Multi-vehicle platoon speed planning method and system

Abstract

This invention relates to a multi-vehicle platooning speed planning method and system, belonging to the field of platooning speed planning. The method includes: constructing a displacement equation for the vehicle platoon; the displacement equation representing the real-time position of the lead vehicle in the platoon; determining an objective function for the vehicle platoon based on energy consumption, traffic efficiency, and passenger comfort; passenger comfort being determined based on vehicle acceleration; and solving the displacement equation by minimizing the objective function, constrained by traffic light timing, speed limits in traffic rules, and vehicle performance limitations on acceleration. This invention reduces energy consumption, improves passenger comfort, and enhances traffic efficiency.

Description

Technical Field

[0001] This invention relates to the field of fleet planning, and in particular to a method and system for speed planning of multi-vehicle platooning. Background Technology

[0002] Intelligent connected vehicles have gradually become a research hotspot in the field due to their enormous potential to improve road safety, driving economy, and traffic system efficiency. With the help of vehicle-to-everything (V2X) communication, intelligent connected vehicles can obtain traffic system information beyond their field of vision from surrounding vehicles and roadside base stations. Vehicles with the same driving purpose can also form platoons, thereby maximizing the advantages of intelligent connected vehicles in terms of economy, safety, and traffic efficiency.

[0003] Furthermore, due to their environmental friendliness and energy-saving characteristics, the proportion of new energy vehicles in the fleet is increasing year by year. Currently, for traffic scenarios, control methods targeting electric vehicle platoons still have issues such as not considering the constraints of traffic lights in the traffic system, not considering the impact of other participants in the traffic system, not considering the driving economy of vehicles, or being unsuitable for urban road sections. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-vehicle platooning speed planning method and system that reduces energy consumption, improves passenger comfort, and enhances traffic efficiency.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A multi-vehicle platooning speed planning method includes:

[0007] Construct the displacement equation for the car formation; the displacement equation is an equation representing the real-time position of the lead car in the car formation;

[0008] The objective function for the car platoon is determined based on energy consumption, traffic efficiency, and passenger comfort; the passenger comfort is determined based on the vehicle's acceleration.

[0009] The displacement equation is solved by minimizing the objective function, with constraints including traffic light timing, speed limits in traffic rules, and vehicle performance limitations on acceleration.

[0010] Optionally, the displacement equation is:

[0011]

[0012] Where s(t) represents the real-time position of the lead vehicle, t is time, j = 0, 1, 2, 3, a j These are parameters to be determined.

[0013] Optionally, the objective function is:

[0014] J = ω e J e +ω c J c +ω t J t ;

[0015] Among them, J e J is the cost function of the energy consumption. c J is the cost function for the passenger comfort. t Let ω be the cost function of the traffic efficiency. e ω is the weighting factor for energy consumption. c ω is the passenger comfort weighting factor. t This is a weighting factor for traffic efficiency;

[0016]

[0017] J e,i Let ti represent the energy consumption of the i-th vehicle in the convoy, t0 represent the planned trip end time, and ti represent the energy consumption of the ith vehicle. e P indicates the end time of the planned trip. m T represents the instantaneous power of the motor of the i-th vehicle. m (t) represents the torque of a single wheel of the i-th vehicle, ω(t) represents the wheel speed of the i-th vehicle, and N v The number of vehicles in the convoy;

[0018] J c =jerk(t), t0≤t≤t e ,

[0019] J t =t e -t0;

[0020] When no vehicle with a speed lower than the lead vehicle in the convoy is detected ahead of the convoy, the constraint condition of the objective function is:

[0021]

[0022] Wherein, v(t0:t e () represents the time from time t0 to t e The velocity between, a(t0:t e () represents the time from time t0 to t e The acceleration between them, v min v is the minimum speed of the lead car. max a is the maximum speed of the lead car. min Let a be the minimum acceleration of the lead car.max The maximum acceleration of the lead car is s. Nv (t e For the last vehicle in the convoy at t e The position of time, s L is the end position of the journey, \ is the modulo operation, C is the traffic light cycle, and G is the green light time.

