A global optimal energy-saving speed curve generation method for high-speed trains

Abstract

The present invention discloses a method for generating a globally optimal energy-saving speed curve for a high-speed train, which includes obtaining high-speed train data, line data, and section operation data; iteratively calculating the corresponding maximum operation capacity curve based on the line speed limit value, and searching for the lower limit of the range where the optimal speed limit value is located; iteratively calculating the corresponding longest coasting energy-saving optimized speed curve based on the line speed limit value, and searching for the upper limit of the range where the optimal speed limit value is located; calculating the feasible coasting energy-saving optimized speed curve whose running time meets the set running time for each speed limit value according to the lower limit and the upper limit of the range where the optimal speed limit value is located; selecting the feasible coasting energy-saving optimized speed curve with the minimum energy consumption from all the feasible coasting energy-saving optimized speed curves to obtain the globally energy-saving optimal speed curve. The present invention can greatly reduce the average time for one iteration, also narrow the search range, thereby reducing the number of iterations, so as to improve the search efficiency and further reduce the energy consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed train reference speed curve generation, and specifically relates to a method for generating a globally optimal energy-saving speed curve for high-speed trains. Background Art

[0002] In high-speed trains, the traction energy consumption accounts for about 70% of the total railway energy consumption of high-speed trains, and there is a large energy-saving space. Therefore, under the constraints of train operation safety and punctuality, aiming at minimizing the traction energy consumption, researching the generation algorithm of the globally energy-saving optimal speed curve for high-speed trains can provide a reference speed curve and operation tips for the driver, thereby reducing the train traction energy consumption. This work is of great significance.

[0003] Most of the existing energy-saving optimization speed curve generation algorithms for high-speed trains adopt the optimization strategy of increasing coasting, that is, under the conditions of a given line, train dynamic parameters, and target operation time, during the surplus time, that is, the difference between the set interval operation time and the time required by the maximum operation capacity curve, insert coasting onto the maximum capacity curve as much as possible when allowed to achieve the purpose of energy saving. However, the existing algorithms do not fully consider the global optimum, that is, when the surplus time is long, there is a set of feasible solutions, a set of coasting energy-saving optimization speed curves corresponding to different target constant speeds where the operation time of the speed curve satisfies the set operation time, and among this set of feasible solutions, there is an energy-saving speed curve with the minimum energy consumption, that is, the globally energy-saving optimal speed curve.

[0004] In summary, the generation algorithm of the energy-saving optimal speed curve for high-speed trains is of great significance for reducing the train traction energy consumption and realizing the energy-saving operation of the train. However, the existing speed curve generation algorithms do not fully consider the global optimum. Summary of the Invention

[0005] In view of the above deficiencies in the prior art, the present invention provides a method for generating a globally optimal energy-saving speed curve for high-speed trains that combines the coasting energy-saving optimization strategy and the line speed limit adjustment strategy.

[0006] In order to achieve the above invention purpose, the technical solution adopted by the present invention is:

[0007] A method for generating a globally optimal energy-saving speed curve for high-speed trains, comprising the following steps:

[0008] S1. Obtain high-speed train data, line data, and interval operation data;

[0009] S2. Iteratively calculate the corresponding maximum operation capacity curve based on the line speed limit value, and search for the line speed limit value corresponding to the operation time in the maximum operation capacity curve that satisfies the set operation time as the lower limit of the range where the optimal speed limit value is located;

[0010] S3. Iteratively calculate the corresponding longest coasting energy-saving optimization speed curve based on the line speed limit value, and search for the line speed limit value corresponding to the running time that meets the set running time in the longest coasting energy-saving optimization speed curve, which is used as the upper limit of the range where the optimal speed limit value is located;

[0011] S4. Calculate the feasible coasting energy-saving optimization speed curves with the running time meeting the set running time at each speed limit value according to the lower limit and upper limit of the range where the optimal speed limit value is located;

[0012] S5. Select the feasible coasting energy-saving optimization speed curve with the minimum energy consumption from all feasible coasting energy-saving optimization speed curves to obtain the global energy-saving optimal speed curve.

