Heavy load group train interval control method capable of evaluating vehicle dynamics in real time

Abstract

The invention discloses a heavy load group train interval control method capable of evaluating vehicle dynamics in real time, and the method is based on a distributed interval control strategy of first optimization and second control, and a complex group train interval control problem is decomposed into an optimization stage and a control stage. A group train speed curve optimization model based on a particle swarm algorithm is established, front and back train speed curves meeting collision avoidance and the shortest tracking interval are independently optimized, and then a three-dimensional train dynamics model is designed to achieve high-precision tracking of the optimal speed curve. Finally, the dynamic performance of the train is evaluated while the interval control of the heavy-load group train is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of heavy-load group train interval control, and particularly relates to a heavy-load group train interval control method capable of real-time evaluation of vehicle dynamics. BACKGROUND

[0002] As a key infrastructure of the national economic lifeline, the heavy-load railway system has a profound impact on the development of the national economy. With the rapid development of China's economy, the transportation demand of coal, steel and other goods is increasingly high, which puts forward higher demand for the efficiency of China's heavy-load railway transportation. The heavy-load train volume improvement problem is imminent. The heavy-load train group operation technology controls the train interval between groups based on the relative braking distance, which can significantly shorten the train tracking interval, significantly improve the line occupancy rate, and effectively improve the railway transportation efficiency. The core of the group operation technology is the train interval control within the group. However, the current research on group train interval control is still very insufficient, and most of the existing interval control methods are designed for virtual grouping technology which is also based on relative braking distance for interval control. Its applicability in group operation technology still needs further research.

[0003] Among the existing group train interval control methods, there is an interval control method based on absolute braking distance (ABD). Most of the researches are carried out for moving block system, which controls the interval between the front and rear trains in the group through control algorithm, and calculates the speed curve that meets the safe collision avoidance and multiple index optimization of the train group under the ABD mode. Disadvantages: under the ABD interval control method, the tracking interval of the group train should always be no less than the absolute braking distance of the rear train, the tracking interval is large, and the operation efficiency is low.

[0004] Another one is the interval control method based on relative braking distance (RBD). The research is carried out for group operation technology, which controls the interval between the front and rear trains in the group through control algorithm, and optimizes the speed curve that meets the safe collision avoidance of the train under the RBD mode and the shortest tracking distance. Disadvantages: most of the researches focus on high-speed railway and urban rail transit fields. The model established in these researches cannot effectively guarantee the safe collision avoidance performance when facing longer grouping and more complex line conditions of heavy-load railway.

[0005] However, the above two group train interval control methods at least have the following technical problems: 1) The existing researches on train interval control mainly focus on high-speed railway and urban rail transit fields. The model established in these researches cannot effectively guarantee the safe collision avoidance performance when facing longer grouping and more complex line conditions of heavy-load railway.

[0006] 2) At present, the point mass model is used in the train modeling in the research on train interval control, and the train dynamics performance cannot be evaluated, so whether the research results can guarantee the safe operation of heavy haul trains still needs to be further verified.

[0007] 3) At present, the research on interval control for heavy haul train group operation technology is still very scarce, and more research focuses on the virtual coupling technology also using RBD interval control mode, and the applicability of the research results in the group operation technology still needs to be further explored and verified. SUMMARY

[0008] The purpose of the present application is to provide a heavy haul group train interval control method which can evaluate the vehicle dynamics in real time, which is based on the distributed interval control strategy of "optimization first and control later", and divides the complex group train interval control problem into two stages of optimization and control, establishes a group train speed curve optimization model based on a particle swarm algorithm, independently optimizes the speed curves of the front and rear vehicles to meet the collision avoidance and the shortest tracking interval, then designs a three-dimensional train dynamics model to realize high-precision tracking of the optimal speed curve, and finally realizes the interval control of the heavy haul group train while evaluating the train dynamics performance.

[0009] The present application is realized by the following technical solutions: A heavy haul group train interval control method which can evaluate the vehicle dynamics in real time, comprising the following steps: S1: establishing a group train inter-vehicle safety protection distance function; S2: establishing a group train speed curve optimization model, optimizing the speed curves of the front and rear vehicles and obtaining the optimal speed curves of the front and rear vehicles respectively; S3: establishing a group train speed curve tracking model, controlling the front and rear vehicles through the optimal speed curves of the front and rear vehicles; S4: evaluating the train dynamics results.

[0010] Preferably, in step S1, the group train inter-vehicle safety protection distance function is: Tracking interval of the rear vehicle during the operation of the heavy haul group train S real Group train inter-vehicle safety protection distance under the RBD mode ; In the formula, d break1 is the emergency braking distance of the front vehicle at the current time, d break2 is the emergency braking distance of the rear vehicle at the current time, is a safety margin.

