Line gradient design method with optimal energy saving as target

Abstract

The invention discloses a line gradient design method with optimal energy saving as a target, and relates to the technical field of rail transit. According to the method, firstly, a train operation acceleration curve model, a corresponding traction force model and a braking force model are constructed, corresponding speed value, traction force and braking force data can be searched based on positions, secondly, on the premise that a speed curve is unique, the optimal line gradient design is optimized by taking the minimum traction energy consumption of an objective function as a target through a genetic algorithm, and the optimal line gradient design is obtained. And a certain reference can be provided for rail transit line design.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit, and particularly relates to a method for designing track gradients with the goal of optimal energy conservation. Background Art

[0002] The statements in this section only provide background information related to the present disclosure and may not constitute prior art.

[0003] Urban rail transit has widely applied the train ATO (Automatic Train Operation) system. The ATO system operates under the protection of the train automatic protection system (ATP system). It is an automatic train control system that realizes functions such as automatic train driving, precise parking, platform automation operation, unmanned return, and automatic train operation adjustment, which can greatly reduce the labor intensity of drivers. In 2020, the total electricity consumption of urban rail transit was 17.24 billion kWh, of which the traction energy consumption was 8.4 billion kWh, accounting for 48.7% of the total electricity consumption. By optimizing the operation curve during ATO operation to reduce the traction energy consumption, the operation cost of urban rail transit can be significantly reduced.

[0004] The design of energy-saving slopes, by reasonably planning the gradients and lengths of track sections, enables the train to quickly convert gravitational potential energy into kinetic energy through downhill movement after leaving the station, obtaining the acceleration and target speed required for train operation with less traction electricity consumption. The present invention uses a genetic algorithm to optimize the best track gradient design with the goal of minimizing traction energy consumption, which can provide certain reference for rail transit line design. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for designing track gradients with the goal of optimal energy conservation for the problems existing in the prior art. First, a train operation acceleration curve model, a corresponding traction force model, and a braking force model are constructed, and the corresponding speed values, traction forces, and braking force data can be found based on the position. Secondly, on the premise of a unique speed curve, a genetic algorithm is used to optimize the best track gradient design with the objective function of minimizing traction energy consumption, which can provide certain reference for rail transit line design.

[0006] The technical solution of the present invention is as follows: A method for designing track gradients with the goal of optimal energy conservation, including: Step S1: Construct a train operation acceleration model, a traction force model, and a braking force model; Step S2: Assume that the train runs at the ATO target speed and the equivalent force of the track gradient at all positions is 0, and determine the required traction force at each position through the train operation acceleration model, traction force model, and braking force model ; Step S3: Based on the required tractive force determined in Step S2 , construct an objective function regarding the actual tractive force and the traction energy consumption; Step S4: Let , optimize the value of through the genetic algorithm to ensure the lowest traction energy consumption.

[0007] Furthermore, the train running acceleration model is as follows:

[0008] Where: is the train acceleration; is the tractive force; is the braking force; is the resistance during the train operation; is the equivalent force of the line gradient; is the train mass.

[0009] Furthermore, the tractive force model is as follows:

[0010]

[0011] is the ATO operating speed; is the ATO target speed; and are both empirical values and are constants.

[0012] Furthermore, the braking force model is as follows:

[0013]

[0014] Where: is the ATP emergency braking trigger speed; , and are both empirical values and are constants.

[0015] Furthermore, the equivalent force of the line gradient at all positions is expressed as follows:

[0016] Among them: represents the equivalent line gradient corresponding to the th position.

[0017] Furthermore, determine the required tractive force at each position , including: Step A: According to the line position, query and obtain the fixed ATP emergency braking trigger speed and the ATO target speed at each position; Step B: Based on the fixed ATP emergency braking trigger speed and the ATO target speed at each position, as well as the train operation acceleration model, tractive force model, and braking force model, determine the required tractive force at each position.

