摘要： 随着城市轨道交通能耗日益剧增，如何提高再生制动能量利用以降低列车运行能耗成为关键。本文聚焦多列车协同运行过程的追踪列车运行控制策略优化问题。首先，在传统运行工况演变策略的基础上，针对追踪运行场景提出“牵引-惰行-牵引-巡航-惰行-制动”策略。其次，构建空间域列车动力学模型、状态转移方程以及能耗模型，并应用插值法将时域的运行协同问题转变为空间域的工况转换点求解问题。随后，构建以运行能耗与准时性为目标的优化决策模型，并结合蜣螂优化算法进行高效求解。最后，以北京地铁亦庄线为仿真线路，对比分析了基于通信的列车控制(CBTC)与列车自主控制(TACS)架构以及不同演变策略对优化效果的影响。结果表明：相较于CBTC架构，TACS架构显著提升列车协同运行优化效果；所提出策略满足准时性需求的同时，在不同发车间隔下的能耗表现均优于传统策略。列车净吸收能耗最多可提高14.651 kWh，真实运行能耗最多可降低11.284 kWh。因此，所提出的工况演变策略与优化求解方法可有效改善列车运行能耗，对城轨列车运控技术发展具有一定借鉴意义。代码已在Github公开：https://github.com/eva-777/Tracking-Train-Operation-Optimization.git。

Abstract: With the escalating energy consumption of urban rail transit system, enhancing the utilization of regenerative braking energy to reduce energy consumption of train operation has become a critical issue. This paper focuses on the optimization problem of tracking train operation control strategy in the process of multi-train cooperative operation. Firstly, building upon the traditional transition strategy of operation mode, the strategy of “Traction-Coasting-Traction-Cruising-Coasting-Braking” is proposed specifically for the tracking operation scenario. Secondly, the train dynamics model in spatial-domain, state transition equation, and energy consumption model are constructed. By employing interpolation method, the cooperative operation problem in time-domain is transformed into the problem of solving optimal switch points in spatial-domain. Subsequently, an optimization decision-making model with the goal of energy consumption and punctuality is constructed, which is then efficiently solved by using the Dung Beetle Optimizer. Finally, taking the Yizhuang Line of Beijing Subway as the simulation line, comparative analyses are conducted to evaluate the influence on optimization performance of Communication-Based Train Control (CBTC) and Train Autonomous Control System (TACS) architectures, as well as different transition strategies. The results demonstrate that TACS significantly enhances the optimization performance of cooperative operation, compared to CBTC. The proposed strategy not only meets punctuality requirement but also outperforms the traditional strategy in energy consumption at various departure intervals. The net absorbed energy consumption can be increased by 14.651 kWh at most, and the actual operational energy consumption can be decreased by 11.284 kWh at most. Therefore, the proposed operational mode transition strategy and optimization method effectively improve the energy consumption of train operation, and have certain reference significance for the development of urban rail train operation control technology. The code has been published in Github: https://github.com/eva-777/Tracking-Train-Operation-Optimization.git.