Coordinated operation control method of high-speed railway trains based on virtual marshaling based on carbon emissions

Abstract

The present invention relates to the technical field of virtual marshaling train operation control, and discloses a method for cooperative operation control of virtual marshaling high-speed railway trains based on carbon emissions, comprising: establishing a dynamic model of the virtual marshaling high-speed railway train, and calculating the expected distance of the virtual marshaling high-speed railway train; calculating the additional line resistance during the braking process of the virtual marshaling high-speed railway train; selecting state variables, establishing and discretizing state space equations, and obtaining model predictive control prediction equations for virtual marshaling high-speed railway trains; establishing an intelligent control objective function and constraint conditions for cooperative operation of a virtual marshaling high-speed railway train group based on carbon emissions, and performing quadratic transformation to obtain and solve an intelligent control optimization model for cooperative operation of a virtual marshaling high-speed railway train group based on carbon emissions; the method can realize cooperative operation control of a virtual marshaling high-speed railway train group under changing line conditions, and can achieve a certain degree of carbon reduction effect while improving train operation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual marshaling train operation control, and in particular to a method for coordinated operation control of virtual marshaling high-speed railway trains based on carbon emissions. Background Art

[0002] At present, although various countries have successively introduced a series of carbon reduction policies, there is a large gap between the actual emissions of the implemented carbon reduction policies and the expected emissions. The situation of carbon emission control remains severe. For the railway transportation industry, high-speed railway, as a low-carbon mode of travel, will play an increasingly important role in the development of the low-carbon economy in the future. However, with the rapid development of the economy and society and the continuous increase in the number of people traveling, the shortage of high-speed railway capacity has gradually become prominent. To solve the above problems, domestic and foreign scholars have proposed a virtual coupling (VC) train operation control method. However, this method does not take carbon emissions into consideration and lacks analysis of the impact of train operation carbon emissions on train following control. At the same time, the relevant train operation scenarios are mostly ideal conditions, and there is a lack of precise control of the tracking error of the virtual coupling train group under changing line conditions. Summary of the Invention

[0003] In response to the above-mentioned deficiencies in the prior art, the present invention provides a method for controlling the coordinated operation of virtual marshaling high-speed railway trains based on carbon emissions. By considering the impact of slopes, curvatures and tunnels in line conditions on the expected distance of trains, the energy consumption of train operation is analyzed, and minimizing carbon emissions from train operation is taken as one of the main control objectives, so as to transform the virtual marshaling train operation control problem into an optimal control problem, thereby solving the problems of lack of consideration for carbon emissions and large train group tracking errors in the prior art.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] The method for controlling the coordinated operation of high-speed railway trains using virtual marshaling based on carbon emissions includes the following steps:

[0006] S1. Based on a single-point train model, perform a force analysis on a virtual high-speed railway train, establish a dynamic model of the virtual high-speed railway train, and calculate the expected distance of the virtual high-speed railway train;

[0007] S2. Analyze the impact of line condition changes on the expected distance of the virtual marshaling high-speed railway train based on the expected distance of the virtual marshaling high-speed railway train, and calculate the line additional resistance during the braking process of the virtual marshaling high-speed railway train and the expected distance of the virtual marshaling high-speed railway train running on a non-ideal line;

[0008] S3. Based on the dynamic model of the virtual marshaling high-speed railway train and the expected distance of the virtual marshaling high-speed railway train running on the non-ideal route, select state variables, establish and discretize the state space equation of the virtual marshaling high-speed railway train, and obtain the prediction equation of the virtual marshaling high-speed railway train for model predictive control;

[0009] S4. Based on the principles of minimizing operational errors and minimizing operational carbon emissions, establish an intelligent control objective function for the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions;

[0010] S5. Establish the constraint conditions of the intelligent control objective function of the coordinated operation of virtual high-speed railway train groups based on carbon emissions;

[0011] S6. Based on the prediction equation of virtual marshaling high-speed railway trains using model predictive control, a quadratic transformation is performed on the objective function and constraints of the intelligent control of the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions, thereby obtaining an intelligent control optimization model for the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions;

[0012] S7. Solve the intelligent control optimization model for the coordinated operation of virtual high-speed railway train groups based on carbon emissions to achieve safe and stable operation of virtual high-speed railway train groups under carbon emissions.

[0013] The present invention has the following beneficial effects:

[0014] The carbon emission-based collaborative operation control method for virtual marshaling high-speed railway trains proposed in the present invention can realize the collaborative operation control of virtual marshaling high-speed railway train groups under changing line conditions. While improving the train operation efficiency, it can also achieve a certain degree of carbon reduction effect. It can adapt to the green development requirements of the new era and provide theoretical and methodological guidance for the actual operation of virtual marshaling high-speed railway trains. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the carbon emission-based virtual marshaling high-speed railway train coordinated operation control method proposed in the present invention;

[0016] Figure 2 Schematic diagram of train positions in virtual marshaling relative braking distance mode;

[0017] Figure 3 Schematic diagram of the effect of slope on the expected interval between trains in the virtual marshaling relative braking distance mode;

[0018] Figure 4 Schematic diagram of the effect of curve radius on the expected train interval under the virtual marshaling relative braking distance mode;

[0019] Figure 5Schematic diagram of the effect of tunnels on the expected interval between trains under the virtual marshaling relative braking distance mode;

[0020] Figure 6 It is a schematic diagram of the principle of approximate slope additional resistance under changing line conditions;

[0021] Figure 7 Schematic diagram of the impact of line conditions on the expected interval and braking acceleration of trains;

[0022] Figure 8 Schematic diagram of the coordinated operation control and carbon emission optimization effect of virtual marshaling high-speed railway trains;

[0023] Figure 9 Schematic diagram of power and energy consumption optimization of three following trains under two different control strategies. DETAILED DESCRIPTION

[0024] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0025] like Figure 1 As shown, the carbon emission-based virtual marshaling high-speed railway train coordinated operation control method includes the following steps S1-S7:

[0026] S1. Based on the single-particle train model, a force analysis is performed on the virtual marshaling high-speed railway train, a dynamic model of the virtual marshaling high-speed railway train is established, and the expected distance of the virtual marshaling high-speed railway train is calculated.

[0027] In this embodiment, by carefully analyzing the stress conditions during the operation of the virtual programming train, a calculation method for the expected interval of virtual marshaling high-speed railway trains in the relative braking mode is obtained, laying the foundation for the establishment of the train operation control model.

