Optimization method of dynamic trajectory for high-speed train group based on resilience adjustment
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摘要: 为提高列车控制过程的自主性和智能性，研究了列车群动态运行过程，采用多智能体和图论方法构建了列车群分布式信息交互模型；以节能和准点为优化目标，以安全和乘客舒适度为约束条件，建立了列车群运行轨迹多目标优化模型，利用基于模拟退火思想改进的差分进化算法获取了列车群静态最优运行轨迹；在此基础上，为避免或消解列车运行过程中随机干扰导致的延误传播问题，针对移动闭塞系统，基于弹复力构建了信息交互支撑的列车群动态间隔调整机制，设计了列车群在线协同优化算法，实现了列车群运行轨迹的动态调整，最后采用武广高速铁路实际数据进行了仿真验证。研究结果表明：提出的在线协同优化算法可以有效提升最优解搜索能力，避免Pareto最优解集的频繁更新，在不同干扰场景下算法触发频率平均降低36.7%；在试验设计的一般干扰场景中，优化后的动态调整策略在保证列车群安全平稳运行的同时，将受扰列车的延误度由6.2%降至0，与立即恢复延误策略相比，节能率达4.8%；在试验设计的较大干扰场景中，受扰列车的延误度由13.1%降至1.4%，全局时间偏差恢复为0，节能率达1.8%。可见，提出的方法能够解决运行轨迹静态规划方式无法完全适应外部动态环境变化的问题，有效保障干扰情况下列车运行复合紊态的及时恢复。Abstract: The dynamic operation process of high-speed train groups was investigated to enhance the autonomy and intelligence of train control, and a distributed information interaction model of high-speed train groups was constructed based on the multi-agent and graph theoretic approaches. A multiobjective optimization model was formulated to optimize the energy saving and punctuality of train groups and ensure the safety and passengers' comfort. The static optimal trajectories of train groups were determined through the differential evolution algorithm modified based on the simulated annealing. On this basis, a resilience-based dynamic interval adjustment mechanism for the train groups supported by the information exchange was specifically established for the moving block system to prevent or eliminate the train delay propagation caused by the stochastic disturbances during the operation. Moreover, an online cooperative optimization algorithm was developed to achieve the dynamic adjustment of the train group trajectories. Finally, simulations were performed based on the actual field data of the Wuhan-Guangzhou High-Speed Railway. Research results show that the proposed online cooperative optimization algorithm can effectively improve the optimal solution searching ability, and avoid excessively frequent updates of the Pareto optimal set. The average algorithm trigger times under different disturbance scenarios decreases by 36.7%. In typical disturbance scenarios, the optimized dynamic adjustment approach decreases the delay degree of the disturbed train from 6.2% to 0, and guarantees the safe and smooth operation of the train group. The optimized approach can save the energy consumption by up to 4.8% compared with the immediate delay recovery approach. Even with more significant disturbance scenarios, the delay degree of the disturbed train decreases from 13.1% to 1.4%, and the global time deviation decreases to 0 with an energy-saving rate of 1.8%. The proposed method can solve the problem that the static trajectory planning is unable to fully adapt to the change in the external dynamic environment, and effectively and timely restore the train operation despite complex disturbances. 7 tabs, 24 figs, 31 refs.
