Study For ATO Multi-objective Optimization Model And Algorithm Of Urban Rail Train In Different Conditions

With the acceleration of urbanization,the urban population is increasing,so urban traffic is confronted with more serious challenges.Because urban railway has the characteristics of large volume,high speed,small footprint,it become the best way to relieve urban traffic pressure.How to keep the train running with smooth,efficient,comfortable become an important issue during the developing of urban railway.As the research progresses,Automatic Train Operation(ATO)become the key to solve those problems.ATO is a control system which can realize multiple performance indicators such as punctuality,safety,energy saving,comfort,precise parking,etc.However,in the current research on ATO optimization strategies,it is difficult to fully reflect the characteristics of multi-objective optimization,or to consider the incompleteness of multi-objective optimization in the process of overall optimization of the operation and most of studies also have the defect of complex model,inefficient calculation,less universality,ect.With previous studies,this paper proposes an universality and efficient multi-objective optimization strategy.First a multi-objective optimization strategy which based on analyzing working condition is proposed after an in-depth analysis of the current ATO optimization theory,the urban rail transit operation mode and its operating environment.This new strategy use the idea of ‘pieces' in order to make sure the strategy more concise and efficient.The main content of the strategy;the whole line will be segmented according to the characteristics of each parts.Then it will optimize the indexes of energy consumption and comfort on motoring part.On the braking part,it also optimize energy consumption and comfort indexes.When it finished each end of the line,the strategy will establish the model to optimize the energy consumption,punctuality and precise parking by using information of the other two parts.After the model is established,the model is solved by the genetic algorithm,and the optimization results of the three-segment model are integrated to form the final optimization result on the line.Finally the goal which optimize energy consumption,punctuality,comfort and precise parking is achieved.The strategy method aims to improve the ATO optimization efficiency.By segmenting the whole line,it can reduce the difficulty of multi-objective optimization of ATO in operation,improved efficiency,and also make the method has practical function which avoid result to deviate from actual operation.This optimization strategy create a new idea to optimize ATO is that make the middle section can be adjusted.It improves the universality of the strategy plan and also provides a new way for optimize the two parameters on time and precise parking.Finally,an speed curve is formed based on the optimization results to guide thetrain operation.The simulation experiments is performed in the MATLAB platform by using the optimization strategy and optimization model which proposed in this paper.Comparing the final optimization results of energy consumption,punctuality,precision parking and comfort with traditional train driving data.it shows that the strategy this paper proposes can achieve the goal which optimize the four indexes at the same time,although the effect of optimization is not obvious on a single goal.Among them,the idea of using working conditions to divide whole line into three parts and the designed single objective optimization model also have a good reference for ATO multi-objective optimization research.