A highway train-oriented energy consumption optimal cooperative speed planning method

Abstract

The application discloses a kind of energy consumption optimal cooperative speed planning methods for highway train, belong to intelligent transportation technical field.The method first obtains driving environment and vehicle parameter;Second, based on dynamic physical model, slope, wind resistance and other dynamic weight coefficients are calculated;Then, construct the joint objective function of fusion energy consumption, efficiency and safety, and load speed, spacing and formation sequence constraint, establish mixed integer programming model;Next, the nonlinear term in model is linearized to obtain standardization model;Finally, real-time optimization is solved using solver, and the speed, spacing and formation sequence of each vehicle are output to make the global energy consumption of formation optimal.The application overcomes the problem that the prior art lacks formation cooperation and has poor environmental adaptability, through multi-vehicle dynamics coupling and real-time optimization, realizes the joint cooperative planning of highway train speed and spacing in dynamic environment, and significantly reduces the overall transportation energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation and vehicle cooperative speed planning technology, specifically involving an energy-optimal cooperative speed planning method for highway trains. The method of this invention is based on dynamic physical modeling and mixed integer programming for highway train platooning cooperative planning. Through multi-vehicle dynamics coupling analysis and real-time optimization solution, it realizes joint optimization of platooning speed and spacing in dynamic environment, significantly reducing the energy consumption of highway train transportation. Background Technology

[0002] With increasing focus on energy consumption and environmental impact in the transportation industry, the application of energy efficiency optimization in transportation systems has become particularly important. Existing highway train speed planning methods often focus on factors such as traffic flow and road conditions, neglecting energy consumption optimization. Highway train energy consumption is influenced by multiple factors, including vehicle load, road gradient, and wind speed. Failure to effectively incorporate these factors into speed planning can lead to energy waste and increased operating costs. Therefore, developing a speed planning algorithm that can consider multiple energy consumption factors is of great significance for achieving efficient energy use and reducing the overall energy consumption of transportation systems.

[0003] Traditional highway freight systems typically operate in a single-vehicle mode, lacking coordination mechanisms even in temporary platooning. Application CN201610291150.1 optimizes the speed curve of a single vehicle but lacks a strategy for regenerative braking energy distribution within the platoon, resulting in an energy recovery rate of less than 10%. This "vehicle-centric" decision-making model leads to overall platoon energy consumption exceeding the theoretical value of collaborative optimization by more than 30%. Furthermore, existing technologies such as CN105785795A, which employ a fixed energy consumption matrix, cannot adapt to congestion or speed-limited events; frequent acceleration and deceleration increase energy consumption. CN101767592B, on the other hand, only achieves energy saving through mechanical drag reduction without considering time, comfort, or other objectives, resulting in a decline in overall performance.

[0004] In contrast, the highway train system adopts a "lead train navigating + following train navigating" mode, and through high-speed communication and unified control strategies between trains, it achieves information sharing, dynamic adjustment, and coordinated driving strategies within the convoy. Especially with the support of automatic driving and intelligent sensing technologies, highway trains have the ability to uniformly schedule and control the formation, driving rhythm, and energy consumption modes of multiple trains, providing structural prerequisites and communication foundations for achieving energy-optimal speed planning.

[0005] Existing algorithms suffer from serious shortcomings in multi-vehicle collaboration. While application CN202210345678.9 (Multi-vehicle Speed ​​Planning Method) achieves collision avoidance, each vehicle still calculates its optimal trajectory independently, lacking energy consumption optimization at the platoon level. Test data shows that this independent planning mode leads to overall platoon energy consumption being more than 25% higher than collaborative planning. These limitations collectively result in existing planning schemes often only achieving local optima in practical applications, failing to achieve global energy consumption optimization in platooning scenarios. Especially in long-distance transportation, accumulated energy consumption differences can increase operating costs by 10-15%. This highlights the urgency of developing new intelligent planning algorithms that can comprehensively consider multiple dynamic factors and support multi-vehicle collaborative decision-making. Summary of the Invention

[0006] In view of the above problems, the present invention provides an energy-optimal cooperative speed planning method for highway trains, which solves the following technical problems in the prior art: (1) Lack of real-time communication and platooning coordination and scheduling mechanism: Traditional highway freight systems mostly operate in the form of "independent single vehicle" or physical platooning. There is a lack of real-time communication and unified scheduling capabilities between vehicles, which makes it impossible to achieve path coordination, energy consumption sharing and system optimization at the platooning level. This "vehicle-centric" decision-making mode is slow to respond and lacks flexibility, which easily leads to low overall efficiency and energy waste.

