Energy-saving cooperative control method and device for vehicle platoon

Abstract

The present application relates to the technical field of vehicle automatic driving technology, and discloses a vehicle platoon energy-saving cooperative control method and device.The method comprises the following steps: obtaining vehicle platoon information, global environment information and vehicle state information; determining the road environment constraint condition of the vehicle platoon within a preset step length based on the global environment information and the vehicle state information; generating an initial speed sequence of the vehicle platoon according to the road environment constraint condition and the vehicle platoon information; determining the overall energy consumption index of the initial speed sequence based on the vehicle state information; iteratively updating the initial speed sequence based on the road environment constraint condition and the overall energy consumption index to obtain an optimized speed sequence; and determining the optimal driving control instruction of each vehicle in the vehicle platoon based on the optimized speed sequence.The present application can solve the problem of high overall energy consumption of the vehicle platoon caused by single dimension of perception information during the vehicle platoon process, thereby reducing the energy consumption of the platoon and optimizing the platoon process.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a method and device for energy-saving collaborative control of vehicle platooning. Background Technology

[0002] With the continuous development of intelligent transportation and autonomous driving technologies, platooning has gradually become an important technical means to improve road traffic efficiency and reduce vehicle energy consumption. Platooning relies on vehicle-to-vehicle (V2V) communication to enable multiple vehicles to maintain a small following distance; the following vehicle can use the wake of the preceding vehicle to reduce air resistance, thereby effectively reducing fuel or electric energy consumption.

[0003] Current formation control schemes are mainly based on Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) technologies. The ACC system relies on radar and cameras to sense the speed, acceleration, and other operating status of the vehicle in front, and controls the vehicle to follow the vehicle. CACC introduces V2V communication on the basis of ACC, allowing the vehicle to directly obtain the acceleration information of the vehicle in front, further shortening the following distance and improving the trajectory following accuracy.

[0004] The existing technology has the following shortcomings: (1) The perceived information dimension is singular and lacks prior information about the global environment. Traditional ACC / CACC control only uses local state information such as the speed, acceleration and relative distance of the vehicle in front, and cannot predict global environmental constraints such as changes in road slope, traffic light phase and traffic congestion ahead. When passing through traffic light intersections or uphill sections, the following vehicles can only passively follow the acceleration and deceleration of the vehicle in front, which can easily induce ghost congestion and frequent braking behavior, thus increasing the overall energy consumption of the formation.

[0005] (2) The control objective does not achieve global optimization at the formation level. The single vehicle controller only uses the following performance and driving comfort of its own vehicle as optimization objectives, which is a distributed local optimization and does not use the minimum total energy consumption of all vehicles in the formation as the optimization index. In dynamic traffic scenarios, local optimization can easily lead to an increase in the overall energy consumption of the formation; for example, if the lead vehicle continues to drive at high speed, the vehicles behind will operate in an inefficient operating range for a long time.

[0006] (3) It lacks the ability to coordinate and anticipate control in complex dynamic scenarios. When the formation approaches an intersection or a slope, the existing solution cannot coordinate and plan the speed trajectory of all vehicles in advance, resulting in the formation frequently starting and stopping before the intersection and accelerating significantly in the slope area for power compensation, making it difficult to fully realize the overall energy-saving potential of the formation. Summary of the Invention

[0007] The purpose of this invention is to provide a vehicle platooning energy-saving collaborative control method and device, so as to at least solve the problem of high overall energy consumption of the platooning due to the single dimension of perception information during the vehicle platooning process, thereby reducing platooning energy consumption and optimizing the platooning process.

[0008] To address the aforementioned technical problems, in a first aspect, the present invention provides a vehicle platooning energy-saving cooperative control method, comprising at least: S1. Obtain vehicle platooning information, global environment information, and vehicle status information; S2. Determine the road environment constraints for vehicle formation within a preset step size based on the global environment information and the vehicle status information. S3. Generate the initial speed sequence of the vehicle formation based on the road environment constraints and the vehicle formation information; S4. Determine the overall energy consumption index of the initial speed sequence based on the vehicle status information; S5. Iteratively update the initial speed sequence based on the road environment constraints and the overall energy consumption index to obtain an optimized speed sequence; S6. When the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than the preset degree of change, the optimal driving control command for each vehicle in the vehicle formation is determined based on the optimized speed sequence.

[0009] Optionally, step S2 specifically includes: S21. Determine the safety distance constraint between adjacent vehicles in a vehicle platoon within a preset step size based on vehicle speed and safe time interval; S22. Determine the speed safety constraints of vehicle platooning within a preset step length based on road environment speed limit signs and road traffic light status. S23. Determine the acceleration constraints of vehicle formation within a preset step size based on vehicle acceleration.