[0013] Optionally, in step S1,

[0014] The high-speed train data specifically includes: vehicle length, load mass, rotary mass coefficient, rotary adhesion coefficient, auxiliary power, transmission efficiency, regeneration efficiency, traction characteristics, braking characteristics, deceleration characteristics;

[0015] The line data specifically includes: line speed limit, ramp slope, curve radius, curve length, tunnel length, station position kilometer marker, electrical phase separation kilometer marker, whether there are long and short chains;

[0016] The interval operation data specifically includes: starting station, terminal station, planned departure time, planned arrival time.

[0017] Optionally, step S2 specifically includes the following sub-steps:

[0018] S21. Take the initial line speed limit value as the upper limit of the iteration interval, and the lowest allowable line speed limit value as the lower limit of the iteration interval, and select a line speed limit value between the upper limit and the lower limit of the iteration interval as the initial iteration speed limit value;

[0019] S22. Take the current iteration speed limit value as the new line speed limit value, and calculate the maximum running capacity curve of the high-speed train and the corresponding running time under this line speed limit condition;

[0020] S23. Judge whether the running time corresponding to the maximum running capacity curve is equal to the set running time;

[0021] If so, jump to step S26;

[0022] Otherwise, jump to step S24;

[0023] S24. Determine the next iteration speed limit value according to the difference between the running time corresponding to the maximum running capacity curve and the set running time;

[0024] S25. Determine whether the determined next iteration speed limit value exceeds the upper and lower limits of the iteration range;

[0025] If so, end the process;

[0026] Otherwise, return to step S22;

[0027] S26. Output the current iteration speed limit value as the lower limit of the range where the optimal speed limit value is located.

[0028] Optionally, in step S22, calculating the maximum operating capacity curve of the high-speed train under the speed limit conditions of this line specifically includes:

[0029] Construct a high-speed train operation model, and obtain the optimal control conditions of the high-speed train according to the constraints that the running speed and running time of the high-speed train meet the speed limit and the overall time limit;

[0030] Take the current iteration speed limit value as the new line speed limit value, accelerate to the line speed limit value under the full traction condition under this line speed limit condition, then maintain a constant speed using the partial traction condition and the partial braking condition, perform early braking when encountering a low speed limit or stop ahead, and calculate the speed-position curve in reverse according to the full braking condition to intersect with the constant speed part, and finally generate the maximum operating capacity curve of the high-speed train.

[0031] Optionally, step S3 specifically includes the following sub-steps:

[0032] S31. Take the initial line speed limit value as the upper limit of the iteration range, and take the lower limit of the range where the optimal speed limit value obtained in step S2 is located as the lower limit of the iteration range, and select a line speed limit value between the upper and lower limits of the iteration range as the initial iteration speed limit value;

[0033] S32. Take the current iteration speed limit value as the new line speed limit value, and calculate the longest coasting energy-saving optimization speed curve and the corresponding running time of the high-speed train under this line speed limit condition;

[0034] S33. Determine whether the running time corresponding to the longest coasting energy-saving optimization speed curve is equal to the set running time;

[0035] If so, jump to step S36;

[0036] Otherwise, jump to step S34;

[0037] S34. Determine the next iteration speed limit value according to the difference between the running time corresponding to the longest coasting energy-saving optimization speed curve and the set running time;

[0038] S35. Determine whether the determined next iteration speed limit value exceeds the upper and lower limits of the iteration range;

[0039] If so, the process ends;

[0040] Otherwise, return to step S32;

[0041] S36. Output the current iterative speed limit value as the upper limit of the range where the optimal speed limit value is located.

[0042] Optionally, calculating the longest coasting energy-saving optimization speed curve of the high-speed train under the speed limit condition of this line in step S32 specifically includes:

[0043] Based on the maximum operating capacity curve calculated in step S2, replace the traction-then-braking working condition section with the coasting working condition, and the replacement condition is that the speed-position curve under the replaced coasting working condition is connected to the original curve and the coasting working condition curve in each replaced section is as long as possible;

[0044] Until all traction-then-braking working conditions that meet the replacement conditions are replaced to obtain the longest coasting energy-saving optimization speed curve of the high-speed train.

