A speed optimization method for energy storage trams based on pseudospectral method

Abstract

This invention relates to a speed optimization method for energy storage trams based on pseudospectral method, comprising the following steps: Step 1: Establishing a scenario-based environmental model of the energy storage tram in urban roads; Step 2: Establishing a kinematic model of the energy storage tram; Step 3: Establishing model constraints for the energy storage tram with the goal of minimizing total energy consumption during operation; Step 4: Optimizing the traction and braking force output of the energy storage tram using pseudospectral method to further optimize its speed trajectory, ensuring efficient passage through intersections during green light phases and minimizing total energy consumption during operation. This invention addresses the situation where energy storage trams share traffic lights with motor vehicles in urban areas, considering the impact of traffic lights on the operating status of the energy storage tram. It optimizes the speed trajectory, traction, and braking force of the energy storage tram to enable it to smoothly pass through intersections without stopping, thereby reducing total energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology in cities, and specifically relates to a speed optimization method for energy storage trams based on pseudospectral method. Background Technology

[0002] Energy storage trams, positioned between buses and trains, effectively alleviate traffic congestion, improve travel efficiency, and reduce air pollution, and have gradually become an important component of urban rail transit in my country. However, with the development of urban rail transit, the energy consumption of urban rail transit systems is constantly increasing. Therefore, it is essential to study energy-saving and emission-reduction methods for trains. Currently, the main energy-saving methods include improving motor efficiency, reducing train resistance, and optimizing train operating speed. Among these, optimizing train operating conditions is an effective, simple, and reliable method. It does not require additional infrastructure but improves traffic efficiency by optimizing train speed and traction distribution. By optimizing the train speed curve between stations, the traction energy consumption of the train can be reduced, thereby reducing the total energy consumption of the rail transit system.

[0003] Currently, scholars both domestically and internationally have conducted extensive research on the speed optimization problem between stations for energy storage trams. Some scholars have used energy conservation as the optimal control objective for trains and employed an indirect method based on the maximum principle to solve for the optimal control strategy, obtaining the optimal operating speed. However, this method is difficult to apply when there are many constraints. Other scholars have used dynamic programming to study the optimal operating speed of trains, finding that this method only has online optimization capabilities when the distance is relatively short. While the above studies have conducted extensive research on the speed optimization control of energy storage trams, energy storage trams in cities often share traffic lights with motor vehicles. How to consider the impact of traffic lights on the operating status of energy storage trams between stations and how to optimize the speed trajectory of energy storage trams to allow them to pass smoothly through intersections without stopping remains a lack of relevant research.

[0004] Therefore, this application will take energy storage trams as the research object, considering the actual constraints of energy storage trams such as braking energy recovery, speed limit, traffic lights, maximum acceleration, and maximum output power, and study the speed optimization control process of energy storage trams passing through traffic lights between platforms. An optimization model will be constructed with time as the independent variable, traction / braking force as the control variable, and operating energy consumption as the objective function. The optimal operating speed of the energy storage tram will be solved using the pseudospectral method-based GPOPS, providing a reference speed curve and optimal control input for the operation of energy storage trams, and effectively reducing the traction energy consumption of energy storage trams operating between stations. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a speed optimization method for energy storage trams based on pseudospectral method to address the issue of how to consider the impact of traffic lights on the operation of energy storage trams when they share traffic lights with motor vehicles, and how to optimize the speed trajectory of energy storage trams so that they can pass through intersections smoothly without stopping, thereby reducing energy consumption.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A method for speed optimization of energy storage trams based on pseudospectral method is provided, the innovation of which lies in the following steps:

[0007] Step 1: Establish a scenario-based environment model for energy storage trams on urban roads;

[0008] Step 2: Establish the kinematic model of the energy storage tram;

[0009] Step 3: The energy storage tram takes minimizing the total energy consumption during operation as the optimization objective, and establishes model constraints based on the state when leaving platform A and arriving at platform B, as well as the timing information of traffic lights;

[0010] Step 4: The energy storage tram optimizes its traction and braking force output through the pseudospectral method, further optimizing its speed trajectory to ensure efficient passage through intersections during green light phases, minimizing total energy consumption during operation, thereby completing the speed optimization of the energy storage tram.

