Multi-objective train energy-saving optimization method based on hybrid operation mode

Abstract

The embodiment of the present invention discloses a multi-objective train energy-saving optimization method based on a hybrid operation mode. In this method, the basic scheme and related parameters of the train based on the hybrid operation mode are input into the test platform to establish a variable set of train operation strategies. The multi-objective particle swarm optimization algorithm in this mode obtains the Pareto frontier solution of the mixed operation mode of the train. The train evaluation simulation module in the test platform will analyze the train operation solution of each mixed operation mode, and simulate the train operation according to the solution, and obtain the corresponding specific For the evaluation value of the strategy, the obtained Pareto frontier solution is applied to the train system, and the train control system is designed to select one of the Pareto frontier solutions as the train operation strategy according to the situation. Considering the safety, energy saving, punctuality, stop accuracy, and comfort indicators of the train, the ATO system can select the train operation tracking curve in a timely manner according to different operating environments, thereby reducing the energy consumption of the train operation, ensuring the efficient operation of the train, and improving passenger satisfaction .

Description

technical field [0001] The invention relates to the field of signal control of urban rail trains, in particular to a multi-objective train energy-saving optimization method based on a mixed operation mode. Background technique [0002] In recent years, with the development of automatic train control system (CBTC) and controller technology, automatic train operation (ATO) has been widely used in the field of urban rail transit. How to adopt appropriate train energy-saving driving strategies has become a research hotspot in the field of rail transit. [0003] The traditional ATO train energy-saving driving strategy is mainly based on the existing train operation information and line conditions, considering the train’s parking accuracy index, energy-saving index, comfort index and train punctuality index, and generates the optimal speed curve. The optimal speed curve and the current position of the train are used to achieve the purpose of energy saving by inertia tracking. The...

AI Technical Summary

Problems solved by technology

The traditional ATO operation focuses on the multi-objective train control research under a single operation mode, and lacks consideration of multiple train operation modes such as cruise, coasting and cruise plus coasting at the same time, and there is a problem that energy saving under specific targets cannot be achieved under specific circumstances. policy requirements

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