[0010] Optionally, step S4 specifically includes: S41. Based on the vehicle status information, determine the candidate consumption index for each subsequence in the initial speed sequence; S42. The candidate consumption index with the smallest value is determined as the individual energy consumption index of the corresponding subsequence; S43. The overall energy consumption index is determined by the individual energy consumption index with the smallest value among all the subsequences.

[0011] Optionally, step S41 specifically includes: S411. Determine the overall energy consumption of the formation, the battery state of charge balance, and the driving comfort based on the vehicle status information. S412. Based on the overall energy consumption of the formation, the battery state of charge balance, the driving comfort, and the preset weights, determine the candidate consumption index for each subsequence in the initial speed sequence.

[0012] Optionally, step S411 specifically includes: S401. When the vehicle platoon consists entirely of fuel vehicles, the overall energy consumption of the platoon is determined based on the vehicle mass, vehicle speed and vehicle acceleration, and the battery state of charge balancing item is set to 0. S402. When all vehicles in the platoon are new energy vehicles, the overall energy consumption of the platoon is determined based on the vehicle traction force and motor efficiency, and the battery state of charge is determined based on the average load state of the platoon. S403. Determine the driving comfort item based on the vehicle acceleration change rate.

[0013] Optionally, it also includes: S7. Adjust the preset weights based on the global environmental information of the vehicle formation.

[0014] Optionally, step S5 specifically includes: S51. Obtain the subsequence corresponding to the overall energy consumption index in the initial velocity sequence; S52. Determine whether the subsequence satisfies the road environment constraints, and when the subsequence satisfies the road environment constraints, determine the subsequence as the optimized speed sequence.

[0015] Optionally, the method further includes the following after step S5: S81. When the number of iterations of the optimized speed sequence has not reached the maximum number of iterations and the degree of change is not less than the preset degree of change, the optimized speed sequence is iteratively updated according to the preset speed update formula, the preset inertia weight and the vehicle state information. S82. Determine whether the optimized speed sequence satisfies the road environment constraints. S83. If satisfied, when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than the preset degree of change, the optimal driving control command for each vehicle in the vehicle formation is determined based on the optimized speed sequence.

[0016] Optionally, the preset inertia weight can be determined as follows: ; In the formula, W This indicates the preset inertia weight. W max This represents the maximum value of the preset inertia weight. W min This represents the minimum value of the preset inertia weight. kIndicates the number of iterations. k max This indicates the maximum number of iterations.

[0017] Secondly, the present invention also provides a vehicle platooning energy-saving cooperative control device, comprising at least: The information acquisition module is used to acquire vehicle platooning information, global environment information, and vehicle status information; The constraint determination module is used to determine the road environment constraint conditions for vehicle formation within a preset step size based on the global environment information and the vehicle status information. A sequence generation module is used to generate an initial speed sequence of the vehicle formation based on the road environment constraints and the vehicle formation information. The index calculation module is used to determine the overall energy consumption index of the initial speed sequence based on the vehicle state information; The sequence optimization module is used to iteratively update the initial speed sequence based on the road environment constraints and the overall energy consumption index to obtain an optimized speed sequence; The formation determination module is used to determine the optimal driving control command for each vehicle in the vehicle formation based on the optimized speed sequence when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than a preset degree of change.

[0018] The technical solution provided by this invention firstly acquires vehicle platooning information, global environmental information, and vehicle status information; further, it determines road environment constraints for the vehicle platooning within a preset step size based on the global environmental information and vehicle status information; further, it generates an initial speed sequence for the vehicle platooning based on the road environment constraints and vehicle platooning information; further, it determines the overall energy consumption index of the initial speed sequence based on the vehicle status information; further, it iteratively updates the initial speed sequence based on the road environment constraints and the overall energy consumption index to obtain an optimized speed sequence; finally, when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than a preset degree of change, it determines the optimal driving control command for each vehicle in the vehicle platoon based on the optimized speed sequence.