[0018] Furthermore, the objective function is as follows:

[0019] is the minimum traction energy consumption; represents the actual tractive force corresponding to the th position; represents the ATO operating speed corresponding to the th position.

[0020] Furthermore, the fitness function adopted in the genetic algorithm is:

[0021] represents the comprehensive gradient value of uphill and downhill for all positions within the section.

[0022] Furthermore, .

[0023] Furthermore, the resistance during the train operation is calculated by the following formula:

[0024] Among them: and are both empirical values and are constants.

[0025] Compared with the existing technology, the beneficial effects of the present invention are: A line gradient design method aiming at optimal energy saving. First, a train operation acceleration curve model, corresponding traction force model, and braking force model are constructed. Based on the position, the corresponding speed values, traction force, and braking force data can be found. Secondly, on the premise of a unique speed curve, the genetic algorithm is used to optimize the best line gradient design with the minimum traction energy consumption as the objective function, which can provide certain reference for rail transit line design. Description of the Drawings

[0026] Figure 1 It is a relative relationship diagram of speeds at all levels related to train operation control; Figure 2 It is a disposal process based on the genetic algorithm; Figure 3 It is a sub-process of the fitness function. Detailed Implementation Modes

[0027] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0028] The features and performance of the present invention will be further described in detail below in conjunction with the embodiments.

[0029] Embodiment 1 First of all, it should be noted that as Figure 1 shown, this embodiment defines the relative relationship between different speeds, specifically as follows: ATP system restricted speed: The speed that the train cannot exceed in the curve section, turnout and platform section on the premise of ensuring train operation safety and meeting certain comfort. It includes curve critical speed, turnout critical speed and platform critical speed. Also known as ATP ceiling speed; ATP emergency braking trigger speed : The speed at which the ATP system automatically implements emergency braking safety protection measures to prevent the train operation speed from exceeding the ATP ceiling speed; ATO target speed : Under the ATO mode, the expected speed for the continuous operation of the train. The signal ATO subsystem controls the running speed of the train (i.e., the ATO running speed described below) to fluctuate slightly above and below the ATO target speed ; The ATO running speed : The actual speed of the train control curve in ATO, which can float up and down with the ATO target speed as the standard.

[0030] Since the line conditions of one line are stable, that is, the above four speeds can be determined according to the position of the line. After determining the operation level, the running speed of a single train on a certain line is fixed.

[0031] Now define the ATP emergency braking trigger speed as , the ATO target speed as , and the ATO running speed as ; The fixed , can be queried according to the line position, which is expressed as follows: (Equation 1) (Equation 2) represents the ATP emergency braking trigger speed corresponding to the th position; represents the ATO target speed corresponding to the th position.

[0032] Please refer to Figure 1 , a method for designing the line gradient with the goal of optimal energy saving, including: Step S1: Construct a train running acceleration model, a traction force model, and a braking force model; Step S2: Assume that the train runs at the ATO target speed and the equivalent force of the line gradient at all positions is 0. Through the train running acceleration model, the traction force model, and the braking force model, determine the required traction force at each position; Step S3: Based on the required traction force determined in Step S2, construct an objective function regarding the actual traction force and the traction energy consumption; Step S4: Let , and optimize the value of through the genetic algorithm to ensure the lowest traction energy consumption.

[0033] In this embodiment, it should be noted that during the train operation, it is mainly affected by three aspects: traction force, braking force, and resistance. Therefore, the constructed train operation acceleration model is as follows: (Equation 3) Where: is the train acceleration; is the traction force; is the braking force; is the resistance during the train operation; is the equivalent force of the track gradient; is the train mass.

[0034] Furthermore, by referring to relevant experience manuals, the constructed traction force model is as follows: (Equation 4) (Equation 5) is the ATO operating speed; is the ATO target speed; and are both empirical values and constants; It can be seen from (Equation 4) that when the difference between the ATO operating speed and the ATO target speed is within a certain range, the traction force is 0; It can be seen from (Equation 5) that the traction force increases in an equal-proportion curve in the low-speed range and decreases inversely in the medium- and high-speed ranges; In the specific operation process, (Equation 4) is used as the first judgment condition with a higher priority than (Equation 5).

