【摘要】：列車的運行是在一個復雜多變的環(huán)境下,由多種因素共同作用的結果。在我國列車的運行控制主要依靠機車駕駛員的經驗及操作技術水平。盡管鐵路運輸?shù)膯挝荒芎闹鹉杲档?但能耗總量依然巨大。因此,研究列車節(jié)能優(yōu)化操縱對于鐵路行業(yè)的節(jié)能減排具有重大意義。本文從列車的區(qū)間運行控制和停車制動控制兩方面研究了列車節(jié)能優(yōu)化操縱問題,在保證列車運行安全的前提下,建立了以能耗、正點、停車準確為目標的多目標列車節(jié)能優(yōu)化操縱模型,提出并改進了模型的優(yōu)化算法,進行了現(xiàn)場試驗。論文的主要研究內容包括以下幾個方面:1.以列車運動過程為基礎,研究了列車運行過程中所受力的情況,分析了列車運行能耗的主要形態(tài),通過理論分析和專家經驗指出保持列車區(qū)間運行速度的均衡性和減少不必要制動是降低列車運行能耗的關鍵。建立了以能耗、運行時間和停車準確度為目標的列車運行過程優(yōu)化模型。2.以遺傳算法為基礎研究列車優(yōu)化操縱問題,并對優(yōu)化算法進行改進。首先針對列車運行環(huán)境和操縱狀態(tài)的不同,提出了基于混合編碼的并行遺傳算法,同時,為了加快算法的收斂速度,將機車駕駛員經驗作為約束信息,融入到解的更新過程中,引導算法的尋優(yōu)過程向最優(yōu)解區(qū)域運動。將模擬退火算法用于求解列車優(yōu)化操縱模型,經驗證,計算結果能夠滿足列車運行控制要求。3.研究了列車節(jié)能操縱的相關方法,提出了將模擬退火算法結合遺傳算法求解多目標列車優(yōu)化操縱問題的方法,實際操縱列車與優(yōu)化模型仿真對比結果表明,該算法具有較好的靈活性,不僅能適應不同的線路狀況,同時,在滿足安全、準點、停車準確的前提下,也能有效降低列車運行能耗。4.分析研究了列車的制動過程及操作要求,指出列車制動的關鍵是合理選擇制動的初始點和緩解點,以及同時滿足線路約束和避免二次制動的前提下盡可能減小列車動能損失。探討了列車制動的控制變量及約束條件,建立了停車制動的模糊神經網絡模型。仿真結果表明,采用模糊神經網絡控制列車制動,能夠在保證安全、平穩(wěn)的前提下,實現(xiàn)列車的一次制動停車,有效避免了二次制動停車的控制方式,從而有利于降低列車運行能耗。本文從理論與實際應用兩方面研究了列車優(yōu)化操縱問題,建立了列車優(yōu)化操縱模型,經現(xiàn)場實際操縱與仿真計算,該模型能有效降低列車運行能耗,對鐵路行業(yè)的節(jié)能減排具有一定的理論意義和應用價值。
[Abstract]:The train operation is the result of the joint action of many factors in a complex and changeable environment. The train operation control in our country mainly depends on the locomotive driver's experience and operation technical level. Although the unit energy consumption of railway transportation is decreasing year by year, the total energy consumption is still huge. Therefore, it is of great significance to study the optimal operation of train energy saving for railway industry. In this paper, the optimal operation of train energy saving is studied from the two aspects of interval operation control and stop braking control. On the premise of ensuring the safety of train operation, the energy consumption and punctuality are established. This paper presents and improves the optimization algorithm of the multi-objective train energy saving operation model, and carries out the field test. The main contents of this paper include the following aspects: 1. Based on the train motion process, this paper studies the force acting on the train operation process, and analyzes the main forms of energy consumption in the train operation. Through theoretical analysis and expert experience, it is pointed out that the key to reduce the energy consumption of train operation is to maintain the equalization of train running speed and reduce unnecessary braking. The optimization model of train operation process is established, which aims at energy consumption, running time and stopping accuracy. Based on genetic algorithm (GA), train control problem is studied, and the optimization algorithm is improved. In order to speed up the convergence of the algorithm, the locomotive driver's experience is used as the constraint information to update the solution. The optimization process of the guidance algorithm moves towards the optimal solution region. The simulated annealing algorithm is used to solve the train control model. It is verified that the calculated results can meet the requirements of the train operation control. 3. In this paper, the related methods of train energy saving operation are studied, and the method of combining simulated annealing algorithm with genetic algorithm is proposed to solve the multi-objective train optimization problem. The simulation results show that the actual train operation is compared with the optimization model. The algorithm has good flexibility, not only can adapt to different line conditions, but also can effectively reduce the energy consumption of train operation. This paper analyzes the braking process and operation requirements of the train, and points out that the key of train braking is to reasonably select the initial braking point and the relief point, and to reduce the train kinetic energy loss as much as possible on the premise of satisfying the line constraints and avoiding the secondary braking. The control variables and constraint conditions of train braking are discussed, and the fuzzy neural network model of stopping braking is established. The simulation results show that using fuzzy neural network to control the train braking can realize the primary braking stop of the train on the premise of safety and stability, and effectively avoid the control mode of the secondary braking stop. Therefore, it is helpful to reduce the energy consumption of train operation. In this paper, the problem of train optimal operation is studied from both theoretical and practical aspects, and a train optimal operation model is established. After practical operation and simulation, the model can effectively reduce the energy consumption of train operation. It has certain theoretical significance and application value to the railway industry energy saving and emission reduction.
【學位授予單位】：北京交通大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：U268.6

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