[0045] Optionally, step S4 specifically includes the following steps:

[0046] S41. Determine the optimal speed limit value range determined by the lower limit of the range where the optimal speed limit value is located obtained in step S2 and the upper limit of the range where the optimal speed limit value is located obtained in step S3 as the search interval, set the iteration step size according to the solution accuracy, and use the lower limit of the range where the optimal speed limit value is located as the initial iterative speed limit value;

[0047] S42. Use the current iterative speed limit value as the new line speed limit value, and calculate the coasting energy-saving optimization speed curve of the high-speed train under the speed limit condition of this line and the corresponding running time;

[0048] S43. Judge whether the running time corresponding to the coasting energy-saving optimization speed curve is equal to the set running time;

[0049] If so, jump to step S44;

[0050] Otherwise, jump to step S45;

[0051] S44. Save the coasting energy-saving optimization speed curve corresponding to the current iterative speed limit value as a feasible solution;

[0052] S45. Determine the next iterative speed limit value according to the current iterative speed limit value and the iteration step size;

[0053] S46. Judge whether the determined next iterative speed limit value exceeds the search interval;

[0054] If so, the process ends;

[0055] Otherwise, return to step S42.

[0056] Optionally, calculating the coasting energy-saving optimized speed curve under the line speed limit condition in step S42 specifically includes:

[0057] S421. Based on the maximum operation capacity curve calculated in step S2, replace the traction-then-braking working condition section with the coasting working condition;

[0058] S422. Calculate the surplus time according to the maximum operation capacity curve and the set operation time,

[0059] S423. Judge whether the surplus time is less than or equal to zero;

[0060] If so, use the maximum operation capacity curve as the coasting energy-saving optimized speed curve;

[0061] Otherwise, jump to step S425;

[0062] S424. Judge whether the set operation time is greater than the longest coasting time required under the current speed limit;

[0063] If so, use the longest coasting energy-saving optimized speed curve calculated in step S3 as the coasting energy-saving optimized speed curve;

[0064] Otherwise, jump to step S425;

[0065] S425. Determine the length of the coasting working condition in each section replaced with the coasting working condition according to the surplus time, and use the replaced speed-position curve as the coasting energy-saving optimized speed curve.

[0066] The present invention has the following beneficial effects:

[0067] (1) By comprehensively adopting the coasting energy-saving optimization strategy and adjusting the speed limit strategy, the present invention realizes searching for a set of feasible solutions of the coasting energy-saving optimized speed curves under different line speed limits in the full speed range that satisfy the curve operation time equal to the set operation time, and outputs the speed curve with the minimum energy consumption among them, that is, the global energy-saving optimal speed curve, further reducing the energy consumption.

[0068] (2) By calculating the maximum capacity curve in the iteration to quickly determine the upper limit of the feasible speed limit interval and calculating the longest coasting energy-saving optimized speed curve in the iteration to quickly determine the lower limit of the feasible speed limit interval, the present invention greatly reduces the calculation times of the coasting energy-saving optimized curve, which is time-consuming due to the need to allocate the coasting interval. Therefore, the average time for one iteration is greatly reduced, and the search range is also reduced, thus reducing the number of iterations, and improving the search efficiency. Description of the Drawings

[0069] Figure 1Schematic flow diagram of a method for generating a globally optimal energy-saving speed curve for a high-speed train in an embodiment of the present invention. Detailed implementation manners

[0070] The following describes the detailed implementation manners of the present invention to facilitate those skilled in the art to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the detailed implementation manners. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions made using the inventive concept of the present invention are within the scope of protection.

[0071] As Figure 1 shown, an embodiment of the present invention provides a method for generating a globally optimal energy-saving speed curve for a high-speed train, including the following steps:

[0072] S1. Obtain high-speed train data, line data, and section operation data;

[0073] In an alternative embodiment of the present invention, step S1 of the present invention needs to obtain the basic data for generating the energy-saving speed curve, including high-speed train data, line data, and section operation data.