[0011] Furthermore, the scenario-based environment model of the energy storage tram in urban roads in step 1 is as follows: Station A and Station B are set up, and a crossroads with traffic lights is located between Station A and Station B. A control unit (ICU) is also located at the crossroads. The distance from the traffic lights to Station A is s1, and the distance from the traffic lights to Station B is s2. The energy storage tram travels from Station A to Station B at an initial speed of v0, passing through the crossroads. When the red light is on, the energy storage tram must stop. During the travel of the energy storage tram between Station A and the traffic lights, the control unit (ICU) sends the traffic light signal timing scheme to the energy storage tram.

[0012] Furthermore, the kinematic model of the energy storage tram in step 2 is as follows:

[0013]

[0014]

[0015] Where s is the mileage of the energy storage tram in meters; v is the speed of the energy storage tram in m / s; t is the time of travel of the energy storage tram in seconds; M is the mass of the energy storage tram in tons; and λ is the rotational mass coefficient of the energy storage tram. a F b R(v) represents the traction force and braking force of the energy storage tram, respectively, in N; R(v) represents the basic resistance of the energy storage tram during operation, in N; G(s) represents the track adhesion resistance of the energy storage tram during operation, including turning resistance and gradient resistance, in N.

[0016] Furthermore, the basic resistance and track adhesion resistance of the energy storage tram during operation can be expressed as follows:

[0017] R(v) = α + β·v + χ·v 2

[0018]

[0019] Where α, β, and χ are the basic drag coefficients, i(s) represents the gradient percentage (‰), r represents the turning radius of the track (m), and g represents the acceleration due to gravity (m / s²). 2 .

[0020] Furthermore, the optimization objective in step 3 is expressed as:

[0021]

[0022] Where J represents the total energy consumption of the energy storage system, in kWh; t0 represents the departure time of the energy storage tram from platform A, in seconds; t f This indicates the arrival time of the energy storage tram at platform B, in seconds (s); p. a The output power of the energy storage system of the energy storage tram when discharging to provide traction is expressed in kW; pb represents the input power of the energy storage system when the energy storage tram brakes, expressed in kW.

[0023] Furthermore, the output power of the energy storage system of the energy storage tram when discharging to provide traction and the input power of the energy storage system when recovering energy during braking can be expressed as follows:

[0024]

[0025] p b =ε·F b ·v·μ g ·u m ·u i·u ch ·u dc

[0026] Where μg represents the gearbox transmission efficiency; u m Indicates motor efficiency; u i Inverter efficiency; ud is Indicates the discharge efficiency of the energy storage system; u dc Indicates the efficiency of the DC / DC converter; u ch ε represents the charging efficiency of the energy storage system when recovering energy; ε represents the energy recovery and utilization rate during braking.

[0027] Furthermore, the model constraints in step 3 are expressed as follows:

[0028] 0≤v(t)≤v max

[0029] a min ≤a(t)≤a max

[0030] 0≤F a ≤F a max

[0031] F b min ≤F b ≤0

[0032] Among them, v max This indicates the maximum speed of the energy storage tram, in m / s; a min This represents the minimum acceleration of an energy storage tram, measured in m / s². 2 ;a max This indicates the maximum acceleration of the energy storage tram, in m / s². 2 F b min This indicates the minimum braking force of an energy storage tram, measured in N (N); F. a max This indicates the maximum traction force of the energy storage tram, in N.

[0033] To optimize the speed of energy storage trams passing through traffic lights and to enable them to pass through traffic lights without stopping, it is necessary to add constraints on the speed of energy storage trams based on traffic light phase timing, specifically:

[0034]

[0035]

[0036] Among them, v low and v high These represent the minimum and maximum speeds of the energy storage tram as it passes through the intersection during the green light phase, respectively, in m / s; v minThe minimum speed of the energy storage tram is indicated by m / s; D represents the distance from the starting point to the traffic light; t g t represents the time remaining until the next green light begins; r This represents the time remaining until the next green light ends; the cycle of the traffic light is T; t f Indicates the terminal time.

