine the optimal solution of the train's total energy consumption model, so as to determine the optimal speed of each working condition and obtain the optimal speed curve. The present invention systematically analyzes the ups and downs of the heavy-duty train, and performs relative energy calculation according to the up and down slopes, and the energy calculation method is more accurate. At the same time, the present invention improves the traditional gray wolf algorithm, adopts the gray wolf algorithm with a new nonlinear convergence factor to optimize the energy consumption of heavy-duty trains, introduces reverse learning strategies, mutation operators, etc., and reduces the risk of premature convergence of the algorithm. Probability, improving the analysis accuracy of energy consumption optimization.

Description

technical field [0001] The invention belongs to the technical field of energy-saving optimization control of heavy-duty trains, and in particular relates to a method for optimizing energy consumption of heavy-duty trains based on a gray wolf optimization algorithm. Background technique [0002] Huge kinetic energy and potential energy will be occupied in the process of heavy-haul railway transportation, so it is very necessary to reduce the energy consumption during the operation of heavy-haul trains. At present, the energy-saving issues of trains are mainly considered to take measures from the aspects of energy-saving line design, lightweight vehicles, and the use of mobile block train control systems. Theoretically, some theories of energy-saving slope research can also be applied to heavy-duty railway lines. The problem of energy-saving control of trains running on undulating slope lines is much more complicated than that of running on flat roads. This invention considers...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of optimizing energy consumption in the operation of heavy-duty trains, the present invention provides a method for optimizing energy consumption of heavy-duty trains based on the gray wolf optimization algorithm. By improving the gray wolf optimization algorithm, the energy consumption optimization analysis of heavy-duty trains is carried out

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