[0019] Therefore, this invention, on the one hand, determines the road environment constraints of vehicle formation within a preset step size based on global environmental information and vehicle state information, providing prior information for the global environment. This allows the formation to plan speed changes in advance, avoiding frequent acceleration and deceleration caused by passively following through intersections and slopes, thus reducing overall formation energy consumption. On the other hand, this invention uses the overall comprehensive performance of the formation as the target to iteratively optimize the speed sequence, coordinating the driving conditions of all vehicles. This avoids the problem of increased global formation energy consumption caused by local optima of individual vehicles, achieving coordinated energy saving at the formation level. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a vehicle platooning energy-saving collaborative control method provided by the present invention; Figure 2 This is a schematic diagram of another energy-saving collaborative control method for vehicle platooning provided by the present invention; Figure 3 This is a schematic diagram of a vehicle platooning energy-saving collaborative control device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0022] Figure 1 This is a schematic diagram of a vehicle platooning energy-saving collaborative control method provided by the present invention. The present invention is applicable to platooning scenarios for at least various types of autonomous vehicles. This vehicle platooning energy-saving collaborative control method can be, but is not limited to, executed by the vehicle platooning energy-saving collaborative control device of the present invention as the executing entity, which can be implemented in software and / or hardware. For example... Figure 1 As shown, the vehicle platooning energy-saving cooperative control method includes at least the following steps: S1. Obtain vehicle platooning information, global environment information, and vehicle status information.

[0023] The vehicle platooning information includes the initial platooning status. Global environmental information includes road traffic light status (traffic light phase, remaining green / red light time, etc.), road speed limit signs, information on upcoming traffic accidents or construction, traffic flow density of the upcoming road segment, average vehicle speed, congestion prediction, and road gradient and elevation map data. Global environmental information can be acquired in real-time via roadside units deployed along the road or through cellular networks (4G / 5G) or C-V2X networks. Vehicle status information includes vehicle weight, vehicle position, vehicle speed, vehicle acceleration, relative distances to adjacent vehicles sensed by onboard radar / cameras, battery SOC, braking status, vehicle traction, motor efficiency, and rate of change of vehicle acceleration. Vehicle status information is shared between vehicles via Dedicated Short Range Communication (DSRC) or C-V2X, and shared information includes vehicle speed, vehicle acceleration, battery SOC, and braking status.

[0024] S2. Determine the road environment constraints for vehicle formation within a preset step size based on global environmental information and vehicle status information.

[0025] The road environment constraints can include safety distance constraints, speed constraints, acceleration constraints, and formation stability constraints. The preset step size can be N future time steps (e.g., N=20, step size Δt=0.5s). Safety distance constraints require that the distance between any two adjacent vehicles is greater than the safety distance. Speed ​​constraints require that the speed of each vehicle meets the road speed limit. Acceleration constraints ensure that vehicle acceleration is within the dynamic capability range. Formation stability constraints ensure that the vehicle spacing error and speed error meet the asymptotic convergence condition, preventing the "accordion effect" in formation.

[0026] S3. Generate the initial speed sequence of the vehicle formation based on road environment constraints and vehicle formation information.

[0027] The initial velocity sequence can be the target velocity-acceleration sequence of each vehicle in the platoon. The initial velocity sequence can be generated by creating a population of initial velocity sequences based on the current platoon information and the road information ahead (i.e., global environment information), while ensuring that it meets the road environment constraints.

[0028] S4. Determine the overall energy consumption index of the initial speed sequence based on vehicle status information.

[0029] The overall energy consumption index represents the energy consumption of a vehicle platoon. The lower the overall energy consumption index, the lower the energy consumption of the vehicle platoon.

[0030] S5. Iteratively update the initial speed sequence based on road environment constraints and overall energy consumption index to obtain the optimized speed sequence.

[0031] The iteration can be performed by calculating a new speed according to the PSO standard speed update formula, and then updating the initial speed sequence to redetermine the overall energy consumption index. If the redetermined overall energy consumption index is less than the previously determined overall energy consumption index, then the optimized speed sequence is determined based on the updated initial speed sequence.

[0032] S6. When the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than the preset degree of change, determine the optimal driving control command for each vehicle in the vehicle formation based on the optimized speed sequence.

[0033] The maximum number of iterations and the preset degree of variability can be adaptively set. The degree of variability represents the difference between two adjacent generations of optimized speed sequences. The optimal driving control command includes at least the target speed / acceleration of each vehicle in the formation. The optimal driving control command can be sent to the corresponding vehicle via V2X communication. After receiving the command, the vehicle sends it to the vehicle execution layer. The vehicle execution layer includes a longitudinal motion controller and a powertrain system. After receiving the target speed or target acceleration from the decision layer, each vehicle's adaptive cruise / drive traction control controller converts it into engine torque or motor torque commands to drive the vehicle along the planned trajectory.

