An energy-saving operation optimization method for trains under the limitation of the overall vehicle energy and power

Abstract

The present invention provides an optimization method for the energy-saving operation of a train under the limitation of the overall vehicle energy power, which relates to the technical field of energy-saving optimization of train operation. The method includes obtaining train information and line information under the limitation of the overall vehicle energy power; constructing a train energy storage battery model and obtaining the traction characteristics of the train powered by the energy storage battery; dividing the discrete space domain according to the train traction characteristics; and traversing and searching for the optimal operation result by using the dynamic programming algorithm according to the division result, so as to complete the optimization of the energy-saving operation of the train under the limitation of the overall vehicle energy power. By adding the energy power constraint of the energy storage to adjust the energy power distribution during the train operation, the present invention obtains an optimized speed curve that can satisfy the smooth arrival of the train at the rescue point as well as the energy-saving and comfortable operation indexes, and solves the deficiencies of the existing method for generating the energy-saving speed curve of the emergency self-propelled train.

Description

Technical Field

[0001] The present invention belongs to the technical field of train operation energy-saving optimization, and in particular relates to a train energy-saving operation optimization method under the condition of limited vehicle energy power. Background Art

[0002] Due to the limited capacity of the on-board energy storage equipment and the limitations of the weight and space of the train, the energy storage device cannot provide enough electrical energy to support long-term, high-power operation. As the energy storage device continues to discharge, its output power will gradually decrease, affecting the performance of the traction system. On complex routes, such as sections with multiple slopes, small curve radii, or sections that require frequent acceleration and deceleration, the energy consumption of the train is higher and the traction force requirements are greater. When the power of the energy storage device decays, the operating performance of the train in these sections will be significantly affected, which is manifested as insufficient traction and reduced speed, which in turn leads to reduced train operation efficiency. In addition, the train auxiliary systems (such as air conditioning, lighting, etc.) will also continue to consume electrical energy, exacerbating the lack of power output. This phenomenon of energy and power limitation directly affects the train's ability to travel on complex routes and reduces the overall operating efficiency. Therefore, the present invention proposes a method of reasonable allocation taking into account the limited energy and power.

[0003] The energy consumption of on-board energy storage powered trains mainly comes from the train traction energy consumption and auxiliary system energy consumption. Most of the existing research on train operation optimization control powered by on-board energy storage batteries is based on the situation where the output power of the energy storage battery is fixed and the energy consumption of the auxiliary system is fixed. The train operation energy consumption is calculated under the given battery capacity and running time constraints. The power limitation problem of a type of energy storage train on complex lines is mainly reflected in the insufficient capacity and power output capacity of the energy storage device. However, the actual on-board energy storage device power supply output power will gradually decay as the energy storage device discharges, restricting the train traction transmission performance, and then causing the train running speed to gradually decline as the train running time and running distance increase. The train running time will further increase, and the auxiliary energy consumption will further increase, thereby affecting the success rate of the train emergency self-rescue, and even endangering the lives of passengers under extreme conditions.

[0004] Based on the characteristics of the train emergency self-driving system, the influence of the two-way station distance (line conditions), train speed level, life support system requirements, regenerative braking energy feedback and on-board energy storage system status on the running direction and operating energy consumption of the emergency self-driving system is comprehensively considered, and a train emergency self-driving solution under energy and power constraints is proposed. Disadvantages of existing technology:

[0005] 1) The constraints of the on-board energy storage power reduction characteristics on train operation are not considered.

[0006] 2) The coordination between train operation efficiency and passenger comfort when the remaining power of on-board energy storage can be distributed is not comprehensively considered.

[0007] Due to the limited capacity of the on-board energy storage equipment and the limitations of the weight and space of the train, the energy storage device cannot provide enough electrical energy to support long-term, high-power operation. As the energy storage device continues to discharge, its output power will gradually decrease, affecting the performance of the traction system. On complex routes, such as sections with multiple slopes, small curve radii, or sections that require frequent acceleration and deceleration, the train consumes more energy and requires greater traction. When the power of the energy storage device decays, the operating performance of the train in these sections will be significantly affected, which is manifested as insufficient traction and reduced speed, which in turn leads to reduced train operating efficiency. In addition, the train's auxiliary systems (such as air conditioning, lighting, etc.) will also continue to consume electrical energy, exacerbating the lack of power output. This phenomenon of energy and power limitation directly affects the train's ability to travel on complex routes and reduces overall operating efficiency. Therefore, this patent proposes a method for optimizing the speed curve under power limitation by reasonably allocating the remaining energy of the energy storage, taking into account the limited energy and power, to achieve safe, efficient, and comfortable train operation.