[0011] Preferably, in step S2, the particle swarm algorithm is used to optimize the speed curve of the front vehicle and the rear vehicle respectively.

[0012] Preferably, in step S2, the optimization target of the front vehicle is the shortest running time, and the optimization target of the rear vehicle is the shortest tracking interval of the group train.

[0013] Preferably, in step S2, the optimization target function of the front vehicle and the rear vehicle is established, and the constraint condition of the front vehicle and the rear vehicle is established.

[0014] Preferably, in step S2, the optimization target function of the front vehicle is: Wherein: is the calculation step length, m is the number of line sections, is the train speed.

[0015] Preferably, in step S2, the optimization target function of the rear vehicle is: Wherein is the safety protection distance between the group trains in the RBD mode, is the tracking interval of the group trains.

[0016] Preferably, the constraint conditions of the front vehicle and the rear vehicle include: train running speed limit constraint, train traction and braking constraint, train running actual motion acceleration constraint, group train tracking interval constraint and working condition switching and working condition keeping constraint.

[0017] Preferably, in the working condition switching and working condition keeping constraint, the traction working condition and the braking working condition are switched through the inertial working condition transition.

[0018] Preferably, in step S3, the group train speed curve tracking model includes a train three-dimensional dynamics model built by Simpack and a fuzzy PID speed controller built by Simulink, and the train three-dimensional dynamics model and the fuzzy PID speed controller are connected through a SIMAT interface.

[0019] Compared with the prior art, the present application has the following advantages and beneficial effects: 1) In the present application, the method is based on the distributed interval control strategy of "optimization first and control later", which decomposes the complex group train interval control problem into two stages of optimization and control, establishes a group train speed curve optimization model based on particle swarm algorithm, independently optimizes the speed curves of the front and rear trains that meet the collision avoidance and shortest tracking interval, then designs a three-dimensional train dynamics model to realize high-precision tracking of the optimal speed curve, and finally realizes the interval control of heavy group trains, the optimal speed curve can adaptively calculate the departure time interval of the group train, which can ensure the safe collision avoidance of the heavy group train while maintaining a short tracking interval, compared with the moving block technology using ABD interval control mode, the train tracking interval can be greatly shortened, 2) Compared with the existing inventions for virtual coupling technology and group operation technology, the present application can comprehensively evaluate the dynamic performance of the group train under the optimal speed curve obtained by optimization while shortening the train tracking interval, ensuring train safety. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings in the embodiments will be briefly introduced as follows, and it should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1 The figure is a distributed interval control logic diagram for heavy group trains in the present application.

[0022] Figure 2 The figure is a HXN3 locomotive model in the present application.

[0023] Figure 3 The figure is a C80 freight car model in the present application.

[0024] Figure 4 The figure is a front train model in the present application.

[0025] Figure 5 The figure is a HXD1 locomotive model in the present application.

[0026] Figure 6 The figure is a rear train model in the present application.

[0027] Figure 7 The figure is a group train speed curve tracking model in the present application.

[0028] Figure 8 The figure is a group train optimal speed curve in the present application.

[0029] Figure 9 The figure is a group train tracking interval under the group operation RBD mode in the present application.

[0030] Figure 10 This refers to the train tracking interval in the moving block ABD mode of this invention.

[0031] Figure 11 This is the derailment coefficient of the group of trains under the optimal speed curve in this invention.

[0032] Figure 12 This refers to the wheel load reduction rate of the train group under the optimal speed curve in this invention.

[0033] Figure 13 This refers to the lateral force of the train wheel axles under the optimal speed curve in this invention.

[0034] Figure 14 This refers to the coupler force of the train group under the optimal speed curve in this invention.

[0035] Figure 15 This refers to the vertical acceleration of the train body under the optimal speed curve in this invention.

[0036] Figure 16 This refers to the lateral acceleration of the train body under the optimal speed curve in this invention.

[0037] Figure 17 This is the process for optimizing train speed curves based on particle swarm optimization in this invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0039] Example 1: A method for controlling the intervals of heavy-load train groups that can evaluate vehicle dynamics in real time, such as... Figure 1 As shown, it includes the following steps: S1: Establish the safety protection distance function between train groups; S2: Establish a group train speed curve optimization model, optimize the speed curves of the preceding and following trains, and obtain the optimal speed curves of the preceding and following trains respectively; S3: Establish a group train speed curve tracking model and control the leading and trailing trains through the optimal speed curves of the leading and trailing trains; S4: Conduct train dynamics result evaluation.

