CN112507464A - Freight train operation curve optimization method based on improved multi-target wolf algorithm - Google Patents
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Abstract
The invention discloses a freight train operation curve optimization method based on an improved multi-target wolf algorithm. Aiming at the parameter change when the train passes through a ramp or a curve in the running process, a speed self-adaptive homogeneous rod model and a multi-mass-point model of the freight train are established. The coupler force of the freight train is often large, and the phenomenon of hook breakage is easy to occur, so that the modeling of the train buffer is considered. And comprehensively considering the force, the running time and the energy consumption of the safe coupler of the train as multi-objective optimization by combining the constraint conditions of the line, and taking the conditions of driving comfort, parking accuracy and speed limit as constraints. And (3) according to the train operation indexes and the limiting conditions of the operation road conditions, researching the operation optimal working condition sequence by using an improved multi-target gray wolf algorithm. For a complex nonlinear train operation process, the train operation curve can be optimized through the method, and the safe, energy-saving and punctual operation of the train is realized.
Description
Technical Field
The invention relates to a method for model switching and operation curve optimization in a freight train operation process, and belongs to the technical field of automatic train driving.
Background
Railway transportation is an important link in the field of modern transportation, and the development of freight trains is well-developed, so that the prospect is wide. The freight train has the advantages of large transportation volume, high speed, low energy consumption, low cost, high safety, strong punctuality and the like, and shows great advantages in transporting goods. However, the energy consumption required by freight trains does not vary considerably. In order to realize energy conservation and emission reduction of a railway system, research needs to be carried out on the problems of energy conservation and consumption reduction of freight trains. Meanwhile, as the transportation volume of the freight train is continuously increased, the transportation safety problem of the train begins to be highlighted, such as the problems of hook breakage and the like, and the development of railway transportation is hindered.
In the running process of the train, in addition to the problem of energy consumption, the problems of safety, punctuality, accurate parking, comfort and the like need to be considered, and the method is a multi-objective optimization problem. The traditional single-substance-point model ignores the interaction force between the vehicles, does not consider the coupling relation between the vehicles, and cannot accurately describe the actual running process of the train, but the train coupling force is crucial to the safe running of the train. In practice, the train consist is a multi-point system that is not rigidly connected by the coupler draft gear, and forces act between the vehicles. The train dynamic model is modeled, the train running curve is optimized, and energy conservation, safety, punctuality, accurate parking and the like of the train can be guaranteed.
At present, the train model in the railway operation of China still mostly adopts a single-quality-point model, and as the actual operation process of the train cannot be accurately described, China gradually deepens into the research of the multi-quality-point model. For the optimization of the train speed curve, only the single-target optimization of energy saving is usually considered, and the others are used as constraint conditions. The difference between the present invention and the conventional method is that: and switching a homogeneous rod model and a multi-mass point model according to the self-adaption of the train speed, establishing a coupler buffer device model, and taking the coupler force as one target in multi-target optimization. The safe, energy-saving and punctual running of the train is considered, and the optimal working condition sequence is researched by using the improved multi-target wolf algorithm. The scheme is simple and practical, the operation curve of the freight train is optimized, and the safe, energy-saving and punctual operation of the train is realized.
Disclosure of Invention
The invention aims to provide a freight train operation curve optimization method based on an improved multi-target gray wolf algorithm aiming at the defects of the prior art, which is used for optimizing a train speed curve and realizing safe, energy-saving and punctual operation of a train.
The invention can be realized by the following technical scheme:
a freight train operation curve optimization method based on an improved multi-target wolf algorithm is used for establishing a speed self-adaptive homogeneous rod model and a multi-mass point model of a freight train. According to the change of the running state, a speed conversion formula is utilized to ensure the smoothness of the whole switching process. Modeling a coupler buffer of a train and constructing a multi-objective optimization model. The train optimization operation model is established by using the improved multi-target gray wolf algorithm, so that the distribution and convergence of the algorithm are ensured, and a train operation optimization curve is obtained. The invention is realized by the following steps:
the method comprises the following steps: analyzing a dynamic model of the freight train in the running process, and respectively establishing a homogeneous rod model and a multi-mass-point model of the train, wherein the running state of the train consists of traction, uniform speed, inertia and braking.
