Energy consumption optimization method and system for heterogeneous traffic flow based on adaptive cruise control

Abstract

The present invention discloses a heterogeneous traffic flow energy consumption optimization method and system based on adaptive cruise control, including: determining the expected following distance according to the variable headway; constructing a heterogeneous traffic flow following model based on the expected following distance and considering the headway distance under different following modes and the penetration rate of different types of vehicles; constructing a discrete adaptive cruise controller for making the target vehicle follow the preceding vehicle at the expected following distance based on the heterogeneous traffic flow following model and the expected following distance; according to the discrete adaptive cruise controller and the heterogeneous traffic flow following model, taking the minimum total energy consumption of the heterogeneous traffic system and the minimum loss of safety, comfort and traffic efficiency as the objective function, obtaining the energy consumption optimization result of the heterogeneous traffic flow following system. The method solves the problem of excessive acceleration and deceleration of vehicles in the random following process in the intelligent networked heterogeneous traffic environment, and reduces the energy consumption of the traffic flow system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method and system for optimizing energy consumption of heterogeneous traffic flows based on adaptive cruise control. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the introduction of intelligent connected vehicles (V2V) and new energy electric vehicles (EV), the types of vehicles on the road are gradually increasing. Under the condition that road hardware is difficult to change, it is necessary to solve the congestion problems of heterogeneous traffic flows and how to reduce energy consumption.

[0004] In the existing technology, scholars have conducted a lot of research on the homogeneous traffic flow following behavior of connected autonomous vehicles (CAV) and human driven vehicles (HDV), but there are few studies on the heterogeneous traffic flow following behavior of mixed vehicle types. In the existing heterogeneous mixed traffic flow research, scholars are committed to the establishment of mixed traffic flow models and the study of control strategies to improve the stability of heterogeneous traffic flow and suppress traffic congestion. They ignore the problem of excessive acceleration and deceleration generated by vehicles in the process of random following driving to maintain stability. In addition, they have not taken into account the energy consumption in the process of traffic flow following, and the energy consumption optimization problem also needs to be solved urgently. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a heterogeneous traffic flow energy consumption optimization method and system based on adaptive cruise control, which solves the problem of excessive acceleration and deceleration of vehicles during random following in an intelligent networked heterogeneous traffic environment and reduces the energy consumption of the traffic flow system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for optimizing energy consumption of heterogeneous traffic flows based on adaptive cruise control, comprising:

[0008] Determine the expected following distance based on the variable headway;

[0009] According to the expected following distance, a heterogeneous traffic flow following model based on random factors and stable control of headway is constructed on the basis of considering the headway between different following modes and the penetration rate of different types of vehicles.

[0010] Based on the heterogeneous traffic flow following model and the expected following distance, a discrete adaptive cruise controller is constructed to make the target vehicle follow the leading vehicle at the expected following distance.

[0011] According to the discrete adaptive cruise controller and the heterogeneous traffic flow following model, a heterogeneous traffic flow dual-control following model is constructed.

[0012] The energy consumption of the heterogeneous traffic system is determined according to the dual-control following model of heterogeneous traffic flow. The objective function is to minimize the total energy consumption of the heterogeneous traffic system and minimize the loss of safety, comfort and traffic efficiency. The energy consumption optimization result of the heterogeneous traffic flow following system is obtained by solving the objective function.

[0013] As an optional implementation, the expected following distance determined according to the variable headway is: Where, δ is the safety distance; v i represents the speed of the i-th vehicle; represents a positive correlation coefficient; t h is the headway time.

[0014] As an optional implementation, the heterogeneous traffic flow following model is:

[0015]

[0016]

[0017] In the formula, v i is the speed of the i-th vehicle; λ, a, m and β are sensitivity coefficients; V(·) is the optimal speed; Δx i (t) is the headway between two adjacent vehicles; d is the expected following distance; d-Δx i (t) represents the stable control of the headway; ai-1(t) is the acceleration of the leading vehicle; is the time derivative of acceleration; Δv i is the speed difference between two adjacent vehicles; σ is the noise intensity; dW i is the Wiener process, indicating random factors; p c is the penetration rate of autonomous vehicles.

