High-speed Railway Train Energy Driving Strategy Based on Improved Genetic Algorithm

In terms of the high-speed railway trains driving strategy, considering the environmental constraints such as speed limit and slope of the actual route, as well as the vehicle’s constraints such as the traction and braking performance of the multiple unit, the multi-objective train control model is constructed based on comfort, punctuality, energy-saving, and precise stopping. The complex multi-objective optimization problem is decomposed into a sub-stage optimization problem by genetic algorithm and the optimal driving profile of the whole process of train operation is generated by the reverse learning mechanism. In order to improve the performance of the algorithm, the reverse learning mechanism and the data-driven strategy is introduced to adjust the factors of the algorithm dynamically. Compared with the traditional genetic algorithm, the simulation results show that the optimized genetic algorithm has the obvious energy-efficient effect, which has a certain reference significance for optimizing the recommended train speed profile.