Urban rail transit train energy-saving curve optimization method, equipment and medium

Abstract

The invention relates to an urban rail transit train energy-saving curve optimization method and device and a medium. The method comprises the steps that S1, a train steepest running speed curve of a to-be-optimized interval and a coasting speed curve of a to-be-optimized train model are collected; s2, discretizing an interval to be optimized in a distance domain; s3, calculating coasting basic resistance of the train to be optimized; and S4, establishing an energy-saving curve optimization model based on the data in the steps S2 and S3 and the convex optimization model, and performing optimization calculation on the train energy-saving curve to be optimized. Compared with the prior art, the method has the advantages that a solution result is a globally optimal solution, the numerical stability is good, the calculation speed is high, the convergence speed is high and the like.

Description

Technical Field

[0001] This invention relates to rail transit signaling systems, and more particularly to a method, equipment, and medium for optimizing energy-saving curves of urban rail transit trains. Background Technology

[0002] Urban rail transit is a major consumer of electricity, with traction energy consumption accounting for 40-50% of total electricity consumption. Furthermore, traction energy consumption is closely related to train driving strategies. Currently, urban rail transit signaling systems are generally equipped with Automatic Train Operation (ATO) systems. ATO systems must meet multiple objectives when driving trains: ensuring safety while achieving smooth driving, precise stopping, punctual arrival, and energy-efficient operation.

[0003] Currently, closely adhering to the development goals of dual-carbon and green urban rail transit, how to further reduce train operation energy consumption under the constraints of safe and stable driving, precise stopping, and punctual arrival has become a hot topic in academic research and engineering applications. ATO driving typically includes two main functions: target speed curve planning and target speed curve tracking. Among these, designing and calculating target speed curves that conform to the actual operating characteristics of trains is particularly important for reducing the energy consumption of ATO driving. Extensive research has been conducted in academia and industry on this issue, which can generally be categorized as an optimal control problem. Modeling and designing optimization problems can be divided into two types: finding the transition points of operating conditions given a preset driving sequence, or finding the optimal solution based on distance (time) infinitesimal discretization. The former requires the use of human experience to analyze the preset driving sequence and employs heuristic numerical algorithms such as genetic algorithms and particle swarm optimization for optimization calculations. This type of optimization modeling method is limited by the need to analyze and design sequences for different intervals (gradient and speed limit changes), and both the sequence design and the search results of the heuristic algorithm will affect the optimality of the final result. The latter typically employs algorithms such as dynamic programming and reinforcement learning, which require a large amount of computation, especially for long-range scenarios.

[0004] A search of Chinese Patent Publication No. CN119428803A reveals a train ATO energy-saving method, device, equipment, medium, and program product, belonging to the field of rail transit technology. The method includes: constructing an objective function with the goal of minimizing traction energy consumption; constructing constraint functions during train operation; and using a dynamic programming algorithm to solve the optimal solution of the objective function under the constraints to obtain the minimum traction energy consumption. The train ATO energy-saving method provided by this invention constructs an objective function with the goal of minimizing traction energy consumption, and combines it with specific constraints during train operation to form constraint functions. Using a dynamic programming algorithm to solve the objective function can accurately find the optimal solution under these constraints, thus obtaining the minimum traction energy consumption. This achieves the reduction of traction energy consumption by optimizing the operation curve during ATO operation, improving the energy efficiency of train operation and significantly reducing the operating costs of urban rail transit. However, this existing patent uses a dynamic programming algorithm, which requires a large amount of computation. Therefore, how to overcome the limitations of existing energy-saving curve optimization model algorithms and the difference between the planning results and the actual operation curve, which affects the energy-saving effect, becomes a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to overcome the defects of the prior art by providing a method, equipment and medium for optimizing the energy-saving curve of urban rail transit trains.

[0006] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a method for optimizing the energy-saving curve of urban rail transit trains is provided, the method comprising: Step S1: Collect the train's fastest operating speed curve and the coasting speed curve of the train model to be optimized for the section to be optimized. Step S2: Discretize the region to be optimized in the distance domain; Step S3: Calculate the basic coasting resistance of the train to be optimized; Step S4: Based on the data from steps S2 and S3 and the convex optimization model, establish an energy-saving curve optimization model and perform optimization calculations on the energy-saving curve of the train to be optimized.