[0037] Furthermore, the method for optimizing traction and braking force output using the pseudospectral method in step 4 is as follows: The kinematic model of the energy storage tram is converted into the following form using the Matlab optimization tool GPOPS based on the pseudospectral method:

[0038]

[0039] Convert the model constraints to:

[0040] q min ≤q(x(t),u(t),t)≤q max (t)

[0041] in:

[0042] q(x(t),u(t),t)=[x2,dx1 / dx2,u1,u2]

[0043] q min =[0,a min ,0,0] T

[0044] q max =[v max (t),a max Fa max ,Fb max ] T

[0045] The nonlinear programming problem obtained by transforming the kinematic model and model constraints as described above is: based on the kinematic model of the energy storage tram, find the optimal control variable F while satisfying the model constraints and the speed constraints of the energy storage tram under traffic light phase timing. a and F b To minimize total energy consumption during operation.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] (1) In the case of energy storage trams and motor vehicles sharing traffic lights in cities, the impact of traffic lights on the operation status of energy storage trams is considered.

[0048] (2) The speed trajectory, traction force and braking force of the energy storage tram were optimized so that it could pass through the intersection smoothly without stopping, thereby reducing the total energy consumption.

[0049] (3) Taking the energy storage tram as the research object, the actual constraints such as braking energy recovery, speed limit, maximum acceleration and maximum output power of the energy storage tram are considered. The pseudo-spectral method is used to solve the optimal operating speed of the energy storage tram, providing a reference speed curve and optimal control input for the operation of the energy storage tram. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0051] Figure 1 This is a flowchart of the method of the present invention.

[0052] Figure 2 This is a schematic diagram of the scenario construction of the present invention.

[0053] Figure 3 This is a schematic diagram of an energy storage tram formation.

[0054] Figure 4a This is a speed comparison chart of the energy storage tram before and after optimization.

[0055] Figure 4b This is a comparison chart of the positions of the energy storage tram before and after optimization.

[0056] Figure 4c This is a comparison chart of dynamic energy consumption before and after optimization of the energy storage tram. Detailed Implementation

[0057] To enable those skilled in the art to better understand the present invention, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0058] This invention provides a speed optimization method for energy storage trams based on the pseudospectral method, the specific process of which is as follows: Figure 1 As shown, it includes the following steps:

[0059] Step 1: Establish a scenario-based environment model for the energy storage tram on urban roads. Specifically, the scenario-based environment model for the energy storage tram on urban roads is as follows: Station A and Station B are set up, and a crossroads with traffic lights is located between Station A and Station B. A control unit (ICU) is also located at the crossroads. The distance from the traffic lights to Station A is s1, and the distance from the traffic lights to Station B is s2. The energy storage tram travels from Station A to Station B with an initial speed of v0, passing through the crossroads. When the red light is on, the energy storage tram must stop. During the travel of the energy storage tram between Station A and the traffic lights, the control unit (ICU) sends the traffic light signal timing scheme to the energy storage tram.

[0060] Step 2: Establish the kinematic model of the energy storage tram. The kinematic model of the energy storage tram is as follows:

[0061]

[0062]

[0063] Where s is the mileage of the energy storage tram in meters; v is the speed of the energy storage tram in m / s; t is the time of travel of the energy storage tram in seconds; M is the mass of the energy storage tram in tons; and λ is the rotational mass coefficient of the energy storage tram. a F b R(v) represents the traction force and braking force of the energy storage tram, respectively, in N; R(v) represents the basic resistance of the energy storage tram during operation, in N; G(s) represents the track adhesion resistance of the energy storage tram during operation, including turning resistance and gradient resistance, in N.

[0064] The basic resistance and track adhesion resistance of the energy storage tram during operation can be expressed as follows:

[0065] R(v) = α + β·v + χ·v 2

[0066]

[0067] Where α, β, and χ are the basic drag coefficients, i(s) represents the gradient percentage (‰), r represents the turning radius of the track (m), and g represents the acceleration due to gravity (m / s²). 2 .

[0068] Step 3: The energy storage tram aims to minimize total energy consumption during its journey. Model constraints are established based on the states when leaving platform A and arriving at platform B, as well as the traffic light timings. The optimization objective is expressed as:

[0069]

[0070] Where J represents the total energy consumption of the energy storage system, in kWh; t0 represents the departure time of the energy storage tram from platform A, in seconds; t f This indicates the arrival time of the energy storage tram at platform B, in seconds (s); p. a This refers to the output power of the energy storage system of an energy storage tram when it discharges to provide traction, measured in kW (kilowatts). b This indicates the input power of the energy storage system of the energy storage tram during braking, expressed in kW.