[0008] The energy consumption of on-board energy storage power supply trains mainly comes from the train traction energy consumption and auxiliary system energy consumption. Most of the existing research on train operation optimization control for on-board energy storage battery power supply is based on the fixed output power of the energy storage battery and the fixed energy consumption of the auxiliary system. The train operation energy consumption is calculated under the given battery capacity and running time constraints. The power limitation problem of a type of energy storage train on a complex line is mainly reflected in the insufficient capacity and power output capacity of the energy storage device. However, the actual on-board energy storage device power supply output power will gradually decay with the discharge of the energy storage device, restricting the train traction transmission performance, and then making the train running speed gradually decay with the increase of the train running time and running distance, while the train running time will further increase, and the auxiliary energy consumption will further increase, thus affecting the success rate of the train emergency self-rescue, and even endangering the lives of passengers under extreme conditions. In order to achieve safe, efficient and comfortable self-rescue of the train under the condition of no electricity in the traction network, on the premise that the train successfully arrives at the rescue point, the train operation efficiency and passenger comfort are coordinated to make full use of the residual energy of the on-board energy storage. This patent fully considers the operating characteristics of the train, focuses on improving train operating efficiency and passenger comfort, and implements speed curve optimization. Summary of the invention

[0009] In view of the above-mentioned deficiencies in the prior art, the present invention provides a train energy-saving operation optimization method under vehicle energy power limitation, which solves the problem of the deficiencies of the existing train emergency self-running energy-saving speed curve generation method.

[0010] In order to achieve the above objectives, the technical solution adopted by the present invention is: a train energy-saving operation optimization method under vehicle energy power limitation, comprising the following steps:

[0011] S1. Obtain train information and line information under vehicle energy power limitation;

[0012] S2. Construct a train energy storage battery model and obtain the traction characteristics of the train powered by the energy storage battery;

[0013] S3, dividing the discrete space domain according to the train traction characteristics;

[0014] S4. Based on the division results, the dynamic programming algorithm is used to traverse and search for the optimal operation result, and the energy-saving operation optimization of the train is completed under the energy and power constraints of the whole vehicle.

[0015] The beneficial effects of the present invention are as follows: the present invention realizes the optimization of train operation under limited energy and power while taking into account the output power constraints of the equipment. The proposed strategy achieves better optimization performance with lower energy consumption under the premise of ensuring passenger safety, thereby improving the self-driving efficiency of the train under limited energy storage power. In addition, in the case of emergency traction of trains with severe energy shortages, the method proposed by the present invention still has better planning capabilities than existing methods.

[0016] Furthermore, step S3 includes the following steps:

[0017] S301, setting the speed limit of the start and end positions, and setting Vmax and Vmin, where Vmax represents the maximum value of the speed allowed during the search process, and Vmin represents the minimum value of the speed allowed during the search process;

[0018] S302, using the interval discrete space domain defined by Δs in the space domain, the train operation process is divided into K sub-stages with the same conditions, wherein in the sub-stage x k , the expression for specifying trajectory points at intervals of Δv is as follows:

[0019] s k,j =(x k ,jΔν),0≤jΔν≤V max (x k ),j∈N

[0020] Among them, s k,j represents a single trajectory point, k represents the kth position after position division, Δs represents the division step of the position interval, j represents the jth trajectory point at the same position, Δν represents the division step of the speed interval, and N represents the total space size of the speed division at the current position;

[0021] S303, using Δν to separate the sub-stages, and determining the state set of the sub-stages according to different speeds, wherein the state set includes the trajectory points;

[0022] S304, calculating the maximum power for traction at each trajectory point;

[0023] S305, calculating the maximum speed of the next track point based on the maximum power and the train traction characteristics;

[0024] S306. According to the maximum speed and the trajectory points divided by speed in each sub-stage, a state space of the solution is formed, and the minimum energy consumption of adjacent trajectory points is calculated to complete the division of the discrete space domain.

