Research on Urban Rail Train Energy-saving Control Strategy Based on Genetic Algorithm

Abstract

Objective Urban rail transit generates a considerable amount of energy consumption during operation, with traction energy consumption of train operation offering significant optimization potential. Therefore, there is an urgent need to study a train traction energy-saving control strategy. Method Firstly, a dynamic equation of train operation is established, and the constraint conditions for the train operation process are defined. Secondly, the optimization strategy for train traction energy consumption is decomposed to establish the objective function.Based on the genetic algorithm model, the allocation of train operation energy consumption in different intervals and the optimal cruising speed within intervals are solved. Then, through the cross mutation process of the genetic algorithm, the optimal energy-saving effect of the driving speed recommendation curve is obtained. Finally, a simulation model is built using Matlab software, with real-line train parameters and operational data to simulate the inter-station operation process of the train. Result & Conclusion Experimental results show that the control strategy based on the genetic algorithm model, compared to the conventional fixed working condition sequence control strategies, significantly improves traction energy-saving indicators.

Keywords

• urban rail transit train
• traction energy consumption
• genetic algorithm
• energy-saving control