[0040] First, establish a safety protection strategy for group trains: throughout the entire operation, the following train tracking interval... S realThe safety protection distance between the group trains under the RBD mode should be not less than .

[0041] In the formula, d break1 is the emergency braking distance of the front train at the current time, d break2 is the emergency braking distance of the rear train at the current time, is the safety margin.

[0042] It is an important prerequisite to construct a reasonable group train safety protection strategy to ensure the group train safety collision avoidance. The group train safety protection distance function constructed above is added into the rear train target function in the form of a penalty function to realize the group train safety collision avoidance. As shown in Figure 2 , then a group train speed curve optimization model is established, and the optimal speed curve of the front train and the rear train is obtained through the group train speed curve optimization model combined with the particle swarm algorithm.

[0043] The RBD control method is a spacing control method based on the relative braking distance, and is researched for the group operation technology. The spacing between the front train and the rear train in the group is regulated through the control algorithm to optimize the speed curve that meets the safety collision avoidance of the trains under the RBD mode and has the shortest tracking distance.

[0044] As shown in Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 , a group train speed curve tracking model is established: a three-dimensional dynamic model of the train is built by using Simpack; a fuzzy PID speed controller is built by using Simulink, and the two are connected through the SIMAT interface to establish a group train speed curve tracking model based on the fuzzy PID; during the model establishment process, other speed controllers except the fuzzy PID can also be used to track the group train speed curve, and in addition, multi-body dynamic software except Simpack, such as UM, can also be used to build a three-dimensional train model and combine the built fuzzy PID controller to build a group train speed curve tracking model.

[0045] As shown in Figure 7As shown, the optimal speed curve is tracked by using the speed curve tracking model: the calculation process of the group train speed curve tracking model is as follows: first, the SIMPACK established train three-dimensional dynamic model calculates the running speed of the train in real time, and feeds back the speed information to the fuzzy PID controller through the SIMAT interface, the fuzzy PID controller takes the optimal speed curve calculated by the speed curve optimization model as the expected speed, and takes the deviation between the expected speed and the actual speed as the input, and calculates the output control force. Subsequently, the calculated control force is compared with the maximum traction force Ft_Max and the maximum electric braking force Fb_Max provided by the train through the control force limiter, and is converted into a traction force or an electric braking force that can meet the locomotive traction and braking capacity. The significance of comparing the control force with the maximum traction force Ft_Max and the maximum electric braking force Fb_Max is that if the control force is greater than the maximum traction force Ft_Max, the maximum traction force is used to control the train, and if the control force is less than the maximum traction force Ft_Max, the control force is used to control the train. Similarly, if the control force is greater than the maximum electric braking force Fb_Max, the maximum electric braking force Fb_Max is used to control the train, and if the control force is less than the maximum electric braking force Fb_Max, the control force is used to control the train. The final limited control force is input into the train dynamics model through the SIMAT interface, and is applied to the locomotive as a traction force or an electric braking force, thereby realizing the speed control of the train.

[0046] In the process of tracking the optimal speed curve of the group train that meets the requirements of group train collision avoidance and shortest tracking interval, the train three-dimensional dynamic model can output the train dynamics calculation results in real time, based on which the group train interval control is realized while the train dynamics performance is comprehensively evaluated; according to the national standard "Evaluation and Test Identification Specification for Locomotive and Rolling Stock Dynamics Performance" (GB / T5599-2019), the dynamics performance of the group train under the optimal speed curve is evaluated, and all of them do not exceed the national standard limit, which verifies that the optimal speed curve optimized by the method proposed in this paper can significantly shorten the tracking interval of the group train while ensuring safe collision avoidance and safe operation.

[0047] The feasibility verification is completed on the actual line data of the Baohe line from Hanjiacun to Aobaogou three stations and two sections, the measurement section mileage used in the verification work is 21.8km, the model used is a front car composed of 1HXN3 locomotive and 50C80 freight cars, a rear car composed of 1HXD1 locomotive and 50C80 freight cars, and the front car and the rear car together form a group train.

[0048] As shown in Figure 8 The optimal speed curve of the group train optimized under the above line conditions is shown in Figure 9The RBD interval control is used for group train tracking interval. The results show that the group train tracking interval is always not less than the safety protection distance under the RBD interval control mode during the whole running process, which meets the safety requirements of group train collision avoidance. In addition, the group train runs according to the optimal speed curve obtained under the optimal speed curve. The average tracking interval of the group train is 642 m, which is close to the test results of the group train operation test of China Shenhua on Baoshen line. In the test of China Shenhua, two 5000-ton unit trains composed of SS4B locomotives realize a dynamic tracking interval of 769 m. Figure 10 The ABD interval control is used for train tracking interval. The verification results show that compared with the ABD interval control mode used in the existing moving block system, the average tracking interval of the group train under the optimal speed curve obtained in the RBD mode is reduced by 38.6%.