[0071] In the above optimization objective formula, the output power of the energy storage system of the energy storage tram when discharging to provide traction and the input power of the energy storage system when the energy storage tram brakes can be expressed as follows:

[0072]

[0073] p b =ε·F b ·v·μ g ·u m ·u i ·u ch ·u dc

[0074] Where, μ g Indicates the gearbox transmission efficiency; u m Indicates motor efficiency; u i Inverter efficiency; u dis Indicates the discharge efficiency of the energy storage system; u dc Indicates the efficiency of the DC / DC converter; u ch ε represents the charging efficiency of the energy storage system when recovering energy; ε represents the energy recovery and utilization rate during braking.

[0075] The model constraints are expressed as follows:

[0076] 0≤v(t)≤v max

[0077] a min ≤a(t)≤a max

[0078] 0≤F a ≤F a max

[0079] F b min ≤F b ≤0

[0080] Among them, v maxThis indicates the maximum speed of the energy storage tram, in m / s; a min This represents the minimum acceleration of an energy storage tram, measured in m / s². 2 ;a max This indicates the maximum acceleration of the energy storage tram, in m / s². 2 F b min This indicates the minimum braking force of an energy storage tram, measured in N (N); F. a max This indicates the maximum traction force of the energy storage tram, in N.

[0081] To optimize the speed of energy storage trams passing through traffic lights and to enable them to pass through traffic lights without stopping, it is necessary to add constraints on the speed of energy storage trams based on traffic light phase timing, specifically:

[0082]

[0083]

[0084] Among them, v low and v high These represent the minimum and maximum speeds of the energy storage tram as it passes through the intersection during the green light phase, respectively, in m / s; v min The minimum speed of the energy storage tram is indicated by m / s; D represents the distance from the starting point to the traffic light; t g t represents the time remaining until the next green light begins; r This represents the time remaining until the next green light ends; the cycle of the traffic light is T; t f Indicates the terminal time.

[0085] Step 4: The energy storage tram optimizes its traction and braking force output through the pseudospectral method, further optimizing its speed trajectory to ensure efficient passage through intersections during green light phases, minimizing total energy consumption during operation, thereby completing the speed optimization of the energy storage tram.

[0086] The method for optimizing traction and braking force output using the pseudospectral method is as follows: The kinematic model of the energy storage tram is transformed into the following using the Matlab optimization tool GPOPS, which is based on the pseudospectral method:

[0087]

[0088] Convert the model constraints to:

[0089] q min ≤q(x(t),u(t),t)≤q max (t)

[0090] in:

[0091] q(x(t),u(t),t)=[x2,dx1 / dx2,u1,u2]

[0092] q min =[0,a min ,0,0] T

[0093] q max =[v max (t),a max Fa max ,Fb max ] T

[0094] The nonlinear programming problem obtained by transforming the kinematic model and model constraints as described above is: based on the kinematic model of the energy storage tram, find the optimal control variable F while satisfying the model constraints and the speed constraints of the energy storage tram under traffic light phase timing. a and F b To minimize total energy consumption during operation.

[0095] The present invention will be further illustrated by the following specific embodiments in response to the above technical solutions:

[0096] Example 1

[0097] A speed optimization method for energy storage trams based on the pseudospectral method was simulated and tested using MATLAB software. The flowchart of the method is shown below. Figure 1 As shown, it includes the following steps:

[0098] Step 1: Establish a scenario-based environment model for energy storage trams on urban roads, such as... Figure 2 As shown, the scenario-based environment model of the energy storage tram in urban roads is as follows: Platform A and Platform B are set up, and a crossroads with traffic lights is located between Platform A and Platform B. A control unit (ICU) is also located at the crossroads. The distance from the traffic lights to Platform A is 1 km, and the distance from the traffic lights to Platform B is 1 km. The energy storage tram travels from Platform A to Platform B at an initial speed of 0 m / s, passing through the crossroads. When the red light is on, the energy storage tram must stop. During the travel of the energy storage tram between Platform A and the traffic lights, the control unit (ICU) sends the traffic light signal timing scheme to the energy storage tram.