[0023] Optionally, when a vehicle with a speed lower than the lead vehicle in the convoy is detected ahead of it, it is denoted as a slow vehicle being detected, and the constraint condition of the objective function is:

[0024]

[0025] t d For the detected slow time, v h The speed of the lead vehicle is represented by s. d Indicates the position of the detected slow train, d min The minimum following distance is given by s1(t), which represents the real-time position of the lead vehicle.

[0026] Optionally, the step of minimizing the objective function and solving the displacement equation under constraints such as traffic light timing, speed limits in traffic rules, and vehicle performance limitations on acceleration specifically includes:

[0027] The displacement equation is solved using the interior point method.

[0028] This invention discloses a multi-vehicle platooning speed planning system, comprising:

[0029] The displacement equation determination module is used to construct the displacement equation of the car formation; the displacement equation is an equation representing the real-time position of the lead car in the car formation.

[0030] The objective function determination module is used to determine the objective function of the car platoon based on energy consumption, traffic efficiency, and passenger comfort; the passenger comfort is determined based on the vehicle's acceleration.

[0031] The displacement equation solving module is used to solve the displacement equation by minimizing the objective function, with constraints such as traffic light timing, speed limits in traffic rules, and vehicle performance limitations on acceleration.

[0032] Optionally, the displacement equation is:

[0033]

[0034] Where s(t) represents the real-time position of the lead vehicle, t is time, j = 0, 1, 2, 3, a j These are parameters to be determined.

[0035] Optionally, the objective function is:

[0036] J = ω e J e +ω c J c +ω t J t ;

[0037] Among them, J e J is the cost function of the energy consumption. c J is the cost function for the passenger comfort. t Let ω be the cost function of the traffic efficiency. e ω is the weighting factor for energy consumption. c ω is the passenger comfort weighting factor. t This is a weighting factor for traffic efficiency;

[0038]

[0039] J e,i Let ti represent the energy consumption of the i-th vehicle in the convoy, t0 represent the planned trip end time, and ti represent the energy consumption of the ith vehicle. e P indicates the end time of the planned trip. m T represents the instantaneous power of the motor of the i-th vehicle. m (t) represents the torque of a single wheel of the i-th vehicle, ω(t) represents the wheel speed of the i-th vehicle, and N v The number of vehicles in the convoy;

[0040] J c =jerk(t), t0≤t≤t e ,

[0041] J t =t e -t0;

[0042] When no vehicle with a speed lower than the lead vehicle in the convoy is detected ahead of the convoy, the constraint condition of the objective function is:

[0043]

[0044] Wherein, v(t0:t e () represents the time from time t0 to t e The velocity between, a(t0:t e () represents the time from time t0 to t e The acceleration between them, v min v is the minimum speed of the lead car. max a is the maximum speed of the lead car. minLet a be the minimum acceleration of the lead car. max The maximum acceleration of the lead car is s. Nv (t e For the last vehicle in the convoy at t e The position of time, s L is the end position of the journey, \ is the modulo operation, C is the traffic light cycle, and G is the green light time.

[0045] Optionally, when a vehicle with a speed lower than the lead vehicle in the convoy is detected ahead of it, it is denoted as a slow vehicle being detected, and the constraint condition of the objective function is:

[0046]

[0047] t d For the detected slow time, v h The speed of the lead vehicle is represented by s. d Indicates the position of the detected slow train, d min The minimum following distance is given by s1(t), which represents the real-time position of the lead vehicle.

[0048] Optionally, the displacement equation solving module specifically includes:

[0049] The displacement equation solving unit is used to solve the displacement equation using the interior point method.