[0018] As an optional implementation, the discrete adaptive cruise controller is:

[0019]

[0020] Among them, a, m and β are sensitivity coefficients; is the driving time delay; X2 is the virtual error; V(·) is the optimal speed; Δx i (t) is the headway between two adjacent vehicles; d is the expected following distance; v iis the speed of the i-th vehicle; a i-1 (t) is the acceleration of the front vehicle; v i-1 is the speed of the front vehicle; X1 is the following distance error; k2 and k1 are both constants greater than 0.

[0021] As an optional implementation, the heterogeneous traffic flow dual-control following model is:

[0022]

[0023]

[0024] Among them, v i is the speed of the i-th vehicle; d is the expected following distance; Δx i (t) is the headway between two adjacent vehicles; λ, a, m and β are sensitivity coefficients; is the driving delay; v x is the partial derivative of vehicle speed with respect to vehicle position; v(x+ΔM,t) is the speed of the vehicle at a distance ΔM from the target vehicle; k2 and k1 are both constants greater than 0; dW i is the Wiener process, representing the random factor; M is the intermediate parameter; Δv i is the speed difference between two adjacent vehicles; σ is the noise intensity.

[0025] As an optional implementation, the energy consumption of the heterogeneous transportation system is:

[0026]

[0027] in, Energy consumption coefficient; sp and ap are the reaction coefficients of velocity and acceleration.

[0028] As an optional implementation, the objective function is:

[0029]

[0030] The four items on the right side of the equation are the expressions of total energy consumption, safety, comfort and traffic efficiency of heterogeneous traffic systems; ω1, ω2, ω3, ω4 are all coefficients greater than 0, s is the distance between vehicles, d max and d min It is the maximum following distance and the minimum following distance under the controlled vehicle speed.

[0031] In a second aspect, the present invention provides a heterogeneous traffic flow energy consumption optimization system based on adaptive cruise control, comprising:

[0032] an expected following distance determination module, configured to determine an expected following distance according to a variable headway;

[0033] A following model building module is configured to build a heterogeneous traffic flow following model based on random factors and stable control of headway according to the expected following distance, taking into account the headway between vehicles in different following modes and the penetration rate of different types of vehicles;

[0034] A controller building module is configured to build a discrete adaptive cruise controller for making the target vehicle follow the leading vehicle at the expected following distance based on the heterogeneous traffic flow following model and the expected following distance;

[0035] A dual-control car-following model building module is configured to build a heterogeneous traffic flow dual-control car-following model according to a discrete adaptive cruise controller and a heterogeneous traffic flow car-following model;

[0036] The optimization module is configured to determine the energy consumption of the heterogeneous traffic system according to the dual-control following model of heterogeneous traffic flow, with the minimum total energy consumption of the heterogeneous traffic system and the minimum loss of safety, comfort and traffic efficiency as the objective function, thereby obtaining the energy consumption optimization result of the heterogeneous traffic flow following system by solving the objective function.

[0037] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention proposes a heterogeneous traffic flow energy consumption optimization method and system based on adaptive cruise control. In order to solve the problem of excessive acceleration and deceleration during vehicle following in an intelligent networked heterogeneous traffic environment, the present invention combines adaptive cruise control with a heterogeneous traffic flow following model. The adaptive cruise control can automatically adjust the vehicle speed according to driving conditions such as vehicle speed, vehicle distance, and the operating status of the vehicle in front, which can improve the safety and efficiency of the vehicle following process. At the same time, a heterogeneous traffic system energy consumption model is constructed to address the energy consumption problem. The energy consumption of the traffic flow system is reduced by taking the minimum total energy consumption of the heterogeneous traffic system and the minimum loss of safety, comfort and traffic efficiency as the objective function.

[0041] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0043] Figure 1 Flowchart of a method for optimizing energy consumption of heterogeneous traffic flows based on adaptive cruise control provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0045] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0046] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "comprise" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0047] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0048] Example 1

[0049] This embodiment provides a method for optimizing energy consumption of heterogeneous traffic flow based on adaptive cruise control. Figure 1 As shown, including:

[0050] Determine the expected following distance based on the variable headway;

[0051] According to the expected following distance, a heterogeneous traffic flow following model based on random factors and stable control of headway is constructed on the basis of considering the headway between different following modes and the penetration rate of different types of vehicles.

[0052] Based on the heterogeneous traffic flow following model and the expected following distance, a discrete adaptive cruise controller is constructed to make the target vehicle follow the leading vehicle at the expected following distance.