[0028] In addition, the stress analysis of the virtual marshaling high-speed railway train is carried out. The stress can generally include six parts: traction, braking force, basic resistance, slope resistance, curve resistance, and tunnel resistance. The established dynamic model is as follows:

[0029] Specifically, the dynamic model of the virtual high-speed railway train in step S1 is:

[0030]

[0031] in, represents the first-order derivative of the position of the i-th virtual marshaling high-speed railway train, v i represents the speed of the i-th virtual marshaling high-speed railway train, represents the first-order derivative of the speed of the i-th virtual marshaling high-speed railway train, a i represents the acceleration of the i-th virtual marshaling high-speed railway train, represents the differential acceleration of the i-th virtual high-speed railway train, u i represents the control acceleration output by the i-th virtual marshaling high-speed railway train, τ represents the dynamic inertia lag of the virtual marshaling high-speed railway train, c1, c2, c3 represent the aerodynamic coefficient, drag coefficient, rolling friction and bearing sliding friction coefficient of the unit basic resistance of the virtual marshaling high-speed railway train, g represents the acceleration of gravity, f represents the unit basic resistance of the virtual marshaling high-speed railway train, w s Indicates the additional resistance per unit slope, w r Indicates the additional resistance of the unit curve, w l represents the additional resistance of the unit tunnel, slo represents the slope of the ramp, R represents the curve radius of the line, L s Indicates the length of the tunnel.

[0032] In this embodiment, the position s of the i-th virtual marshaling high-speed railway train is i , in m; the speed v of the i-th virtual marshaling high-speed railway train i , in m / s; the acceleration a of the i-th virtual marshaling high-speed railway train i , unit is m / s 2 ; Control acceleration u output by the i-th virtual marshaling high-speed railway train i , unit is m / s 2 ; Unit basic resistance f of the virtual marshaling high-speed railway train, unit is N / kN; Unit slope additional resistance w s , unit is N / kN; unit curve additional resistance w r , unit is N / kN; unit tunnel additional resistance w l , unit is N / kN; the slope θ of the ramp is the ratio of the vertical height to the horizontal distance in thousandths. In this embodiment, the uphill is set to a positive value and the downhill is set to a negative value. The value depends on the current position and line conditions, unit is %; the curve radius R of the line, unit is m; the length L of the tunnel, unit is m.

[0033] in, is the Davis empirical formula for the unit basic resistance of a virtual marshaling high-speed railway train. At the same time, the Davis empirical formula for the unit basic resistance of a virtual marshaling high-speed railway train is closely related to the train type and train parameters. In order to reduce the impact of uncertain factors on the train operation control problem, the same vehicle type is considered as the control object, and it is assumed that c1, c2, and c3 do not change during the train operation.

[0034] In addition, since the present invention is based on a single-particle train model, it does not consider the situation where a uniform train traverses different line conditions, that is, it is assumed that the slope, curve and tunnel conditions at the train location are unique.

[0035] Specifically, the calculation formula for the expected distance of the virtual high-speed railway train in step S1 is:

[0036]

[0037] Among them, d des,i represents the expected distance between the i-th virtual marshaling high-speed railway train and the preceding train, S i,brake represents the braking distance of the i-th virtual marshaling high-speed railway train, i.e. the braking distance of the following train, S i-1,brake represents the braking distance of the i-1th virtual marshaling high-speed railway train, that is, the braking distance of the preceding train, d0 represents the safety margin, L represents the length of the virtual marshaling high-speed railway train, S extra Indicates the distance that the following train maintains its current speed during the information transmission and response process, t response , t delay They represent the influence of following train reaction time and information transmission time respectively.

[0038] In this embodiment, by analyzing the tracking interval of trains in the relative braking distance mode, the braking distance requirement of the train stopping position is met, and a calculation method for the expected distance of virtual marshaling high-speed railway trains is obtained. The position of the two trains in the relative braking distance mode is as follows: Figure 2 As shown, the calculation formula for the expected distance of the virtual high-speed railway train is d des,i =S i,brake -S i-1,brake +d0+L+S extra 、S extra =(t response +t delay )×v i .

[0039] In addition, in order to facilitate the calculation of the subsequent state space equations, the virtual marshaling high-speed railway trains considered in the present invention have the same braking mode, that is, i =v i-1 Conditions, Si,brake =S i-1,brake , specifically: when the rear vehicle braking distance S i,brake Braking distance to the vehicle in front S u-1,brake When the difference is large, a d des,i <0, in this case, if d des,i If the train runs at the expected distance, a rear-end collision will occur. Therefore, it is necessary to judge this situation so that: des,i =max(S i,brake -S i-1,brake +S extra ,0)+d0+L, in this case, if the braking distance S of train i-1 i-1,brake Compared to the braking and reaction distance S of train i i,brake +S extra If S is long, then the expected distance between trains is just the pre-set safety margin d0; on the contrary, if S i,brake -S i-1,brake +S extra >0, the braking distance and reaction distance of the two trains should be considered in detail to avoid rear-end collisions.

[0040] S2. Based on the expected distance of the virtual marshaling high-speed railway train, analyze the impact of line condition changes on the expected distance of the virtual marshaling high-speed railway train, and calculate the line additional resistance during the braking process of the virtual marshaling high-speed railway train and the expected distance of the virtual marshaling high-speed railway train running on a non-ideal line.

[0041] In this embodiment, by analyzing the impact of slopes, curvatures, and tunnels on the expected train running interval, i.e., the expected distance, and starting from the train braking process, a method for calculating an approximate line additional resistance is considered, providing an effective solution for the operation process of virtual marshaling high-speed railway trains under changing line conditions.

[0042] Specifically, step S2 includes S21-S:

[0043] S21. Based on the expected distance of a virtual high-speed railway train, analyze the impact of changes in line conditions on the expected distance of a virtual high-speed railway train, including the impact of slopes, curves, and additional resistance in tunnels.

