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Energy consumption analysis of urban rail fast and slow train modes based on train running curve optimization

Lianbo Deng, Cai Li, Guiqing Zhang, Shiyu Tang

2023Energy Reports11 citationsDOIOpen Access PDF

Abstract

According to the operation characteristics of urban rail transit in fast and slow train modes, this paper studies the energy-saving problem of the two train modes from the perspective of optimization of the train running curve. A method based on the control parameters including control force and speed is proposed. Under the constraint of the interval running time, considering the station speed limit and curve speed limit, and with the goal of minimizing the energy consumption during operation, an optimization model of an urban rail transit train running curve under fast and slow train modes is established, and a solution method based on the simulated annealing algorithm and multi-parameter control strategy is designed. Using the line and operation data of Guangzhou Metro Line 21, the interval energy-saving running curves under two modes are obtained, and the data of the energy consumption and interval running time are analyzed, verifying the effectiveness of the energy-saving strategy. By comparing with the slow train, it is concluded that the energy consumption each kilometer of the fast train is reduced by 1.468 kW·h, saving 18.04%, and the energy consumption each hour is reduced by 36.213 kW·h, saving 6.76%, which provides support for energy-saving and cost reduction of the transportation sector under fast and slow train modes.

Topics & Concepts

Energy consumptionUrban rail transitTrainEnergy (signal processing)Interval (graph theory)Automotive engineeringSimulationSimulated annealingLimit (mathematics)Computer scienceControl theory (sociology)EngineeringControl (management)AlgorithmMathematicsTransport engineeringElectrical engineeringGeographyMathematical analysisCombinatoricsCartographyStatisticsArtificial intelligenceRailway Systems and Energy EfficiencyTransportation Planning and OptimizationTraffic Prediction and Management Techniques