[0025] The beneficial effects of the above further scheme are: the present invention can further compress the solution space by dividing the search space more finely, significantly improve the computational efficiency of the method, and find feasible solutions that meet the conditions more quickly; in the search process, a detailed search is performed on the speed range of each step to ensure that the optimal solution can still be accurately found under the condition that the energy power of the whole vehicle is limited. The energy utilization rate of the energy storage device is improved, so that the train can use energy more efficiently during operation, reduce energy consumption costs, and significantly improve the overall operation safety and the robustness of the optimization method.

[0026] Furthermore, the expression of the maximum power is as follows:

[0027] P maxk,j =min[(U OCVk,j -I k,j R 0 )I k,j -P aux ,P rate ]

[0028] Among them, P maxk,j Indicates the maximum power available for train traction, U OCVk,j Indicates the open circuit voltage, I k,j Represents state point s k,j The circuit current at .

[0029] Furthermore, the expression of the maximum speed is as follows:

[0030]

[0031] Among them, ν nextk,j 、F nextk,j and a nextk,j Respectively represent the maximum velocity, force and acceleration of the next state, ν k,j represents the current speed, M represents the vehicle weight, P naxk,j Indicates the power of the next state.

[0032] Furthermore, the constraints of the train operation process are as follows:

[0033]

[0034] Among them, F t (v) represents the train traction force, F t,max Indicates the maximum value of traction force, F b Indicates braking force, F t Indicates braking force, F b,max represents the maximum value of the comprehensive braking force, v represents the train running speed, F elc,max Indicates the maximum value of the electric braking force, V lim Indicates speed limit value, SOC min and SOC max They respectively represent the minimum and maximum SOC values ​​during the operation of the on-board energy storage battery.

[0035] The beneficial effect of the above further scheme is: the present invention can not only effectively avoid collision risks and improve the safety of train operation by monitoring the speed, position, acceleration and other data of the train, but also use it as the basis for dynamic planning to optimize the scheduling path of subsequent trains and improve the overall line utilization efficiency; at the same time, this constraint helps the optimization algorithm to more reasonably plan the train acceleration and deceleration process, reduce energy consumption, and improve the passenger experience by reducing sudden acceleration and deceleration; and enhance the adaptability of the optimization algorithm in emergency situations and improve the robustness of the system.

[0036] Furthermore, step S4 includes the following steps:

[0037] S401. Based on the state transition principle of dynamic programming, a minimum energy consumption optimization function of the train running speed curve is designed, and minimum energy consumption is used as the main criterion for each state transition. The minimum energy consumption optimization function takes into account speed, acceleration and braking state, evaluates the minimum energy consumption path at each decision point, and updates the local optimal solution in each state transition in turn;

[0038] S402, when calculating the optimal train speed curve, determine whether all state points separated by Δv in the current sub-stage have been traversed. If so, use the backtracking mechanism of dynamic programming to update the final value of the minimum energy consumption optimization function, obtain the local optimal solution, and enter step S403; otherwise, transfer to the next state point separated by Δv in the current sub-stage based on the local optimal solution, and return to step S401;

[0039] S403. According to the principle of sub-stage division, determine whether all sub-stages separated by Δs have been traversed. If so, output the optimal speed curve by backtracking the generated path to optimize the energy-saving operation of the train under the energy and power constraints of the whole vehicle; otherwise, move to the next sub-stage separated by Δs, discretize the speed domain of the next sub-stage, and return to step S401 to continue the state search and transfer of dynamic programming.

[0040] The beneficial effect of the above further scheme is: the present invention can efficiently realize the optimal path planning and accurately meet multiple constraints such as speed, position, acceleration, time, energy consumption, etc. by utilizing the dynamic programming algorithm to traverse and search for the optimal operation result. At the same time, relying on the characteristics of memory storage and avoiding repeated calculations, it can reduce the calculation complexity, improve the real-time performance, optimize the energy consumption management, minimize unnecessary energy consumption, enhance the robustness and adaptability of the algorithm, maintain the optimization effect in the face of uncertain factors, and can be easily expanded and applied to different scenarios and conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a diagram of the train power system structure and power flow relationship.

[0042] Figure 2 This is the train operation force diagram.

[0043] Figure 3 This is a linear equivalent circuit model diagram.

