内燃牵引货物列车节能操纵模型与实时优化算法
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摘要
列车运行控制是一个典型的多目标、多约束、非线性的复杂时变过程。在我国铁路运输既有条件下,列车运行控制品质基本依赖于司乘人员的操纵技术水平。铁路运输虽具有单位能耗低的优点,但消耗能源总量很大,其中牵引耗能所占比重高达60%-70%,具有很大的节能潜力。因此,研究动态运行环境下的列车节能操纵实时优化问题,可以在安全、正点的前提下实现铁路运输节能减排,为今后研究列车自动驾驶奠定理论基础,具有重要的研究价值和现实意义。
在借鉴国内外已有研究成果的基础上,论文主要从区间运行和停车制动两个方面研究了列车节能操纵优化问题,提出了基于实际约束条件的列车节能操纵实时优化算法,并探讨了车载指导系统的实现技术和实证效果。论文主要内容包括以下几个方面：
1.从机车牵引力做功的角度分析了列车运行能耗的主要作用形态,即克服列车运行阻力做功和补偿制动导致的动能损失。通过理论分析验证了提高区间运行速度均衡性和避免不必要制动是列车节能运行的关键。根据仿真得出,合理的控制列车区间运行速度上下限,可在运行时分不变的情况下节约行车能耗6.8%；通过避免不必要制动和增加停站制动前的惰行距离,可在运行时分增加0.5%的情况下使列车运行能耗降低9%。
2.从模糊推理和预测控制相结合的角度,构建了面向区间运行的列车节能操纵模糊预测模型。在考虑运行安全、防止纵向冲动和机车操纵规范等实际约束的基础上,设计了基于当前运行速度、目标速度和加算坡道的模糊控制规则,构造了模糊规则后件在滚动时域内的行车能耗和运行延误评价函数,提出了列车节能操纵滚动在线优化算法。算例分析表明,模糊预测控制算法具有较好的灵活性,能适应不同的运行线路环境。在长大上坡道前,该算法通过提高机车手柄,使列车以较高的运行速度完成动能闯坡,保证运行速度的均衡性；在长大下坡道和低限速区段前,通过及时降低机车手柄,能有效避免调速制动减少动能损失。
3.分析了列车制动过程及操纵要求,指出调速制动的关键是合理地选择制动初始点和缓解点,即在同时满足最低缓解速度约束和避免二次调速制动的前提下尽可能减少列车动能损失。探讨了空气制动方式的控制变量及其约束条件,构建了停车制动双层模糊神经网络模型,第一层子网通过样本训练计算基于制动初速和制动距离的初始控制变量,第二层子网考虑加算坡道和牵引计算误差对制动距离的影响,对初始控制变量进行模糊修正,最后采用四点信息三段制动的方法通过追加减压控制提高停车精度。算例结果表明,双层模糊神经网络控制方法能在保证列车运行安全、平稳和满足操纵实际约束的前提下,实现货物列车的一次停车制动,可改进现有的二次制动进站停车控制方法,有利于降低行车能耗。
4.研究了列车节能操纵实时指导系统的设计方法和实现关键技术。在变步长计算的基础上,通过采样反馈减少牵引计算误差对指导系统计算效果的影响。采用串口通信的方式实现了列车动态运行信息的共享,分析了屏蔽通信数据噪音的方法,提出了列车运行公里标突变的辨识和换算方法,实现了多条运行线路基础数据的衔接与融合。
5.根据我国铁路运输特点,研制了可用于车载的内燃牵引货物列车节能操纵指导系统。现场测试结果表明,指导系统可在满足列车运行安全、正点和平稳等要求的前提下,通过合理运用动能闯坡、节能有利区段增加惰行比例、避免不必要制动等节能操纵策略,在线计算基于动态环境的列车运行前方优化操纵方案,为乘务员操纵提供实时指导。合肥机务段宁西线的测试结果表明,在节能操纵指导系统的实时指导下,列车运行能耗降低5.88%,初步实现了内燃牵引货物列车的节能操纵。
Reducing the energy consumption and emissions of rail vehicles is one of the main concerns of today's railway industry. However, train operation optimization is a typical time-varying problem with such characteristics as multi-objective, multi-constrictions, and non-linear. Under the existing railway condition of China, its performances largely depend on drivers'operational proficiency. Consequently, it is essential to explore the real-time energy-efficient operation algorithms under dynamic conditions sheds light on an energy-saving, safe, and punctual running of the train. This also sets a foundation for Auto Train Operation study.