[0007] (2) The assumption of static environment leads to insufficient robustness: Traditional planning methods usually assume that the environment is static and the state is completely known. Therefore, they are not adaptable to real-time traffic dynamic changes, sudden events and uncertainties. Once a sudden change in road conditions or information is missing, they cannot be adjusted quickly, which affects the effectiveness and safety of planning.

[0008] (3) Lack of collaborative decision-making capability for multi-vehicle clusters: Although the highway train system has V2V communication and unified control foundation, the existing methods are mostly single-vehicle decision-making and lack multi-agent collaborative mechanism; under multi-vehicle formation, it is difficult to take into account the energy consumption minimization and driving safety of individuals and the whole, and it is easy to fall into local optima.

[0009] This invention provides a method for energy-optimal cooperative speed planning for highway trains, comprising the following steps: S1: Obtain driving environment parameters and vehicle parameters; S2: Calculate the dynamic weighting coefficient based on the driving environment parameters and vehicle parameters; S3: Based on the dynamic weighting coefficients, construct an energy-optimal coordinated speed planning model with the objective of minimizing the joint objective function; the joint objective function includes at least a total energy consumption function, a transportation efficiency function, and a safety penalty function; wherein, the total energy consumption function is obtained by weighted summation of the gradient drag function, aerodynamic drag function, and acceleration penalty function based on the dynamic weighting coefficients; S4: Load constraints including speed constraints, vehicle spacing constraints, and formation sequence constraints onto the energy-optimal cooperative speed planning model; S5: Linearize the nonlinear terms in the energy consumption optimal coordinated speed planning model after applying constraints to obtain a standardized model; S6: Solve the standardized model to output the optimal speed, optimal spacing, and optimal formation sequence for each vehicle in the formation.

[0010] Optionally, in step S1, the driving environment parameters include at least the road slope angle, road speed limit and wind speed; the vehicle parameters include at least the mass of the lead vehicle and following vehicles in the formation, the initial distance between vehicles, the frontal area of ​​the vehicles and the initial formation order.

[0011] Optionally, in step S2, the dynamic weighting coefficients include slope coefficient weights, wind resistance coefficient weights, and basic weighting coefficients; the slope coefficient weights are positively correlated with the maximum slope angle of the current road segment, the wind resistance coefficient weights are positively correlated with the current wind speed, and the basic weighting coefficients are used to ensure that the sum of all dynamic weighting coefficients is one.

[0012] Optionally, in step S3, the slope drag function is positively correlated with the product of vehicle mass, speed, and the sine of road slope angle; the aerodynamic drag function is positively correlated with the product of air density, wind speed coefficient, vehicle frontal area, and the cube of the difference between vehicle speed and wind speed, and the influence of wind drag correction function on vehicle spacing is corrected.

[0013] Optionally, the drag correction function is a monotonically increasing function that asymptotically approaches 1 as the distance between vehicles increases.

[0014] Optionally, in step S3, the acceleration penalty function is positively correlated with the square of the acceleration difference between two adjacent vehicles, and its acceleration weighting coefficient is related to the total mass of the two adjacent vehicles.

[0015] Optionally, in step S3, the transportation efficiency function is positively correlated with the ratio of the average platoon speed to the speed limit of the road segment; the safety penalty function is an exponential function that increases rapidly as the difference between the actual vehicle spacing and the ideal safe distance increases.

[0016] Optionally, in step S4, the speed constraint requires that the speed of each vehicle be within a preset ratio range of the road segment speed limit; the vehicle spacing constraint requires that the spacing between any two adjacent vehicles is not less than a dynamic minimum safe distance related to vehicle speed and total vehicle mass.

[0017] Optionally, in step S5, the linearization approximation of the nonlinear term includes: piecewise linearization of the velocity cubic term in the aerodynamic drag function, and first-order Taylor expansion approximation of the safety penalty function.

[0018] Optionally, in step S6, a mixed integer programming solver is used to solve the standardized model.

[0019] Compared with the prior art, the present invention has at least the following beneficial effects: 1. The energy-optimal cooperative speed planning method of the present invention incorporates dynamic physical modeling: it constructs coupling equations based on real-time sensor data and achieves online updates of the energy consumption model through dynamic weight coefficients, thereby improving the robustness of the system.

[0020] 2. The energy-optimal collaborative speed planning method of the present invention comprehensively considers dynamic environmental parameters such as slope, load, and wind speed, constructs a multi-dimensional energy consumption optimization model, and realizes a more energy-efficient speed planning scheme.

[0021] 3. The energy-optimal cooperative speed planning method of the present invention adopts dynamic formation reconstruction technology, which automatically adjusts the vehicle position according to real-time energy consumption assessment results and road condition information to achieve optimal energy utilization efficiency. Attached Figure Description

[0022] Figure 1 is a schematic diagram of the technical route of the present invention; Figure 2 This is a schematic diagram of the data flow in this invention; Figure 3 This is a schematic diagram of the algorithm flow of the present invention. Detailed Implementation

[0023] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0024] A specific embodiment of the present invention discloses an energy-optimal cooperative speed planning method for highway trains, the specific steps of which are as follows: Step 1. Obtain the driving environment and vehicle parameter information.