[0044] In this embodiment, based on the established method for calculating the expected distance of a train under the relative braking distance mode, the influence of line condition changes on the expected distance is analyzed, wherein the influence of the additional resistance of the slope, curve and tunnel is as follows: Figure 3 、 4As shown in Figure 5, it is assumed that the positions and track conditions of the two trains do not change during the braking process, the train runs at a speed of 30m / s, and the sum of the information transmission time and the following vehicle response time is 100ms; the expected interval (expected distance) between the two trains on different slopes is as follows: Figure 3 As shown in , it can be observed that the expected interval (expected distance) increases with the increase of the slope of the leading vehicle, and then decreases with the increase of the slope of the following vehicle. At the same time, when the leading vehicle is at the maximum upslope (20‰) and the following vehicle is at the maximum downslope (-20‰), the expected interval (expected distance) between the leading vehicle and the following vehicle reaches its maximum value. This value is related to the speed difference of the trains in the current state and the slope difference between the two trains. The influence of curve radius on the expected train interval is shown in Figure 2. Figure 4 As shown in the figure, it can be seen that the expected interval (expected distance) decreases as the curve radius of the leading train increases, and increases as the curve radius of the following train increases. Furthermore, when the leading train is at the minimum curve radius and the curvature of the following train is zero, the expected interval (expected distance) reaches its maximum value. This is related to the current train speed and the curvature difference between the two trains. The additional resistance of the curve is not simply linearly related to the curve radius. When the curve radius is less than 1000m, its impact on the expected interval (expected distance) is particularly significant, while when the curve radius is greater than 1000m, the impact is smaller. Furthermore, through comparison, it is found that under the same operating conditions, the slope has a more significant impact on the expected interval of the virtual marshaling. Figure 5 The effect of tunnel added resistance on the expected separation between trains under different line conditions is shown. Tunnel added resistance is directly linearly related to tunnel length, with longer tunnels generating greater added resistance. The expected separation (expected distance) increases with increasing tunnel length at the leading train and decreases with increasing tunnel length at the following train. The expected separation (expected distance) reaches its maximum when the leading train is at its maximum tunnel length and the following train is on its normal route.

[0045] S22. Based on the impact of line condition changes on the expected distance of the virtual marshaling high-speed railway train, the additional resistance of multiple continuous lines during the braking process of the virtual marshaling high-speed railway train is approximated by a single additional resistance line. The additional resistance line during the braking process of the virtual marshaling high-speed railway train is calculated, namely:

[0046]

[0047] Among them, G eb It represents the additional resistance of the line during the braking process of the virtual marshaling high-speed railway train, G eb,1 , G eb,2 , G eb,3They represent the approximate slope additional resistance, the approximate curve additional resistance, and the approximate tunnel additional resistance during the braking process of the virtual marshaling high-speed railway train, sinθ′ represents the sine value of the approximate slope, and w r ′、w l ′ represents the average curve additional resistance and average tunnel additional resistance experienced by the virtual marshaling high-speed railway train during braking, H e ' b represents the height of the virtual marshaling high-speed railway train during braking, L′ represents the length of each slope section experienced by the virtual marshaling high-speed railway train during braking, n represents the total number of slope sections experienced by the virtual marshaling high-speed railway train during braking, l1, l j 、l n They represent the 1st, jth and nth slopes experienced by the virtual marshaling high-speed railway train during braking, θ1, θ j ,θ n represents the slopes of the 1st, jth, and nth sections of the ramp experienced by the virtual marshaling high-speed railway train during braking, sinθ j represents the approximate sine value of the slope of the first and jth sections of the ramp experienced by the virtual marshaling high-speed railway train during braking, a eb Indicates the emergency braking acceleration of a virtual high-speed railway train.

[0048] In this embodiment, based on the analysis of the impact of line condition changes, the additional resistance of multiple continuous lines during the train braking process is approximated by the additional resistance of a single line, so that the two are equivalent during the entire train braking process; wherein, Figure 6 The principle process of approximating the slope additional resistance under changing line conditions is demonstrated, including: using the formula Calculate the approximate slope additional resistance. Since the emergency braking distance of the train cannot be accurately calculated under the influence of the slope, the emergency braking distance of the train under ideal line conditions is used for approximation, that is: Therefore, the sine value sinθ′ of the approximate slope can be expressed as: Therefore, the additional acceleration of the train during the entire braking process can be calculated from the approximate slope, that is, the approximate slope additional resistance G eb,1 For: G eb,1 =gsinθ′; Similarly, the same approximate method can be used to obtain the approximate curve additional resistance G eb,2 G eb,2 =w′ r g×10 -3 , approximate additional tunnel resistance G eb,3 G eb,3 =w′ l g×10 -3 ;and Figure 6 In, L eband L′ eb They represent the actual horizontal displacement of the train and the horizontal displacement of the train after approximation by the additional resistance of the line.

[0049] S23. Calculate the expected distance of a virtual marshaling high-speed railway train running on a non-ideal route, namely:

[0050]

[0051] Where d′ des,i represents the expected distance of the i-th virtual marshaling high-speed railway train running on a non-ideal line, v i-1 Indicates the speed of the i-1th virtual marshaling high-speed railway train.

[0052] S3. Based on the dynamic model of the virtual marshaling high-speed railway train and the expected distance of the virtual marshaling high-speed railway train running on a non-ideal line, state variables are selected, and the state space equation of the virtual marshaling high-speed railway train is established and discretized to obtain the prediction equation of the virtual marshaling high-speed railway train for model predictive control.

[0053] In this embodiment, the prediction equation of the virtual marshaling high-speed railway train for model predictive control is derived. Under the premise of knowing the current actual operating status of the train, the predicted value of the train's operating status in the entire prediction time domain can be obtained. The derivation of the prediction equation is the key to establishing the virtual marshaling high-speed railway train operation control model.

[0054] Specifically, step S3 includes S31-S34:

[0055] S31. According to the dynamic model of the virtual marshaling high-speed railway train and the expected distance of the virtual marshaling high-speed railway train running on the non-ideal line, select the state variables, namely:

[0056] x=[Δs i Δv i a i v i ] T =[s i-1 -s i -d′ des,i v i-1 -v i a i v i ] T

[0057] Where x represents the state variable, T represents the transpose, and Δs i represents the position error between the i-th virtual marshaling high-speed railway train and the i-1-th virtual marshaling high-speed railway train, Δv irepresents the speed error between the ith virtual marshaling high-speed railway train and the i-1th virtual marshaling high-speed railway train, s i-1 represents the position of the i-1th virtual marshaling high-speed railway train, s i Represents the position of the i-th virtual marshaling high-speed railway train.

[0058] In this embodiment, the position error Δs of the front and rear trains is selected based on the established dynamic equation and the characteristics of the virtual marshaling high-speed railway train operation control. i , speed error Δv i , train acceleration a i and the current train speed v i As a set of state variables of the system, that is: x=[Δs i Δv i a i v i ] T =[s i-1 -s i -d′ des,i v i-1 -v i a i v i ] T .

[0059] S32. Based on the state variables, a state space equation of a virtual high-speed railway train is established, namely:

[0060]

[0061] in, represents the first-order derivative of the state variable, Represent different input coefficient matrices, u represents the control quantity, d represents the measurable interference, represents the output coefficient matrix, y represents the output of the state variable, f i-1 represents the unit basic resistance of the i-1th virtual marshaling high-speed railway train, f i It represents the unit basic resistance of the i-th virtual high-speed railway train.