On the basis of domestic and international achievements, this study probes into reducing energy consumption by means of improved train control. A real-time optimization algorithm for train operation is proposed, and an on-board driving aided system is presented. The following components are involved:
1. The forms of train energy consumption are explained by work analysis of locomotive traction, which includes work against running resistance and the kinetic energy loss caused by apply the brake. The core of energy-efficient operation is illustrated with theoretical analysis, such as ensuring running speed equilibrium and avoiding unnecessary brake. The simulation result also demonstrates that 6.8% energy consumption can be reduced by a reasonable control of upper and lower limit of running speed in the section; the 9% energy consumption decrease with a 0.5% time increase are obtained by reducing unnecessary brake and extending the coasting distance.
2. A fuzzy predictive model for energy-efficient running is developed with fuzzy inference and predictive control theory. The fuzzy rule set is designed considering running speed, target speed, and modified gradient. An on-line optimization algorithm is presented based on moving horizon strategy, energy consumption and schedule delays are selected as objectives, and the constraints include safety, longitudinal impulse, and operating regulations factors. The case study indicates that the proposed algorithm performs well under different railway conditions. Running on the long sharp slope, the applied high power traction is capable of a high-speed pass, which greatly guarantees speed equilibrium. In the area of heavy down slope and front of speed-limited section, the kinetic energy loss can be effectively reduced by replacing the handle to coasting position.
3. The key point of speed regulating braking is to determine the initial point and braking releasing point. The optimal strategy is to reduce kinetic energy loss, satisfying the constraints of lowest releasing speed and avoiding second brake regulating. With illustration of the air-braking crucial factors and its constraints, a new bi-level fuzzy neural network model of train stop braking is formulated. The initial control variable associated with initial speed and braking distance are first provided by sample training. Considering the influences from calculation error and gradients in the forward section, the correction values of the control variable are obtained by fuzzy inference. In which, the approach of "four position and three step braking" is presented improve the braking accuracy. According to the results of simulation case, the fuzzy network control method is proved to be more effective, especially that it ensures the safe, stable, accurate and energy-saving train brake with the real operational constraints.
4. The designing procedure and crucial techniques of the on-board system are addressed for energy-efficient train operation. Sampling feedback compensation is able to diminish the impact of calculation errors on the effectiveness of recommend proposal. Serial communication module is designed to share the train dynamic information, and the control approaches for shielding the noisy data and kilometer post conversion are presented, which makes a good basic-data fusion for railway lines data.
5. The on-board system for train energy-efficient operation with diesel locomotive is developed in accordance with the railway condition of China, and its effectiveness is illustrated by wide field tests. Referring to the strategies like reaching steep uphill slope with high speed, extending coasting distance in steep downhill section, and avoiding unnecessary brake, the system provides real-time optimized operating schemes for train drivers and insures an efficient, safe, punctual, and stable running. The energy consumption is cut by 5.88%, and the safe and relatively low-cost operation for freight train with diesel locomotive is realized.
在借鉴国内外已有研究成果的基础上,论文主要从区间运行和停车制动两个方面研究了列车节能操纵优化问题,提出了基于实际约束条件的列车节能操纵实时优化算法,并探讨了车载指导系统的实现技术和实证效果。论文主要内容包括以下几个方面：
1.从机车牵引力做功的角度分析了列车运行能耗的主要作用形态,即克服列车运行阻力做功和补偿制动导致的动能损失。通过理论分析验证了提高区间运行速度均衡性和避免不必要制动是列车节能运行的关键。根据仿真得出,合理的控制列车区间运行速度上下限,可在运行时分不变的情况下节约行车能耗6.8%；通过避免不必要制动和增加停站制动前的惰行距离,可在运行时分增加0.5%的情况下使列车运行能耗降低9%。
2.从模糊推理和预测控制相结合的角度,构建了面向区间运行的列车节能操纵模糊预测模型。在考虑运行安全、防止纵向冲动和机车操纵规范等实际约束的基础上,设计了基于当前运行速度、目标速度和加算坡道的模糊控制规则,构造了模糊规则后件在滚动时域内的行车能耗和运行延误评价函数,提出了列车节能操纵滚动在线优化算法。算例分析表明,模糊预测控制算法具有较好的灵活性,能适应不同的运行线路环境。在长大上坡道前,该算法通过提高机车手柄,使列车以较高的运行速度完成动能闯坡,保证运行速度的均衡性；在长大下坡道和低限速区段前,通过及时降低机车手柄,能有效避免调速制动减少动能损失。
3.分析了列车制动过程及操纵要求,指出调速制动的关键是合理地选择制动初始点和缓解点,即在同时满足最低缓解速度约束和避免二次调速制动的前提下尽可能减少列车动能损失。探讨了空气制动方式的控制变量及其约束条件,构建了停车制动双层模糊神经网络模型,第一层子网通过样本训练计算基于制动初速和制动距离的初始控制变量,第二层子网考虑加算坡道和牵引计算误差对制动距离的影响,对初始控制变量进行模糊修正,最后采用四点信息三段制动的方法通过追加减压控制提高停车精度。算例结果表明,双层模糊神经网络控制方法能在保证列车运行安全、平稳和满足操纵实际约束的前提下,实现货物列车的一次停车制动,可改进现有的二次制动进站停车控制方法,有利于降低行车能耗。
4.研究了列车节能操纵实时指导系统的设计方法和实现关键技术。在变步长计算的基础上,通过采样反馈减少牵引计算误差对指导系统计算效果的影响。采用串口通信的方式实现了列车动态运行信息的共享,分析了屏蔽通信数据噪音的方法,提出了列车运行公里标突变的辨识和换算方法,实现了多条运行线路基础数据的衔接与融合。
