A method for solving an optimized speed curve based on a train cruising speed

Abstract

The application discloses a kind of solving methods of optimization speed curve based on train cruise speed, comprising the following steps: basic data input;Line section division;Population initialization;For given, use, back-propagation section i Optimization speed curve;Construct genetic algorithm objective function, and calculate individual fitness;Perform genetic algorithm process: selection, cross, mutation until reach maximum iteration number, mark optimal individual as;Utilize generation optimal energy-saving speed curve, output optimal result;Through information interaction mode generation driving assistance information, guide driver to drive.The application innovatively establishes the solving method of genetic algorithm model based on cruise speed, realizes the solving of optimization speed curve by encoding operation energy consumption and cruise speed, so that the overall speed curve of train is more optimal.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of train auxiliary driving technology, and particularly relates to a method for solving an optimized speed curve based on a train cruising speed. BACKGROUND

[0002] There are various feasible speed curves for a subway train or other railway train running between stations, and different speed curves will result in different energy consumptions of the train running between stations. The problem of a single train energy-saving speed curve aims to solve an optimal energy-saving running strategy and speed curve of the train running between stations under the condition of timing constraints, so that the train runs under the condition of meeting running time, and minimizes the traction energy consumption of the train between stations. Early research results show that for a running section with constant slope, the optimal driving strategy is composed of a combination of "maximum acceleration-cruise-idling-maximum braking" working conditions. However, in the actual situation, the energy-saving working condition conversion sequence of the train running in the section has various combinations according to the number of conversion points, and therefore the method of listing the working condition sequence usually needs to determine the working condition conversion sequence of the train in each section in advance, that is, the optimization result depends on experience, and it is difficult to ensure that the result is globally optimal. SUMMARY

[0003] In view of the defects in the prior art, the present applicant provides a reasonable and effective method for solving an optimized speed curve based on a train cruising speed.

[0004] The technical scheme adopted by the present application is as follows:

[0005] A method for solving an optimized speed curve based on a train cruising speed, comprising the following steps:

[0006] Step S1: basic data input, including line data, train characteristic data, timetable data and genetic algorithm parameters;

[0007] Step S2: line section division, the line is divided into n sections according to the basic line data, and it is ensured that the line slope and speed limit in each section are constant;

[0008] Step S3: population initialization, initializing a population P, and the population size is M; each individual in the initial population is discretized according to the energy consumption of running by section and the optimal cruising speed of section, to obtain a corresponding train running state matrix C=[c1, c2, c3, … c M ] of the section; wherein each individual c f in the population corresponds to the energy consumption and cruising speed of the train running in each section, that is, c f =[E1, u1, E2, u2, … E n , u n ]; wherein E j represents energy consumption, and u j represents cruising speed;

[0009] Step S4: For a given c f = [E1, u1, E2, u2, … E n , u n ], the optimization speed curve of interval j is deduced by using E j , u j , and the method is as follows:

[0010] Step 1: Let the initial speed of the train running in the interval be v0, the speed limit at the exit of the interval be v t , and the total length of the running interval be s. Divide the interval into N equal parts, and let the mileages from the starting point of the interval to the end points of each of the N equal parts be x1, x2, … x N . The acceleration in each Δx is constant, and the speed sequence of the train in the acceleration stage is calculated.

[0011]

[0012] Wherein, E represents the traction energy consumption of the train running, F represents the maximum traction of the train, W represents the total resistance of the train running, v i represents the speed of the train from the starting point of the interval to the end point of the i-th equal part, a represents the acceleration, Δx represents one N-th of the running interval, x i represents the mileage of the train from the starting point of the interval to the end point of the i-th equal part, and m represents the weight of the train.

[0013] Step 2: Let i = i + 1 and repeat Step 1, when E = 0 and v i = u, u represents the low-speed sliding speed when the traction energy consumption of the train running is 0, k represents the position when the traction energy consumption of the train running is 0, and Step 4 is entered. Otherwise, when v i = u and E > 0, the train enters the cruising working condition, and the speed sequence of the train in the cruising working condition is calculated.