[0007] As a preferred technical solution, the train's fastest running speed curve in step S1 is a time series data with a fixed time interval, including the train's current running time, current position, current speed, and gradient acceleration information at the current position, with the departure time and position as the origin.

[0008] As a preferred technical solution, the train's maximum operating speed curve is collected by ATO actual driving of the train or calculated using ATO simulation tools; if simulation tools are used for calculation, the curve calculated by the simulation tools is guaranteed to be consistent with the actual operating curve.

[0009] As a preferred technical solution, the coasting speed curve in step S1 is the curve of the actual operation of the ATO-driven train, specifically a time series data with a fixed time interval, including the train's current running time, current position, current speed, gradient acceleration at the current position, and the control commands output by the current ATO.

[0010] As a preferred technical solution, step S2 specifically includes: Step S201: Discretize the interval to be optimized with distance step size d[i], define the number of discretization ranges as N, and set the speed value of the fastest running curve at the end position of the discretization step size as the speed value V[i] of the discretization range; Step S201: Select the average slope within the discretization range as the slope value within the discretization range, and calculate the slope acceleration g[i] within each discretization range.

[0011] As a preferred technical solution, step S3 specifically includes: Step S301: Select steady-state coasting curves from the collected coasting curves of the train models to be optimized; Step S302: Considering the influence of gradient acceleration on the steady-state coasting curve, use the optimization model to estimate the coasting resistance of the train under the current steady-state coasting curve; Step S303: By combining several steady-state coasting curves, estimate the train coasting resistance and obtain the train coasting resistance coefficients c0, c1 and c2.

[0012] As a preferred technical solution, the steady-state coasting curve refers to the curve that satisfies the condition that the train has entered the coasting state for more than the preset coasting entry time and that the continuous coasting exceeds the preset coasting duration.

[0013] As a preferred technical solution, step S4 specifically includes: Step S41: Define the decision variables to be optimized and the upper and lower bounds of each decision variable; Step S42: Establish the objective function of the energy-saving curve optimization model; Step S43: Establish the constraints for the energy-saving curve optimization model; Step S44: Optimize the train's energy-saving curve using the established energy-saving curve optimization model.

[0014] As a preferred technical solution, the decision variables include: energy consumption per unit train mass E[i] in each discretization range; endpoint speed v[i], resistance per unit train mass r[i], control command per unit train mass u[i], and control command change delta_u[i] in each discretization range; and slack variables q1[i], q2[i], and q3[i] in each discretization range.

[0015] As a preferred technical solution, the objective function is the weighted sum of the energy consumption term and the comfort index penalty term, with a weight of w, where the energy consumption term is the sum of the energy consumption terms E[i] of each discretization range; and the comfort index penalty term is the sum of q3[i] of each discretization range.

[0016] As a preferred technical solution, the constraints include relaxation conditions for running time, the square of the endpoint speed, and the square of the change in control command within each discretization range; train coasting resistance constraints; kinematic equation constraints based on the kinetic energy theorem; timetable constraints; control command change constraints; traction power constraints within each discretization range; actual traction energy consumption constraints within each discretization range; and actual braking feedback energy consumption constraints within each discretization range.

[0017] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0018] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0019] Compared with the prior art, the present invention has the following advantages: 1) This invention uses a convex optimization model to establish an energy-saving curve optimization model, which has advantages such as solving a globally optimal solution, good numerical stability, fast calculation speed, and fast convergence speed.

[0020] 2) This invention has the advantage of not relying on human experience to pre-set manipulation sequences; 3) This invention comprehensively considers various factors during train operation, including: train traction and braking characteristics, resistance characteristics, external track gradient, overspeed protection of ATP system during operation, driving comfort constraints, and punctuality timetable constraints from the dispatching system. 4) This invention has the characteristic of high consistency with the actual ATO operating curve. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the calculation process of the train energy-saving curve calculation method in an embodiment of the present invention. Figure 2 This is an example diagram of train resistance parameters obtained from multiple sets of coasting steady-state curves in an embodiment of the present invention; Figure 3 This is an example diagram showing the optimization results of the interval energy-saving curve in an embodiment of the present invention; Figure 4 This is an example diagram comparing the interval planning results in the real-time example of the present invention with the actual operation results. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] This invention proposes an energy-saving curve optimization method for urban rail transit trains based on a convex optimization model. The convex optimization model offers advantages such as providing a globally optimal solution, good numerical stability, fast computation speed, and no reliance on pre-set control sequences based on human experience. The proposed method comprehensively considers the influence of various factors, including train traction and braking characteristics, resistance characteristics, external track gradient, the overspeed protection effect of the ATP system during operation, driving comfort constraints, and punctuality constraints from the dispatching system. This results in a high degree of consistency between the curve planning results and the actual ATO (Automatic Train Operation) curve.

