Energy-Efficient Optimization Method For Trains In Urban Rail Transit Based On Particle Swarm Optimization Algorithm

Urban rail transit is an important part of public transport system in China. As a kind of safe, comfortable, punctual and large-capacity means of transportation, the construction of urban rail traffic has achieved rapid development in recent years. However, energy consumption is an urgent problem due to the large overall traffic volume. Therefore, the research of energy-saving for urban rail transit has important practical significance.Nowadays, the communication-based train control(CBTC) system has been widely applied to urban rail transit automatic control system, based on these, the optimization of train operation trajectory methods for single train and two tracking trains are proposed in this thesis. Aiming to the optimization of single train operation trajectory, minimum energy consumption and practical travel time optimized are the research target and multi-objective particle swarm algorithm is applied to calculate train curve. Aiming to the optimization of two tracking trains’ operation trajectory, the tracking dynamic process and the energy saving space for the following train are deeply analyzed with the consideration of the leading train’s running information, dynamic particle swarm algorithm is applied to optimize the operation curve of the tracking train. At last, the proposed methods are applied to simulate with the actual line data, the results demonstrate the proposed methods can reduce the energy consumption. Detailed contents are as follows:(1) Train dynamics model is set up based on analysis of train operation process. According to train tracking operation characteristics and individual tracking scenario, train tracking operation model is set up. The key factors affecting the train energy-saving operation is analyzed, and train energy consumption calculation model is set up.(2) Multi-objective particle swarm optimization algorithm is applied to optimize the single train operation curve. The single train operation trajectory is optimized for minimum energy consumption and practical travel time. Because of good convergence characteristics, multi-objective particle swarm optimization algorithm is applied to calculate train optimal operation curve in order to achieve optimization goals. Compared with the traditional method for multi-objective optimization problem, multi-objective particle swarm optimization algorithm does not rely on experience value to select coefficient and increase solutions diversity.(3) Dynamic multi-objective particle swarm algorithm is applied to optimize the train tracking operation curve. The tracking dynamic process of two trains in a section is analyzed with the consideration of the leading train’s running information, dynamic particle swarm algorithm is applied to optimize the operation curve of the tracking train.(4) This thesis presents some numerical examples based on the operation data from the Beijing Subway CHANGPING line. By comparing the results of the train operation, the multi-objective particle swarm algorithm is energy-effective to be used for the single train’s energy-saving, energy consumption can be reduced by 11.98%. Dynamic multi-objective particle swarm algorithm is energy-effective to be used for the two trains’tracking and energy consumption can be reduced by 6.59%.