When the train runs in a constant speed state, the whole train can be regarded as a whole, and a 'homogeneous rod' model is adopted for the train. The train dynamics equation is:
when the train runs at a non-uniform speed, relative displacement and speed difference exist among the trains (if the front train runs and the rear train does not move when the train is started, stress is different and acceleration is different in the running process). Therefore, a multi-point model of the train needs to be considered. The kinetic equation for the ith vehicle is:
in the formula: m isiIs the mass of the vehicle;
is the acceleration of the vehicle; fTFor tractive effort, FBIs braking force; fCLIs the front car coupler force; fCRIs the rear coupler force; fWRunning a basic resistance for the vehicle; fWcIs curve resistance; fWrIs the ramp resistance.
When the train reaches a certain speed after being started, the train usually runs in a cruising mode in order to realize energy conservation. And according to a speed conversion formula, smoothly switching the model of the train.
Step two: and establishing a coupler buffer device model.
The coupler force may be described by the following formula:
in the formula: fcThe coupler force in the running process of two vehicles is represented, and the coupler force can be simply calculated in real time; c represents a coupler stiffness coefficient; k represents a damping coefficient; x is the number of1，x2，
Respectively the displacement and speed of the front and rear vehicles.
Step three: and constructing a multi-objective optimization model, and defining a target fitness function according to the train operation optimization model.
(1) Train safety evaluation model:
in the formula, FciThe coupler force of the train at each moment.
(2) Energy-saving evaluation model of train:
in the formula: mu is energy consumption utilization rate; rho is the utilization rate of the regenerative braking energy; omega is the power of the train auxiliary equipment; and delta t is train running time.
(3) Train punctual evaluation model:
in the formula:
the actual running time; and T is the scheduled operation time.
Therefore, the multi-objective optimization model of the train is as follows:
f＝min(f1，f2，f3) (8)
step four: and solving the optimization model by adopting an improved multi-target wolf algorithm to obtain a plurality of groups of non-inferior solutions of the safety, the traction energy consumption and the operation time of the train in a single region.
And 4.1, initializing a wolf pack population, namely selecting a wolf pack meeting the fitness and the constraint condition according to the constraint condition of the actual train running road condition.
And 4.2, calculating objective function values of all individuals, and storing the non-inferior solution into the Archive population according to the domination relation.
And 4.3, determining the congestion value of the Archive individuals, determining and selecting alpha, beta and sigma according to the mode of a roulette plate, updating the positions of all the individuals in the wolf group according to the three solutions, and calculating an objective function value.
And 4.4, determining the gray wolf patrol wolf and randomly searching the field of the gray wolf patrol wolf. Non-dominant solution sets non-bases of the population after the update are calculated.
And 4.5, combining non-hosts and Archive, calculating non-dominant solution sets of the non-hosts and Archive, judging whether the non-dominant solution sets exceed the size of the specified Archive, and if so, eliminating overcrowded individuals according to the crowding distance. And judging whether the maximum iteration number is reached, and outputting Archive if the maximum iteration number is reached. Otherwise, go to step 4.2.
In step 4.3, compared with the traditional multi-target gray wolf algorithm, the Archive population has the current optimal solution, and the heading wolf is selected from the Archive population by adopting a roulette mode, wherein alpha, beta and sigma wolf are collectively called the heading wolf. Therefore, the selection mechanism of the leading wolf is optimized, and the exploration capability of the algorithm is enhanced.
In step 4.4, compared with the traditional multi-target gray wolf algorithm, a gray wolf patrol mechanism is introduced, and in each iteration, the worst individual is selected as the patrol wolf to randomly search the field of the patrol wolf, so that other potential non-dominant solutions are avoided being ignored.
j＝rand(1，2，...，n) (10)
In the formula: a and B are respectively the upper limit and the lower limit of the search, and n is the number of the wolf individuals. After the search is finished, the dominant relationship of the two individuals is compared, and the non-dominant solution is stored.