[0014]

[0015] Step 3: Let i = i + 1 and repeat Step 2 until E = 0, and the stopping point is recorded as i = k, and Step 4 is entered.

[0016] Step 4: Let i = k + 1, and the speed sequence of the train in the coasting stage is calculated.

[0017]

[0018] Step 5: Repeat Step 4 until the speed sequence of the train intersects with the pre-calculated speed protection curve, and record the stopping point as i = j.

[0019] Step6: From the speed sequence after i = j, use the points of the pre-calculated speed protection curve as the generated speed sequence;

[0020] Step7: Return the calculated optimized speed sequence and calculate the total running time of the train in the running section:

[0021]

[0022] v(t) represents the speed of the train at time t, and s represents the total length of the train in the running section;

[0023] Step S5: Construct the genetic algorithm objective function and calculate the individual fitness;

[0024]

[0025] In the formula, is the total value of train running energy consumption, and α, β, γ are the penalty coefficients of train running time error, running overspeed, and parking point respectively; min J represents the objective function without constraints, |T s -T p | represents the train running time error, T s represents the actual running time of the train, T p represents the specified train inter-station running time, |V t -V line | represents the running overspeed, V t represents the actual running speed, V line represents the specified maximum running speed, |S s -S| represents the parking point error, S s represents the actual parking location, and S represents the specified parking location;

[0026] Step S6: Execute the genetic algorithm process: selection, crossover, mutation until the maximum number of iterations is reached, mark the optimal individual as x best ; use x best to generate the optimal energy-saving speed curve, and output the optimal result;

[0027] Step S7: Generate driving assistance information through information interaction to guide the driver when the actual driving trajectory of the train deviates from the optimized speed curve, and the system generates corresponding prompt information.

[0028] As an improvement, the display mode of the driving assistance information adopts text, image visual mode, voice mode or combination of the above modes.

[0029] As an improvement, when the train deviates from its intended path, the actual operating speed is compared with the recommended speed to generate a corresponding target speed and operating condition information, so that the train's actual operating speed curve returns to the energy-saving speed curve.

[0030] The beneficial effects of this invention are as follows:

[0031] This invention addresses the challenge of random and arbitrary selection of train driving speed curves, which makes it difficult to obtain effective optimized speed curves. It innovatively establishes a solution method based on a genetic algorithm model of cruising speed. This method calculates and analyzes the energy-saving model algorithm for timed train operation, solving for the optimized speed curve by encoding operating energy consumption and cruising speed. The optimized speed curve obtained by this invention can be ultimately transmitted to the DAS (Driver Assistance System) terminal for display via driving assistance information, providing guidance for the driver and achieving energy-saving effects in train operation. The algorithm proposed in this invention does not require a pre-determined operating condition sequence table, increasing the degree of freedom in model optimization and resulting in a smoother overall train speed curve. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0033] The specific embodiments of the present invention will now be described with reference to the accompanying drawings.

[0034] like Figure 1 As shown, the method for solving the optimized speed curve based on train cruising speed described in this invention calculates and analyzes the energy-saving model algorithm for timed train operation to solve for the optimized speed curve. Specifically, it includes the following steps:

[0035] Step S1: Basic data input. This includes route data, train characteristic data, timetable data, and parameters related to the genetic algorithm.

[0036] Step S2: Route Section Division. Divide the route into n sections based on the basic route data, ensuring that the gradient and speed limit remain unchanged within each section.

[0037] Step S3: Population Initialization. Initialize the population P with a population size of M. Discretize the energy consumption and optimal cruising speed of each individual in the initial population according to the interval operation, and obtain the corresponding train operation state matrix C = [c1, c2, c3, ... c M ], where each individual c in the population f The energy consumption and cruising speed corresponding to a train operating in each section, i.e., c f =[E1,u1,E2,u2,…E n ,u n ]; where E jEnergy consumption, u j Cruise speed.

