Method for generating double-deck multi-objective locomotive optimized manipulating sequence

Abstract

The invention discloses a method for generating a double-deck multi-objective locomotive optimized manipulating sequence. The method is characterized by comprising steps of 1. collecting current vehicle parameters of locomotives, route data and historical data of driving of drivers and preprocessing the data; 2. defining all routes as different types of child segments based on the equivalent summing gradient obtained in route data preprocessing; 3. confirming a manipulating method based on the segmentation result in the step 2, and building a base layer multi-objective calculation model comprising a traction calculation model; 4. building an upper-layer multi-objective calculation optimized model based on the output provided by the base-layer multi-objective calculation model; 5. finishing the optimized locomotive manipulating by combining the upper-layer optimized calculation model and the base-layer optimized calculation model. The method greatly shortens running time, and can be used in offline optimization and short-distance online optimization.

Description

technical field [0001] The invention relates to a method for generating an optimal maneuvering sequence of a double-layer multi-objective railway locomotive, belonging to the technical field of railway locomotive control. Background technique [0002] With the increasingly prominent energy problems, how to reduce energy consumption and reduce operating costs has become a major concern of the railway department under the condition of ensuring train safety and punctual operation. Therefore, research on energy-saving control is of great significance. In the case of known train formation, running time, and running distance, there are many different control methods, which will directly lead to different energy consumption. Therefore, finding an optimized control method has become an important issue. [0003] Gears are directly related to power and energy consumption. In the actual driving process, the train driver controls the speed of the railway locomotive by controlling the d...

AI Technical Summary

Problems solved by technology

There are two main problems with this method: one is that the search space is too large without introducing domain knowledge, and it takes too long to obtain the optimal solution, which cannot meet the needs of online real-time computing, and even for longer or more complex The time cost of offline calculation cannot meet the requirements (it takes more than 1 hour to optimize a 5km-long section); the second is that the segmented method with a fixed step size ignores the influence of road conditions, and the design of a single step with the same gear Contrary to domain knowledge, it is difficult to obtain the ideal optimized manipulation sequence

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