Urban Rail Transit Energy Saving Research Based On Train Operation Control Strategy Optimization

With the continuous expansion of urban rail network scale,energy consumption has become an urgent problem to be solved in China's urban rail transit industry.Reducing the traction energy consumption of trains is an effective way to reduce overall energy consumption and operating costs.The ATO system can adjust the train speed in real time,to ensure the safe,punctual,comfortable operation of the train.Therefore,this paper starts from the aspect of optimizing the ATO system recommended speed curve,to achieve the purpose of reducing the traction energy consumption of the train.This paper discussed the control strategies of trains in different speed limits and working conditions,and summarized the optimization principles of train operation control.This paper proposed six kinds of ATO system performance evaluation indicators: overspeed protection index,parking accuracy index,punctuality index,passenger comfort index,speed fluctuation index and energy saving index.In the train optimization model,this paper used an improved multi-particle train model,which can simulate the smooth change of the resistance when the train nearing the change point of slope and curve.In the algorithm,this paper proposed a double-layer genetic algorithm with a working condition selection layer and a speed protection layer,and used a variable length real number coding chromosome for calculation.And then optimized the recommended speed curve of the ATO system based on the above performance evaluation indicators.Finally,this paper used the algorithm to carry out an example calculation,and proved effectiveness and reliability of this algorithm.