[0025] The parameters of the driving environment include road slope angle, road curvature, road speed limit, wind speed, and air density; further, the parameters of the vehicles include the lead vehicle, the following vehicles, the vehicle mass, the initial distance between the two vehicles, the frontal area, and the formation order.

[0026] For example, the total number of vehicles is 3, and the formation order is: lead vehicle 1, follower vehicle 2 and follower vehicle 3; Road slope angle =0.02, road curvature =0 indicates a straight road with a speed limit. The wind speed is 20 m / s. =5m / s, air density =1.225kg / m 3 The three vehicles have the following weights: =|1000080006000|kg, the initial distance between each vehicle is... =|30 25|m, the frontal area of ​​each vehicle =|1086|m 2 Formation order =|123|, where, i and j This is the vehicle's serial number.

[0027] Step 2. Obtain dynamic weights based on driving environment and vehicle parameter information.

[0028] The expression for dynamic weights is: ; ;

[0029] in, The slope coefficient is the weight. Weighting for drag coefficient; These are acceleration weighting coefficients, ensuring that the sum of the coefficients is 1; This represents the maximum gradient angle of the current road section. It is the acceleration due to gravity; This is a scalar value for wind speed; The empirical wind speed coefficient is set to 20.

[0030] Specifically, by substituting the parameter information of the driving environment and vehicle in the example into the weight calculation formula, the slope weight is obtained. =0.002, drag weight =0.25, base weight =0.748.

[0031] Step 3. Construct the joint objective function with optimal energy consumption efficiency; Specifically, the expression for the joint objective function is:

[0032] in, The joint objective function representing the optimal energy consumption efficiency consists of three parts: the total energy consumption function, the transportation efficiency function, and the safety penalty function. Represents the total energy consumption function; Represents the transportation efficiency function; Represents a safety penalty function; Indicates a safe distance.

[0033] Specifically, the expression for the total energy consumption function is:

[0034] in, Represents the slope resistance function; Represents the aerodynamic drag function; This represents the acceleration penalty function.

[0035] For example, substituting the dynamic weights obtained in step 2 into the joint objective function yields the energy consumption function:

[0036] Furthermore, the expression for the slope resistance function is:

[0037] in, Indicates the first i The total mass of the vehicle; Represents gravitational acceleration; Indicates the first i The speed of the vehicle; Indicates the first i The slope angle of the road where the vehicle is located.

[0038] Furthermore, the expression for the aerodynamic drag function is:

[0039] in, This is the drag correction function; Indicates air density; For the first i Wind speed coefficient of the vehicle, lead car Intermediate car ; tail car ; For the first i The frontal area of ​​a vehicle.

[0040] Furthermore, the drag correction function The expression is:

[0041] in, Indicates the minimum attenuation coefficient. The decay rate coefficient, To determine the minimum safe distance considering dynamic safety boundaries; e It is the natural logarithm; Furthermore, in the aerodynamic drag function In the function, since the wind speed is 5 m / s and it is a downwind, the cubic velocity term is reduced by the wind speed. If the driving condition is headwind, then the cubic velocity term in the function is: The aerodynamic drag function is specifically as follows:

[0042] in, This refers to wind speed.

[0043] Furthermore, the expression for the acceleration penalty function is:

[0044] in, This represents the acceleration weighting coefficient, which is used to ensure a comfortable acceleration of less than 2 m / s². 2 ; Indicates the first i The total mass of the vehicle Indicates the first j The total mass of the vehicle, the i Car and the j The vehicles are adjacent vehicles; Indicates the first i The acceleration of a vehicle; Indicates the first j The acceleration of a vehicle; Furthermore, the transportation efficiency function is expressed as follows:

[0045] in, The average speed of the formation; Speed ​​limits are set for this section of road; Efficiency weighting coefficient; Sensitivity coefficient; security penalty function ; This is a safety penalty coefficient; For ideal safe distance; This is the penalty intensity coefficient.

[0046] Step 4. Load constraints; The expression for the constraint is:

[0047] Furthermore, the first i Vehicle speed is based on road section speed limit The following constraints exist: Considering vehicle dynamic safety boundaries and vehicle spacing Constraints: Formation sequence variables By influencing the wind speed coefficient of each vehicle To influence the total energy consumption function; This indicates the maximum number of formations in the team; Represents a positive integer.

[0048] For example, the speed constraint is: 14 m / s < <22m / s; Spacing constraint: Simplified, take 20m; Grouping sequence: fixed. =|123|.

