Research On Energy-saving Driving Strategy For High-speed Train With Multi-objective Optimization

The energy-saving train driving strategy can guide the train to run on time while reducing the energy consumption of train operation,which is of positive significance to promoting the goal of"carbon peaking and carbon neutrality".To investigate the energy-saving effect of continuous speed control mode and discrete speed control mode,and to solve the problem of complicated modeling due to excessive reliance on algorithm characteristics in existing methods,the following researches were conducted:The optimization benchmark of energy-efficient driving strategies for high-speed train was studied.The inverse problem of the energy-saving train driving strategy,the fastest train speed driving strategy,was studied,and the corresponding generation method was proposed to generate the fastest train speed driving strategy for CRH_3C EMU from Yueyangdong Station to Miluodong Station.The results show that the running time of the fastest train speed driving strategy of the section is lower than the given running time of the section.The multi-objective optimization of high-speed train driving strategy with continuous control and discrete control was studied.The multi-objective optimization mathematical model of train driving strategy was constructed according to the train operation evaluation indexes,and the multi-objective optimization methods of train driving strategy for different speed control modes and their corresponding general setting rules of decision variables of the optimization algorithm were proposed,respectively.Particle Swarm Optimization,Simulated Annealing,and Pattern Search were used to find the energy-saving train driving strategies with continuous control and discrete control for CRH_3C EMU from Yueyangdong Station to Miluodong Station for two given running time scenarios,respectively.The results show that:the energy-saving train driving strategy with continuous control is more effective on the variable gradient line containing the downhill slope;extending the train running time can effectively reduce the energy consumption of train operation;the energy-saving train driving strategies with running time scenarios of 1140s and 1200s can save up to about 19.83%and 24.41%of energy consumption respectively;the energy-saving effect of Particle Swarm Optimization is better among the three algorithms.The impact of additional resistance of slopes on the driving strategy of high-speed train was compared and analyzed.The mapping model of train position and unit slope additional resistance was constructed,the real-time invocation algorithm of unit slope additional resistance was proposed,and a comparison was made to analyze the driving strategies of high-speed train with different line gradient conditions.The results show that:the downhill slope has a facilitating effect on the optimization of train energy saving;on the level line,the energy-saving train driving strategy with discrete control has a better energy-saving effect;the line slope affects the energy consumption of train operation throughout the line,while it affects the train running time only in acceleration phase and braking phase.