[0053] According to the discrete adaptive cruise controller and the heterogeneous traffic flow following model, a heterogeneous traffic flow dual-control following model is constructed.

[0054] The energy consumption of the heterogeneous traffic system is determined according to the dual-control following model of heterogeneous traffic flow. The objective function is to minimize the total energy consumption of the heterogeneous traffic system and minimize the loss of safety, comfort and traffic efficiency. The energy consumption optimization result of the heterogeneous traffic flow following system is obtained by solving the objective function.

[0055] In actual car-following, if the speed of the front vehicle is much greater than that of the target vehicle and the safety distance is small, the target vehicle may not slow down because it can predict that the distance between the vehicles will increase. Therefore, when modeling vehicle platoons, it is necessary to consider not only the influence of the speed of adjacent vehicles, but also the acceleration of the front vehicle and the bumps of the target vehicle.

[0056] Therefore, in this embodiment, in order to more accurately describe the actual traffic scene, the variable headway is used to further describe the driver's expected following distance d, which is expressed as:

[0057]

[0058] Where, δ is the safety distance; v i represents the speed of the i-th vehicle; represents a positive correlation coefficient; t h is the headway time, expressed as:

[0059]

[0060] Where t0 is the basic headway; m v and m a Sensitivity coefficient; t h,max is the maximum headway; Δv i and Δa i It represents the speed difference and acceleration difference between two adjacent vehicles.

[0061] Therefore, based on the desired following distance, the headway between two adjacent vehicles is made to approach the desired headway, thereby achieving stable control of the desired following distance.

[0062] This embodiment is based on the homogeneous traffic flow following model based on the full speed difference model. The expression of the above process is:

[0063]

[0064] Where λ, β and a are sensitivity coefficients; V(Δx i (t)) is the optimal speed; Δx i (t) is the distance between the front ends of two adjacent vehicles.

[0065] With the gradual implementation of autonomous vehicles, the operation of manually driven vehicles has been affected to a certain extent, resulting in instability of the traffic flow system and additional energy consumption. Traffic instability is due to the cumulative effect of random factors, which can be of many types, such as additional stimuli, vehicle powertrain, time changes in driver behavior, or some unconscious decisions of the driver.

[0066] There are four following modes on mixed traffic flow roads, and each mode has an obvious safe headway, as shown in Table 1; the average headway in mixed traffic flow can be expressed as:

[0067]

[0068] Assumptions and Then formula (4) is transformed into:

[0069]

[0070] In the formula, is the scale parameter.

[0071] Therefore, this embodiment constructs a heterogeneous traffic flow following model based on the basic homogeneous traffic flow following model, specifically:

[0072]

[0073] Where Δv i (t)=v(x+ΔM,t)-v(x,t),

[0074] Where m, γ and β are sensitivity coefficients; Δxi(t) represents the headway between two adjacent vehicles; V(·) is the optimal speed; d-Δx i (t) represents the difference between the headway distance between two adjacent vehicles and the expected headway distance. The headway distance between two adjacent vehicles tends to the expected headway distance, which indicates stable control of the headway distance. i-1 (t) represents the acceleration of the preceding vehicle; represents the time derivative of acceleration; σ represents the intensity or dissipation term of noise; dW i is the Wiener process, which is used to represent the random factors in the traffic flow system; p c is the penetration rate of autonomous driving vehicles; M is an intermediate parameter.

[0075] Table 1 Proportions of different following modes

[0076]

[0077] Set the adaptive cruise control to In, it is expressed as:

[0078]

[0079] In the formula, represents the driving delay; u i (t) is the adaptive cruise controller.

[0080] Substituting formula (7) into the heterogeneous traffic flow following model, we can obtain:

[0081]

[0082] In this embodiment, based on the heterogeneous traffic flow following model and the expected following distance, a discrete adaptive cruise controller is designed using the backstepping method to make the target vehicle follow the preceding vehicle at the expected following distance. The specific design steps include:

[0083] Step 1: Define the following distance error X1;

[0084]

[0085] Define the first Lyapunov function The derivative of the Lyapunov function is expressed as:

[0086] Introducing dummy variable control At the same time, define the virtual error Based on this, the derivative of L1 is expressed as:

[0087] Through Young's inequality Make changes and get

[0088] Thus, the virtual controller is obtained The expression is:

[0089]

[0090] Step 2: Define another Lyapunov function L2:

[0091]

[0092] The derivative of L2 is expressed as:

[0093]

[0094] To ensure the stability of the heterogeneous traffic flow following system, it is required Therefore, the expression of the designed discrete adaptive cruise controller is:

[0095]

[0096] Among them, k2 and k1 are constants greater than 0; a, m and β are sensitivity coefficients; is the driving time delay; X2 is the virtual error; V(·) is the optimal speed; Δxi(t) is the headway between two adjacent vehicles; d is the expected following distance; v i is the speed of the i-th vehicle; a i-1 (t) is the acceleration of the front vehicle; v i-1 is the speed of the front vehicle; X1 is the following distance error.