[0034] The technical solution provided by this invention firstly acquires vehicle platooning information, global environmental information, and vehicle status information; further, it determines road environment constraints for the vehicle platooning within a preset step size based on the global environmental information and vehicle status information; further, it generates an initial speed sequence for the vehicle platooning based on the road environment constraints and vehicle platooning information; further, it determines the overall energy consumption index of the initial speed sequence based on the vehicle status information; further, it iteratively updates the initial speed sequence based on the road environment constraints and the overall energy consumption index to obtain an optimized speed sequence; finally, when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than a preset degree of change, it determines the optimal driving control command for each vehicle in the vehicle platoon based on the optimized speed sequence.

[0035] Therefore, this invention, on the one hand, determines the road environment constraints of vehicle formation within a preset step size based on global environmental information and vehicle state information, providing prior information for the global environment. This allows the formation to plan speed changes in advance, avoiding frequent acceleration and deceleration caused by passively following through intersections and slopes, thus reducing overall formation energy consumption. On the other hand, this invention uses the overall comprehensive performance of the formation as the target to iteratively optimize the speed sequence, coordinating the driving conditions of all vehicles. This avoids the problem of increased global formation energy consumption caused by local optima of individual vehicles, achieving coordinated energy saving at the formation level.

[0036] Based on the above embodiments or implementation methods Figure 2 This is a schematic diagram of another energy-saving collaborative control method for vehicle platooning provided by the present invention. The present invention is based on the above embodiments and includes additions and refinements. Figure 2 As shown, the vehicle platooning energy-saving cooperative control method includes at least the following steps: S1. Obtain vehicle platooning information, global environment information, and vehicle status information.

[0037] S21. Determine the safe distance constraint between adjacent vehicles in the vehicle formation within the preset step size based on vehicle speed and safe time interval.

[0038] The safety distance constraint can be any distance di(t) between two adjacent vehicles satisfying: di(t) ≥ d min +τ vi(t), where d min Let τ be the minimum stationary following distance, t be the safe following distance, and vi(t) be the speed of the following vehicle. The safe following distance refers to the safe distance maintained between a vehicle and the vehicle in front during driving to ensure safe stopping in an emergency and avoid rear-end collisions.

[0039] S22. Determine the speed safety constraints of vehicle platoons within a preset step length based on road environment speed limit signs and road traffic light status.

[0040] Among them, the speed safety constraint can be v min(t) ≤vi(t)≤v limit(t) , where v limit(t) Determined by the current speed limit and traffic light status (v when the light is red). limit(t) =0).

[0041] S23. Determine the acceleration constraints of vehicle formation within a preset step size based on vehicle acceleration.

[0042] The acceleration constraint can be: | a i(t) | ≤ a max . a i(t) Indicates vehicle acceleration. a max This indicates the maximum permissible acceleration of the vehicle.

[0043] S3. Generate the initial speed sequence of the vehicle formation based on road environment constraints and vehicle formation information.

[0044] The initial velocity sequence can be generated within the constraints of the road environment using the Particle Swarm Optimization (PSO) algorithm. Each particle in the PSO (i.e., the subsequence below) represents the velocity sequence of all vehicles in the formation over the next N steps, with a particle dimension of M×N (M being the number of vehicles and N being the number of prediction steps). Real-number encoding is used, with each dimension corresponding to the velocity value of a specific vehicle at a given moment.

[0045] S41. Determine the candidate consumption index for each subsequence in the initial speed sequence based on vehicle status information.

[0046] In one specific implementation, step S41 may optionally include: S411. Determine the overall energy consumption of the formation, the battery state of charge balance, and the driving comfort based on vehicle status information.

[0047] In another specific embodiment, step S411 may optionally include: S401. When all vehicles in a platoon are fuel-powered vehicles, the overall energy consumption of the platoon is determined based on the vehicle mass, vehicle speed, and vehicle acceleration, and the battery state of charge balance item is set to 0.

[0048] For gasoline-powered vehicles, energy consumption Pi is mainly related to engine power and can be simplified into a polynomial model related to vehicle speed v and acceleration a: Pi≈c0 +c1 v i +c2 v i ²+c3 v i ³+m a i v i In the formula, c0~c3 are fitting coefficients, and m is the total vehicle mass. The overall energy consumption term of the formation is the sum of the energy consumption integrals of all vehicles over the next N steps.

[0049] S402. When all vehicles in a platoon are new energy vehicles, the overall energy consumption of the platoon is determined based on the vehicle's traction force and motor efficiency, and the battery state of charge is determined based on the average load state of the platoon.

