A Novel Dual Speed-Curve Optimization Based Approach for Energy-Saving Operation of High-Speed Trains

This paper studies the problem of high-speed train operation with special attention to minimizing the energy consumption. The performance characteristics of a high-speed train, including traction characteristic and regenerative braking, and the railway geographical conditions consisting of slope, curve, and tunnel parameters, are fully considered in the dynamic model in order to make it more effective and practical. A new optimal strategy for train operation is developed, and its novelty lies in the fact that it is the first time to optimize the actual speed curve using the method of dual speed curve optimization, which contains two processes of offline global optimization and online local optimization, thus leading to more energy saving as compared with most existing methods with only one-time optimization process. We utilize combination optimization techniques, in tandem with the speed codes and subsections, to solve the global optimization problem with a genetic algorithm. Predictive control is developed for local optimization to refine the global optimization in real time, more particularly, the train operation modes including traction, cruise, coast, and braking are switched on the base of the line slope information, from which a more energy-efficient speed trajectory is generated under the constraints of fixed time and distance. To verify the effectiveness of the proposed strategy, operation of CHR-3 on high-speed railway is tested. Through the comparison of energy consumption in two typical cases, it verifies that the proposed energy-saving strategy works better than that of single optimization strategy. At the same time, the actual speed deviation can be corrected in a timely manner with the proposed method.