An Energy-Efficient Cooperative Control Method For Train Operation By Considering Braking Power In Urban Rail Transit

Urban rail transit(URT)has the characteristics of safety,punctuality,and large transportation capacity.In recent years,URT has been developing rapidly and the operation mileage has been increasing.As a result,the overall energy consumption increases rapidly,among which the traction energy consumption accounts for the highest proportion.The energy-efficient cooperative train operation and control is a method that maximizes the regenerative braking energy(RBE)and reduces the traction energy consumption by coordinating the traction and braking processes of multiple trains in the same power supply section.This method can increase the utilization of the RBE which accounts for 30%～50% of the traction energy consumption and becomes a trend in the research on the energy-efficient train operation and control.The existing methods generally regard the overlapping time between the traction train and the braking train as the optimization goal.However,the influence of the instantaneous power change during the actual utilization of RBE is not considered,resulting in the deviation of the actual energyefficient effect from the solution of the optimization model.Aiming at solving the above problems,this thesis studies the optimization approaches for the energy-efficient cooperative train operation and control.Specifically,based on the real-time data of current and voltage during train traction and braking,the utilization regularity of the RBE is analyzed.Then,considering electric power during train operation,the maximum utilization model of the RBE is constructed,and an efficient solution algorithm is proposed based on branch and bound and adaptive particle swarm optimization.For better illustrations,an analysis and evaluation tool of the RBE utilization is developed.The main contributions and results are as follows:(1)The factors affecting the utilization rate of RBE are sorted out.There are four scenarios where the RBE can be exploited by the coordinated operation of trains.The linear regression and double-hidden-layer neural network are developed to derive the utilization regularity of the RBE between upward and downward trains.(2)Based on the data of a line of Beijing Subway,the branch and bound method is used to solve the maximum utilization model of the RBE in train cooperative operation.The results show that the proposed model for the RBE calculation with consideration of electric power will increase the RBE utilization rate by 14.0% during peak hours and17.6% during off-peak hours.Compared with the existing model based on the kinetic energy theorem,the proposed method is 3.3% higher during peak hours and 9.2% higher during off-peak hours,which indicates its effectiveness.(3)In order to improve the solution efficiency,an adaptive particle swarm optimization algorithm is developed.According to the data of a line of the Beijing subway,an example is analyzed and compared with the optimization effect of the branch and bound method.The results show that the adaptive particle swarm optimization algorithm only obtains the suboptimal solution,which improves the RBE utilization rate by 12.9% during the peak hours and 11.6% during the off-peak hours.Nevertheless,the adaptive particle swarm optimization algorithm improves the solution time by about 92.5% during the peak hours and 90.0% during the off-peak hours.(4)An analysis and evaluation tool of the RBE utilization is developed based on the WPF user interface under the.NET framework,using the hybrid programming technology of XAML+C#+MATLAB.This tool can visualize the RBE utilization effect after optimization,which is more intuitive and bridges the research gap of the analysis and evaluation of the RBE utilization.In summary,the proposed methods considering the utilization regularity of the RBE and calculating the RBE based on electric braking power in this thesis can increase the utilization rate of the RBE effectively.The research result provides theoretical support for the practical applications of the energy-efficiency cooperative train operation and control.59 figures,28 tables,and 79 references.