[0044] Figure 4 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0045] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0046] Example

[0047] like Figure 4 As shown, the present invention provides a train energy-saving operation optimization method under vehicle energy power limitation, and the implementation method is as follows:

[0048] S1. Obtain train information and line information under vehicle energy power limitation;

[0049] In this embodiment, train information (train weight, train turning mass coefficient, train traction / braking characteristics) and line information (speed limit, slope, curve, tunnel, electrical phase separation) are obtained.

[0050] S2. Construct a train energy storage battery model and obtain the traction characteristics of the train powered by the energy storage battery;

[0051] S3. Divide the discrete space domain according to the train traction characteristics. The implementation method is as follows:

[0052] S301, setting the speed limit of the start and end positions, and setting Vmax and Vmin, where Vmax represents the maximum value of the speed allowed during the search process, and Vmin represents the minimum value of the speed allowed during the search process;

[0053] S302, using the interval discretization space domain defined by Δs in the space domain, dividing the train operation process into K sub-stages with consistent conditions;

[0054] S303, using Δν to separate the sub-stages, and determining the state set of the sub-stages according to different speeds, wherein the state set includes the trajectory points;

[0055] S304, calculating the maximum power for traction at each trajectory point;

[0056] S305, calculating the maximum speed of the next track point based on the maximum power and the train traction characteristics;

[0057] S306, forming a state space of a solution according to the maximum speed and the trajectory points divided by speed in each sub-stage, and calculating the minimum energy consumption of adjacent trajectory points to complete the division of the discrete space domain;

[0058] S4. Based on the division results, the dynamic programming algorithm is used to traverse and search for the optimal operation result to complete the energy-saving operation optimization of the train under the limited energy and power of the whole vehicle. The implementation method is as follows:

[0059] S401. Based on the state transition principle of dynamic programming, a minimum energy consumption optimization function of the train running speed curve is designed, and minimum energy consumption is used as the main criterion for each state transition. The minimum energy consumption optimization function takes into account speed, acceleration and braking state, evaluates the minimum energy consumption path at each decision point, and updates the local optimal solution in each state transition in turn;

[0060] S402, when calculating the optimal train speed curve, determine whether all state points separated by Δv in the current sub-stage have been traversed. If so, use the backtracking mechanism of dynamic programming to update the final value of the minimum energy consumption optimization function, obtain the local optimal solution, and enter step S403; otherwise, transfer to the next state point separated by Δv in the current sub-stage based on the local optimal solution, and return to step S401;

[0061] S403. According to the principle of sub-stage division, determine whether all sub-stages separated by Δs have been traversed. If so, output the optimal speed curve by backtracking the generated path to optimize the energy-saving operation of the train under the energy and power constraints of the whole vehicle; otherwise, move to the next sub-stage separated by Δs, discretize the speed domain of the next sub-stage, and return to step S401 to continue the state search and transfer of dynamic programming.

[0062] Before describing the complete technology of the present invention, a summary of the theoretical results is first described.

[0063] (1) Emergency traction system architecture

[0064] like Figure 1 As shown in the figure, the train power system is mainly composed of a pantograph, a traction transformer, a double four-quadrant rectifier, a traction inverter, a traction motor, a DC / DC converter, an on-board energy storage device, an auxiliary converter, etc. The on-board energy storage device is connected to the intermediate DC link of the converter through a DC / DC converter. In the emergency traction condition, the on-board energy storage device provides energy to the traction system and the auxiliary system through a DC / DC converter, and the traction system converts the electrical energy into train kinetic energy to drive the train to run. In the process of emergency self-running, the regenerative braking energy generated by the train braking is not considered for charging the on-board energy storage device. There are certain differences in the power flow relationship between the normal operation and emergency operation of the train. When the train is running normally, the main power source of the train is the overhead line. The overhead line mainly supplies power to the traction motor through a transformer, a double four-quadrant rectifier, a traction inverter, etc. The overhead line can provide part of the energy to the auxiliary system through a traction transformer, a double four-quadrant rectifier and a DC / DC converter, or charge the on-board energy storage device when the on-board energy storage device needs to be charged. The power flow relationship during normal train operation is as follows: Figure 1 The blue line path is shown in the figure.

[0065] When the overhead line fails and the train is in emergency operation, all the energy required for the train operation is provided by the on-board energy storage device. The on-board energy storage device can supply energy to the traction motor through the DC / DC converter and traction inverter; at the same time, the on-board energy storage device can provide energy to the auxiliary system through the DC / DC converter and auxiliary inverter. This is shown in the red line path in the figure.

