基于黄金比例遗传算法的动车组列车节能优化研究

为研究注重最小化能耗的动车组列车运行控制,针对列车单质点模型受力分析不准确问题,提出一种对附加阻力进行处理的多质点方法,进而以多质点模型为基础进行2次优化。为解决遗传算法寻优时容易陷入局部最优的问题,提出一种基于黄金比例遗传算法的优化方法,1次优化通过该算法为列车运行寻求一组满足约束条件的目标速度集合,获得列车节能运行速度曲线。考虑过电分相对列车运行的影响,进行2次优化,将运行区间划分为操纵固定段和操纵可优化段,并通过黄金比例遗传算法搜索出一组操纵可优化段内满意的工况转换点,结合1次优化得到列车最终运行曲线。以兰考南-开封北线路CRH3型动车组为仿真实例,列车运行能耗降低了10.83%,表明所提方法是可行的。

Research on energy-saving optimization of EMU trains based on golden ratio genetic algorithm

In order to study EMU(electric multiple units) trains operation control with attention to minimizing the energy consumption, a multi-particle method to deal with additional resistances was proposed aiming at the problem that the force analysis of single-particle model for the train was inaccurate, and two optimizations were carried out based on the multi-particle model. Then, a method with golden ratio genetic algorithm was proposed to solve the problem that genetic algorithm was easy to fall into local optimum, by which a set of target speed sets satisfying constraints were sought for the train in the first optimization, thus the train energy-saving operation speed curve was determined. Considering the influence of electrical phases for the train operation, the second optimization was carried out. The operation interval was divided into fixed segments and optimizable segments of manipulation, and a set of satisfactory operation switching points were searched by golden ratio genetic algorithm. The final operation curve of the train was obtained in tandem with the first optimization. Taking CRH3 of Lankao South-Kaifeng North line as a simulation case, the energy consumption of the train operation is reduced by 10.83%, which shows that the proposed method is feasible.