And finally obtaining a group of pareto fronts of the train optimization working condition sequence, thereby obtaining a group of train S-V train optimal operation curves and related data. And the optimal optimization curve is selected according to the actual situation, so that the scheme is more scientific and reasonable.
Compared with the prior art, the invention has the following advantages: the invention designs a freight train operation curve optimization method based on an improved multi-target wolf algorithm. And analyzing a dynamic model in the running process of the freight train, respectively establishing a homogeneous rod model and a multi-mass-point model of the train, and ensuring the smoothness of the whole switching process by utilizing a speed transformation formula according to the running state change. And establishing a coupler buffer device model, and taking the coupler force as one target in multi-target optimization. And (3) according to the limiting conditions of the train running line condition and the running road condition, researching the running optimal working condition sequence by using an improved multi-target gray wolf algorithm. The technical scheme is simple and practical, optimizes the operation curve of the freight train, and realizes safe, energy-saving and punctual operation of the train.
Drawings
In order to make the reader more clearly understand the embodiments of this patent, the following brief description of the drawings in the detailed description of this patent is provided:
fig. 1 is a structural diagram of a freight train operation curve optimization method based on an improved multi-objective gray wolf algorithm.
FIG. 2 is a flow chart of a freight train operating curve optimization method based on an improved multi-objective gray wolf algorithm.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
As shown in FIG. 1, the invention provides a freight train operation curve optimization method based on an improved multi-objective Grey wolf algorithm, and a speed-adaptive homogeneous rod model and a multi-mass point model of a freight train are established. According to the change of the running state, a speed conversion formula is utilized to ensure the smoothness of the whole switching process. Modeling a coupler buffer of a train and constructing a multi-objective optimization model. The train optimization operation model is established by using the improved multi-target gray wolf algorithm, so that the distribution and convergence of the algorithm are ensured, and a train operation optimization curve is obtained.
As shown in fig. 2, the invention can be verified by simulation, comprising the following steps:
the method comprises the following steps: analyzing a dynamic model of the freight train in the running process, and respectively establishing a homogeneous rod model and a multi-mass-point model of the train, wherein the running state of the train consists of traction, uniform speed, inertia and braking.
When the train runs in a constant speed state, the whole train can be regarded as a whole, and a 'homogeneous rod' model is adopted for the train. The train dynamics equation is:
when the train runs at a non-uniform speed, relative displacement and speed difference exist among the trains (if the front train runs and the rear train does not move when the train is started, stress is different and acceleration is different in the running process). Therefore, a multi-point model of the train needs to be considered. The kinetic equation for the ith vehicle is:
in the formula: m isiIs the mass of the vehicle;
is the acceleration of the vehicle; fTFor tractive effort, FBIs braking force; fCLIs the front car coupler force; fCRIs the rear coupler force; fWRunning a basic resistance for the vehicle; fWcIs curve resistance; fWrIs the ramp resistance.
When the train reaches a certain speed after being started, the train usually runs in a cruising mode in order to realize energy conservation. And according to a speed conversion formula, smoothly switching the model of the train.
Step two: and establishing a coupler buffer device model.
The coupler force may be described by the following formula:
in the formula: fcThe coupler force in the running process of two vehicles is represented, and the coupler force can be simply calculated in real time; c denotes a car couplerA stiffness coefficient; k represents a damping coefficient; x is the number of1，x2，
Respectively the displacement and speed of the front and rear vehicles.
Step three: and constructing a multi-objective optimization model, and defining a target fitness function according to the train operation optimization model.
(1) Train safety evaluation model:
in the formula, FciThe coupler force of the train at each moment.
(2) Energy-saving evaluation model of train:
in the formula: mu is energy consumption utilization rate; rho is the utilization rate of the regenerative braking energy; omega is the power of the train auxiliary equipment; and delta t is train running time.
(3) Train punctual evaluation model:
in the formula:
the actual running time; and T is the scheduled operation time.