[0050] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0051] This invention discloses a multi-vehicle platooning speed planning method and system. By determining the objective function of the vehicle platooning based on energy consumption, traffic efficiency, and passenger comfort, and using traffic signal timing, speed limits in traffic rules, and vehicle performance limitations on acceleration as constraints, the method reduces the energy consumption of vehicle platooning, improves passenger comfort, and enhances traffic efficiency. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of a multi-vehicle platooning speed planning method according to the present invention;

[0054] Figure 2 This is a schematic diagram of the structure of a multi-vehicle platooning speed planning system according to the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] The purpose of this invention is to provide a multi-vehicle platooning speed planning method and system that reduces energy consumption, improves passenger comfort, and enhances traffic efficiency.

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] Example 1

[0059] In this invention, each vehicle in an electric vehicle platoon is designated as a node vehicle, and the longitudinal dynamic equation of a node vehicle is:

[0060]

[0061] Among them, F x It is the longitudinal force of the vehicle, M is the curb weight of the vehicle, and a x ρ is the vehicle's longitudinal acceleration, ρ is the air density, A is the vehicle's frontal area, and v is the air density. x Where is the vehicle's longitudinal velocity, g is the acceleration due to gravity, f is the total rolling resistance coefficient, θ is the road gradient, and C is the longitudinal velocity of the vehicle. d This represents the air drag coefficient.

[0062] The longitudinal force of a vehicle can be calculated from the total wheel torque:

[0063] Among them, T tot It is the total torque of the wheel, and r is the rolling radius of the wheel.

[0064] Based on the motor loss MAP diagram, the instantaneous power of the motor can be obtained through the motor power equation:

[0065] P m =T tot ω+P loss ;

[0066] Among them, P m P is the instantaneous power of the motor, ω is the wheel speed, and P is the instantaneous power of the motor. loss This is for power loss.

[0067] The relationship between wheel speed and longitudinal vehicle speed is as follows:

[0068] This embodiment provides a basic framework for multi-vehicle speed planning, without considering vehicles outside the queue. External vehicles include slow-moving vehicles in front of the queue and vehicles cutting in line. This embodiment is applicable to smooth roads.

[0069] Figure 1 This is a schematic diagram of a multi-vehicle platooning speed planning method according to the present invention, as shown below. Figure 1 As shown, a multi-vehicle platooning speed planning method includes the following steps:

[0070] Step 101: Construct the displacement equation of the car formation; the displacement equation is an equation representing the real-time position of the lead car in the car formation.

[0071] The car formation specifically refers to a formation of electric vehicles.

[0072] Step 102: Determine the objective function of the car platoon based on energy consumption, traffic efficiency, and passenger comfort; the passenger comfort is determined based on the vehicle's acceleration.

[0073] Step 103: Solve the displacement equation by minimizing the objective function, with constraints such as traffic light timing, speed limits in traffic rules, and vehicle performance limitations on acceleration.

[0074] The velocity sequence of the car formation is obtained by solving the displacement equation.

[0075] This invention's speed planning only covers the range from the initial position to the stop line at the next intersection. As the convoy passes an intersection, the initial position moves to the stop line at that intersection. The speed planning problem for long-haul routes is then transformed into a multi-stage planning problem.

[0076] The optimization of the velocity curve is based on a fourth-order polynomial with four undetermined parameters. The time-dependent displacement equation for the velocity curve optimization is expressed as follows:

[0077]

[0078] Where s(t) represents the real-time position of the lead vehicle, t is time, j = 0, 1, 2, 3, a j These are parameters to be determined.

[0079] Based on the displacement equation, the velocity, acceleration, and impact curves can be obtained through differentiation. The basic objective of the optimization algorithm in this invention is to find the optimal set of fourth-order polynomial parameters that satisfy the optimization objective, under the premise of satisfying a series of constraints.

[0080] The objective function is: J = ω e J e +ω c J c +ω t Jt ;

[0081] Among them, J e J is the cost function of the energy consumption. c J is the cost function for the passenger comfort. t Let ω be the cost function of the traffic efficiency. e ω is the weighting factor for energy consumption. c ω is the passenger comfort weighting factor. t This is a weighting factor for traffic efficiency.