[0066] The power transmission relationship in the emergency traction system can be expressed as:

[0067] P bat (SOC) = P aux +P Ft (1)

[0068] Among them, P bat (SOC) represents the discharge power of the vehicle lithium battery, P aux Indicates the electric power of the auxiliary system (fixed value), P Ft Indicates the electrical power of the traction motor.

[0069] (2) Train kinematic equation:

[0070]

[0071] C(n)=F t (n)-Fb (n)-F g -F r (n)

[0072] F b (n) = F elc (n)+F air (n)

[0073] F r (n) = M·g·(a+bn+cn 2 )

[0074] F g =M·g·sinq (2)

[0075] Where v represents the train speed, x represents the train running distance, t represents the train running time, C(v) represents the resultant force during the train running process, M represents the train mass, γ represents the rotation quality factor, and F t (v) represents the train traction force, F b (v) represents the train braking force, F g Indicates the additional force of the line, F r (v) represents the basic operating resistance, F elc (v) represents the electric braking force, F air (v) represents the air braking force, G represents the acceleration due to gravity, a, b, and c represent the basic running resistance coefficients, n represents the number of vehicles, C(n) represents the total length of the train, g represents the acceleration due to gravity, and F b (n) represents the comprehensive braking force, F r (n) represents the air resistance of the train, and q represents the slope.

[0076] (3) Battery model

[0077] Different from the train in normal traction mode, the train in emergency self-driving mode is powered by energy storage device. The present invention proposes a simplified model - linear equivalent circuit model. Figure 3 As shown, the circuit model consists of an open circuit voltage (OCV) representing the voltage source and an ohmic internal resistance R0 used to describe the resistance of the battery component.

[0078] According to Kirchhoff's theorem, the battery model can be obtained as shown in formula (3):

[0079] U=U ocv (SOC)-R 0 I (3)

[0080] Where, U represents the output voltage, U ocv (SOC) represents the open circuit voltage of the voltage source, R 0 It represents the internal resistance in ohms which describes the resistance of the battery component, and I represents the output current.

[0081] Open circuit voltage U ocv It can be expressed as formula (4):

[0082] U OCV (SOC) = s 0 +s 1 SOC+,…,+s i SOC n (4)

[0083] Among them, s i represents the polynomial coefficient, n represents the polynomial order, SOC n Indicates the battery's state of charge, usually expressed as a percentage.

[0084] Based on the above theoretical analysis, in order to achieve the purpose of the present invention, the algorithm is designed as follows:

[0085] Based on the simplified circuit model above, substituting into formula (3), the further definition of the electric power transmission relationship of the emergency traction train can be rewritten as formula (5):

[0086]

[0087] Among them, P bat (SOC) represents the discharge power of the vehicle lithium battery, U ocv (SOC) represents the open circuit voltage of the vehicle lithium battery, F t (ν) represents the traction force, P Ft Indicates the train traction F t The instantaneous power of the train at speed v under the action of (ν).

[0088] In this case, according to formula (1), the traction force can be calculated by formula (6):

[0089]

[0090] However, the traction of the train must be lower than the maximum traction related to the motor rated power Prate, which is defined as a constant value. The final traction of the train can be expressed as formula (7):

[0091]

[0092] For the emergency traction system, the global goal is to minimize the train traction energy consumption. After considering the regenerative braking energy generated during the train operation, the total energy consumption can be expressed as formula (8):

[0093]

[0094] Among them, J represents the value of the objective function, xsta and x end They represent the starting point and end point of the train respectively, and dx represents the set single-step distance; the traction efficiency and braking efficiency during the train operation are defined as η t and η b , T represents the total time of the train's emergency self-driving. Due to the unique characteristics of the train's emergency self-driving, the travel time can be a free output rather than a fixed constraint. The constraints imposed on the train during operation are shown in formula (9):

[0095]

[0096] Among them, F t,max Indicates the maximum value of traction force, F b,max Indicates the maximum value of the comprehensive braking force, F elc,max Indicates the maximum value of the electric braking force, which is limited by the characteristics of the train itself. lim is the speed limit value, SOC min and SOC max They represent the minimum and maximum SOC values ​​of the on-board energy storage battery during operation, and F b Indicates braking force, F t Indicates braking force.