[0050] For new energy vehicles, energy consumption Pi includes the power consumption of the drive motor and the energy recovered through regenerative braking, Pi = (F traction v i ) / η motor F traction For traction force, η motor Let η represent motor efficiency (η<1 during driving, η>1 during braking indicates energy recovery). The overall energy consumption of the formation is the sum of the energy consumption integrals of all vehicles over the next N steps. Introducing a battery state of charge (SOC) term can minimize the difference in SOC between vehicles in the formation: J SOC = Σ|SOC i - SOC avg |², where SOC avg This represents the formation's average state of charge (SOC). When a vehicle's SOC is low, the optimization algorithm tends to reduce that vehicle's acceleration and regenerative braking intensity to help it retain more battery power.

[0051] It is understandable that existing platooning control strategies do not take into account the differences in the state of charge (SOC) of each electric vehicle's battery, nor do they coordinate the distribution of regenerative braking energy recovery. If a vehicle with a low SOC in the platoon continues to execute an aggressive acceleration strategy, it may cause the vehicle's battery to run out prematurely and be forced to leave the platoon. At the same time, the regenerative energy generated by the braking of the vehicle in front is difficult to be utilized by the vehicles behind, resulting in energy loss. The configuration of this invention can effectively solve the above-mentioned technical problems.

[0052] S403. Determine the driving comfort term based on the vehicle acceleration change rate.

[0053] The driving comfort term can be determined as the sum of squares of the rate of change of acceleration (jerk): J comfort = Σ(j i )², where j i = da i / dt. This option helps avoid sudden acceleration and braking in the optimization results, improving ride comfort.

[0054] S412. Based on the overall energy consumption of the formation, the battery state of charge balance, the driving comfort, and the preset weights, determine the candidate consumption index for each subsequence in the initial speed sequence.

[0055] The candidate consumption index J can be determined in the following ways: J = w1 J energy + w2 J SOC + w3 J comfort ; In the formula, J energy This represents the overall energy consumption of the formation. J SOC This represents the battery state-of-charge balance term. J comfort This represents the driving comfort item. w1, w2, and w3 together represent the preset weights.

[0056] In yet another specific implementation, optionally, it also includes: S7. Adjust the preset weights based on the global environmental information of the vehicle formation.

[0057] The adjustments can be made based on the vehicle's environment. For example, in a highway scenario where energy consumption is a priority, W1 can be increased, while in an urban road scenario where comfort is a priority, W2 can be increased.

[0058] S42. The candidate consumption index with the smallest value is determined as the individual energy consumption index of the corresponding subsequence.

[0059] The subsequences change continuously with each iteration, and one subsequence can correspond to multiple candidate consumption indices. The PSO algorithm records the historical best position pbest (corresponding to the smallest candidate consumption index) for each particle (i.e., subsequence).

[0060] S43. Determine the overall energy consumption index by taking the individual energy consumption index with the smallest value among all subsequences.

[0061] The overall energy consumption index can be used to represent the current best particle (subsequence) retained in each iteration of the PSO algorithm. Furthermore, a small-scale chaotic perturbation can be applied to the subsequence corresponding to the overall energy consumption index to generate several neighborhood solutions, preventing the algorithm from prematurely converging to a local optimum.

[0062] S51. Obtain the subsequence corresponding to the overall energy consumption index in the initial velocity sequence.

[0063] S52. Determine whether the subsequence meets the road environment constraints, and if the subsequence meets the road environment constraints, determine the subsequence as the optimal speed sequence.

[0064] Understandably, during the iteration process, the speed and acceleration of the platooned vehicles are updated, necessitating further checks to determine if the sequence meets road environment constraints and prevent road traffic risks. Understandably, for subsequences that violate road environment constraints, a penalty function method is used to add a penalty term P = λ·Σ[max(0, g] to their overall energy consumption index. j )]², where g j Let λ be the violation amount of the j-th constraint, and λ be the penalty coefficient. By gradually increasing λ, the population gradually converges towards the feasible region.

[0065] S6. When the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than the preset degree of change, determine the optimal driving control command for each vehicle in the vehicle formation based on the optimized speed sequence.

[0066] In one specific implementation, after step S52, the method further includes: S81. When the number of iterations for optimizing the speed sequence has not reached the maximum number of iterations and the degree of change is not less than the preset degree of change, the speed sequence is iteratively updated according to the preset speed update formula, preset inertia weight, and vehicle state information.

[0067] The preset velocity update formula can be the PSO standard velocity update formula. In another specific implementation, optionally, it further includes: the method for determining the preset inertia weight can be: .