[0099] Step 2: Establish the kinematic model of the energy storage tram. The kinematic model of the energy storage tram is as follows:

[0100]

[0101]

[0102] Where s is the mileage of the energy storage tram in meters; v is the speed of the energy storage tram in m / s; t is the time of travel of the energy storage tram in seconds; M is the mass of the energy storage tram, which is 80t; λ is the rotational mass coefficient of the energy storage tram, which is 0.06; F a F b R(v) represents the traction force and braking force of the energy storage tram, respectively, in N; R(v) represents the basic resistance of the energy storage tram during operation, in N; G(s) represents the track adhesion resistance of the energy storage tram during operation, including turning resistance and gradient resistance, in N.

[0103] The basic resistance and track adhesion resistance of the energy storage tram during operation can be expressed as follows:

[0104] R(v) = α + β·v + χ·v 2

[0105]

[0106] Where α, β, and χ are the basic drag coefficients, taken as 6.891, 0.0912, and 0.0045 respectively; i(s) represents the gradient percentage (0‰); r represents the turning radius of the track (0m); and g represents the acceleration due to gravity (9.81m / s²). 2 .

[0107] Step 3: The energy storage tram aims to minimize total energy consumption during its journey. Model constraints are established based on the states when leaving platform A and arriving at platform B, as well as the traffic light timings. The optimization objective is expressed as:

[0108]

[0109] Where J represents the total energy consumption of the energy storage system, in kWh; t0 represents the departure time of the energy storage tram from platform A, in seconds; t f This indicates the arrival time of the energy storage tram at platform B, in seconds (s); p. a This refers to the output power of the energy storage system of an energy storage tram when it discharges to provide traction, measured in kW (kilowatts). b This indicates the input power of the energy storage system of the energy storage tram during braking, expressed in kW.

[0110] In this invention, the specific train formation sequence of the energy storage tram is WMTW, that is, the order of the carriages, such as... Figure 3As shown. Where W represents the motor vehicle with a driver's cab, M represents the motor vehicle, and T represents the trailer. The output power of the energy storage system of the energy storage tram when discharging to provide traction and the input power of the energy storage system recovering energy during braking can be expressed as follows:

[0111]

[0112] p b =ε·F b ·v·μ g ·u m ·u i ·u ch ·u dc

[0113] Where, μ g This represents the gearbox transmission efficiency, taken as 0.9; u m This represents the motor efficiency, taken as 0.9; u i Inverter efficiency, taken as 0.9; u dis This represents the discharge efficiency of the energy storage system, taken as 0.85; u dc This represents the efficiency of the DC / DC converter, taken as 0.9; u ch ε represents the charging efficiency of the energy storage system when recovering energy, taken as 0.8; ε represents the energy recovery and utilization rate during braking, taken as 0.74.

[0114] The model constraints are expressed as follows:

[0115] 0≤v(t)≤v max

[0116] a min ≤a(t)≤a max

[0117] 0≤F a ≤F amax

[0118] F b min ≤F b ≤0

[0119] Among them, v max This indicates the maximum speed of the energy storage tram is 20 m / s; a min The minimum acceleration of the energy storage tram is -1 m / s². 2 ;a max This indicates the maximum acceleration of the energy storage tram, which is 1.2 m / s². 2 ;F b min The minimum braking force for an energy storage tram is -110 kN; F a max This indicates that the maximum traction force of the energy storage tram is 94 kN;

[0120] To optimize the speed of energy storage trams passing through traffic lights and to enable them to pass through traffic lights without stopping, it is necessary to add constraints on the speed of energy storage trams based on traffic light phase timing, specifically:

[0121]

[0122]

[0123] Among them, v low and v high These represent the minimum and maximum speeds of the energy storage tram as it passes through the intersection during the green light phase, respectively, in m / s; v min The minimum speed of the energy storage tram is 0 m / s; D represents the distance from the starting point to the traffic light, which is 1 km; t g t represents the time remaining until the next green light begins; r This indicates the time remaining until the next green light ends; the traffic light cycle is T, which is 120 seconds; t f Indicates the terminal time.

[0124] Step 4: The energy storage tram optimizes its traction and braking force output through the pseudospectral method, further optimizing its speed trajectory to ensure efficient passage through intersections during green light phases, minimizing total energy consumption during operation, thereby completing the speed optimization of the energy storage tram.