[0074] Among them, the high-speed train data specifically includes: vehicle length, load mass, rotary mass coefficient, rotary adhesion coefficient, auxiliary power, transmission efficiency, regeneration efficiency, traction characteristics, braking characteristics, deceleration characteristics;

[0075] The line data specifically includes: line speed limit, ramp slope, curve radius, curve length, tunnel length, kilometer post of station location, kilometer post of electrical phase separation, whether there are long and short chains;

[0076] The section operation data specifically includes: starting station, terminal station, planned departure time, planned arrival time.

[0077] S2. Iteratively calculate the corresponding maximum operating capacity curve based on the line speed limit value, and search for the line speed limit value corresponding to the case where the running time in the maximum operating capacity curve meets the set running time, as the lower limit of the range where the optimal speed limit value is located;

[0078] In an alternative embodiment of the present invention, step S2 of the present invention searches for the lower limit of the feasible speed limit value range, that is, by continuously changing the speed limit value to calculate the corresponding maximum operating capacity curve, and searching for the speed limit value when the running time of the maximum capacity curve meets the set running time, as the feasible speed limit value range, that is, the lower limit of the range where the optimal speed limit value is located.

[0079] In an alternative embodiment of the present invention, step S2 of the present invention specifically includes the following sub-steps S21 to S26:

[0080] S21. Initialize the iteration range and the initial iteration speed limit value. Set the upper limit of the iteration range as the initial line speed limit value, and the lower limit as the minimum line speed limit allowed. Select a line speed limit value between the upper and lower limits of the iteration range as the initial iteration speed limit value.

[0081] S22. Use the current iteration speed limit value as the new line speed limit value, and calculate the maximum operating capacity curve and the corresponding operating time of the high-speed train under this line speed limit condition.

[0082] In an optional embodiment of the present invention, calculating the maximum operating capacity curve of the high-speed train under the line speed limit condition in step S22 specifically includes the following sub-steps S221 to S222:

[0083] S221. Construct a high-speed train operation model. According to the constraints that the running speed and running time of the high-speed train satisfy the speed limit and the overall time limit, obtain the optimal control condition of the high-speed train.

[0084] In step S221, the high-speed train operation model constructed in the present invention is:

[0085]

[0086]

[0087] where u t , u b are the control coefficients of the train traction force and braking force respectively, satisfying u t ∈[0, 1], u b ∈[0, 1] and u t ·u b =0, F(v) is the maximum traction force per unit mass of the train, B(v) is the maximum braking force per unit mass of the train, w(v) is the train running resistance, g r (x) is the external ramp resistance, v is the train running speed, x is the train position, and t is the running time.

[0088] The constraints that the high-speed train running speed and time need to satisfy the speed limit and the overall time limit are:

[0089] v(0) = 0, v(X) = 0, v(x) ≤ v lim (x)

[0090] t(X) - t(0) = T

[0091] where v lim (x) is the maximum allowable speed, T is the given overall running time, and X is the running end position.

[0092] Construct the Hamiltonian function according to the train operation model:

[0093]

[0094] The adjoint variables λ1 = λ1(x) and λ2 = λ2(x) are the solutions of the adjoint equations

[0095]

[0096]

[0097] where M is the complementary slack factor.

[0098] Define new adjoint variables The Hamiltonian function is transformed into

[0099]

[0100] Thus, the optimal control condition of the train is obtained, as shown in Table 1.

[0101] Table 1

[0102]

[0103] S222. Take the iterative speed limit value at this time as the new line speed limit value. Under this line speed limit condition, accelerate to the line speed limit value using the full power traction condition, and then use the partial traction condition and partial braking condition to maintain a constant speed. When encountering a low speed limit or stopping ahead, perform early braking, and calculate the speed-position curve in reverse according to the full power braking condition to intersect with the constant speed part, and finally generate the maximum operating capacity curve of the high-speed train.

[0104] In step S222, the present invention obtains the maximum operating capacity curve of the high-speed train by solving under the given speed limit, in the sequence of full power traction condition, constant speed condition (including constant speed partial traction and constant speed partial braking), and full power braking condition, based on the line conditions, train characteristics, and target constraints. This curve takes the least time and has the highest energy consumption under this speed limit.