[0068] In the formula, W This indicates the preset inertia weight. W max This represents the maximum value of the preset inertia weight. W min This represents the minimum value of the preset inertia weight. k Indicates the number of iterations. k maxThis represents the maximum number of iterations. It is understood that, as in this invention, a larger w in the early stages is beneficial for global exploration, while a smaller w in the later stages is beneficial for fine-grained local search.

[0069] S82. Determine whether the optimized speed sequence meets the road environment constraints.

[0070] S83. If satisfied, when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than the preset degree of change, the optimal driving control command for each vehicle in the vehicle formation is determined based on the optimized speed sequence.

[0071] In a specific implementation scenario, the technical solution provided by this invention is as follows: Intersection Coordination: When the Internet of Things (IoT) senses the remaining red light duration (Tred) at the intersection ahead, the platoon decision-making layer pre-calculates a platoon coordination deceleration scheme, controlling all vehicles in the platoon to coast or lightly brake, so that they arrive at the stop line just as the green light turns on, eliminating the extra energy consumption caused by starting again after the vehicle has come to a complete stop; if the current green light duration is sufficient, the platoon speed is optimized so that the platoon can pass through the intersection continuously within the green light window at an economical speed.

[0072] Slope coordination: When an uphill section is detected ahead, the algorithm appropriately increases the platoon speed in advance within the road safety speed limit, using vehicle kinetic energy to assist in climbing the slope and reducing the demand for high-power drive during the uphill phase; when entering a downhill section, the algorithm controls the platoon speed in advance, using gravitational potential energy to achieve slow driving, increasing the amount of regenerative braking energy recovered, and avoiding the risk of speeding downhill.

[0073] Congestion Coordination: When the cloud platform predicts traffic congestion ahead, the algorithm reduces the overall speed of the convoy and appropriately increases the following distance, so that the convoy passes through the congested section at a stable low speed and avoids frequent acceleration and deceleration. If the congestion level is high, the algorithm can issue a convoy disbanding command, and each vehicle switches to single-vehicle autonomous driving mode. After leaving the congested area, the convoy is reassembled.

[0074] SOC Coordination: Each vehicle in the platoon broadcasts its own battery SOC status in real time. When a vehicle's SOC falls below a set threshold, the algorithm automatically reduces the vehicle's acceleration intensity, prioritizing vehicles with high SOCs to take on the task of leading the platoon and breaking the wind, while vehicles with low SOCs follow in the wake region to reduce energy consumption. During braking, the algorithm coordinates and adjusts the braking force of the vehicle in front, reserving a reasonable following distance so that the following vehicles have sufficient buffer space to carry out regenerative braking and avoid energy waste caused by emergency braking.

[0075] Therefore, this invention, on the one hand, determines the road environment constraints of vehicle formation within a preset step size based on global environmental information and vehicle state information, providing prior information for the global environment. This allows the formation to plan speed changes in advance, avoiding frequent acceleration and deceleration caused by passively following through intersections and slopes, thus reducing overall formation energy consumption. On the other hand, this invention uses the overall comprehensive performance of the formation as the target to iteratively optimize the speed sequence, coordinating the driving conditions of all vehicles. This avoids the problem of increased global formation energy consumption caused by local optima of individual vehicles, achieving coordinated energy saving at the formation level.

[0076] Figure 3 This is a schematic diagram of a vehicle platooning energy-saving and cooperative control device provided by the present invention. The present invention is applicable to at least various types of autonomous vehicle platooning scenarios, and the vehicle platooning energy-saving and cooperative control device can be implemented using software and / or hardware. Figure 3 As shown, the vehicle platooning energy-saving collaborative control device includes at least: The information acquisition module 110 is used to acquire vehicle platooning information, global environment information, and vehicle status information.

[0077] The constraint determination module 120 is used to determine the road environment constraints of vehicle formation within a preset step size based on global environment information and vehicle status information.

[0078] The sequence generation module 130 is used to generate the initial speed sequence of the vehicle formation based on road environment constraints and vehicle formation information.

[0079] The index calculation module 140 is used to determine the overall energy consumption index of the initial speed sequence based on vehicle status information.

[0080] The sequence optimization module 150 is used to iteratively update the initial speed sequence based on road environment constraints and the overall energy consumption index to obtain an optimized speed sequence.

[0081] The formation determination module 160 is used to determine the optimal driving control command for each vehicle in the vehicle formation based on the optimized speed sequence when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than the preset degree of change.

[0082] Optionally, the constraint determination module 120 is specifically used for: The system determines the safe distance constraints between adjacent vehicles in a vehicle platoon within a preset step size based on vehicle speed and safe time distance; and determines the speed safety constraints of the vehicle platoon within a preset step size based on road environment speed limit signs and road traffic light status; and determines the acceleration constraints of the vehicle platoon within a preset step size based on vehicle acceleration.