[0082]

[0083] J e,i This represents the energy consumption of the i-th vehicle in the vehicle platoon. The vehicle's energy consumption is the cumulative result of the energy generated by the drive system. This invention considers regenerative braking; therefore, the two modes of energy consumption variation are electric motor drive and regenerative braking. The vehicle's energy consumption is:

[0084]

[0085] t0 represents the planned end time of the trip, t e P represents the end time of the planned journey (the end time of the optimized speed curve). m T represents the instantaneous power of the motor of the i-th vehicle. m (t) represents the torque of a single wheel of the i-th vehicle, ω(t) represents the wheel speed of the i-th vehicle, and N v The number of vehicles in the convoy.

[0086] In this embodiment, the preceding slow-moving vehicles and lane-jumping vehicles are not considered. Therefore, for the lead vehicle in the platoon, the start and end times correspond to the starting position and stop line of the previous intersection. Based on the pre-set vehicle following distance, the starting position of each vehicle in the platoon can be obtained:

[0087] s 0,i =s0-(i-1)×(d+L) v (i = 1, 2, ..., N) v );

[0088] Among them, s 0,i Let s0 represent the starting position of the i-th vehicle, d represent the following distance, and L represent the starting position of the lead vehicle. v N represents the length of the vehicle at the node. v This represents the number of vehicles in the formation.

[0089] The cost function J of passenger comfort cThis represents the passenger comfort characteristics of the planned speed curve. The jerk curve is chosen as the indicator, as jerk reflects the degree of suddenness and corresponds highly to passenger comfort. Jerk is the third derivative of the displacement equation, expressed as:

[0090] Where a3 is a parameter to be determined;

[0091] Based on the acceleration curve of the lead car, J c =jerk(t), t0≤t≤t e .

[0092] The cost function J of traffic efficiency t This corresponds to the impact of the planned speed sequence on traffic rate. Traffic efficiency can be expressed as the planned travel time from the initial location to the destination location, J. t The expression is:

[0093] J t =t e -t0.

[0094] The optimized constraints include traffic light timing, speed limits in traffic rules, and vehicle performance limitations on acceleration. When no vehicle with a speed lower than the lead vehicle in the platoon is detected ahead of the platoon, the constraints of the objective function are:

[0095]

[0096] Wherein, v(t0:t e () represents the time from time t0 to t e The velocity between, a(t0:t e () represents the time from time t0 to t e The acceleration between them, v min v is the minimum speed of the lead car. max a is the maximum speed of the lead car. min Let a be the minimum acceleration of the lead car. max The maximum acceleration of the lead car is s. Nv (t e For the last vehicle in the convoy at t e The position of time, s L is the end position of the journey, \ is the modulo operation, C is the traffic light cycle, and G is the green light time.

[0097] Based on the objective function and constraints described above, the optimization framework is as follows:

[0098]

[0099] Among them, a j These are the polynomial coefficients.

[0100] The displacement equations are solved using the interior point method (the interior point method is used to solve the above nonlinear programming problem with equality and inequality constraints).

[0101] Example 2

[0102] Urban roads are frequently congested, especially during peak hours. Therefore, it is necessary to consider speed planning schemes when there are slow-moving vehicles ahead.

[0103] Assume the warning time for the previous slow vehicle to be detected is t. d The previous slow train was detected at position s. d When vehicles continuously follow a slower vehicle with minimal following intervals, energy consumption cannot be optimized. To reduce energy consumption, the speed curve of the queuing, which is obstructed by slower vehicles, needs to be redesigned.

[0104] When a vehicle with a speed lower than the lead vehicle in the convoy is detected ahead, it is denoted as a slow vehicle. As slow vehicle information is acquired, the constraints of the objective function are expanded to:

[0105]

[0106] t d For the detected slow time, v h The speed of the lead vehicle is represented by s. d Indicates the position of the detected slow train, d min The minimum following distance is given by s1(t), which represents the real-time position of the lead vehicle.

