Train adaptive energy saving strategy based on iterative induced genetic algorithm - MEI Wenqing; SHI Ke; ZHANG Zhengfang (机车电传动)
In response to the inefficient consideration of complex manipulation characteristics constraints and dynamic traction system efficiency issues in existing train energy-saving strategies, a detailed energy consumption calculation model for train operation was proposed based on the efficiency characteristics of the traction system and the mechanical work of the wheel rotation. An adaptive energy-saving strategy based on an iterative induced genetic algorithm was presented, and an energy consumption objective function considering the traction system efficiency was designed. The objective function was solved under manipulation characteristic constraints to achieve optimal energy-saving planning. Firstly, the optimization objective was designed as several sub-intervals based on the ramp of the train route. The route characteristic information of the sub-intervals was transformed into state and control constraints, improving the adaptivity of the energy-saving objective to the manipulation characteristics under changes in route information. Then, to overcome the difficulty of traditional genetic algorithms in solving continuous sub-interval optimization problems due to discrete independence, an iterative induced genetic algorithm was designed to calculate the Pareto optimal solution set for the energy-saving objective function. Finally, the algorithm was simulated and validated on the MATLAB platform. The simulation results of different line information show that the adaptive energy-saving strategy based on iterative induced genetic algorithm has good energy-saving performance under the condition of ensuring running time, with an energy-saving index improvement of 3%-13% compared to constant speed and constant force operation.