Therefore, the multi-objective optimization model of the train is as follows:
f＝min(f1，f2，f3) (8)
step four: and solving the optimization model by adopting an improved multi-target wolf algorithm to obtain a plurality of groups of non-inferior solutions of the safety, the traction energy consumption and the operation time of the train in a single region.
And 4.1, initializing a wolf pack population, namely selecting a wolf pack meeting the fitness and the constraint condition according to the constraint condition of the actual train running road condition.
And 4.2, calculating objective function values of all individuals, and storing the non-inferior solution into the Archive population according to the domination relation.
And 4.3, determining the congestion value of the Archive individuals, determining and selecting alpha, beta and sigma according to the mode of a roulette plate, updating the positions of all the individuals in the wolf group according to the three solutions, and calculating an objective function value.
And 4.4, determining the gray wolf patrol wolf and randomly searching the field of the gray wolf patrol wolf. Non-dominant solution sets non-bases of the population after the update are calculated.
And 4.5, combining non-hosts and Archive, calculating non-dominant solution sets of the non-hosts and Archive, judging whether the non-dominant solution sets exceed the size of the specified Archive, and if so, eliminating overcrowded individuals according to the crowding distance. And judging whether the maximum iteration number is reached, and outputting Archive if the maximum iteration number is reached. Otherwise, go to step 4.2.
In step 4.3, compared with the traditional multi-target gray wolf algorithm, the Archive population has the current optimal solution, and the heading wolf is selected from the Archive population by adopting a roulette mode, wherein alpha, beta and sigma wolf are collectively called the heading wolf. Therefore, the selection mechanism of the leading wolf is optimized, and the exploration capability of the algorithm is enhanced.
In step 4.4, compared with the traditional multi-target gray wolf algorithm, a gray wolf patrol mechanism is introduced, and in each iteration, the worst individual is selected as the patrol wolf to randomly search the field of the patrol wolf, so that other potential non-dominant solutions are avoided being ignored.
j＝rand(1，2，...，n) (10)
In the formula: a and B are respectively the upper limit and the lower limit of the search, and n is the number of the wolf individuals. After the search is finished, the dominant relationship of the two individuals is compared, and the non-dominant solution is stored.
And finally obtaining a group of pareto fronts of the train optimization working condition sequence, thereby obtaining a group of train S-V train optimal operation curves and related data. And the optimal optimization curve is selected according to the actual situation, so that the scheme is more scientific and reasonable.
The above description is a preferred embodiment of the present invention, but the scope of protection is not limited thereto. Those skilled in the art can make changes or modifications to the embodiment examples, but such changes or modifications are within the scope of the present invention.
Claims (5)
1. A freight train operation curve optimization method based on an improved multi-target wolf algorithm is used for establishing a speed self-adaptive homogeneous rod model and a multi-mass point model of a freight train. According to the change of the running state, a speed conversion formula is utilized to ensure the smoothness of the whole switching process. Modeling a coupler buffer of a train and constructing a multi-objective optimization model. The train optimization operation model is established by using the improved multi-target gray wolf algorithm, so that the distribution and convergence of the algorithm are ensured, and a train operation optimization curve is obtained. The invention is realized by the following steps:
the method comprises the following steps: analyzing a dynamic model in the running process of the freight train, and respectively establishing a homogeneous rod model and a multi-mass-point model of the train, wherein the running state of the train consists of traction, uniform speed, inertia and braking;
step two: establishing a coupler buffer device model;
step three: constructing a multi-objective optimization model, and defining a target fitness function according to the train operation optimization model;
step four: and solving the optimization model by adopting an improved multi-target wolf algorithm to obtain a plurality of groups of non-inferior solutions of the safety, the traction energy consumption and the operation time of the train in a single region.
