Research on Multi-Objective Optimization of Urban Rail Train Tracking Operation Considering Collaboration

doi:10.19678/j.issn.1000-3428.0070654

Abstract

Abstract:

With the increasing energy consumption of urban rail transit systems, enhancing the utilization of regenerative braking energy to reduce the energy consumption of train operations has become a critical issue. This study focuses on the optimization problem of tracking train operation control strategies in the multi-train cooperative operation process. First, building upon the traditional transition strategy based on operation mode, the study proposes the ″Traction—Coasting—Traction—Cruising—Coasting—Braking (TCTCCB)″ strategy for the tracking operation scenario. Second, the study constructs a train dynamics model in the spatial domain, a state transition equation, and an energy consumption model. By employing the interpolation method, the cooperative operation problem in the time domain is transformed into a problem of solving optimal switch points in the spatial domain. Subsequently, an optimization decision-making model with the goals of energy consumption and punctuality is constructed, which is then efficiently solved using the Dung-Beetle Optimizer (DBO). Finally, considering the Yizhuang Line of the Beijing Subway as a simulation line, comparative analyses are conducted to evaluate the influence of Communication-Based Train Control (CBTC) and Train Autonomous Control System (TACS) architectures, as well as different transition strategies, on optimization performance. The results demonstrate that TACS significantly enhances the optimization performance of cooperative operations compared with CBTC. The proposed strategy not only meets the punctuality requirement but also outperforms the traditional strategy in terms of energy consumption at various departure intervals. The net absorbed energy consumption increases by a maximum of 14.651 kWh, and the actual operational energy consumption decreases by a maximum of 11.284 kWh. Therefore, the proposed operational mode transition strategy and optimization method effectively improve the energy consumption of train operations and have reference significance for the development of urban rail train operation control technologies.

Key words: urban rail transit, Train Autonomous Control System (TACS), tracking operation, collaboration, regenerative braking

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。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(TCTCCB)"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(DBO)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。实验结果表明, 相较于CBTC架构, TACS架构显著提升列车协同运行优化效果, 所提策略在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。

关键词: 城市轨道交通, 列车自主运行系统, 追踪运行, 协同, 再生制动

ZHANG Lei, LI Shihua, GAO Hao, WANG Xiaoyong. Research on Multi-Objective Optimization of Urban Rail Train Tracking Operation Considering Collaboration[J]. Computer Engineering, 2026, 52(7): 377-389.

张雷, 李世华, 高豪, 汪小勇. 考虑协同的城轨列车追踪运行多目标优化研究[J]. 计算机工程, 2026, 52(7): 377-389.

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Figures/Tables 15

Fig.1 Process of utilizing regenerative braking energy for trains under TACS architecture

Fig.2 Schematic diagram of multi-train cooperative operation under different working condition sequences

Fig.3 Solution flow of the optimization problem on tracking train operation considering cooperation

Fig.4 Traction and braking characteristics of trains, as well as basic resistance characteristics

Fig.5 Optimal operation control strategy and traction energy consumption of preceding train

Fig.6 Tracking train operation trajectories under the CBTC architecture

Fig.7 Net absorbed energy consumption of tracking trains under different departure interval under the CBTC architecture

Fig.8 Optimal operation control strategy for tracking train under TCCB working condition strategies

Fig.9 Optimal operation control strategy for tracking train under TCTCCB working condition strategies

Fig.10 Comparison of TCCB and TCTCCB in operation energy consumption optimization

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