[0097] (2) Dynamic programming algorithm

[0098] The present invention proposes a dynamic programming (opc-DP) considering the output power constraint of the on-board energy storage device as a solution to the problem, taking into account the flexibility of time constraints and the dynamic changes of battery characteristics during the emergency traction of high-speed trains, as well as the Markov property of train operation, so that it does not depend on past states and controls. The above energy consumption calculation function is used and added to the dynamic programming algorithm as an evaluation indicator.

[0099] In this embodiment, dynamic programming is used to solve the optimization problem. First, it is modeled as a multi-stage decision process. The train operation process is divided into K sub-stages using the interval defined by Δs in the spatial domain to ensure that the line conditions in each section are consistent. k , with Δv as the interval, specify the trajectory points as shown in formula (10)

[0100] s k,j =(x k ,jΔν),0≤jΔν≤V max (x k ),j∈N (10)

[0101] Among them, s k,jRepresents a single trajectory point, k represents the kth position after position division, Δs represents the division step of the position interval, j represents the jth trajectory point at the same position, Δν represents the division step of the speed interval, and N represents the total space size of the speed division at the current position.

[0102] The state point that satisfies the constraints is the starting state point of the train operation. In the multi-stage decision process, there is a unique initial state s 1,0 and the only terminal state s K+1,0 Each sub-stage state is represented by selecting points along the train trajectory to ensure that the no-aftereffect principle is followed in the dynamic programming optimization. At the same time, based on the above discrete rules, the dynamic programming optimization process always runs in the train's operating state space. The optimization result must satisfy the safe operation constraints provided by formula (9).

[0103] In this embodiment, the values ​​of Δs and Δv will affect the accuracy of the results. The smaller the values, the more candidate points will be brought, which will lead to the dimensional disaster caused by excessive calculation. Therefore, it is necessary to take appropriate values ​​for the two. At the same time, due to the limited capacity and power of the energy storage device, the speed of the train must be lower than the maximum speed under the real-time power, so the solution space can be further compressed to improve the calculation speed. The minimum energy consumption calculation formula is shown in formula (11):

[0104]

[0105] Among them, F tk Indicates the traction force, F elck Indicates the electric braking force, P bat,tk (SOC k ) represents the output power of the battery at point k. Considering the limited capacity of the train energy storage device, the train k,j The maximum power available for traction at a given moment can be calculated by formula (12):

[0106] P maxk,j =min[(U OCVk,j -I k,j R 0 )I k,j -P aux ,P rate ] (12)

[0107] Among them, P maxk,j Indicates the maximum power available for train traction, U OCVk,j Indicates the open circuit voltage, I k,j Represents state point s k,j Therefore, the train runs from the current state point s k,j The maximum speed to the next state can be calculated according to formula (13):

[0108]

[0109] The maximum velocity, force, and acceleration of the next state are defined as ν nextk,j , F nextk,j and a nextk,j , ν k,j is the current speed. Therefore, by calculating the minimum consumption between each adjacent state point, the optimal train operation and optimized speed curve can be obtained according to dynamic programming.

[0110] In this embodiment, according to the optimal train operation and the optimized speed curve, the dynamic programming algorithm is used to traverse and search for the optimal operation result, and the optimization of the energy-saving operation of the train is completed as follows:

[0111] Design the minimum energy consumption optimization function of the train speed curve, such as formula (8), as the search standard of dynamic programming. Then, through dynamic programming, traverse and search for the optimal operation result, first determine whether all state points separated by Δv have been traversed. If all state points have been traversed, update the minimum energy consumption. If not all state points have been traversed, move to the next state point. Then determine whether all sub-stages separated by Δs have been traversed. If not all sub-stages have been traversed, move to the next sub-stage and discretize the speed domain of the next sub-stage. If all state points have been traversed, update and output the optimal speed curve, and output the train optimization is completed.

[0112] In summary, based on the existing invented train speed curve optimization method, the present invention proposes a train energy consumption model that introduces the dynamic characteristics of the on-board energy storage device. And an algorithm based on dynamic programming is designed, which realizes the optimization of train operation under limited energy and power while considering the output power constraints of the equipment. The proposed strategy achieves better optimization performance with lower energy consumption while ensuring the safety of passengers, thereby improving the efficiency of the train self-drive. In addition, in the case of emergency traction of trains with severe energy shortages, the method proposed in this patent still has better planning capabilities than the existing methods.