[0062] In this embodiment, it is assumed that the reaction time of the following vehicle is t response and information transmission time t delay are all fixed, so (t response +t delay ) is set to t0, on this basis, Δs i and Δv i The differential form of the state space equation is: in, represents d′ des,iThe first-order derivative of , since the present invention takes into account the influence of line conditions on the train operation process, it can be known from the dynamic model of the virtual marshaling high-speed railway train: Therefore, for the higher-order terms of the differential form of the state space equation, the first-order Taylor expansion method can be used. Since the speed of the first train in the virtual marshaling state is predetermined, and the final expected speed state of the virtual marshaling high-speed railway train formation is v0=v1=...=v i , so v0 is the equilibrium state of the train, and the dynamic equation is expanded by first-order Taylor near v0, that is: v i 2 =v0 2 +2v0(v i -v0)=2v0v i -v0 2 , v i 3 =v0 3 +3v0 2 (v i -v0)=3v0 2 v i -2v0 2 , so the state space equation of the virtual marshaling high-speed railway train can be derived, namely:

[0063] In addition, the discretized different input coefficient matrices A, B u 、B d , Z can be expressed as: In addition, the discretized output coefficient matrix can be expressed as: Similarly, let the acceleration of the front vehicle be a i-1 , the unit basic resistance f at the position of the preceding vehicle i-1 And the unit basic resistance f at the current position of the car i as interference.

[0064] S33. The state space equation of the virtual marshaling high-speed railway train is discretized using the forward Euler method to obtain the discretized state space equation of the virtual marshaling high-speed railway train, namely:

[0065]

[0066] Among them, k represents the time, x(k+1) represents the state variable discretized at the k+1th time, x(k) represents the state variable discretized at the kth time, A, B u 、B d , Z represent the discretized different input coefficient matrices, A(k), B u (k), B d(k) and Z(k) represent the different input coefficient matrices discretized at the kth moment, u(k) represents the control quantity discretized at the kth moment, d(k) represents the measurable disturbance discretized at the kth moment, C represents the discretized output coefficient matrix, and y(k+1) represents the output of the state variable at the kth moment.

[0067] S34. Perform model predictive control based on the state space equation of the discretized virtual marshaling high-speed railway train to obtain a prediction equation of the virtual marshaling high-speed railway train for model predictive control.

[0068] In this embodiment, the state of each stage of the system in the entire prediction time domain is derived from the known current state quantity of the system according to the principle of model predictive control (MPC).

[0069] Specifically, step S34 includes S341-S345:

[0070] S341. Set the prediction time domain to p, the control time domain to m, and m≤p, and assume that the control quantity outside the control time domain does not change, that is: u(k)=u(k+j1), j1=m,m+1,...,p-1, and the measurable interference is approximately the predicted value of the previous stage; where u(k+j1) represents the discretized control quantity at the k+j1th moment.

[0071] S342. Based on the state space equation of the discretized virtual marshaling high-speed railway train, the state variables of p steps in the prediction time domain are obtained, namely:

[0072]

[0073] Among them, x(k+m) represents the discretized state variable at the k+mth moment, A m 、A m-1 They represent the input coefficient matrices under the control time domain of m and m-1 respectively, u(k+m-1) represents the control quantity discretized at the k+m-1th moment, d(k+m-1) represents the measurable disturbance discretized at the k+m-1th moment, x(k+p) represents the state variable discretized at the k+pth moment, A p 、A p-1 They represent the input coefficient matrices in the prediction time domain of p and p-1 respectively, u(k+p-1) represents the control quantity discretized at the k+p-1th moment, and d(k+p-1) represents the measurable interference discretized at the k+p-1th moment.

[0074] S343. Based on the state variables of step p in the prediction time domain, the predicted output of the state variables at time k+p is calculated, that is:

[0075]

[0076] Among them, y(k+m / k) represents the predicted output of the state variable at the k+mth moment predicted by the state variable at the kth moment, and y(k+p / k) represents the predicted output of the state variable at the k+pth moment predicted by the state variable at the kth moment.

[0077] S344. Based on the predicted output of the state variable at the k+pth moment, generate the predicted output vector and the control quantity vector for p steps in the prediction time domain, that is:

[0078]

[0079] Among them, Y p (k+1 / k) represents the predicted output vector of the state variable at moment k+1 predicted by the state variable at moment k in p steps within the prediction domain, y(k+1 / k) represents the predicted output of the state variable at moment k+1 predicted by the state variable at moment k, y(k+2 / k) represents the predicted output of the state variable at moment k+2 predicted by the state variable at moment k, U(k) represents the control quantity vector at moment k, D(k) represents the measurable interference vector at moment k, u(k+1) represents the discretized control quantity at moment k+1, and d(k+1) represents the discretized measurable interference at moment k+1.

[0080] S345. Based on the predicted output vector and the control quantity vector of the p-step prediction time domain, a prediction equation for a virtual marshaling high-speed railway train of model predictive control is obtained, namely:

[0081] Y p (k+1 / k)=Ψx(k)+ΘU(k)+ΓD(k)+H

[0082] Among them, Ψ, Θ, Γ, and H represent the state variable coefficient matrix, control quantity vector coefficient matrix, measurable interference vector coefficient matrix, and constant matrix at the kth moment, respectively.

[0083] In this embodiment,

[0084] S4. Based on the principle of minimizing operating error and carbon emissions, an intelligent control objective function for the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions is established, namely:

[0085]

[0086] Among them, J represents the control objective function, ξ represents the relaxation factor, ρ represents the weight coefficient, and q 1,j1 ,q 2,j1 They represent the weighting factors of the position error and speed error of the virtual high-speed railway train at the k+j1th moment, d i(k+j1 / k) represents the actual running distance between the i-1th virtual marshaling high-speed railway train and the i-th virtual marshaling high-speed railway train at the k+j1th time, d′ des,i (k+j1) represents the expected distance between the i-1th virtual marshaling high-speed railway train and the i-th virtual marshaling high-speed railway train running on the non-ideal line at the k+j1th time, v i-1 (k+j1 / k) represents the speed of the i-1th virtual marshaling high-speed railway train at the k+j1th time, v i (k+j1 / k) represents the speed of the i-th virtual marshaling high-speed railway train at the k+j1th time, s j1 It represents the energy consumption weight of the virtual marshaling high-speed railway train at the k+j1th moment, and E(k+j1) represents the operating energy consumption value of the virtual marshaling high-speed railway train at the k+j1th moment.