2. The method for optimizing the operation curve of the freight train based on the improved multi-target wolf algorithm as claimed in claim 1, wherein the method for establishing the homogeneous rod model and the multi-mass point model of the train in the step 1 comprises the following steps:
when the train runs in a constant speed state, the whole train can be regarded as a whole, and a 'homogeneous rod' model is adopted for the train. The train dynamics equation is:
when the train runs at a non-uniform speed, relative displacement and speed difference exist among the trains (if the front train runs and the rear train does not move when the train is started, stress is different and acceleration is different in the running process). Therefore, a multi-point model of the train needs to be considered. The kinetic equation for the ith vehicle is:
in the formula: m isiIs the mass of the vehicle;
is the acceleration of the vehicle; fTFor tractive effort, FBIs braking force; fCLIs the front car coupler force; fCRIs the rear coupler force; fWRunning a basic resistance for the vehicle; fWcIs curve resistance; fWrIs the ramp resistance;
when the train reaches a certain speed after being started, the train usually runs in a cruising mode in order to realize energy conservation. And according to a speed conversion formula, smoothly switching the model of the train.
3. The freight train operation curve optimization method based on the improved multi-objective wolf algorithm as claimed in claim 2, wherein the method for establishing the coupler draft gear device model in step 2 is as follows:
the coupler force may be described by the following formula:
in the formula: fcThe coupler force in the running process of two vehicles is represented, and the coupler force can be simply calculated in real time; c represents a coupler stiffness coefficient; k represents a damping coefficient; x is the number of1，x2，
Respectively the displacement and speed of the front and rear vehicles.
4. The freight train operation curve optimization method based on the improved multi-objective wolf algorithm as claimed in claim 3, wherein the construction method of the multi-objective optimization model in step 3 is as follows:
(1) train safety evaluation model:
in the formula, FciThe car coupler force of the train at each moment;
(2) energy-saving evaluation model of train:
in the formula: mu is energy consumption utilization rate; rho is the utilization rate of the regenerative braking energy; omega is the power of the train auxiliary equipment; delta t is train running time;
(3) train punctual evaluation model:
in the formula:
the actual running time; t is the planned operation time;
therefore, the multi-objective optimization model of the train is as follows:
f＝min(f1，f2，f3) (8)
5. the method for optimizing freight train operation curves based on the improved multi-target gray wolf algorithm as claimed in claim 4, wherein the established improved multi-target gray wolf algorithm of step 4 is:
step 4.1, initializing a wolf pack population, namely selecting a wolf pack meeting the fitness and the constraint condition according to the constraint condition of the actual train running road condition;
step 4.2, calculating objective function values of all individuals, and storing non-inferior solutions into the Archive population according to the domination relationship;
step 4.3, determining the congestion degree value of the Archive individuals, determining and selecting alpha, beta and sigma according to the mode of a roulette plate, updating the positions of all the individuals in the wolf group according to the three solutions, and calculating an objective function value;
and 4.4, determining the gray wolf patrol wolf and randomly searching the field of the gray wolf patrol wolf. Calculating non-dominant solution sets non-bases of the population after updating;
and 4.5, combining non-hosts and Archive, calculating non-dominant solution sets of the non-hosts and Archive, judging whether the non-dominant solution sets exceed the size of the specified Archive, and if so, eliminating overcrowded individuals according to the crowding distance. And judging whether the maximum iteration number is reached, and outputting Archive if the maximum iteration number is reached. If not, turning to step 4.2;
in step 4.3, compared with the traditional multi-target gray wolf algorithm, the Archive population has the current optimal solution, and the heading wolf is selected from the Archive population by adopting a roulette mode, wherein alpha, beta and sigma wolf are collectively called the heading wolf. Therefore, the selection mechanism of the leading wolf is optimized, and the exploration capability of the algorithm is enhanced;
in step 4.4, compared with the traditional multi-target wolf algorithm, a wolf patrol mechanism is introduced, and in each iteration, the worst individual is selected as a patrol wolf to randomly search the field of the wolf, so that other potential non-dominant solutions are avoided being ignored;
j＝rand(1，2，...，n) (10)
in the formula: a and B are respectively the upper limit and the lower limit of the search, and n is the number of the wolf individuals. After the search is finished, comparing the domination relation of the two individuals, and storing a non-domination solution;
and finally obtaining a group of pareto fronts of the train optimization working condition sequence, thereby obtaining a group of train S-V train optimal operation curves and related data. And the optimal optimization curve is selected according to the actual situation, so that the scheme is more scientific and reasonable.
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