[0125] The method for optimizing traction and braking force output using the pseudospectral method is as follows: The kinematic model of the energy storage tram is transformed into the following using the Matlab optimization tool GPOPS, which is based on the pseudospectral method:

[0126]

[0127] Convert the model constraints to:

[0128] q min ≤q(x(t),u(t),t)≤q max (t)

[0129] in:

[0130] q(x(t),u(t),t)=[x2,dx1 / dx2,u1,u2]

[0131] q min =[0,a min ,0,0] T

[0132] q max =[v max (t),amax ,F a max ,F b max ] T

[0133] The nonlinear programming problem obtained by transforming the kinematic model and model constraints as described above is: based on the kinematic model of the energy storage tram, find the optimal control variable F while satisfying the model constraints and the speed constraints of the energy storage tram under traffic light phase timing. a and F b To minimize total energy consumption during operation.

[0134] To verify the effectiveness of the algorithm, a control group was set up in this embodiment. The control group traveled with constant acceleration, that is, the acceleration was randomly generated within the range of minimum and maximum values. Based on the kinematic model of the energy storage tram and the optimization objective, the travel trajectory, travel speed and energy consumption of the control group were calculated to verify the effectiveness of the invention.

[0135] Figure 4a and Figure 4b The relationship between speed and position of the energy storage tram before and after optimization was compared. In the figure, the solid line represents the situation before optimization, and the dashed line represents the situation after applying the method of this invention. Figure 4a and Figure 4b As can be seen, the energy storage tram starts from platform A with an initial speed of 0 m / s. Without optimization, it uses constant acceleration and travels at its maximum speed when it is far from the traffic light. When it reaches 822m, it decelerates sharply, with the tram speed dropping from 20 m / s to 9.2 m / s in 11 seconds. After passing the traffic light, it accelerates sharply again. The acceleration and deceleration are large and too frequent, resulting in poor passenger comfort and significant energy loss. When the energy storage tram is optimized using the method of this invention, it can receive the signal timing information of the next traffic light throughout its journey from platform A, and begin speed trajectory optimization. As shown in the figure, compared to before optimization, the speed change is relatively slow, especially when approaching the traffic light. The energy storage tram's speed decreases by only 3.33 m / s within 11 seconds, and it quickly passes the traffic light at 16.67 m / s. After passing the traffic light, it accelerates slowly. The acceleration and deceleration amplitudes are small throughout the entire operation, avoiding sudden deceleration, sudden acceleration, and stopping caused by reaching the stop line at the traffic light within the red light time window. Figure 4c As shown, the rapid acceleration of the energy storage tram when passing through traffic lights greatly increases its energy consumption. The optimized energy consumption trajectory is relatively smooth, without any intense energy consumption. This indicates that the optimization algorithm proposed in this invention can significantly reduce the energy consumption of the train during operation by optimizing the travel speed of the energy storage tram. The energy consumption after optimization is reduced by 23.45% compared to before optimization.

[0136] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, all modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope of the present invention. The technical content for which protection is sought in the present invention has been fully described in the technical requirements.

Claims

1. A speed optimization method for energy storage trams based on pseudospectral method, characterized in that: Includes the following steps: Step 1: Establish a scenario-based environment model for energy storage trams on urban roads; Step 2: Establish the kinematic model of the energy storage tram; Step 3: The energy storage tram takes minimizing the total energy consumption during operation as the optimization objective, and establishes model constraints based on the state when leaving platform A and arriving at platform B, as well as the timing information of traffic lights; The model constraints are expressed as follows: 0≤v(t)≤v max a min ≤a(t)≤a max 0≤F a ≤F amax F bmin ≤F b ≤0 Among them, v max This indicates the maximum speed of the energy storage tram, in m / s; a min This represents the minimum acceleration of an energy storage tram, measured in m / s². 2 ;a max This indicates the maximum acceleration of the energy storage tram, in m / s². 2 F bmin This indicates the minimum braking force of an energy storage tram, measured in N (N); F. amax This indicates the maximum traction force of the energy storage tram, in N. To optimize the speed of energy storage trams passing through traffic lights and to enable them to pass through traffic lights without stopping, it is necessary to add constraints on the speed of energy storage trams based on traffic light phase timing, specifically: Among them, v low and v high These represent the minimum and maximum speeds of the energy storage tram as it passes through the intersection during the green light phase, respectively, in m / s; v min The minimum speed of the energy storage tram is indicated by m / s; D represents the distance from the starting point to the traffic light; t g t represents the time remaining until the next green light begins; r This represents the time remaining until the next green light ends; the cycle of the traffic light is T; t f Indicates the terminal time; Step 4: The energy storage tram optimizes its traction and braking force output through the pseudospectral method, further optimizing its speed trajectory to ensure efficient passage through intersections during green light phases, minimizing total energy consumption during operation, thereby completing the speed optimization of the energy storage tram.