[0035] Furthermore, by referring to relevant experience manuals, the constructed braking force model is as follows: (Equation 6) (Equation 7) Where: is the ATP emergency braking trigger speed; 、 and are all empirical values and constants.

[0036] Furthermore, by referring to relevant experience manuals, the resistance during the train operation is calculated by the following formula: (Equation 8) Where: and are both empirical values and are constants.

[0037] That is, in this embodiment, the equivalent line gradient is set to , The determination of the value determines the optimal energy-saving scheme; C is represented in matrix form (for example, with a step of 50 meters) as follows: (Equation 9) Where: represents the equivalent line gradient corresponding to the th position.

[0038] In this embodiment, it is assumed that the train maintains the highest ATO speed, that is, the actual ATO running speed of the train deviates from the ATO target speed by within ±5 km / h (1.3 m / s) at all times, that is: (Equation 10) To ensure this speed, the train needs to ensure the switching between traction and braking at all times; specifically, traction is applied when the ATO running speed is lower than the ATO target speed range, and braking is applied when the ATO running speed is higher than the ATO target speed range.

[0039] In this embodiment, specifically, the required traction force at each position is determined, including: Step A: According to the line position, the fixed ATP emergency braking trigger speed and the ATO target speed at each position are queried; specifically, see (Equation 1) and (Equation 2); Step B: Based on the fixed ATP emergency braking trigger speed and the ATO target speed at each position, as well as the train running acceleration model, traction force model, and braking force model, the required traction force at each position is determined.

[0040] In this embodiment, specifically, assuming that the equivalent line gradient at all positions is 0, according to the above formulas (except (Equation 9)), the traction force, braking force, and speed curves based on different train positions can be obtained, which are represented as follows: (Equation 11) (Equation 12) (Equation 13) Indicates the traction force corresponding to the th position; Indicates the braking force corresponding to the th position; Indicates the ATO operating speed corresponding to the th position; That is, when the traction force and the braking force are fixed values, the equivalent line gradient can be solved with the minimum energy consumption as the target .

[0041] In this embodiment, it should be noted that the genetic algorithm (Genetic Algorithm, GA) was first proposed by Professor J. Holland of the United States in 1975. It is an optimization algorithm that simulates the genetic evolution process of species in the biological world and is essentially a probabilistic-based randomized search method. The advantage of the GA algorithm is that it can automatically obtain and adjust the search direction through operations such as gene exchange (i.e., gene crossover) and mutation, and has global optimization capabilities. As Figure 2 shown, the flow of the genetic algorithm is presented, and a brief description of the GA algorithm is given below: The equivalent value of the line gradient can be equivalent to the acceleration and deceleration. Each value in (Equation 9) has a value range of ±0.3% gradient value, that is, it is expressed as: (Equation 14) Where: represents the acceleration due to gravity.

[0042] Assume there are two pairs of paired chromosomes and , which are called the initial chromosomes and have the same form as (Equation 9).

[0043] Determine the fitness function: First, it is necessary to ensure that the values in (Equation 9) accumulate to 0, ensuring that the combined gradient value of uphill and downhill within the interval is 0, which is expressed as: (Equation 15) Secondly, by changing the values of the matrix , ultimately ensure that the actual traction force decreases, thereby reducing the lowest traction energy consumption. That is: (Equation 16) is the lowest traction energy consumption; Indicates the actual traction force corresponding to the th position; Indicates the ATO operating speed corresponding to the th position.