[0107] Solving the displacement equations using the interior point method (solving the aforementioned nonlinear programming problem with equality and inequality constraints using the interior point method) specifically includes:

[0108] Based on the objective function, construct a barrier function:

[0109]

[0110] Where f(x) is the objective function, x is the optimization variable, and c e For the equality constraints in the objective function's constraints, c in For the inequality constraints in the objective function, m c Let μ be the dimension of the inequality constraint, μ be a positive parameter, and λ be a vector that transforms the inequality constraint into an equality constraint.

[0111] To solve for the barrier function, a Lagrange function was constructed:

[0112]

[0113] Where y and z are both Lagrange multipliers, and λ i Represent the elements in the λ vector. Transform the Lagrange equation into Newton's method form:

[0114]

[0115] Where L represents the Lagrangian function L(x,λ,y,z), A e (x) and A in (x) represents the gradient of the constraint, and Z and Λ are diagonal matrices, with diagonal elements given by z and λ. k =(d x d λ d y d z ( ) represents the search direction for each variable in Newton's method equations. Then, an optimal solution is found through iterative loops, as shown in Table 1.

[0116]

[0117] The Karush-Kuhn-Tucker condition is as follows: Carlo needs - Kuhn - Tucker conditions x0 is the initial variable, μ k x is the positive parameter of the k-th iteration. k+1 , λ k+1 y k+1 and z k+1 The optimization variables for the (k+1)th iteration, the vector that transforms inequality constraints into equality constraints, the Lagrange multipliers y and z, are respectively used to obtain the polynomial parameters that meet the constraint adjustment and minimize the cost function, thereby obtaining the speed sequence of the vehicle formation.

[0118] This paper presents a multi-vehicle platooning speed planning method that simultaneously considers the constraints of traffic light timing and the constraints of slower vehicles ahead in congested sections. This method can reduce the energy consumption of vehicle platooning, improve comfort, and enhance traffic efficiency. Based on polynomial optimization, this speed planning method significantly reduces the computational complexity of traditional vehicle platooning speed planning methods, enabling the algorithm to be applied to real-vehicle calculations without the need for cloud computing resources.

[0119] This invention utilizes a speed planning method based on polynomial optimization. Compared to traditional planning methods that use control sequences as optimization variables, it reduces the number of optimization variables from dozens or even hundreds to just a few, significantly lowering the computational burden and enabling the vehicle platooning speed planning algorithm to be applied in real-world scenarios without relying on cloud computing resources. Furthermore, this invention considers the impact of traffic light timing signals at intersections on vehicle platooning speed planning, as well as the situation of slower vehicles ahead of the platoon in congested conditions. This makes the application scenarios more realistic and promotes the application of vehicle platooning speed planning algorithms in real-world traffic scenarios.

[0120] Figure 2 This is a schematic diagram of a multi-vehicle platooning speed planning system according to the present invention, as shown below. Figure 2 As shown, a multi-vehicle platooning speed planning system includes:

[0121] The displacement equation determination module 201 is used to construct the displacement equation of the car formation; the displacement equation is an equation representing the real-time position of the lead car in the car formation.

[0122] The objective function determination module 202 is used to determine the objective function of the car formation based on energy consumption, traffic efficiency and passenger comfort; the passenger comfort is determined based on the vehicle's acceleration.

[0123] The displacement equation solving module 203 is used to solve the displacement equation by minimizing the objective function, with constraints such as traffic light timing, speed limits in traffic rules, and vehicle performance limitations on acceleration.

[0124] The displacement equation is:

[0125]

[0126] Where s(t) represents the real-time position of the lead vehicle, t is time, j = 0, 1, 2, 3, a j These are parameters to be determined.