[0087] In this embodiment, the objective function of the model is designed based on the goals of minimizing operational error and minimizing carbon emissions from train operation. For the selection of the objective function for virtual marshaling high-speed railway train operation control, the primary goal is to make the controlled output close to the reference input. In this embodiment, the quadratic form of the output error is selected, and its control objective is: According to the derivation in the previous steps, y(k+j1 / k) is a two-dimensional vector composed of the train position error Δs and the train speed error Δv. Therefore, the above control objective can also be written as: Among them, q 1,j1 and q 2,j1 Represented as q j1 The two components of represent the weighting factors of the train position error Δs and the train speed error Δv in each stage, respectively. On the basis that the controlled target gradually approaches the expectation, the present invention hopes to reduce the energy consumption of train operation as much as possible to achieve the purpose of reducing carbon emissions from high-speed railway train operation. Therefore, the present invention takes train operation energy consumption as one of the main control targets. Therefore, the train operation energy consumption in each stage can be expressed as: Where T s ′ represents the unit time of train operation control sampling; since the system model changes in real time, the constantly changing line conditions may make the optimization model infeasible at certain moments. In order to ensure the feasibility of the model, it is necessary to properly process the objective function obtained so far. The most common and effective way is to add a relaxation factor ξ to the optimization objective. Therefore, the overall objective function of train operation, that is, the objective function of intelligent control of cooperative operation of virtual marshaling high-speed railway train group based on carbon emissions, is Among them, the weight coefficient ρ is set to 10.

[0088] S5. Establish the constraint conditions of the intelligent control objective function of the coordinated operation of virtual high-speed railway train groups based on carbon emissions.

[0089] In this embodiment, by considering train running interval constraints, speed constraints, control performance constraints, power constraints and passenger comfort constraints, the results obtained by model optimization can better meet the needs of actual operation scenarios and be more in line with actual conditions.

[0090] Specifically, step S5 includes S51-S55:

[0091] S51. Establish safety interval constraints, namely:

[0092] d′ des,i -ξ≤s i-1 -s i ≤d max +ξ

[0093] Among them, d max Indicates the maximum operating interval.

[0094] In this embodiment, during the train operation, it is first necessary to ensure that no rear-end collision occurs during the train operation. Therefore, for each operation state of the virtual marshaling train, the safety interval constraint condition should be met.

[0095] S52. Establish speed constraints, namely:

[0096] 0≤v i ≤v max

[0097] Among them, v max Indicates maximum speed.

[0098] In this embodiment, due to the characteristics of the train itself and the influence of the line speed limit, it is necessary to constrain the train running speed.

[0099] S53. Establish control performance constraints, namely:

[0100] -a br_max ≤u i ≤a ac_max

[0101] Among them, a br_max represents the maximum braking deceleration of a virtual high-speed railway train, a ac_2ax Indicates the maximum traction acceleration of a virtual high-speed railway train.

[0102] In this embodiment, since the control quantity u considered in the present invention is the acceleration of the train, according to the dynamic model, the constraints on the train traction and braking force can be converted into constraints on acceleration, that is, the control performance constraints are met.

[0103] S54. Establish operating power constraints, namely:

[0104] -P br ≤Mu i v0≤P ac

[0105] Among them, P br represents the maximum braking power of a virtual high-speed railway train, P ac represents the maximum traction power of the virtual marshaling high-speed railway train, M represents the mass of the virtual marshaling high-speed railway train, and v0 represents the expected running speed of the virtual marshaling high-speed railway train.

[0106] In this embodiment, since the maximum value of the train's traction braking force is not fixed but is affected by the train's operating power, its traction force can reach a specified maximum value at the start. As the operating speed continues to increase, its traction force is limited by the maximum operating power and is numerically inversely proportional to the train's operating speed. In this embodiment, the train's operating speed is approximated to the expected speed of the preceding vehicle, that is, an operating power constraint is established.

[0107] S55. Establish comfort constraints, namely:

[0108] Δu min -ξ≤Δu≤Δu max +ξ

[0109] Where Δu min Indicates the minimum value of the acceleration change rate, Δu indicates the acceleration change rate, that is, the acceleration change rate is used to express the passenger comfort, Δu max Indicates the maximum value of the acceleration rate of change.

[0110] In this embodiment, the need to ensure passenger comfort is taken into consideration in the operation control of high-speed railway trains. This embodiment mainly considers the acceleration change rate Δu. Its purpose is: because sudden acceleration or emergency braking during train operation will have a greater impact on passengers, passenger comfort is measured by Δu. The larger Δu is, the lower the passenger comfort is, and the smaller Δu is, the higher the passenger comfort is. At the same time, it may be difficult to ensure passenger comfort in each train operation state when the line conditions change. This constraint can be expressed as Δu min -ξ≤Δu≤Δu max +ξ.

[0111] S6. Based on the prediction equation of virtual marshaling high-speed railway trains using model predictive control, the objective function and constraints of the intelligent control of the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions are transformed into a quadratic form to obtain the intelligent control optimization model of the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions.

[0112] In this embodiment, by converting the established train operation control model into a quadratic form, it is convenient to use the existing quadratic programming optimization method to solve it. Representing the model in a matrix form is easier to understand and is suitable for programming using computer language.

[0113] Specifically, step S6 includes S61-S66:

[0114] S61. Based on the prediction equation of virtual marshaling high-speed railway trains under model predictive control, a quadratic transformation is performed on the intelligent control objective function of the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions to obtain the transformed intelligent control objective function of the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions, namely:

[0115]

[0116] Wherein, J′ represents the converted carbon emission-based virtual marshaling high-speed railway train group coordinated operation intelligent control objective function, V represents the coefficient matrix composed of the square of the expected running speed of the virtual marshaling high-speed railway train, v0(k) represents the expected running speed of the virtual marshaling high-speed railway train at the kth moment, S represents the symmetric energy consumption weight coefficient matrix, Q represents the symmetric position and speed error weight coefficient matrix, T s Represents a discrete time interval.

[0117] In this embodiment, the objective function of the intelligent control of the coordinated operation of a virtual high-speed railway train group based on carbon emissions is first converted. According to the prediction equation of the virtual high-speed railway train group based on model predictive control, the above model objective can be converted into: definition Where diag represents a diagonal matrix, T s The value is 0.1s; therefore, the converted carbon emission-based virtual marshaling high-speed railway train group coordinated operation intelligent control objective function is J′=U(k) T (Θ T QΘ+VS)U(k)+(2x(k) T Ψ T QΘ+2d(k) T Γ T QΘ+2H T QΘ)U(k).

[0118] S62. Perform a quadratic transformation on the safety interval constraint and the speed constraint, namely:

[0119] -Ψx-ΓD-H≤ΘU≤2d0-Ψx-ΓD-H.