[0097] In this embodiment, based on equations (6) and (13), the adaptive cruise controller and the heterogeneous traffic flow following model are combined to obtain a heterogeneous traffic flow dual control following model that considers adaptive cruise control, stability control and random factors:

[0098]

[0099]

[0100] Among them, v x is the partial derivative of the vehicle speed with respect to the vehicle position; v(x+ΔM,t) is the speed of the vehicle ΔM away from the target vehicle, that is, the speed of the adjacent preceding vehicle.

[0101] In this embodiment, a basic micro energy consumption model is used, which is expressed as:

[0102]

[0103] In the formula, Energy consumption coefficient; v is vehicle speed; a is vehicle acceleration; sp and ap are the reaction coefficients of speed and acceleration.

[0104] Based on the micro energy consumption model, a hybrid traffic flow following model with dual control and random factors is constructed to construct the energy consumption of heterogeneous traffic systems.

[0105]

[0106] In this embodiment, based on the vehicle-to-vehicle distance distribution, the objective function is to minimize the total energy consumption of the heterogeneous transportation system and minimize the loss of safety, comfort and traffic efficiency; specifically:

[0107]

[0108] In the formula, the four terms on the right side of the equation are the expressions of total energy consumption, safety, comfort and traffic efficiency of heterogeneous traffic systems; ω1, ω2, ω3, ω4 are all coefficients greater than 0, s is the distance between vehicles, d is the distance between vehicles, max and d min is the maximum and minimum following distance under the controlled vehicle speed, expressed as:

[0109]

[0110] Based on the mixed traffic flow characteristics, driving safety and driving comfort requirements and driving efficiency, the constraints are constructed; the constraints are:

[0111]

[0112] In the formula, l n is the vehicle body length; the first three items represent the upper and lower bounds of the vehicle following distance, vehicle speed and acceleration respectively; the fourth item represents the vehicle following behavior; and the fifth item is the constraint on the vehicle's optimal speed.

[0113] In this embodiment, the weight factor of driving safety in the optimization model is 1, that is, ω2=1, that is, under the premise of ensuring driving safety, driving comfort, traffic efficiency and minimization of energy consumption are considered; genetic algorithm is used to optimize ω1, ω3 and ω4; finally, based on the constraints, the objective function is solved, and the comprehensive optimal solution that takes into account various objectives is selected without compromising the following performance and comfort, safety and traffic efficiency. Under the premise of ensuring vehicle safety and driving comfort, the traffic efficiency is maximized and the total energy consumption of the system is minimized, thereby improving the energy economy of the heterogeneous transportation system.

[0114] Example 2

[0115] This embodiment provides a heterogeneous traffic flow energy consumption optimization system based on adaptive cruise control, including:

[0116] an expected following distance determination module, configured to determine an expected following distance according to a variable headway;

[0117] A following model building module is configured to build a heterogeneous traffic flow following model based on random factors and stable control of headway according to the expected following distance, taking into account the headway between vehicles in different following modes and the penetration rate of different types of vehicles;

[0118] A controller building module is configured to build a discrete adaptive cruise controller for making the target vehicle follow the leading vehicle at the expected following distance based on the heterogeneous traffic flow following model and the expected following distance;

[0119] A dual-control car-following model building module is configured to build a heterogeneous traffic flow dual-control car-following model according to a discrete adaptive cruise controller and a heterogeneous traffic flow car-following model;

[0120] The optimization module is configured to determine the energy consumption of the heterogeneous traffic system according to the dual-control following model of heterogeneous traffic flow, with the minimum total energy consumption of the heterogeneous traffic system and the minimum loss of safety, comfort and traffic efficiency as the objective function, thereby obtaining the energy consumption optimization result of the heterogeneous traffic flow following system by solving the objective function.

