Cooperative control of metro trains to minimize net energy consumption
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
With the increasing concerns on energy consumption and operating cost in metro systems, energy saving on train operation attracts significant attentions. Previous studies have mainly focused on optimal control of a single train and energy-efficient train timetabling. The former does not consider the synchronization of motoring and braking trains, which cannot ensure the proper utilization of regenerative energy on the metro lines without energy storage systems. The latter includes scheduling train operations to synchronize motoring and braking trains for the better utilization of regenerative energy. However, the overlapping time of motoring and braking trains is usually as short as a few seconds and the energy reduction might be made impossible by train delays, which are common in practice. This paper presents a model framework, on the extents of motoring/braking of train acceleration and station stopping, as well as the locations of switching train operation modes, for real-time cooperative control of multiple metro trains. The objective is to minimize the net energy consumption with the consideration of utilizing regenerative energy. A cooperative co-evolutionary algorithm is developed to attain the solution of the proposed model. Case studies on a real-life metro line demonstrate the energy saving performance of the proposed approach compared with separate train control and timetable optimization, from no disturbance to a good range of delays. The results also indicate that the partial motoring in train acceleration and partial braking in station stopping achieve better net energy reduction, in comparison with full motoring/braking preferred in previous studies.
|Number of pages||15|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 26 Apr 2019|
- Metro Train, Cooperative control, Energy savings, Regenerative brakinig, Co-evolutionary algorithms