[0105] Finally, the present invention calculates the running time of the maximum operating capacity curve of the high-speed train, and the calculation formula is:

[0106]

[0107] S23. Judge whether the running time corresponding to the maximum operating capacity curve is equal to the set running time; the set running time here is specifically the interval running time specified in the train operation schedule;

[0108] If so, jump to step S26;

[0109] Otherwise, jump to step S24;

[0110] S24. Determine the next iterative speed limit value according to the difference between the running time corresponding to the maximum operating capacity curve and the set running time;

[0111] Specifically, the present invention can adopt iterative methods such as the bisection method and the steepest descent method to determine the next iterative speed limit value according to the difference between the running time corresponding to the maximum operating capacity curve and the set running time;

[0112] S25. Determine whether the determined next iterative speed limit value exceeds the upper and lower limits of the iterative interval;

[0113] If so, the process ends;

[0114] Otherwise, return to step S22;

[0115] S26. Output the iterative speed limit value at this time as the lower limit of the range where the optimal speed limit value is located.

[0116] S3. Based on the line speed limit value, iteratively calculate the corresponding longest coasting energy-saving optimized speed curve, and search for the line speed limit value corresponding to the running time that meets the set running time in the longest coasting energy-saving optimized speed curve as the upper limit of the range where the optimal speed limit value is located;

[0117] In an alternative embodiment of the present invention, step S3 of the present invention searches for the upper limit of the range of feasible speed limit values, that is, by continuously changing the speed limit value and then calculating the longest coasting energy-saving optimized speed curve, searching for the speed limit value when the running time of the energy-saving optimized speed curve meets the set running time as the range of feasible speed limit values, that is, the upper limit of the range where the optimal speed limit value is located.

[0118] In an alternative embodiment of the present invention, step S3 of the present invention specifically includes the following sub-steps:

[0119] S31. Use the initial line speed limit value as the upper limit of the iterative interval, and use the lower limit of the range where the optimal speed limit value obtained in step S2 is located as the lower limit of the iterative interval, and select a line speed limit value between the upper and lower limits of the iterative interval as the initial iterative speed limit value, so as to realize the initialization of the iterative interval and the initial iterative speed limit value;

[0120] S32. Use the iterative speed limit value at this time as the new line speed limit value, and calculate the longest coasting energy-saving optimized speed curve of the high-speed train and the corresponding running time under this line speed limit condition;

[0121] In an alternative embodiment of the present invention, in step S32 of the present invention, calculating the longest coasting energy-saving optimized speed curve of the high-speed train under this line speed limit condition specifically includes:

[0122] Based on the maximum operating capacity curve calculated in step S2, replace the traction-to-braking condition section with the coasting condition. The replacement condition is that the speed-position curve under the replaced coasting condition is connected to the original curve and the coasting condition curve within each replaced section is as long as possible, that is, the longest replacement length can be guaranteed under the condition of connecting the original curve.

[0123] Until all traction-to-braking conditions that meet the replacement conditions are replaced, the longest coasting energy-saving optimized speed curve of the high-speed train is obtained.

[0124] Each time the operation time is increased after replacing it with the coasting condition. Until all traction-to-braking conditions that meet the conditions are replaced, the speed-position curve at this time is the longest coasting energy-saving optimized speed curve. This curve has the minimum energy consumption and the longest time-consuming at this speed limit.

[0125] S33. Determine whether the operation time corresponding to the longest coasting energy-saving optimized speed curve is equal to the set operation time;

[0126] If so, jump to step S36;

[0127] Otherwise, jump to step S34;

[0128] S34. Determine the next iterative speed limit value according to the difference between the operation time corresponding to the longest coasting energy-saving optimized speed curve and the set operation time;

[0129] Specifically, the present invention can adopt iterative methods such as the bisection method and the steepest descent method to determine the next iterative speed limit value according to the difference between the operation time corresponding to the longest coasting energy-saving optimized speed curve and the set operation time;

[0130] S35. Determine whether the determined next iterative speed limit value exceeds the upper and lower limits of the iterative interval;

[0131] If so, the process ends;

[0132] Otherwise, return to step S32;

[0133] S36. Output the iterative speed limit value at this time as the upper limit of the range where the optimal speed limit value is located.

