An integrated approach for the energy-efficient driving strategy optimization of multiple trains by considering regenerative braking

With the rapid development of urban rail transit networks, the energy consumption problem of urban rail trains becomes more and more prominent. A critical issue is to reduce the energy consumption of trains through advanced control and optimization techniques. The majority of existing studies in this field focus on (1) the optimization of single train operation strategy and (2) the scheduling of multiple trains, separately. In this paper, we propose an integrated energy-efficient operation optimization method for multiple trains in multiple inter stations by considering regenerative braking. In particular, this paper integrates the energy-efficient operation strategy with optimized train timetable under the operation scenario of two trains in multiple interstations. First, a genetic algorithm is used to optimize the energy-efficient operation strategy for single train in multiple interstations and obtain the relationship between the operation time and operation energy consumption of trains. Then, a particle swarm optimization method is adopted to obtain the optimal operation time of trains (i.e. train timetable) for the purpose of minimizing the overall energy consumption. Finally, real-world case studies based on the operation data of the Beijing subway line 7 are presented to verify the effectiveness of the proposed approach. Numerical experiments show that the proposed approach by optimizing the train operation strategy and timetable can reduce the total energy consumption by 16.24% compared with the real-world train operation strategy.