[0038] Step S4: Inverse the optimization speed curve of each interval. Specifically, for a given c f = [E1, u1, E2, u2, … E n , u n ], using E j , u j Inverse the optimization speed curve of interval j, the method is as follows:

[0039] Step1: Set the initial speed of the train running in the interval as v0, the speed limit at the exit of the interval as v t , and the total length of the running interval as s. Divide the interval into N equal parts, and the mileages of the train from the starting point of the interval to the end points of each of the N equal parts are denoted as x1, x2, … x N . The acceleration in each Δx is constant, and the speed sequence of the train in the acceleration stage is calculated.

[0040]

[0041] Wherein, E represents the traction energy consumption of the train running, F represents the maximum traction of the train, W represents the total resistance of the train running, v i represents the speed of the train from the starting point of the interval to the end point of the i-th equal part, a represents the acceleration, Δx represents the N-th part of the running interval, x i represents the mileage of the train from the starting point of the interval to the end point of the i-th equal part, and m represents the weight of the train.

[0042] Step2: Let i = i + 1 and repeat Step1, when E = 0 and v i The stopping point is recorded as i = k, u represents the low-speed sliding speed when the traction energy consumption of the train running is 0, and k represents the position when the traction energy consumption of the train running is 0. Enter Step4, otherwise when v i = u and E > 0, the train enters the cruise working condition, and the speed sequence of the train in the cruise working condition is calculated.

[0043]

[0044] Step3: Let i = i + 1 and repeat Step2 until E = 0, record the stopping point as i = k, and enter Step4;

[0045] Step4: Let i = k + 1, and calculate the speed sequence of the train in the coasting stage.

[0046]

[0047] Step5: Repeat Step4 until the train speed sequence intersects with the pre-computed speed protection curve. Mark the stopping point as i=j;

[0048] Step6: From the speed sequence after i=j, use the points of the pre-computed speed protection curve as the generated speed sequence;

[0049] Step7: Return the calculated optimized speed sequence and calculate the total running time of the train in the running section:

[0050]

[0051] v(t) represents the speed of the train at time t, s represents the total length of the train in the running section;

[0052] Step S5: Construct the genetic algorithm objective function and calculate the individual fitness;

[0053]

[0054] In the formula, is the total value of train running energy consumption, and α, β, γ are the penalty coefficients of train running time error, running overspeed, and stopping point respectively.

[0055] minJ represents the objective function without constraints. For the constraint problem in the model, the method of introducing penalty function is considered, that is, by adding the constraint term to the objective function, the constraint term far from the optimal value is punished, and the constrained minimization problem is converted into an optimization problem of a single objective function without constraints. s -T p | represents the train running time error, T s represents the actual running time of the train, T p represents the specified train station running time. t -V line | represents the running overspeed, V t represents the actual running speed, V line represents the specified maximum running speed. s -S| represents the stopping point error, S s represents the actual stopping point, and S represents the specified stopping point. | represents the absolute value (taking positive numbers).

[0056] Step S6: Execute the genetic algorithm process: selection, crossover, mutation until the maximum number of iterations is reached, and mark the optimal individual as x best . Use x best to generate the optimal energy-saving speed curve and output the optimal result.

[0057] Step S7: driving assistance information is generated by information interaction mode to guide the driver. When the actual track of the train deviates from the optimized speed curve, the system can generate corresponding prompt information.

[0058] The display mode of the driving assistance information adopts text, image visual mode, voice mode or combination of the above modes. When the train operation deviates, the actual running speed of the current train is compared with the suggested speed to generate corresponding target speed, operation condition and other information to make the actual running speed curve of the train return to the energy-saving speed curve.

[0059] The application establishes a solving method of the genetic algorithm model based on the cruise speed, calculates and analyzes the train timing operation energy-saving model algorithm, and realizes the solving of the optimized speed curve through the coding of the running energy consumption and the cruise speed. The optimized speed curve solved by the application can be finally transmitted to the DAS vehicle-mounted terminal for display to guide the driver, and the effect of train driving energy saving is realized.

[0060] The algorithm proposed by the application does not need to determine the working condition sequence table in advance, increases the freedom degree of the model optimization, and makes the overall speed curve of the train more stable.