2. The speed optimization method for energy storage trams based on pseudospectral method according to claim 1, characterized in that: The specific environmental model of the energy storage tram in urban roads in step 1 is as follows: Station A and Station B are set up, and a crossroads with traffic lights is located between Station A and Station B. A control unit (ICU) is also located at the crossroads. The distance from the traffic lights to Station A is s1, and the distance from the traffic lights to Station B is s2. The energy storage tram travels from Station A to Station B at an initial speed of v0, passing through the crossroads. When the red light is on, the energy storage tram must stop. During the travel of the energy storage tram between Station A and the traffic lights, the control unit (ICU) sends the traffic light signal timing scheme to the energy storage tram.

3. The speed optimization method for energy storage trams based on pseudospectral method according to claim 1, characterized in that: The kinematic model of the energy storage tram in step 2 is as follows: Where s is the mileage of the energy storage tram in meters; v is the speed of the energy storage tram in m / s; t is the time of travel of the energy storage tram in seconds; M is the mass of the energy storage tram in tons; and λ is the rotational mass coefficient of the energy storage tram. a F b R(v) represents the traction force and braking force of the energy storage tram, respectively, in N; R(v) represents the basic resistance of the energy storage tram during operation, in N; G(s) represents the track adhesion resistance of the energy storage tram during operation, including turning resistance and gradient resistance, in N.

4. The speed optimization method for energy storage trams based on pseudospectral method according to claim 3, characterized in that: The basic resistance and track adhesion resistance of the energy storage tram during operation can be expressed as follows: R(v)=α+β·v+x·v 2 Where α, β, and χ are the basic drag coefficients, i(s) represents the gradient percentage (‰), r represents the turning radius of the track (m), and g represents the acceleration due to gravity (m / s²). 2 .

5. The speed optimization method for energy storage trams based on pseudospectral method according to claim 1, characterized in that: The optimization objective in step 3 is expressed as: Where J represents the total energy consumption of the energy storage system, in kWh; t0 represents the departure time of the energy storage tram from platform A, in seconds; t f This indicates the arrival time of the energy storage tram at platform B, in seconds (s); p. a This refers to the output power of the energy storage system of an energy storage tram when it discharges to provide traction, measured in kW (kilowatts). b This indicates the input power of the energy storage system of the energy storage tram during braking, expressed in kW.

6. The speed optimization method for energy storage trams based on pseudospectral method according to claim 5, characterized in that: The output power of the energy storage system of the energy storage tram when discharging to provide traction and the input power of the energy storage system when recovering energy during braking can be expressed as follows: P b =ε·F b ·v·μ g ·u m ·u i ·u ch ·u dc Where, μ g Indicates the gearbox transmission efficiency; u m Indicates motor efficiency; u i Inverter efficiency; u dis Indicates the discharge efficiency of the energy storage system; u dc Indicates the efficiency of the DC / DC converter; u ch ε represents the charging efficiency of the energy storage system when recovering energy; ε represents the energy recovery and utilization rate during braking.

7. The speed optimization method for energy storage trams based on pseudospectral method according to claim 1, characterized in that: The method for optimizing traction and braking force output in step 4 is as follows: The kinematic model of the energy storage tram is converted using the Matlab optimization tool GPOPS, which is based on the pseudospectral method. Convert the model constraints to: q min ≤q(x(t),u(t),t)≤q max (t) in: q(x(t),u(t),t)=[x2,dx1 / dx2,u1,u2] q min =[0,a min ,0,0] T q max =[v max (t),a max ,Fa max ,Fb max ] T The nonlinear programming problem obtained by transforming the kinematic model and model constraints as described above is: based on the kinematic model of the energy storage tram, find the optimal control variable F while satisfying the model constraints and the speed constraints of the energy storage tram under traffic light phase timing. a and F b To minimize total energy consumption during operation.

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