[0024] Figure 1 This is a flowchart illustrating the calculation process of the train operation online time adjustment method in this embodiment of the invention. The specific process is as follows: Step 100: Calculate the train's fastest running curve for the section to be optimized using the ATO simulation tool. This curve is a time series data with a fixed time interval (0.1s), including: the train's current running time (unit: s), position (unit: m), speed (unit: m / s), and gradient acceleration (unit: m / s², positive values ​​indicate uphill, negative values ​​indicate downhill) with the departure time and time as the origin. The train's fastest running curve refers to the speed-distance (or speed-time) relationship curve that allows the train to travel from the starting point to the destination in the shortest time under given track conditions and train performance. The train's fastest running speed typically follows the basic pattern of "maximum traction—constant speed—maximum braking" (without considering energy-saving requirements).

[0025] Step 101: Collect the actual running curve by driving the train through ATO. The curve is a time series data with a fixed time interval (0.1s), which includes: the current running time, position, speed, gradient acceleration, and control commands output by ATO (unit: m / s2, positive value indicates traction, negative value indicates braking, 0 indicates coasting).

[0026] Step 200: Discretize the selected interval with a distance step size d[i] = 5m. Select the speed value V[i] of the discretization range as the speed of the fastest running curve at the end of the discretization step size.

[0027] Step 201: Select the average slope within the discretization range as the slope value within that discretization range, and calculate the slope acceleration g[i] within each discretization range. For example, the average slope value can be obtained by dividing the elevation difference between the two endpoints within the discretization range by d[i].

[0028] Step 300: Select steady-state coasting curves from the collected coasting curves of the train models to be optimized. The steady-state coasting curve is selected based on the following conditions: the train enters the coasting condition for more than the preset coasting entry time (e.g., defined as 3s), and the continuous coasting exceeds the preset coasting duration (e.g., defined as 10s).

[0029] Step 301: Using the j-th coasting steady-state curve, considering the influence of gradient acceleration on the steady-state coasting curve, the optimization model and algorithm are used to estimate the coasting resistance parameters c0[j], c1[j], and c2[j] of the train under the current steady-state coasting curve. Specifically, with the same 0.1s time step, the coasting acceleration of the train at time k is calculated: coast_accel(k) = gradient_accel(k) + c0[j] + c1[j] × v(k) + c2[j] × v(k) × v(k). The estimated velocity at time k is calculated: v_hat(k) = v_hat(k-1) - coast_accel(k) × 0.1. An optimization model is established with the objective of minimizing the sum of squared velocity deviations between the steady-state coasting curve and v_hat(k) at each time step. The coasting resistance parameters c0[j], c1[j], and c2[j] of the j-th steady-state coasting curve are calculated as follows: min J1 = sum(v_sample(k)-v_hat(k))^2. Here, v_sample is the velocity value of the steady-state coasting curve, and k ranges from the number of times the steady-state coasting curve j has elapsed. The constraints are that c0[j], c1[j], and c2[j] are all non-negative. For the above optimization problem, heuristic algorithms, including but not limited to genetic algorithms, can be used to solve for c0[j], c1[j], and c2[j].

[0030] Step 302: By synthesizing several steady-state coasting curves, estimate the train's coasting resistance to obtain c0, c1, and c2. Establish an optimization model: min J2 = sum(c0[j]-c0+v(k) ×(c1[j]-c1)+v(k) ×v(k) ×(c2[j]-c2))^2, where v(k) represents the speed sequence obtained at a certain sampling interval (e.g., 1 kph) for the train's possible speed range (0~80 kph): v(k)=0 kph, 1 kph, 2 kph, ..., 80 kph. The constraint is that c0, c1, and c2 are all non-negative numbers. For the above optimization problem, heuristic algorithms, including but not limited to genetic algorithms, can be used to solve for c0, c1, and c2. Figure 2 The image shows an example of coasting resistance parameters calculated from 111 coasting steady-state curves for a certain subway line.