Claims

1. A train energy-saving operation optimization method under vehicle energy power limitation, characterized in that: The following steps are involved: S1. Obtain train information and line information under vehicle energy power limitation; S2. Construct a train energy storage battery model and obtain the traction characteristics of the train powered by the energy storage battery; S3. Divide the discrete space domain according to the train traction characteristics, which are as follows: S301, setting the speed limit of the start and end positions, and setting Vmax and Vmin, where Vmax represents the maximum value of the speed allowed during the search process, and Vmin represents the minimum value of the speed allowed during the search process; S302, using the interval discrete space domain defined by Δs in the space domain, the train operation process is divided into K sub-stages with the same conditions, wherein in the sub-stage x k , the expression for specifying trajectory points at intervals of Δv is as follows: s k,j =(x k ,jΔν),0≤jΔν≤V max (x k ),j∈N Among them, s k,j represents a single trajectory point, k represents the kth position after position division, Δs represents the division step of the position interval, j represents the jth trajectory point at the same position, Δν represents the division step of the speed interval, and N represents the total space size of the speed division at the current position; S303, using Δν to separate the sub-stages, and determining the state set of the sub-stages according to different speeds, wherein the state set includes the trajectory points; S304, calculating the maximum power for traction at each trajectory point; S305, calculating the maximum speed of the next track point based on the maximum power and the train traction characteristics; S306, forming a state space of a solution according to the maximum speed and the trajectory points divided by speed in each sub-stage, and calculating the minimum energy consumption of adjacent trajectory points to complete the division of the discrete space domain; S4. Based on the division results, the dynamic programming algorithm is used to traverse and search for the optimal operation result, and the energy-saving operation optimization of the train under the limited energy and power of the whole vehicle is completed, which is specifically as follows: S401. Based on the state transition principle of dynamic programming, a minimum energy consumption optimization function of the train running speed curve is designed, and minimum energy consumption is used as the main criterion for each state transition. The minimum energy consumption optimization function takes into account speed, acceleration and braking state, evaluates the minimum energy consumption path at each decision point, and updates the local optimal solution in each state transition in turn; S402, when calculating the optimal train speed curve, determine whether all state points separated by Δv in the current sub-stage have been traversed. If so, use the backtracking mechanism of dynamic programming to update the final value of the minimum energy consumption optimization function, obtain the local optimal solution, and enter step S403; otherwise, transfer to the next state point separated by Δv in the current sub-stage based on the local optimal solution, and return to step S401; S403. According to the principle of sub-stage division, determine whether all sub-stages separated by Δs have been traversed. If so, output the optimal speed curve by backtracking the generated path to optimize the energy-saving operation of the train under the energy and power constraints of the whole vehicle; otherwise, move to the next sub-stage separated by Δs, discretize the speed domain of the next sub-stage, and return to step S401 to continue the state search and transfer of dynamic programming.

2. The train energy-saving operation optimization method under vehicle energy power limitation according to claim 1 is characterized in that: The expression of the maximum power is as follows: P maxk,j =min[(U OCVk,j -I k,j R0)I k,j -P aux ,P rate ] Among them, P maxk,j Indicates the maximum power available for train traction, U OCVk,j Indicates the open circuit voltage, I k,j Represents state point s k,j The circuit current at the point, R0 represents the ohmic internal resistance describing the resistance of the battery component, P rate Indicates the rated power of the motor, P aux Indicates the electrical power of the auxiliary system.

3. The train energy-saving operation optimization method under vehicle energy power limitation according to claim 2 is characterized in that: The expression of the maximum speed is as follows: Among them, ν nextk,j 、F nextk,j and a nextk,j Respectively represent the maximum velocity, force and acceleration of the next state, ν k,j represents the current speed, M represents the vehicle weight, P naxk,j Indicates the power of the next state.

4. The train energy-saving operation optimization method under vehicle energy power limitation according to claim 3 is characterized in that: The constraints of the train operation process are as follows: Among them, F t (v) represents the train traction force, F t,max Indicates the maximum value of traction force, F b Indicates the train braking force, F t Indicates the train traction, F b,max represents the maximum value of the comprehensive braking force, v represents the train running speed, F elc,max Indicates the maximum value of the electric braking force, V lim Indicates speed limit value, SOC min and SOC max They respectively represent the minimum and maximum SOC values ​​during the operation of the on-board energy storage battery.

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