[0049] The dynamic calculation results of the group train running according to the optimal speed curve obtained are evaluated according to the national standard "Evaluation and Test Identification Specification for Dynamic Performance of Locomotive and Rolling Stock" (GB / T5599-2019). As shown in Figure 11 , the derailment coefficients of the front locomotive and the wagon are 0.35 and 0.40, respectively, and the derailment coefficients of the rear locomotive and the wagon are 0.38 and 0.40, respectively, which do not exceed the limit value. As shown in Figure 12 , the wheel load reduction rates of the front locomotive and the wagon are 0.27 and 0.37, respectively, and the wheel load reduction rates of the rear locomotive and the wagon are 0.39 and 0.39, respectively, which do not exceed the limit value. As shown in Figure 13 , the wheel axle lateral forces of the front locomotive and the wagon are 29.0 kN and 47.2 kN, respectively, and the wheel axle lateral forces of the rear locomotive and the wagon are 41.4 kN and 47.5 kN, respectively, which do not exceed the limit value. As shown in Figure 14 , the maximum hook forces of the front and rear trains are 608.05 kN and 716.40 kN, respectively, and the maximum hook forces are 321.73 kN and 493.39 kN, respectively, which do not exceed the safety limit value. As shown in Figure 15 , the vertical acceleration peaks of the front locomotive and the wagon are 0.38 m / s 2 , 1.33 m / s 2 , and the vertical acceleration peaks of the rear locomotive and the wagon are 0.38 m / s 2 , 1.35 m / s 2 , which do not exceed the limit value. As shown in Figure 16 , the lateral acceleration peaks of the front locomotive and the wagon are 0.76 m / s 2 , 1.28 m / s 2, the peak value of the lateral acceleration of the car body of the rear locomotive and the car body of the truck is 0.46 m / s 2 , 1.41 m / s 2 , respectively, which do not exceed the limit value. Therefore, the key indexes such as the derailment coefficient of the train, the wheel load reduction rate, the lateral force of the wheel axle, the acceleration of the car body, and the coupling force do not exceed the safety limit value, and the driving safety of the heavy-haul group train can be ensured.

[0050] Embodiment 2: As shown in Figure 1 and Figure 17 , the embodiment further limits the optimization mode of the train speed curve optimization on the basis of the above-mentioned embodiment, and establishes a group train speed curve optimization model: the optimization target of the front train is set as the shortest running time, and the optimization target of the rear train is set as the shortest tracking interval of the group train; meanwhile, the safety protection constraints in the above-mentioned safety protection strategy are added to the optimization target function of the rear train in the form of a penalty function, so that the speed curve obtained by optimization meets the requirements of group train collision avoidance and the shortest tracking interval; the particle swarm optimization algorithm needs a target function as a driving force and a constraint condition as a limit, and finally converges to obtain an optimal solution. The whole line with a length of L is divided into m sections by a fixed step length using a discrete method, and the optimization target function of the front train is: and the optimization target function of the rear train is: , wherein: is the safety protection distance between the group trains in the RBD mode, is the tracking interval of the group trains.

[0051] Based on the line speed limit, the locomotive gear switching principle and the locomotive traction and braking capacity, the following constraint conditions are set for the front and rear trains.

[0052] 1) Train running speed limit constraint; in order to ensure driving safety, the train running speed cannot exceed the line speed limit at any time, so the running speed of the group train should meet the following constraint: 2) Traction and braking constraint; the actual output traction force and braking force of the train in operation cannot be greater than the maximum traction force and the maximum braking force that can be provided by the train. In addition, the traction force Ft and the braking force Fb cannot be output at the same time, so Ft and Fb meet the following constraint: The actual motion acceleration of the train in operation a i meets the following constraint: , wherein, , respectively are the minimum and maximum values of train acceleration.

[0053] 3) Group train tracking interval constraint; group train tracking interval constraint in RBD mode S real will be limited by the safety protection distance Firstly, S real must be no less than at any time, otherwise the following vehicle may be rear-ended due to insufficient braking when the preceding vehicle brakes urgently, and therefore S real The following hard constraints must be met: S real The calculation method is: 4) Working condition switching and working condition maintaining constraint; the train must meet the working condition switching constraint during actual operation, i.e., the traction working condition and the braking working condition cannot be directly converted, and a section of inert working condition must be inserted for transition. The train operation working condition conversion principle is shown in Table 1.