[0061] The above description is an explanation of the application, not a limitation of the application, and the application can be modified in any form without departing from the spirit of the application.

Claims

1. A method for solving an optimized speed profile based on a train cruise speed, characterized in that, The method comprises the following steps: Step S1: basic data input, including line data, train characteristic data, timetable data and genetic algorithm parameters; Step S2: line section division, the line is divided into n sections according to the basic line data, and it is ensured that the line gradient and speed limit in each section are constant; Step S3: Population initialization. Initialize the population P with a population size of M. Discretize the energy consumption and optimal cruising speed of each individual in the initial population according to the interval operation, and obtain the corresponding train operation state matrix C = [c1, c2, c3, ... c M ], where each individual c in the population f The energy consumption and cruising speed corresponding to a train operating in each section, i.e., c f =[E1,u1,E2,u2,…E n ,u n ]; where E j Indicates energy consumption, u j Indicates cruising speed; Step S4: For a given c f =[E1,u1,E2,u2,…E n ,u n ], using E j ,u j The method for reverse-engineering the optimal velocity curve for interval j is as follows: Step1: Set the initial speed of the train running in the section as v0, the speed limit at the exit of the section as v t , and the total length of the section as s. Divide the section into N equal parts, and the distance from the start point of the section to the end point of each equal part is x1, x2, … x N . The acceleration in each Δx is constant, and the speed sequence of the train in the acceleration stage is calculated. wherein E represents the traction energy consumption of train operation, F represents the maximum traction of the train, W represents the total resistance of train operation, v i represents the speed of the train from the start point of the section operation to the end point of the i-th section of N equal parts, a represents the acceleration, Δx represents one of N of the train operation section, x i represents the mileage of the train from the start point of the section operation to the end point of the i-th section of N equal parts, m represents the weight of the train; Step2: Let i=i+1 and repeat Step1, when E=0 and v i Step3: Let i=i+1 and repeat Step1, when E=0 and v i Step4: Let i=i+1 and repeat Step1, when E=0 and v Step 3: let i=i+1 and repeat Step 2 until E=0, the stopping point is recorded as i=k, and Step 4 is entered; Step 4: let i=k+1, and the speed sequence of the train idling is calculated: Step 5: repeat Step 4 until the train speed sequence intersects with the pre-calculated speed protection curve, and the stopping point is recorded as i=j; Step 6: from the speed sequence after i=j, the points of the pre-calculated speed protection curve are used as the generated speed sequence; Step 7: return the calculated optimized speed sequence, and calculate the total running time of the train in the running section: v(t) represents the speed of the train at the t time point, and s represents the total length of the train in the running section; Step S5: construct a genetic algorithm objective function, and calculate the individual fitness; In the formula, is the total value of train operation energy consumption, and α, β, γ are the penalty coefficients of train operation time error, operation overspeed, and parking point respectively; min J represents the objective function without constraints, |T s -T p represents train operation time error, T s represents the actual running time of the train, T p represents the specified train inter-station running time, |V t -V line represents operation overspeed, V t represents the actual running speed, V line represents the specified maximum running speed, |S s -S| represents parking point error, S s represents the actual parking location, and S represents the specified parking location; Step S6: execute genetic algorithm procedure: selection, crossover, mutation until reaching maximum iteration number, mark the optimal individual as x best ; generate optimal energy-saving speed curve using x best output optimal result; Step S7: generate driving assistance information through information interaction, and guide the driver to drive, when the actual running track of the train deviates from the optimized speed curve, the system generates corresponding prompt information.

2. The method for solving an optimized speed profile based on a train cruise speed according to claim 1, wherein, The display mode of the driving assistance information adopts text, image visual mode, voice mode or combination of the above modes.

3. The method for solving an optimized speed profile based on a train cruise speed according to claim 1, wherein, When the train operation deviates, the actual running speed of the current train is compared with the recommended speed, so as to generate corresponding target speed and operation working condition information, so that the actual running speed curve of the train returns to the energy-saving speed curve.

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