[0083] Optionally, the index calculation module 140 is specifically used for: Based on vehicle state information, candidate energy consumption indices are determined for each subsequence in the initial speed sequence; and the candidate energy consumption index with the smallest value is determined as the individual energy consumption index of the corresponding subsequence; and the individual energy consumption index with the smallest value in all subsequences is determined as the overall energy consumption index.

[0084] Optionally, the index calculation module 140 is also specifically used for: Based on vehicle status information, determine the overall energy consumption of the formation, the battery state of charge balance, and the driving comfort; and based on the overall energy consumption of the formation, the battery state of charge balance, the driving comfort, and preset weights, determine the candidate consumption index for each subsequence in the initial speed sequence.

[0085] Optionally, the index calculation module 140 is also specifically used for: When the vehicle platoon consists entirely of gasoline-powered vehicles, the overall energy consumption of the platoon is determined based on the vehicle mass, vehicle speed, and vehicle acceleration, and the battery state of charge is balanced to 0; when the vehicle platoon consists entirely of new energy vehicles, the overall energy consumption of the platoon is determined based on the vehicle traction force and motor efficiency, and the battery state of charge is determined based on the average load state of the platoon; and the driving comfort is determined based on the rate of change of vehicle acceleration.

[0086] Optionally, it also includes: The weight adjustment module 170 is used to adjust the preset weights based on the global environmental information of the vehicle formation.

[0087] Optionally, the sequence optimization module 150 is specifically used for: Obtain the subsequence corresponding to the overall energy consumption index in the initial velocity sequence; Determine whether the subsequence meets the road environment constraints, and if the subsequence meets the road environment constraints, determine the subsequence as the optimal speed sequence.

[0088] Optionally, the sequence optimization module 150 is also specifically used for: When the number of iterations for optimizing the speed sequence has not reached the maximum number of iterations and the degree of change is not less than the preset degree of change, the optimized speed sequence is iteratively updated according to the preset speed update formula, preset inertia weight, and vehicle state information; and, it is determined whether the optimized speed sequence meets the road environment constraints; and, if it does, and the number of iterations for optimizing the speed sequence has reached the maximum number of iterations or the degree of change is less than the preset degree of change, the optimal driving control command for each vehicle in the vehicle formation is determined based on the optimized speed sequence.

[0089] Optionally, the preset inertia weight can be determined as follows: ; In the formula, W This indicates the preset inertia weight. Wmax This represents the maximum value of the preset inertia weight. W min This represents the minimum value of the preset inertia weight. k Indicates the number of iterations. k max This indicates the maximum number of iterations.

[0090] The technical solution provided by this invention firstly acquires vehicle platooning information, global environmental information, and vehicle status information through an information acquisition module; further, a constraint determination module determines road environment constraints for the vehicle platooning within a preset step size based on the global environmental information and vehicle status information; further, a sequence generation module generates an initial speed sequence for the vehicle platooning based on the road environment constraints and vehicle platooning information; further, an index calculation module determines the overall energy consumption index of the initial speed sequence based on the vehicle status information; further, a sequence optimization module iteratively updates the initial speed sequence based on the road environment constraints and the overall energy consumption index to obtain an optimized speed sequence; finally, when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than a preset degree of change, the platooning determination module determines the optimal driving control command for each vehicle in the vehicle platooning based on the optimized speed sequence.

[0091] Therefore, this invention, on the one hand, determines the road environment constraints of vehicle formation within a preset step size based on global environmental information and vehicle state information, providing prior information for the global environment. This allows the formation to plan speed changes in advance, avoiding frequent acceleration and deceleration caused by passively following through intersections and slopes, thus reducing overall formation energy consumption. On the other hand, this invention uses the overall comprehensive performance of the formation as the target to iteratively optimize the speed sequence, coordinating the driving conditions of all vehicles. This avoids the problem of increased global formation energy consumption caused by local optima of individual vehicles, achieving coordinated energy saving at the formation level.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle platooning energy-saving collaborative control method, characterized in that, At least including: S1. Obtain vehicle platooning information, global environment information, and vehicle status information; S2. Determine the road environment constraints for vehicle formation within a preset step size based on the global environment information and the vehicle status information. S3. Generate the initial speed sequence of the vehicle formation based on the road environment constraints and the vehicle formation information; S4. Determine the overall energy consumption index of the initial speed sequence based on the vehicle status information; S5. Iteratively update the initial speed sequence based on the road environment constraints and the overall energy consumption index to obtain an optimized speed sequence; S6. When the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than the preset degree of change, the optimal driving control command for each vehicle in the vehicle formation is determined based on the optimized speed sequence.