[0049] Step 5. Standardize the energy-optimal coordinated speed planning model; First, because in the joint optimization objective function, the aerodynamic drag energy consumption term Includes nonlinear terms related to velocity. 3 Furthermore, the safety penalty function S(d) is in exponential form, making direct solution to this nonlinear mixed-integer programming problem computationally complex and difficult to meet real-time control requirements. To improve solution efficiency and ensure the feasibility of online algorithm operation, this invention employs a piecewise linearization method to approximate the key nonlinear terms. 3 Piecewise linearization, for example, such as reducing 14 m / s < <22m / s is divided into three segments; the exponential safety penalty function S(d) is approximated by a first-order Taylor expansion. This transformation, while ensuring model accuracy, allows the problem to be solved quickly using efficient solvers such as Gurobi, thus supporting the online real-time application of the energy-optimal cooperative speed planning method.

[0050] Substituting the above parameters into the joint objective function for optimal energy efficiency... J The expression is: + +

[0051] For example, .

[0052]

[0053] Step 6. Solution and Results The Gurobi solver is used to solve the piecewise linearized mixed integer programming problem. The output of the solution is: ,in, To achieve the optimal speed for each vehicle; The optimal spacing between each workshop; This is the optimal grouping sequence.

[0054] For example, the optimal solution is obtained by substituting the input into the solver: ; ; .

[0055] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A highway-oriented train energy-optimal cooperative speed planning method, characterized in that, Includes the following steps: S1: Obtain driving environment parameters and vehicle parameters; S2: Calculate the dynamic weighting coefficient based on the driving environment parameters and vehicle parameters; S3: Based on the dynamic weighting coefficients, construct an energy-optimal coordinated speed planning model with the objective of minimizing the joint objective function; the joint objective function includes at least a total energy consumption function, a transportation efficiency function, and a safety penalty function; wherein, the total energy consumption function is obtained by weighted summation of the gradient drag function, aerodynamic drag function, and acceleration penalty function based on the dynamic weighting coefficients; S4: Load constraints including speed constraints, vehicle spacing constraints, and formation sequence constraints onto the energy-optimal cooperative speed planning model; S5: Linearize the nonlinear terms in the energy consumption optimal coordinated speed planning model after applying constraints to obtain a standardized model; S6: Solve the standardized model to output the optimal speed, optimal spacing, and optimal formation sequence for each vehicle in the formation.

2. The method of claim 1, wherein, In step S1, the driving environment parameters include at least the road slope angle, road section speed limit and wind speed; the vehicle parameters include at least the mass of the lead vehicle and following vehicles in the formation, the initial distance between vehicles, the frontal area of ​​vehicles and the initial formation order.

3. The method according to claim 2, characterized in that, In step S2, the dynamic weighting coefficients include slope coefficient weight, wind resistance coefficient weight, and basic weighting coefficient; the slope coefficient weight is positively correlated with the maximum slope angle of the current road segment, the wind resistance coefficient weight is positively correlated with the current wind speed, and the basic weighting coefficient is used to ensure that the sum of all dynamic weighting coefficients is one.

4. The method according to claim 1, characterized in that, In step S3, the slope drag function is positively correlated with the product of vehicle mass, speed, and the sine of road slope angle; the aerodynamic drag function is positively correlated with the product of air density, wind speed coefficient, vehicle frontal area, and the cube of the difference between vehicle speed and wind speed, and the influence of wind drag correction function on vehicle spacing is corrected.

5. The method according to claim 4, characterized in that, The wind resistance correction function is a monotonically increasing function that asymptotically approaches 1 as the distance between vehicles increases.

6. The method according to claim 1 or 4, characterized in that, In step S3, the acceleration penalty function is positively correlated with the square of the acceleration difference between two adjacent vehicles, and its acceleration weighting coefficient is related to the total mass of the two adjacent vehicles.

7. The method according to claim 1, characterized in that, In step S3, the transportation efficiency function is positively correlated with the ratio of the average platoon speed to the speed limit of the road segment; the safety penalty function is an exponential function that increases rapidly as the difference between the actual vehicle spacing and the ideal safe distance increases.

8. The method according to claim 1, characterized in that, In step S4, the speed constraint is that the speed of each vehicle must be within a preset ratio range of the road segment speed limit; the vehicle spacing constraint is that the spacing between any two adjacent vehicles is not less than a dynamic minimum safe distance related to vehicle speed and total vehicle mass.

9. The method according to claim 1, characterized in that, In step S5, the linearization approximation of the nonlinear term includes: piecewise linearization of the velocity cubic term in the aerodynamic drag function, and first-order Taylor expansion approximation of the safety penalty function.

10. The method according to claim 1, characterized in that, In step S6, a mixed integer programming solver is used to solve the standardized model.

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