[0031] Step 400: Define the decision variables to be optimized and their upper and lower bounds. These are: the energy consumption per unit train mass E[i] within each discretization range; the endpoint speed v[i], resistance per unit train mass r[i], control command per unit train mass u[i], and control command change delta_u[i] within each discretization range; and the slack variables q1[i], q2[i], and q3[i] within each discretization range. Their lower bound is: [eta_b×u_BR×d[i], eps0, 2 / V_max,eps0×eps0, -c0, u_BR, u_BR-u_TR, 0], and their upper bound is: [u_TR×d[i] / eta_t, V[i], 2 / eps0, V[i] ×V[i], 0, u_TR, u_TR-u_BR, (u_TR-u_BR) ×(u_TR-u_BR)].

[0032] Step 401: Establish the objective function of the energy-saving curve optimization model. The objective function is the weighted sum of the energy consumption term and the comfort index penalty term, with a weight of w. The energy consumption term is the sum of the energy consumption terms E[i] for each discretized range. The comfort index penalty term is the sum of q3[i] for each discretized range. That is: min J3 = sum(E[i] + w × q3[i]).

[0033] Step 402, establish the constraints of the energy-saving curve optimization model: (1) 2 / q1[i]-v[i]-v0 <= 0 and 2 / q1[i]-v[i]-v[i-1] <= 0, representing the relaxation conditions of running time in each discretization range. (2) v[i] ×v[i]-q2[i] <= 0, representing the relaxation condition of the square of the velocity at the endpoints of each discretization range. (3) delta_u[i] ×delta_u[i]-q3[i] <= 0, representing the relaxation condition of the square of the change in control command between each discretization range. (4) r[i]+c1×v[i]+c2×q2[i] = -c0, representing the train coasting resistance constraint. (5) 2d[i] ×u[i]-q2[i]+2d[i] ×r[i] = -v0×v0-2g[i] ×d[i] and 2d[i] ×u[i]-q2[i-1]-q2[i]+2d[i] ×r[i] = -2g[i] ×d[i] represent the kinematic equation constraints based on the kinetic energy theorem. (6) sum(d[i] ×q1[i]) = T represents the time constraints of the timetable. (7) u[i+1]-u[i]-delta_u[i] = 0 represents the control command change constraint. (8) u[i]-P_TR×q1[i] <= 0 represents the traction power constraint in each discretization range. (9) d[i] / eta_t×u[i]-E[i] <= 0 represents the actual traction energy consumption constraint in each discretization range. (10) d[i] ×eta_b×u[i]-E[i] <= 0, which means the actual braking feedback energy consumption constraint in each discretization range.

[0034] Step 403: Represent the above constraints using sparse matrices, and give the Jacobian and Hessian matrices of the nonlinear constraints. Solve using gradient-based optimization algorithms, including but not limited to interior-point algorithms and IPOPT solvers.

[0035] Step 404: The parameters involved in the above description and their meanings are as follows: w is the comfort weight, v0 is the speed value of the initial endpoint, c0, c1, and c2 are train resistance parameters, d[i] is the length of the i-th discrete range, g[i] is the average gradient acceleration of the i-th discrete range, T is the interval running time given in the timetable, u_TR is the maximum value of unit traction acceleration, P_TR is the traction power per unit mass, u_BR is the maximum value of unit mass braking deceleration, eta_t is the traction force working efficiency, eta_b is the regenerative braking recovery efficiency, V[i] is the fastest curve speed of the i-th discretized endpoint, eps0 is a parameter very close to 0, and V_max is the maximum speed that the train can reach. All of the above parameters are known parameters and are used as inputs to the optimization model.

[0036] like Figure 3 The figure shows the optimization calculation results of four energy-saving curves in a certain section of a subway line, based on the optimization model algorithm proposed in this invention. The optimization section is divided into N=286 stages, with 2286 decision variables and 2573 constraints. On a regular laptop (Intel i7 CPU, 16GB memory), the algorithm solves the problem in less than 1 second.