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图 1 CRH380AL型列车牵引/制动特性曲线
Figure 1. Traction/braking characteristic curves of Train CRH380AL
图 2 多智能体列车群分布式信息交互过程
Figure 2. Distributed information interaction process of multi-agent train group
图 5 基于弹复力的列车群动态间隔调整机制
Figure 5. Dynamic interval adjustment mechanism of train group based on resilience
图 6 基于弹复力的相邻列车追踪间隔原理
Figure 6. Principle of tracking interval between adjacent trains based on resilience
图 7 高速列车群运行轨迹优化过程
Figure 7. Optimization process of trajectory for high-speed train group
图 11 HDEA解集分布特性
Figure 11. Distribution characteristics of solution set obtained by HDEA
图 15 人为误操作下列车G1521的运行轨迹和弹复力曲线
Figure 15. Moving trajectories and resilience curves of Train G1521 under man-made mistakes
图 16 列车运行过程中基本阻力系数变化情况
Figure 16. Variations of basic resistant parameters during train operation
图 17 恶劣天气下列车G1521的运行轨迹和弹复力曲线
Figure 17. Trajectories and resilience curves of Train G1521 under scene of heavy weather
图 18 临时限速下列车群动态运行轨迹
Figure 18. Dynamic trajectories of train group under scene of temporary speed restriction
图 19 临时限速下列车G1521的运行轨迹和弹复力曲线
Figure 19. Trajectories and resilience curves of Train G1521 under scene of temporary speed restriction
图 20 不同方法下G1521在途运行能耗和延误曲线
Figure 20. Energy consumption and delay evolution curves of Train G1521 with different approaches
图 21 晚点发车下列车群动态运行轨迹
Figure 21. Dynamic trajectories of train group under scene of starting late
图 22 晚点发车下列车G1521和G1003的运行轨迹和弹复力曲线
Figure 22. Trajectories and resilience curves of Train G1521 and G1003 under scene of starting late
图 23 不同干扰场景下算法触发时刻分布
Figure 23. Trigger point distributions of algorithm under different perturbation scenes
图 24 不同干扰场景下列车群动态调整后乘客舒适度
Figure 24. Passengers' comfort after dynamic adjustment of train group under different perturbation scenes
表 1 CRH380AL列车基本参数
Table 1. Basic parameters of CRH380AL train
参数 数值 列车质量/t 890 回转质量系数 0.06 最大允许速度/(km·h-1) 310 启动牵引力/kN 520 牵引功率/kW 20 440 弹复力调节因子 0.02 可接受距离裕度/m 300 可接受时间裕度/s 5 
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表 2 列车群运行时刻表
Table 2. Operation timetable of train group
车站 S1 S2 S3 S4 S5 G1003 AT/s 1 980 5 100 DT/s 0 2 100 G1521 AT/s 1 800 5 460 DT/s 360 1 920 G1005 AT/s 2 460 4 380 6 540 DT/s 1 020 2 580 4 500 G1525 AT/s 7 440 DT/s 2 760
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表 3 列车群计划运行信息
Table 3. Scheduled operation information of train group
车次号 区间 区间里程/km 区间运行时间/s 全程运行时间/s G1003 S1~S3 127 1 980 4 980 S3~S5 234 3 000 G1521 S1~S2 85 1 440 4 980 S2~S5 277 3 540 G1005 S1~S2 85 1 440 5 280 S2~S4 130 1 800 S4~S5 147 2 040 G1525 S1~S5 362 4 680 4 680
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表 4 不同参数下的数据统计结果
Table 4. Statistical results of simulation with different parameters
算法参数 仿真结果 退火速度 种群规模 平均迭代次数 平均运算时间/s 0.6 30 20 1.44 0.6 50 17 2.20 0.6 100 14 3.27 0.6 200 12 6.01 0.4 30 18 1.22 0.4 50 15 1.87 0.4 100 13 3.11 0.4 200 11 5.45 0.2 30 20 1.36 0.2 50 16 1.90 0.2 100 12 2.89 0.2 200 11 5.36
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表 5 协同选择机制效率测试结果
Table 5. Efficiency test results of cooperative selection mechanism
仿真参数 仿真结果 列车A可行解个数 列车B可行解个数 协同运行方案/组 协同选择时间/s 20 20 400 1.36 30 30 900 2.92 50 50 2 500 7.84 100 100 10 000 32.04
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表 6 不同方法优化后的运行结果
Table 6. Operational results optimized by different algorithms
方法 指标 G1003 G1521 G1005 G1525 SOA 能耗/(kW·h) 13 880 13 863 13 754 13 787 时间偏差/s 0 0 0 0 DE 能耗/(kW·h) 13 036 13 006 12 897 12 938 时间偏差/s +1 0 -1 0 HEA 能耗/(kW·h) 12 979 12 954 12 850 12 915 时间偏差/s 0 0 0 0 HEA相对于SOA 节能率/% 6.49 6.56 6.57 6.32 HEA相对于DE 节能率/% 0.44 0.40 0.36 0.18
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表 7 各车次运行时间偏差
Table 7. Operation time deviations of each train
s 车次 S1 S2 S3 S4 S5 G1003 260 28 0 G1521 80 4 0 G1005 0 0 0 G1525 0
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