Study on Energy-Saving Optimization of Train Coasting Control Based on Multi-Population Genetic Algorithm

With the increasing importance of rail transit in urban transportation, the reduction in energy consumption of automatic train operation (ATO) is very significant. Coasting, as one of train's operation modes without energy consumption, is an effective energy-saving way by integrating coast control into ATO. In this paper, a time optimal train running reference curve is designed with least time consuming but highest energy consumption and it is optimized by adding multi-point coasting control to realize energy saving with a relative rise in time. Multi-population genetic algorithm (MPGA) is adopted to solve this multi-point combinatorial optimization problem. Population's diversity of MPGA can improve global search capability and convergence speed by parallel optimization. A multi-particle train model is built to approach the real condition of train. Simulation results, based on real line condition and train parameters of Shanghai line 7, demonstrate the advancement of multi-point coasting control with MPGA. It can save energy substantially and consider the running time synthetically with different energy-saving effect.