[0120] In this embodiment, if the discretized output coefficient matrix is ​​set to C1 = [1 0 0 0], it corresponds to the train safety interval constraint, and if the discretized output coefficient matrix is ​​set to C2 = [0 0 0 1], it corresponds to the train running speed constraint.

[0121] S63. Perform a quadratic transformation on the control performance constraint, namely:

[0122] U min ≤U≤U max

[0123] Among them, U min Indicates the minimum value of the control quantity vector, U max Indicates the maximum value of the control quantity vector.

[0124] S64. Perform a quadratic transformation on the operating power constraint to obtain a transformed operating power constraint, namely:

[0125] P min / M / V0≤U≤P max / M / V0

[0126] Among them, P min Represents the maximum braking power vector, P max represents the maximum traction power vector, and V0 represents the expected operating speed vector.

[0127] S65. Perform a quadratic transformation on the comfort constraint, namely:

[0128] ΔU min ≤ΔU≤ΔU max

[0129] Among them, ΔU min Represents the minimum acceleration change vector, ΔU max It represents the maximum value vector of acceleration change, and ΔU represents the acceleration change vector.

[0130] In this embodiment, Δu(k)=u(k)-u(k-1), so ΔU=U(k)-U(k-1).

[0131] S66. According to the converted intelligent control objective function of the coordinated operation of the virtual marshaling high-speed railway train group based on carbon emissions and the converted constraint conditions, an intelligent control optimization model of the coordinated operation of the virtual marshaling high-speed railway train group based on carbon emissions is obtained, namely:

[0132]

[0133] Among them, min means taking the minimum value, and st means making it satisfy.

[0134] S7. Solve the intelligent control optimization model for the coordinated operation of virtual high-speed railway train groups based on carbon emissions to achieve safe and stable operation of virtual high-speed railway train groups under carbon emissions.

[0135] In order to verify the effectiveness of the carbon emission-based virtual marshaling high-speed railway train coordinated operation control method proposed in the present invention, the simulation parameters of the virtual marshaling high-speed railway train are given as shown in Table 1:

[0136] Table 1. Virtual marshaling high-speed railway train simulation parameters

[0137]

[0138]

[0139] Table 1 gives the simulation parameters of virtual high-speed railway trains, where a dr represents the maximum traction acceleration of the virtual high-speed railway train, a br represents the maximum braking acceleration of the virtual high-speed railway train, j max It represents the passenger comfort expressed by the acceleration change rate, and t0 represents the sum of the information transmission time and the reaction time of the virtual marshaling high-speed railway train.

[0140] Figure 7 The influence of line condition changes on the running status of marshaled trains is shown. Figure 7 (a) and Figure 7 (b) shows the comparison of the expected distance and acceleration of the train at different positions under ideal line conditions and line conditions. Figure 7 The results shown in the figure show that under the influence of line conditions, the maximum difference between the ideal train expected distance and the actual train expected distance can reach 5.53m, and the maximum difference between the ideal braking acceleration and the actual braking acceleration can reach 0.121m / s 2 Therefore, in the virtual marshaling scenario, the impact of line parameters on the operation control of high-speed rail trains cannot be ignored. The constantly changing line conditions will have a huge impact on the virtual marshaling status and train operation safety.

[0141] According to the parameters in Table 1, combined with Figure 7 The influence of line condition changes can be used to simulate the running status of the following trains using MATLAB. The obtained virtual marshaling high-speed railway train coordinated operation control effect and energy consumption optimization are shown in the figure. Figure 8 shown; among them, Figure 8 (a) and Figure 8 (b) shows the position tracking and speed tracking of the model proposed in the present invention. The graphical analysis shows that the virtual train tracking effect is good; Figure 8 (c) to Figure 8 (d) The position error and speed error of the train during tracking are compared. The results show that the proposed model always meets the safety interval requirements during train operation. Figure 8 (e) to Figure 8 (f) The train operating power and total energy consumption before and after the energy consumption optimization target was added were compared, and it was found that the change in train power decreased significantly after optimization. The virtual marshaling train operation control model proposed in this invention can achieve a certain energy consumption optimization effect while ensuring close tracking of the train.

[0142] also, Figure 9 This shows the power and energy consumption optimization comparison of the three following vehicles under two different control strategies. Figure 9 (b) and Figure 9 (d) It can be found that the total energy consumption of the train operation without considering the energy optimization goal gradually increases with the increase of the number of following trains, while the total energy consumption of the train operation considering the energy optimization goal gradually decreases with the increase of the number of following trains. Among them, the energy optimization effect of following train 3 is the best, reaching 13.2 kw·h, reducing carbon emissions by 8.03 kg.

[0143] To sum up, the carbon emission-based collaborative operation control method for virtual marshaling high-speed railway trains proposed in the present invention can realize the collaborative operation control of virtual marshaling high-speed railway train groups under changing line conditions, and can achieve a certain degree of carbon reduction effect while improving the train operation efficiency, adapting to the green development requirements of the new era, and providing theoretical and methodological guidance for the actual operation of virtual marshaling trains.

[0144] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0145] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand 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 descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A virtual marshaling high-speed railway train coordinated operation control method based on carbon emissions, characterized in that: The following steps are involved: S1. Based on a single-point train model, perform a force analysis on a virtual high-speed railway train, establish a dynamic model of the virtual high-speed railway train, and calculate the expected distance of the virtual high-speed railway train; S2. Analyze the impact of line condition changes on the expected distance of the virtual marshaling high-speed railway train based on the expected distance of the virtual marshaling high-speed railway train, and calculate the line additional resistance during the braking process of the virtual marshaling high-speed railway train and the expected distance of the virtual marshaling high-speed railway train running on a non-ideal line; S3. Based on the dynamic model of the virtual marshaling high-speed railway train and the expected distance of the virtual marshaling high-speed railway train running on the non-ideal route, select state variables, establish and discretize the state space equation of the virtual marshaling high-speed railway train, and obtain the prediction equation of the virtual marshaling high-speed railway train for model predictive control; S4. Based on the principles of minimizing operational errors and minimizing operational carbon emissions, establish an intelligent control objective function for the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions; S5. Establish the constraint conditions of the intelligent control objective function of the coordinated operation of virtual high-speed railway train groups based on carbon emissions; S6. Based on the prediction equation of virtual marshaling high-speed railway trains using model predictive control, a quadratic transformation is performed on the objective function and constraints of the intelligent control of the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions, thereby obtaining an intelligent control optimization model for the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions; S7. Solve the intelligent control optimization model for the coordinated operation of virtual high-speed railway train groups based on carbon emissions to achieve safe and stable operation of virtual high-speed railway train groups under carbon emissions.

2. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 1 is characterized in that: The dynamic model of the virtual high-speed railway train in step S1 is: in, represents the first-order derivative of the position of the i-th virtual marshaling high-speed railway train, v i represents the speed of the i-th virtual marshaling high-speed railway train, represents the first-order derivative of the speed of the i-th virtual marshaling high-speed railway train, a i represents the acceleration of the i-th virtual marshaling high-speed railway train, represents the differential acceleration of the i-th virtual high-speed railway train, u i represents the control acceleration output by the i-th virtual marshaling high-speed railway train, τ represents the dynamic inertia lag of the virtual marshaling high-speed railway train, c1, c2, c3 represent the aerodynamic coefficient, drag coefficient, rolling friction and bearing sliding friction coefficient of the unit basic resistance of the virtual marshaling high-speed railway train, g represents the acceleration of gravity, f represents the unit basic resistance of the virtual marshaling high-speed railway train, w s Indicates the additional resistance per unit slope, w r Indicates the additional resistance of the unit curve, w l represents the additional resistance of the unit tunnel, slo represents the slope of the ramp, R represents the curve radius of the line, L s Indicates the length of the tunnel.

3. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 2 is characterized in that: The calculation formula for the expected distance of the virtual high-speed railway train in step S1 is: Among them, d des,i represents the expected distance between the i-th virtual marshaling high-speed railway train and the preceding train, S i,brake represents the braking distance of the i-th virtual marshaling high-speed railway train, i.e. the braking distance of the following train, S i-1,brake represents the braking distance of the i-1th virtual marshaling high-speed railway train, that is, the braking distance of the preceding train, d0 represents the safety margin, L represents the length of the virtual marshaling high-speed railway train, S extra It indicates the distance that the following train maintains its current speed during the information transmission and response process, T response 、T delay They represent the influence of following train reaction time and information transmission time respectively.

4. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 3 is characterized in that: Step S2 specifically includes: S21. Analyze the impact of changes in line conditions on the expected distance of a virtual high-speed train, including the impact of slopes, curves, and additional resistance in tunnels. S22. Based on the impact of line condition changes on the expected distance of the virtual marshaling high-speed railway train, the additional resistance of multiple continuous lines during the braking process of the virtual marshaling high-speed railway train is approximated by a single additional resistance line. The additional resistance line during the braking process of the virtual marshaling high-speed railway train is calculated, namely: Among them, G eb It represents the additional resistance of the line during the braking process of the virtual marshaling high-speed railway train, G eb,1 , G eb,2 , G eb,3 They represent the approximate slope additional resistance, the approximate curve additional resistance, and the approximate tunnel additional resistance during the braking process of the virtual marshaling high-speed railway train, sinθ′ represents the sine value of the approximate slope, and w′ r , w′ l H′ represents the average curve additional resistance and average tunnel additional resistance experienced by the virtual high-speed railway train during braking. eb represents the height of the virtual marshaling high-speed railway train during braking, L′ represents the length of each slope section experienced by the virtual marshaling high-speed railway train during braking, n represents the total number of slope sections experienced by the virtual marshaling high-speed railway train during braking, l1, l j 、l n They represent the 1st, jth and nth slopes experienced by the virtual marshaling high-speed railway train during braking, θ1, θ j ,θ n represents the slopes of the 1st, jth, and nth sections of the ramp experienced by the virtual marshaling high-speed railway train during braking, sinθ j represents the approximate sine value of the slope of the first and jth sections of the ramp experienced by the virtual marshaling high-speed railway train during braking, a eb Indicates the emergency braking acceleration of a virtual high-speed railway train; S23. Calculate the expected distance of a virtual marshaling high-speed railway train running on a non-ideal route, namely: Where d′ des,i represents the expected distance of the i-th virtual marshaling high-speed railway train running on a non-ideal line, v i-1 Indicates the speed of the i-1th virtual marshaling high-speed railway train.

5. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 4 is characterized in that: Step S3 specifically includes: S31. According to the dynamic model of the virtual marshaling high-speed railway train and the expected distance of the virtual marshaling high-speed railway train running on the non-ideal line, select the state variables, namely: x=[Δs i Δv i a i v i ] T =[s i-1 -s i -d′ des,i v i-1 -v i a i v i ] T Where x represents the state variable, T represents the transpose, and Δs i represents the position error between the i-th virtual marshaling high-speed railway train and the i-1-th virtual marshaling high-speed railway train, Δv i represents the speed error between the ith virtual marshaling high-speed railway train and the i-1th virtual marshaling high-speed railway train, s i-1 represents the position of the i-1th virtual marshaling high-speed railway train, s i represents the position of the i-th virtual marshaling high-speed railway train; S32. Based on the state variables, a state space equation of a virtual high-speed railway train is established, namely: in, represents the first-order derivative of the state variable, Represent different input coefficient matrices, u represents the control quantity, d represents the measurable interference, represents the output coefficient matrix, y represents the output of the state variable, f i-1 represents the unit basic resistance of the i-1th virtual marshaling high-speed railway train, f i represents the unit basic resistance of the i-th virtual marshaling high-speed railway train; S33. The state space equation of the virtual marshaling high-speed railway train is discretized using the forward Euler method to obtain the discretized state space equation of the virtual marshaling high-speed railway train, namely: Among them, k represents the time, x(k+1) represents the state variable discretized at the k+1th time, x(k) represents the state variable discretized at the kth time, A, B u 、B d , Z represent the discretized different input coefficient matrices, A(k), B u (k), B d (k) and Z(k) represent the different input coefficient matrices discretized at the kth moment, u(k) represents the control quantity discretized at the kth moment, d(k) represents the measurable disturbance discretized at the kth moment, C represents the discretized output coefficient matrix, and y(k+1) represents the output of the state variable at the kth moment; S34. Perform model predictive control based on the state space equation of the discretized virtual marshaling high-speed railway train to obtain a prediction equation of the virtual marshaling high-speed railway train for model predictive control.

6. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 5 is characterized in that: Step S34 specifically includes: S341. Set the prediction time domain to p and the control time domain to m, with m≤p, and assume that the control variables outside the control time domain do not change, that is, u(k)=u(k+j1), j1=m,m+1,...,p-1. At the same time, the measurable interference is approximately the predicted value of the previous stage; where u(k+j1) represents the discretized control variable at the k+j1th time. S342. Based on the state space equation of the discretized virtual marshaling high-speed railway train, the state variables of p steps in the prediction time domain are obtained, namely: Among them, x(k+m) represents the discretized state variable at the k+mth moment, A m 、A m-1 They represent the input coefficient matrices under the control time domain of m and m-1 respectively, u(k+m-1) represents the control quantity discretized at the k+m-1th moment, d(k+m-1) represents the measurable disturbance discretized at the k+m-1th moment, x(k+p) represents the state variable discretized at the k+pth moment, A p 、A p-1 They represent the input coefficient matrices in the prediction time domain of p and p-1 respectively, u(k+p-1) represents the control quantity discretized at the k+p-1th moment, and d(k+p-1) represents the measurable interference discretized at the k+p-1th moment; S343. Based on the state variables of step p in the prediction time domain, the predicted output of the state variables at time k+p is calculated, that is: Where y(k+m / k) represents the predicted output of the state variable at time k+m predicted by the state variable at time k, and y(k+p / k) represents the predicted output of the state variable at time k+p predicted by the state variable at time k; S344. Based on the predicted output of the state variable at the k+pth moment, generate the predicted output vector and the control quantity vector for p steps in the prediction time domain, that is: Among them, Y p (k+1 / k) represents the predicted output vector of the state variable at time k+1 predicted by the state variable at time k in p steps in the prediction domain, y(k+1 / k) represents the predicted output of the state variable at time k+1 predicted by the state variable at time k, y(k+2 / k) represents the predicted output of the state variable at time k+2 predicted by the state variable at time k, U(k) represents the control quantity vector at time k, D(k) represents the measurable disturbance vector at time k, u(k+1) represents the discretized control quantity at time k+1, and d(k+1) represents the discretized measurable disturbance at time k+1; S345. Based on the predicted output vector and the control quantity vector of the p-step prediction time domain, a prediction equation for a virtual marshaling high-speed railway train of model predictive control is obtained, namely: Y p (k+1 / k)=Ψx(k)+ΘU(k)+ΓD(k)+H Among them, Ψ, Θ, Γ, and H represent the state variable coefficient matrix, control quantity vector coefficient matrix, measurable interference vector coefficient matrix, and constant matrix at the kth moment, respectively.

7. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 6 is characterized in that: The objective function of intelligent control of coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions in step S4 is: Among them, J represents the control objective function, ξ represents the relaxation factor, ρ represents the weight coefficient, and q 1,j1 ,q 2,j1 They represent the weighting factors of the position error and speed error of the virtual high-speed railway train at the k+j1th moment, d i (k+j1 / k) represents the actual running distance between the i-1th virtual marshaling high-speed railway train and the i-th virtual marshaling high-speed railway train at the k+j1th time, d′ des,i (k+j1) represents the expected distance between the i-1th virtual marshaling high-speed railway train and the i-th virtual marshaling high-speed railway train running on the non-ideal line at the k+j1th time, v i-1 (k+j1 / k) represents the speed of the i-1th virtual marshaling high-speed railway train at the k+j1th time, v i (k+j1 / k) represents the speed of the i-th virtual marshaling high-speed railway train at the k+j1th time, s j1 It represents the energy consumption weight of the virtual marshaling high-speed railway train at the k+j1th moment, and E(k+j1) represents the operating energy consumption value of the virtual marshaling high-speed railway train at the k+j1th moment.

8. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 7 is characterized in that: Step S5 specifically includes: S51. Establish safety interval constraints, namely: d′ des,i -ξ≤s i-1 -s i ≤d max +ξ Among them, d max Indicates the maximum operating interval; S52. Establish speed constraints, namely: 0≤v i ≤v max Among them, v max Indicates the maximum speed; S53. Establish control performance constraints, namely: -a br_max ≤u i ≤a ac_max Among them, a br_max represents the maximum braking deceleration of a virtual high-speed railway train, a ac_max Indicates the maximum traction acceleration of the virtual marshaling high-speed railway train; S54. Establish operating power constraints, namely: -P br ≤Mu i v0≤P ac Among them, P br represents the maximum braking power of a virtual high-speed railway train, P ac represents the maximum traction power of the virtual marshaling high-speed railway train, M represents the mass of the virtual marshaling high-speed railway train, and v0 represents the expected running speed of the virtual marshaling high-speed railway train; S55. Establish comfort constraints, namely: Thu min -ξ≤Δu≤Δu max +ξ Where Δu min Indicates the minimum value of the acceleration change rate, Δu indicates the acceleration change rate, that is, the acceleration change rate is used to express the passenger comfort, Δu max Indicates the maximum value of the acceleration rate of change.

9. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 8 is characterized in that: Step S6 specifically includes: S61. Based on the prediction equation of virtual marshaling high-speed railway trains under model predictive control, a quadratic transformation is performed on the intelligent control objective function of the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions to obtain the transformed intelligent control objective function of the coordinated operation of virtual marshaling high-speed railway train groups based on carbon emissions, namely: Wherein, J′ represents the converted carbon emission-based virtual marshaling high-speed railway train group coordinated operation intelligent control objective function, V represents the coefficient matrix composed of the square of the expected running speed of the virtual marshaling high-speed railway train, v0(k) represents the expected running speed of the virtual marshaling high-speed railway train at the kth moment, S represents the symmetric energy consumption weight coefficient matrix, Q represents the symmetric position and speed error weight coefficient matrix, T s represents a discrete time interval; S62. Perform a quadratic transformation on the safety interval constraint and the speed constraint, namely: -Ψx-ΓD-H≤ΘU≤2d0-Ψx-ΓD-H; S63. Perform a quadratic transformation on the control performance constraint, namely: IN min ≤U≤U max Among them, U min Indicates the minimum value of the control quantity vector, U max Indicates the maximum value of the control quantity vector; S64. Perform a quadratic transformation on the operating power constraint to obtain a transformed operating power constraint, namely: P min / M / V0≤U≤P max / M / V0 Among them, P min Represents the maximum braking power vector, P max represents the maximum traction power vector, V0 represents the expected operating speed vector; S65. Perform a quadratic transformation on the comfort constraint, namely: ΔU min ≤ΔU≤ΔU max Among them, ΔU min Represents the minimum acceleration change vector, ΔU max represents the maximum value vector of acceleration change, and ΔU represents the acceleration change vector; S66. According to the converted intelligent control objective function of the coordinated operation of the virtual marshaling high-speed railway train group based on carbon emissions and the converted constraint conditions, an intelligent control optimization model of the coordinated operation of the virtual marshaling high-speed railway train group based on carbon emissions is obtained.

10. The carbon emission-based virtual marshaling high-speed railway train coordinated operation control method according to claim 9, characterized in that: The intelligent control optimization model for the coordinated operation of virtual high-speed railway train groups based on carbon emissions in step S66 is: Among them, min means taking the minimum value, and st means making it satisfy.

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