[0134] S4. Calculate the feasible coasting energy-saving optimized speed curve whose operation time meets the set operation time at each speed limit value according to the lower and upper limits of the range where the optimal speed limit value is located;

[0135] In an alternative embodiment of the present invention, step S4 of the present invention obtains all feasible coasting energy-saving optimized speed curves, that is, by setting an appropriate step size, within the feasible speed limit value range determined by the lower limit of the feasible speed limit value range obtained in step S2 and the upper limit of the feasible speed limit value range obtained in step S3, calculate the feasible coasting energy-saving optimized speed curves for which the running time of the speed curve at each speed limit meets the set running time.

[0136] Step S4 of the present invention specifically includes the following steps:

[0137] S41. Determine the optimal speed limit value range obtained from the lower limit of the range where the optimal speed limit value obtained in step S2 is located and the upper limit of the range where the optimal speed limit value obtained in step S3 is located as the search interval, set the iteration step size according to the solution accuracy, and use the lower limit of the range where the optimal speed limit value is located as the initial iteration speed limit value;

[0138] Specifically, the present invention uses the feasible limit value range determined by the lower limit of the feasible speed limit value range obtained in step S2 and the upper limit of the feasible speed limit value range obtained in step S3 as the search interval, sets the iteration step size according to the solution accuracy, sets the normal solution accuracy iteration step size to 1 km / h, sets the high-precision iteration step size to 0.1 km / h, and uses the lower limit of the feasible speed limit value range as the initial iteration speed limit value, so as to determine the search interval, the initial iteration speed limit value, and the iteration step size.

[0139] S42. Use the current iteration speed limit value as the new line speed limit value, and calculate the coasting energy-saving optimized speed curve of the high-speed train under this line speed limit condition and the corresponding running time;

[0140] In an alternative embodiment of the present invention, calculating the coasting energy-saving optimized speed curve of the high-speed train under this line speed limit condition in step S42 of the present invention specifically includes:

[0141] S421. Based on the maximum operation capacity curve calculated in step S2, replace the traction-then-braking working condition section with the coasting working condition;

[0142] S422. Calculate the surplus time according to the maximum operation capacity curve and the set running time,

[0143] S423. Determine whether the surplus time is less than or equal to zero;

[0144] If so, use the maximum operation capacity curve as the coasting energy-saving optimized speed curve;

[0145] Otherwise, jump to step S425;

[0146] S424. Determine whether the set running time is greater than the longest coasting time required under the current speed limit;

[0147] If so, use the longest coasting energy-saving optimized speed curve calculated in step S3 as the coasting energy-saving optimized speed curve;

[0148] Otherwise, jump to step S425;

[0149] S425. Determine the length of the coasting condition in each section replaced with the coasting condition according to the surplus time, and use the replaced speed-position curve as the coasting energy-saving optimized speed curve.

[0150] Specifically, the coasting energy-saving optimized speed curve here refers to the speed curve between the maximum capacity curve and the longest coasting energy-saving optimized speed curve under the given speed limit.

[0151] The present invention first obtains the maximum capacity curve and the longest coasting energy-saving optimized speed curve under the given speed limit. Still replace the traction and braking conditions in the maximum capacity curve with the coasting condition. There are multiple replaceable coasting condition curves in the same section, and the curve lengths decrease and can all connect the original maximum capacity curve. We know that the larger the proportion of coasting, the lower the energy consumption and the longer the time, but the energy efficiency ratios (energy consumption per unit time) of different-length coasting curves in different sections are different. The present invention distributes the surplus time so that the energy efficiency ratio of the coasting condition curve in each traction and braking condition section is the highest, and the corresponding speed curve is the coasting energy-saving optimized speed curve.

[0152] If the surplus time is less than or equal to 0, the coasting energy-saving optimized speed curve is the maximum operation curve; if the set time T0 is greater than the longest coasting time required under the current speed limit, the coasting energy-saving optimized speed curve is the longest coasting operation curve; in other cases, determine the length of the coasting condition in each section that can be replaced with the coasting condition according to the surplus time.