[0037] like Figure 4 The image shows an example of an energy-saving curve calculated using the method of this invention during the ATO (Automatic Train Operation) process in a certain section of a subway line. This verifies that the energy-saving curve calculated by the method proposed in this invention meets actual operational requirements and can be applied in engineering.

[0038] The above is an introduction to the method embodiments. The following embodiments using electronic devices and storage media will further illustrate the solution of the present invention.

[0039] This invention also provides an electronic device including a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0040] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0041] The processing unit executes the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S4 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S4 by any other suitable means (e.g., by means of firmware).

[0042] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0043] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0044] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0045] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the energy-saving curve of urban rail transit trains, characterized in that, The method includes: Step S1: Collect the train's fastest operating speed curve and the coasting speed curve of the train model to be optimized for the section to be optimized. Step S2: Discretize the region to be optimized in the distance domain; Step S3: Calculate the basic coasting resistance of the train to be optimized; Step S4: Based on the data from steps S2 and S3 and the convex optimization model, establish an energy-saving curve optimization model and perform optimization calculations on the energy-saving curve of the train to be optimized.

2. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 1, characterized in that, The train's fastest running speed curve in step S1 is a time series data with a fixed time interval, including the train's current running time, current position, current speed, and gradient acceleration information at the current position, with the departure time and position as the origin.

3. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 2, characterized in that, The train's maximum operating speed curve is obtained by collecting data from actual ATO-driven trains or by calculating it using ATO simulation tools; if simulation tools are used, the curve calculated by the simulation tools must be consistent with the actual operating curve.

4. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 1, characterized in that, The coasting speed curve in step S1 is the actual running curve of the ATO-driven train, specifically a time series data with fixed time intervals, including the train's current running time, current position, current speed, gradient acceleration at the current position, and the control commands output by the current ATO.

5. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 1, characterized in that, Step S2 specifically involves: Step S201: Discretize the interval to be optimized with distance step size d[i], define the number of discretization ranges as N, and set the speed value of the fastest running curve at the end position of the discretization step size as the speed value V[i] of the discretization range; Step S201: Select the average slope within the discretization range as the slope value within the discretization range, and calculate the slope acceleration g[i] within each discretization range.

6. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 1, characterized in that, Step S3 specifically involves: Step S301: Select steady-state coasting curves from the collected coasting curves of the train models to be optimized; Step S302: Considering the influence of gradient acceleration on the steady-state coasting curve, use the optimization model to estimate the coasting resistance of the train under the current steady-state coasting curve; Step S303: By combining several steady-state coasting curves, estimate the train coasting resistance and obtain the train coasting resistance coefficients c0, c1 and c2.

7. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 6, characterized in that, The steady-state coasting curve refers to the curve that satisfies the condition that the train has entered the coasting state for more than the preset coasting entry time and has been continuously coasting for more than the preset coasting duration.

8. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 1, characterized in that, Step S4 specifically involves: Step S41: Define the decision variables to be optimized and the upper and lower bounds of each decision variable; Step S42: Establish the objective function of the energy-saving curve optimization model; Step S43: Establish the constraints for the energy-saving curve optimization model; Step S44: Optimize the train's energy-saving curve using the established energy-saving curve optimization model.

9. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 8, characterized in that, The decision variables include: energy consumption per unit train mass E[i] in each discretization range; endpoint speed v[i], resistance per unit train mass r[i], control command per unit train mass u[i], and control command change delta_u[i] in each discretization range; and slack variables q1[i], q2[i], and q3[i] in each discretization range.

10. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 8, characterized in that, The objective function is the weighted sum of the energy consumption term and the comfort index penalty term, with a weight of w. The energy consumption term is the sum of the energy consumption terms E[i] for each discretization range, and the comfort index penalty term is the sum of q3[i] for each discretization range.

11. The method for optimizing the energy-saving curve of urban rail transit trains according to claim 8, characterized in that, The constraints include relaxation conditions for running time, the square of the endpoint speed, and the square of the change in control command within each discretization range; train coasting resistance constraints; kinematic equation constraints based on the kinetic energy theorem; timetable constraints; control command change constraints; traction power constraints within each discretization range; actual traction energy consumption constraints within each discretization range; and actual braking feedback energy consumption constraints within each discretization range.

12. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 11.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 11.

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