5.根据我国铁路运输特点,研制了可用于车载的内燃牵引货物列车节能操纵指导系统。现场测试结果表明,指导系统可在满足列车运行安全、正点和平稳等要求的前提下,通过合理运用动能闯坡、节能有利区段增加惰行比例、避免不必要制动等节能操纵策略,在线计算基于动态环境的列车运行前方优化操纵方案,为乘务员操纵提供实时指导。合肥机务段宁西线的测试结果表明,在节能操纵指导系统的实时指导下,列车运行能耗降低5.88%,初步实现了内燃牵引货物列车的节能操纵。
Reducing the energy consumption and emissions of rail vehicles is one of the main concerns of today's railway industry. However, train operation optimization is a typical time-varying problem with such characteristics as multi-objective, multi-constrictions, and non-linear. Under the existing railway condition of China, its performances largely depend on drivers'operational proficiency. Consequently, it is essential to explore the real-time energy-efficient operation algorithms under dynamic conditions sheds light on an energy-saving, safe, and punctual running of the train. This also sets a foundation for Auto Train Operation study.
On the basis of domestic and international achievements, this study probes into reducing energy consumption by means of improved train control. A real-time optimization algorithm for train operation is proposed, and an on-board driving aided system is presented. The following components are involved:
1. The forms of train energy consumption are explained by work analysis of locomotive traction, which includes work against running resistance and the kinetic energy loss caused by apply the brake. The core of energy-efficient operation is illustrated with theoretical analysis, such as ensuring running speed equilibrium and avoiding unnecessary brake. The simulation result also demonstrates that 6.8% energy consumption can be reduced by a reasonable control of upper and lower limit of running speed in the section; the 9% energy consumption decrease with a 0.5% time increase are obtained by reducing unnecessary brake and extending the coasting distance.
2. A fuzzy predictive model for energy-efficient running is developed with fuzzy inference and predictive control theory. The fuzzy rule set is designed considering running speed, target speed, and modified gradient. An on-line optimization algorithm is presented based on moving horizon strategy, energy consumption and schedule delays are selected as objectives, and the constraints include safety, longitudinal impulse, and operating regulations factors. The case study indicates that the proposed algorithm performs well under different railway conditions. Running on the long sharp slope, the applied high power traction is capable of a high-speed pass, which greatly guarantees speed equilibrium. In the area of heavy down slope and front of speed-limited section, the kinetic energy loss can be effectively reduced by replacing the handle to coasting position.
3. The key point of speed regulating braking is to determine the initial point and braking releasing point. The optimal strategy is to reduce kinetic energy loss, satisfying the constraints of lowest releasing speed and avoiding second brake regulating. With illustration of the air-braking crucial factors and its constraints, a new bi-level fuzzy neural network model of train stop braking is formulated. The initial control variable associated with initial speed and braking distance are first provided by sample training. Considering the influences from calculation error and gradients in the forward section, the correction values of the control variable are obtained by fuzzy inference. In which, the approach of "four position and three step braking" is presented improve the braking accuracy. According to the results of simulation case, the fuzzy network control method is proved to be more effective, especially that it ensures the safe, stable, accurate and energy-saving train brake with the real operational constraints.
4. The designing procedure and crucial techniques of the on-board system are addressed for energy-efficient train operation. Sampling feedback compensation is able to diminish the impact of calculation errors on the effectiveness of recommend proposal. Serial communication module is designed to share the train dynamic information, and the control approaches for shielding the noisy data and kilometer post conversion are presented, which makes a good basic-data fusion for railway lines data.
5. The on-board system for train energy-efficient operation with diesel locomotive is developed in accordance with the railway condition of China, and its effectiveness is illustrated by wide field tests. Referring to the strategies like reaching steep uphill slope with high speed, extending coasting distance in steep downhill section, and avoiding unnecessary brake, the system provides real-time optimized operating schemes for train drivers and insures an efficient, safe, punctual, and stable running. The energy consumption is cut by 5.88%, and the safe and relatively low-cost operation for freight train with diesel locomotive is realized.
引文
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