[0153] S43. Determine whether the operation time corresponding to the coasting energy-saving optimized speed curve is equal to the set operation time;

[0154] If so, jump to step S44;

[0155] Otherwise, jump to step S45;

[0156] S44. Save the coasting energy-saving optimized speed curve corresponding to the current iterative speed limit value as a feasible solution;

[0157] S45. Determine the next iterative speed limit value according to the current iterative speed limit value and the iterative step size;

[0158] Specifically, the present invention determines the next iterative speed limit value by adding the iterative step size to the current iterative speed limit value.

[0159] S46. Determine whether the determined next iterative speed limit value exceeds the search interval;

[0160] If so, the process ends;

[0161] Otherwise, return to step S42.

[0162] S5. Select the feasible coasting energy-saving optimization speed curve with the minimum energy consumption from all feasible coasting energy-saving optimization speed curves to obtain the globally energy-saving optimal speed curve.

[0163] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0164] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0166] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

[0167] Those of ordinary skill in the art will realize that the embodiments described herein are provided to assist the reader in understanding the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on these technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for generating a globally optimal energy-saving speed curve for a high-speed train, characterized in that It includes the following steps: S1. Obtain high-speed train data, line data, and section operation data; S2. Iteratively calculate the corresponding maximum operation capacity curve based on the line speed limit value, and search for the line speed limit value corresponding to the running time satisfying the set running time in the maximum operation capacity curve as the lower limit of the range where the optimal speed limit value is located; S3. Iteratively calculate the corresponding longest coasting energy-saving optimization speed curve based on the line speed limit value, and search for the line speed limit value corresponding to the running time satisfying the set running time in the longest coasting energy-saving optimization speed curve as the upper limit of the range where the optimal speed limit value is located; S4. Calculate the feasible coasting energy-saving optimization speed curve with the running time satisfying the set running time at each speed limit value according to the lower and upper limits of the range where the optimal speed limit value is located; S5. Select the feasible coasting energy-saving optimization speed curve with the minimum energy consumption from all the feasible coasting energy-saving optimization speed curves to obtain the global energy-saving optimal speed curve.

2. The method for generating a globally optimal energy-saving speed curve of a high-speed train according to claim 1, wherein, In step S1, the high-speed train data specifically includes: vehicle length, payload mass, rotary mass coefficient, rotary adhesion coefficient, auxiliary power, transmission efficiency, regeneration efficiency, traction characteristics, braking characteristics, deceleration characteristics; the line data specifically includes: line speed limit, ramp gradient, curve radius, curve length, tunnel length, station location kilometer post, electrical phase separation kilometer post, whether there are long and short chains; the section operation data specifically includes: starting station, terminal station, planned departure time, planned arrival time.

3. A global optimal energy-saving speed curve generation method for high-speed trains according to claim 1, characterized in that Step S2 specifically includes the following sub-steps: S21. Take the initial line speed limit value as the upper limit of the iteration interval, take the lowest allowable line speed limit value as the lower limit of the iteration interval, and select a line speed limit value between the upper and lower limits of the iteration interval as the initial iteration speed limit value; S22. Take the current iteration speed limit value as the new line speed limit value, and calculate the maximum operation capacity curve of the high-speed train and the corresponding running time under this line speed limit condition; S23. Judge whether the running time corresponding to the maximum operation capacity curve is equal to the set running time; If so, jump to step S26; Otherwise, jump to step S24; S24. Determine the next iteration speed limit value according to the difference between the running time corresponding to the maximum operation capacity curve and the set running time; S25. Judge whether the determined next iteration speed limit value exceeds the upper and lower limits of the iteration interval; If so, the process ends; Otherwise, return to step S22; S26. Output the current iteration speed limit value as the lower limit of the range where the optimal speed limit value is located.

4. A method for generating a globally optimal energy-saving speed curve of a high-speed train according to claim 3, characterized in that Calculating the maximum operation capacity curve of the high-speed train under this line speed limit condition in step S22 specifically includes: Construct a high-speed train operation model, and obtain the optimal control working condition of the high-speed train according to the constraint conditions that the running speed and running time of the high-speed train satisfy the speed limit and the overall time limit; Take the iterative speed limit value at this time as the new line speed limit value. Under this line speed limit condition, accelerate to the line speed limit value using the full traction condition, and then use the partial traction condition and partial braking condition to maintain a constant speed. When encountering a lower speed limit or a stop ahead, apply early braking, and calculate the speed-position curve in reverse according to the full braking condition to intersect with the constant speed part, and finally generate the maximum operating capacity curve of the high-speed train.