[0127] The objective function is:

[0128] J = ω e J e +ω c J c +ω t J t ;

[0129] Among them, J e J is the cost function of the energy consumption. c J is the cost function for the passenger comfort. t Let ω be the cost function of the traffic efficiency. e ω is the weighting factor for energy consumption. c ω is the passenger comfort weighting factor. t This is a weighting factor for traffic efficiency;

[0130]

[0131] J e,i Let ti represent the energy consumption of the i-th vehicle in the convoy, t0 represent the planned trip end time, and ti represent the energy consumption of the ith vehicle. e P indicates the end time of the planned trip. mT represents the instantaneous power of the motor of the i-th vehicle. m (t) represents the torque of a single wheel of the i-th vehicle, ω(t) represents the wheel speed of the i-th vehicle, and N v The number of vehicles in the convoy;

[0132] J c =jerk(t), t0≤t≤t e ,

[0133] Where a3 is a parameter to be determined;

[0134] J t =t e -t0;

[0135] When no vehicle with a speed lower than the lead vehicle in the convoy is detected ahead of the convoy, the constraint condition of the objective function is:

[0136]

[0137] Wherein, v(t0:t e () represents the time from time t0 to t e The velocity between, a(t0:t e () represents the time from time t0 to t e The acceleration between them, v min v is the minimum speed of the lead car. max a is the maximum speed of the lead car. min Let a be the minimum acceleration of the lead car. max The maximum acceleration of the lead car is s. Nv (t e For the last vehicle in the convoy at t e The position of time, s L is the end position of the journey, \ is the modulo operation, C is the traffic light cycle, and G is the green light time.

[0138] When a vehicle with a speed lower than the lead vehicle in the convoy is detected ahead of it, it is denoted as a slow vehicle being detected. The constraint condition of the objective function is:

[0139]

[0140] t d For the detected slow time, v h The speed of the lead vehicle is represented by s. d Indicates the position of the detected slow train, d min The minimum following distance is given by s1(t), which represents the real-time position of the lead vehicle.

[0141] The displacement equation solving module specifically includes: solving the displacement equation using the interior point method.

[0142] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0143] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-vehicle platooning speed planning method, characterized in that, The displacement equation of the vehicle platoon is constructed. The displacement equation is an equation representing real-time positions of a head vehicle in the vehicle platoon. A target function of the vehicle platoon is determined based on energy consumption, traffic efficiency, and passenger comfort; the passenger comfort is determined according to jerk of the vehicle. The displacement equation is solved with minimization of the target function as a constraint, and traffic signal timing, speed limit in traffic rules, and acceleration limit of vehicle performance are used as the constraint. The displacement equation is: The target function is: where s(t) represents the real-time position of the head vehicle, t is time, j = 0, 1, 2, 3, a j is a pending parameter; When no vehicle with a speed less than a speed of the head vehicle in the vehicle platoon is detected in front of the vehicle platoon, the constraint condition of the target function is: J = ω e J e + ω c J c + ω t J t ; where J e is a cost function for the energy consumption, J c is a cost function for the passenger comfort, J t is a cost function for the traffic efficiency, ω e is a weight factor for the energy consumption, ω c is a weight factor for the passenger comfort, ω t is a weight factor for the traffic efficiency; J e,i Ei(t) represents the energy consumption of the ith vehicle in the vehicle platoon, t0represents the start time of the planned trip, t e Ei(t) represents the energy consumption of the ith vehicle in the vehicle platoon, t0represents the start time of the planned trip, t m Ei(t) represents the energy consumption of the ith vehicle in the vehicle platoon, t0represents the start time of the planned trip, t m Ei(t) represents the energy consumption of the ith vehicle in the vehicle platoon, t0represents the start time of the planned trip, t v Ei(t) represents the energy consumption of the ith vehicle in the vehicle platoon, t0represents the start time of the planned trip, t Ei(t) represents the energy consumption of the ith vehicle in the vehicle platoon, t0represents the start time of the planned trip, t J c = jerk(t), t0≤ t≤ t e , J t = t e - t0; When a vehicle with a speed less than a speed of the head vehicle in the vehicle platoon is detected in front of the vehicle platoon, denoted as a slow vehicle is detected, the constraint condition of the target function is: where v(t0:t e ) is the speed of the lead vehicle between time t0 and t e , a(t0:t e ) is the acceleration of the lead vehicle between time t0 and t e , v min is the minimum speed of the lead vehicle, v max is the maximum speed of the lead vehicle, a min is the minimum acceleration of the lead vehicle, a max is the maximum acceleration of the lead vehicle, s Nv (t e ) is the position of the rear vehicle at time t e , s L is the end position of the trip, \ is the modulo operation, C is the signal cycle, and G is the green time.