[0121] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0122] In further embodiments, there is also provided:

[0123] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, it will not be described in detail here.

[0124] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0125] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0126] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0127] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0128] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0129] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for optimizing energy consumption of heterogeneous traffic flow based on adaptive cruise control, characterized in that: include: Determine the expected following distance based on the variable headway; According to the expected following distance, a heterogeneous traffic flow following model based on random factors and stable control of headway is constructed on the basis of considering the headway between different following modes and the penetration rate of different types of vehicles. The heterogeneous traffic flow following model is: Based on the heterogeneous traffic flow following model and the expected following distance, a discrete adaptive cruise controller is constructed to make the target vehicle follow the leading vehicle at the expected following distance. The discrete adaptive cruise controller is: According to the discrete adaptive cruise controller and the heterogeneous traffic flow following model, a heterogeneous traffic flow dual-control following model is constructed. The dual-control car-following model for heterogeneous traffic flow is: Among them, v i (t) is the speed of the i-th vehicle; λ, a, m, β and γ are sensitivity coefficients; V(·) is the optimal speed; Δx i (t) is the headway between two adjacent vehicles; d is the expected following distance; d-Δx i (t) represents the stable control of the headway; a i-1 (t) is the acceleration of the preceding vehicle; is the time derivative of acceleration; Δv i (t) is the speed difference between two adjacent vehicles; σ is the noise intensity; dW i is the Wiener process, indicating random factors; p c is the penetration rate of autonomous vehicles; is the driving time lag; X2 is the virtual error; v i-1 is the speed of the preceding vehicle; X1 is the following distance error; k2 and k1 are both constants greater than 0; v x is the partial derivative of vehicle speed with respect to vehicle position; v(x+ΔM,t) is the speed of the vehicle at a distance ΔM from the target vehicle; represents the random factor; M is an intermediate parameter; t h is the headway time; The energy consumption of the heterogeneous traffic system is determined according to the dual-control following model of heterogeneous traffic flow. The objective function is to minimize the total energy consumption of the heterogeneous traffic system and minimize the loss of safety, comfort and traffic efficiency. The energy consumption optimization result of the heterogeneous traffic flow following system is obtained by solving the objective function.

2. The method for optimizing energy consumption of heterogeneous traffic flow based on adaptive cruise control according to claim 1, characterized in that: The expected following distance determined based on the variable headway is Where, δ is the safety distance; v i represents the speed of the i-th vehicle; represents a positive correlation coefficient; t h is the headway time.

3. The method for optimizing energy consumption of heterogeneous traffic flow based on adaptive cruise control according to claim 1, characterized in that: The energy consumption of the heterogeneous transportation system is: in, Energy consumption coefficient; sp and ap are the reaction coefficients of velocity and acceleration respectively.

4. The method for optimizing energy consumption of heterogeneous traffic flow based on adaptive cruise control as claimed in claim 3, characterized in that: The objective function is: The four items on the right side of the equation are the expressions of total energy consumption, safety, comfort and traffic efficiency of heterogeneous traffic systems; ω1, ω2, ω3, ω4 are all coefficients greater than 0, s is the distance between vehicles, d max and d min They are the maximum following distance and the minimum following distance under the controlled vehicle speed.

5. Heterogeneous traffic flow energy consumption optimization system based on adaptive cruise control, characterized in that: include: an expected following distance determination module, configured to determine an expected following distance according to a variable headway; A following model building module is configured to build a heterogeneous traffic flow following model based on random factors and stable control of headway according to the expected following distance, taking into account the headway between vehicles in different following modes and the penetration rate of different types of vehicles; A controller building module is configured to build a discrete adaptive cruise controller for making the target vehicle follow the leading vehicle at the expected following distance based on the heterogeneous traffic flow following model and the expected following distance; A dual-control car-following model building module is configured to build a heterogeneous traffic flow dual-control car-following model according to a discrete adaptive cruise controller and a heterogeneous traffic flow car-following model; The optimization module is configured to determine the energy consumption of the heterogeneous traffic system according to the dual-control following model of heterogeneous traffic flow, with the minimum total energy consumption of the heterogeneous traffic system and the minimum loss of safety, comfort and traffic efficiency as the objective function, thereby obtaining the energy consumption optimization result of the heterogeneous traffic flow following system by solving the objective function.

6. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 4 is completed.

7. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 4.

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