2. The vehicle platooning energy-saving collaborative control method according to claim 1, characterized in that, Step S2 specifically includes: S21. Determine the safety distance constraint between adjacent vehicles in a vehicle platoon within a preset step size based on vehicle speed and safe time interval; S22. Determine the speed safety constraints of vehicle platooning within a preset step length based on road environment speed limit signs and road traffic light status. S23. Determine the acceleration constraints of vehicle formation within a preset step size based on vehicle acceleration.

3. The vehicle platooning energy-saving coordinated control method according to claim 1, characterized in that, Step S4 specifically includes: S41. Based on the vehicle status information, determine the candidate consumption index for each subsequence in the initial speed sequence; S42. The candidate consumption index with the smallest value is determined as the individual energy consumption index of the corresponding subsequence; S43. The overall energy consumption index is determined by the individual energy consumption index with the smallest value among all the subsequences.

4. The vehicle platooning energy-saving coordinated control method according to claim 3, characterized in that, Step S41 specifically includes: S411. Determine the overall energy consumption of the formation, the battery state of charge balance, and the driving comfort based on the vehicle status information. S412. Based on the overall energy consumption of the formation, the battery state of charge balance, the driving comfort, and the preset weights, determine the candidate consumption index for each subsequence in the initial speed sequence.

5. The vehicle platooning energy-saving coordinated control method according to claim 4, characterized in that, Step S411 specifically includes: S401. When the vehicle platoon consists entirely of fuel vehicles, the overall energy consumption of the platoon is determined based on the vehicle mass, vehicle speed and vehicle acceleration, and the battery state of charge balancing item is set to 0. S402. When all vehicles in the platoon are new energy vehicles, the overall energy consumption of the platoon is determined based on the vehicle traction force and motor efficiency, and the battery state of charge is determined based on the average load state of the platoon. S403. Determine the driving comfort item based on the vehicle acceleration change rate.

6. The vehicle platooning energy-saving coordinated control method according to claim 4, characterized in that, Also includes: S7. Adjust the preset weights based on the global environmental information of the vehicle formation.

7. The vehicle platooning energy-saving collaborative control method according to claim 1, characterized in that, Step S5 specifically includes: S51. Obtain the subsequence corresponding to the overall energy consumption index in the initial velocity sequence; S52. Determine whether the subsequence satisfies the road environment constraints, and when the subsequence satisfies the road environment constraints, determine the subsequence as the optimized speed sequence.

8. The vehicle platooning energy-saving collaborative control method according to claim 1, characterized in that, The process after step S5 also includes: S81. When the number of iterations of the optimized speed sequence has not reached the maximum number of iterations and the degree of change is not less than the preset degree of change, the optimized speed sequence is iteratively updated according to the preset speed update formula, the preset inertia weight and the vehicle state information. S82. Determine whether the optimized speed sequence satisfies the road environment constraints. S83. If satisfied, when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than the preset degree of change, the optimal driving control command for each vehicle in the vehicle formation is determined based on the optimized speed sequence.

9. The vehicle platooning energy-saving coordinated control method according to claim 8, characterized in that, The preset inertia weight can be determined in the following ways: ； In the formula, W Indicates the preset inertia weight. W max This represents the maximum value of the preset inertia weight. W min This represents the minimum value of the preset inertia weight. k Indicates the number of iterations. k max This indicates the maximum number of iterations.

10. A vehicle platooning energy-saving collaborative control device, characterized in that, The device is used to perform the vehicle platooning energy-saving collaborative control method as described in any one of claims 1-9; The device includes at least: The information acquisition module is used to acquire vehicle platooning information, global environment information, and vehicle status information; The constraint determination module is used to determine the road environment constraint conditions for vehicle formation within a preset step size based on the global environment information and the vehicle status information. A sequence generation module is used to generate an initial speed sequence of the vehicle formation based on the road environment constraints and the vehicle formation information. The index calculation module is used to determine the overall energy consumption index of the initial speed sequence based on the vehicle state information; The sequence optimization module is used to iteratively update the initial speed sequence based on the road environment constraints and the overall energy consumption index to obtain an optimized speed sequence; The formation determination module is used to determine the optimal driving control command for each vehicle in the vehicle formation based on the optimized speed sequence when the number of iterations of the optimized speed sequence reaches the maximum number of iterations or the degree of change is less than a preset degree of change.

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