[0044] Meanwhile, ensure that the sum of the values of matrix and is equal to , which is expressed as: (Equation 17) Gene crossover refers to the process in which, with a certain probability, two paired chromosomes exchange some genes according to certain rules to form two new individuals. First, select a crossover algorithm, and then the genotypes of the offspring after crossover and are as follows: (Equation 18) Where: represents the distribution index of the crossover operation, and the specific calculation formula is as follows: (Equation 19) Where: is a uniformly distributed random variable between 0 and 1.

[0045] Mutation refers to the process in which, with a certain probability, a gene at a certain position in an individual is replaced by another gene to form a new individual. Then, select a mutation algorithm, and the genotype of the offspring after mutation is expressed as: ; the mutation point is , the value range of is, and the mutation rule is as follows: (Equation 20) Where: represents a certain gene after mutation.

[0046] Solve for the optimal value : Using (Equation 14) - (Equation 20), taking the after crossover and mutation as the judged individual, calculate the fitness and the objective function, and the process is as Figure 3 shown. Finally, obtain the optimal matrix to achieve optimal energy saving.

[0047] The above-described embodiments merely represent specific implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the protection scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the technical solution of the present application, several modifications and improvements can be made, and these all fall within the protection scope of the present application.

[0048] This Background of the Invention section is provided to generally present the context of the present invention. Work of the presently named inventors, to the extent it is described in this Background of the Invention section, and aspects of the work that are not yet prior art as of the filing date of this application, are neither expressly nor impliedly admitted to be prior art to the present invention.

Claims

1. A method for designing line gradients with the goal of optimal energy conservation, characterized in that, Including: Step S1: Construct a train running acceleration model, a traction force model, and a braking force model; Step S2: Assume that the train runs at the ATO target speed and the equivalent line gradient at all positions is 0, and determine the required traction force at each position through the train running acceleration model, traction force model, and braking force model ; Step S3: Based on the required traction force determined in Step S2 , construct an objective function regarding the actual traction force and the traction energy consumption; Step S4: Let , and optimize the value of through the genetic algorithm to ensure the lowest traction energy consumption.

2. The line gradient design method aiming at optimal energy saving according to claim 1, characterized in that The train running acceleration model is as follows: Where: is the train acceleration; is the traction force; is the braking force; is the resistance during the train operation; is the equivalent force of the line gradient; is the train mass.

3. A method for designing a line gradient aiming at optimal energy conservation according to claim 2, characterized in that The traction force model is as follows: is the running speed of ATO; is the ATO target speed; and Both are empirical values and are constants.

4. A method for designing line gradients aiming at optimal energy conservation according to claim 3, characterized in that, The braking force model is as follows: Where: is the ATP emergency braking trigger speed; , and are all empirical values and are constants.

5. A method for designing a line gradient aiming at optimal energy saving according to claim 4, characterized in that, Equivalent line gradient at all positions Is expressed as follows: Where: Indicates the equivalent line gradient corresponding to the th position.

6. A method for designing line gradient aiming at optimal energy saving according to claim 5, characterized in that, Determine the required traction force at each position , including: Step A: Query and obtain the fixed ATP emergency braking trigger speed and ATO target speed at each location according to the line position and ; Step B: Based on the ATP emergency braking trigger speeds fixed at various positions and the ATO target speeds as well as the train operation acceleration model, traction force model and braking force model, determine the required traction force at each position .

7. A method for designing line gradient aiming at optimal energy conservation according to claim 6, characterized in that, The objective function is as follows: is the lowest traction energy consumption; Indicates the actual traction force corresponding to the th position; Indicates the ATO operating speed corresponding to the position.

8. A method for designing a line gradient aiming at optimal energy conservation according to claim 7, characterized in that, The fitness function adopted in the genetic algorithm is: Represents the comprehensive slope value of uphill and downhill at all positions within the interval.

9. A method for designing a line gradient aiming at optimal energy saving according to claim 8, characterized in that, 。 10. A method for designing a line gradient aiming at optimal energy saving according to claim 8, characterized in that Resistance during train operation Calculated by the following formula: Where: and are both empirical values and are constants.

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