2. The method of claim 1, wherein, The displacement equation is solved with minimization of the target function as a constraint, and traffic signal timing, speed limit in traffic rules, and acceleration limit of vehicle performance are used as the constraint. t d for the detected slow vehicle time, v h represents the speed of the slow vehicle, s d represents the position of the detected slow vehicle, d min is the minimum following distance, s1(t) represents the real-time position of the leading vehicle.

3. The multi-vehicle platoon speed planning method of claim 2, wherein, The displacement equation is solved by using an interior point method. The displacement equation of the vehicle platoon is constructed.

4. A multi-vehicle platoon speed planning system, comprising: The displacement equation is an equation representing real-time positions of a head vehicle in the vehicle platoon. A target function of the vehicle platoon is determined based on energy consumption, traffic efficiency, and passenger comfort; the passenger comfort is determined according to jerk of the vehicle. The displacement equation is solved with minimization of the target function as a constraint, and traffic signal timing, speed limit in traffic rules, and acceleration limit of vehicle performance are used as the constraint. The displacement equation is: The target function is: When no vehicle with a speed less than a speed of the head vehicle in the vehicle platoon is detected in front of the vehicle platoon, the constraint condition of the target function is: where s(t) represents the real-time position of the head vehicle, t is time, j = 0, 1, 2, 3, a j is a pending parameter; When a vehicle with a speed less than a speed of the head vehicle in the vehicle platoon is detected in front of the vehicle platoon, denoted as a slow vehicle is detected, the constraint condition of the target function is: J = ω e J e + ω c J c + ω t J t ; where J e is a cost function for the energy consumption, J c is a cost function for the passenger comfort, J t is a cost function for the traffic efficiency, ω e is a weight factor for the energy consumption, ω c is a weight factor for the passenger comfort, ω t is a weight factor for the traffic efficiency; J e,i Ei(t) represents the energy consumption of the ith vehicle in the vehicle platoon, t0represents the start time of the planned trip, t e Ei(t) represents the energy consumption of the ith vehicle in the vehicle platoon, t0represents the start time of the planned trip, t m P(t) represents the instantaneous power of the electric motor of the ith vehicle, T m (t) represents the individual wheel torque of the ith vehicle, ω(t) represents the wheel speed of the ith vehicle, N v N represents the number of vehicles in the vehicle platoon; for the head vehicle of the platoon, the start time and the end time correspond to the starting position and the stop line of the previous intersection; J c = jerk(t), t0≤ t≤ t e , J t = t e - t0; The displacement equation is solved with minimization of the target function as a constraint, and traffic signal timing, speed limit in traffic rules, and acceleration limit of vehicle performance are used as the constraint. where v(t0:t e ) is the speed of the lead vehicle between time t0 and t e , a(t0:t e ) is the acceleration of the lead vehicle between time t0 and t e , v min is the minimum speed of the lead vehicle, v max is the maximum speed of the lead vehicle, a min is the minimum acceleration of the lead vehicle, a max is the maximum acceleration of the lead vehicle, s Nv (t e ) is the position of the rear vehicle at time t e , s L is the end position of the trip, \ is the modulo operation, C is the signal cycle, and G is the green time.

5. The multi-vehicle platoon speed planning system of claim 4, wherein, The displacement equation is solved by using an interior point method. t d for the detected slow vehicle time, v h represents the speed of the slow vehicle, s d represents the position of the detected slow vehicle, d min is the minimum following distance, s1(t) represents the real-time position of the leading vehicle.

6. The multi-vehicle platoon speed planning system of claim 5, wherein, ​ ​

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