[0054] Table 1: Train operation working condition conversion principle In Table 1: represents that conversion is allowed; represents that conversion is prohibited; and represents that conversion is not required.

[0055] Let the working condition sequence function under the traction, inert, and braking working conditions be respectively take values 1, 0, and -1, and therefore the working condition switching constraint is represented by the following formula: In addition, a certain time must be maintained after the completion of the above-mentioned three working condition switching to avoid frequent switching of train gears. Generally, the inert distance is generally taken to be no less than 500 m, and in this embodiment, the shortest maintaining distances of the three working conditions are all set to 500 m.

[0056] Developing group train speed curve optimization: based on the above-mentioned optimization objective function and constraint conditions, a particle swarm algorithm is adopted to develop speed curve optimization for the preceding and following trains, and other suitable optimization algorithms can also be adopted for group train speed curve optimization in addition to the particle swarm algorithm. The train operation working condition sequence defined as the position vector of a single particle is determined through iteration to determine the optimal control strategy of the preceding and following trains, and on this basis, the train dynamics model and line data are combined to calculate the optimal speed curve In addition, the rear vehicle can adaptively adjust the group train departure time interval in combination with the line condition, the front vehicle speed and the self traction and braking capacity when optimizing the optimal speed curve, so as to meet the group train collision avoidance and shortest tracking interval requirements. Other parts of the embodiment are the same as the above-described embodiment, and will not be described here again.

[0057] The above is only the preferred embodiment of the present application, and does not limit the present application in any form. Any simple modification or equivalent change made on the basis of the technical essence of the present application to the above embodiment falls within the protection scope of the present application.

Claims

1. A heavy-haul consist spacing control method that can evaluate vehicle dynamics in real time, characterized by, The method comprises the following steps: S1: establishing a safety protection distance function between group trains; S2: establishing a group train speed curve optimization model, optimizing the speed curve of the front and rear trains, and obtaining the optimal speed curve of the front and rear trains respectively; S3: establishing a group train speed curve tracking model, and controlling the front and rear trains through the optimal speed curves of the front and rear trains; S4: evaluating the train dynamics results.

2. The method of claim 1, wherein the real-time evaluation of the vehicle dynamics of the heavy-haul consist is performed by a train management system. In step S1, the safety protection distance function between group trains is: Tracking interval of rear car in heavy-load group train operation process S real Safety protection distance between group trains in RBD mode ; In the formula, d break1 is the emergency braking distance of the preceding vehicle at the current time, d break2 is the emergency braking distance of the following vehicle at the current time, is the safety margin.

3. The method of claim 1, wherein the real-time evaluation of the vehicle dynamics of the heavy-haul consist is performed by a train management system. In step S2, the particle swarm algorithm is used to optimize the speed curve of the front and rear trains respectively.

4. The method of claim 1, wherein the real-time evaluation of the vehicle dynamics of the heavy-haul consist is performed by a train management system. In step S2, the optimization target of the front train is the shortest running time; and the optimization target of the rear train is the shortest tracking interval of the group trains.

5. The method for real-time evaluation of heavy-haul consist control of a train according to claim 1, wherein, In step S2, the optimization target functions of the front and rear trains are established, and the constraint conditions of the front and rear trains are established.

6. The method of claim 5, wherein the real-time evaluation of the vehicle dynamics of the heavy-haul consist is performed by a train management system. In step S2, the optimization target function of the front train is: wherein: is the step length, m is the number of line sections, is the train speed.

7. The method for controlling the interval of heavy-haul consist trains, which can evaluate the dynamics of the vehicle in real time, according to claim 5, wherein In step S2, the optimization target function of the rear train is: wherein: is the safety protection distance between group trains in RBD mode, is the group train tracking interval.

8. The method for real-time evaluation of heavy-haul consist separation control of a train according to claim 5, wherein, The constraint conditions of the front and rear trains include: train running speed limit constraint, train traction and braking constraint, train actual motion acceleration constraint, group train tracking interval constraint, and working condition switching and working condition maintaining constraint.

9. The method of claim 8, wherein the real-time evaluation of the vehicle dynamics of the heavy-haul consist is performed by a train management system (TMS) of the heavy-haul consist. In the working condition switching and working condition maintaining constraint, the traction working condition and the braking working condition are switched through the inertial working condition transition.

10. The method for real-time evaluation of heavy-haul consist control of a train according to claim 1, wherein, In step S3, the group train speed curve tracking model comprises a train three-dimensional dynamics model built by Simpack and a fuzzy PID speed controller built by Simulink, and the train three-dimensional dynamics model and the fuzzy PID speed controller are connected through a SIMAT interface.

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