5. A method for generating a globally optimal energy-saving speed curve of a high-speed train according to claim 1, characterized in that Step S3 specifically includes the following sub-steps: S31. Take the initial line speed limit value as the upper limit of the iterative interval, and take the lower limit of the range where the optimal speed limit value obtained in step S2 is located as the lower limit of the iterative interval. Select a line speed limit value between the upper and lower limits of the iterative interval as the initial iterative speed limit value; S32. Take the iterative speed limit value at this time as the new line speed limit value, and calculate the longest coasting energy-saving optimization speed curve of the high-speed train and the corresponding running time under this line speed limit condition; S33. Determine whether the running time corresponding to the longest coasting energy-saving optimization speed curve is equal to the set running time; If so, jump to step S36; Otherwise, jump to step S34; S34. Determine the next iterative speed limit value according to the difference between the running time corresponding to the longest coasting energy-saving optimization speed curve and the set running time; S35. Determine whether the determined next iterative speed limit value exceeds the upper and lower limits of the iterative interval; If so, the process ends; Otherwise, return to step S32; S36. Output the iterative speed limit value at this time as the upper limit of the range where the optimal speed limit value is located.

6. A method for generating a globally optimal energy-saving speed curve of a high-speed train according to claim 5, characterized in that In step S32, calculating the longest coasting energy-saving optimization speed curve of the high-speed train under this line speed limit condition specifically includes: Based on the maximum operating capacity curve calculated in step S2, replace the traction and braking sections with coasting sections, and the replacement condition is that the speed-position curve under the replaced coasting section is connected to the original curve and the coasting section curve in each replaced section is as long as possible; Until all traction and braking sections that meet the replacement conditions are replaced, the longest coasting energy-saving optimization speed curve of the high-speed train is obtained.

7. A global optimal energy-saving speed curve generation method for high-speed trains according to claim 1, characterized in that, Step S4 specifically includes the following steps: S41. Take the range of the optimal speed limit value determined by the lower limit of the range where the optimal speed limit value obtained in step S2 is located and the upper limit of the range where the optimal speed limit value obtained in step S3 is located as the search interval, set the iteration step according to the solution accuracy, and take the lower limit of the range where the optimal speed limit value is located as the initial iterative speed limit value; S42. Take the iterative speed limit value at this time as the new line speed limit value, and calculate the coasting energy-saving optimization speed curve of the high-speed train and the corresponding running time under this line speed limit condition; S43. Determine whether the running time corresponding to the coasting energy-saving optimization speed curve is equal to the set running time; If so, jump to step S44; Otherwise, jump to step S45; S44. Save the coasting energy-saving optimization speed curve corresponding to the iterative speed limit value at this time as a feasible solution; S45. Determine the next iterative speed limit value according to the iterative speed limit value and the iteration step at this time; S46. Determine whether the determined next iterative speed limit value exceeds the search interval; If so, the process ends; Otherwise, return to step S42.

8. The global optimal energy-saving speed curve generation method for high-speed trains according to claim 7, characterized in that In step S42, calculating the coasting energy-saving optimized speed curve under the speed limit condition of this line specifically includes: S421. Based on the maximum operation capacity curve calculated in step S2, replacing the traction-then-braking working condition section with the coasting working condition; S422. Calculating the surplus time according to the maximum operation capacity curve and the set operation time; S423. Judging whether the surplus time is less than or equal to zero; If so, taking the maximum operation capacity curve as the coasting energy-saving optimized speed curve; Otherwise, jumping to step S425; S424. Judging whether the set operation time is greater than the longest coasting time required under the current speed limit; If so, taking the longest coasting energy-saving optimized speed curve calculated in step S3 as the coasting energy-saving optimized speed curve; Otherwise, jumping to step S425; S425. Determining the length of the coasting working condition in each section replaced with the coasting working condition according to the surplus time, and taking the replaced speed-position curve as the coasting energy-saving optimized speed curve.

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