A method, apparatus, and equipment for the coordinated control of mixed-load truck platoons

Abstract

This application provides a method, apparatus, and equipment for the collaborative control of mixed-load truck platoons, applicable to business scenarios such as ports, border crossings, road freight, urban delivery, mining, and airports. The method includes: acquiring the real-time load mass of each vehicle in the platoon and constructing a vehicle energy consumption model incorporating kinetic energy recovery; dynamically determining the optimal lead vehicle based on this energy consumption model and forward-looking road gradient information, with the goal of minimizing the overall energy consumption of the platoon; and finally, generating and executing an energy-saving control strategy before the platoon reaches the slope, instructing the platoon to complete the collaborative driving configuration switch. This invention achieves refined and adaptive energy-saving control of truck platoons under real-world complex road conditions by integrating load, gradient, and kinetic energy recovery factors.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a cooperative control method, apparatus and device for a mixed heavy-duty truck platoon. Background Technology

[0002] In the logistics and transportation sector, autonomous truck platooning technology, by maintaining a compact formation, effectively reduces air resistance and has become an important means of improving transportation efficiency and achieving energy conservation and emission reduction. However, in actual operation, platooned vehicles often load and unload goods at stations along the route, resulting in dynamic changes in vehicle load status and forming a typical mixed load scenario, which poses a severe challenge to platoon control.

[0003] Existing formation control technologies are ill-suited to the actual needs of dynamic load changes in logistics transportation, and are significantly inadequate in ensuring driving safety and achieving refined energy conservation. There is an urgent need to develop collaborative control methods that can intelligently respond to load changes. Summary of the Invention

[0004] This application provides a collaborative control method for a mixed-load truck platoon. By integrating load, gradient, and kinetic energy recovery factors, it achieves refined, adaptive, and energy-saving control of the truck platoon under real and complex road conditions.

[0005] In a first aspect, the present invention provides a cooperative control method for a mixed-load truck platoon, comprising: The real-time load mass of each vehicle in the truck queue is obtained, and the load mass is used to construct a vehicle energy consumption model that includes kinetic energy recovery. Based on the vehicle energy consumption model and predicted road slope information, the lead vehicle is determined; Before the truck convoy arrives at the ramp, a convoy energy-saving control strategy is generated, which is used to instruct the truck convoy to travel in a coordinated manner.

[0006] As can be seen, the method of the present invention solves the problem of insufficient energy consumption optimization caused by ignoring load differences in existing truck platoon control. The proposed method obtains the vehicle load mass in real time and constructs an energy consumption model that includes kinetic energy recovery. It combines road slope information to dynamically decide the optimal lead vehicle and completes the platoon configuration optimization before the slope. Finally, it realizes refined, adaptive and energy-saving control of truck platoons under real complex road conditions.

[0007] In one possible implementation, determining the lead vehicle includes: When the road gradient information indicates that the road ahead is an uphill section, the vehicle with the lightest load in the queue is selected as the lead vehicle; and / or, when the road gradient information indicates that the road ahead is a downhill section, the vehicle with the heaviest load in the queue is selected as the lead vehicle.

[0008] As can be seen, the method of this invention achieves significant energy-saving effects by intelligently matching load characteristics with road gradient. Specifically, when going uphill, lightly loaded vehicles lead the way, effectively reducing the core energy consumption required to overcome gradient resistance; when going downhill, heavily loaded vehicles lead the way, making full use of their greater inertia to maintain speed while maximizing kinetic energy recovery efficiency. This strategy enables the convoy to adaptively adjust its formation structure according to real-time road conditions, significantly improving the overall energy efficiency of mixed-load convoys in complex terrain while ensuring driving safety.

[0009] In one possible implementation, when the road gradient information indicates that there is a continuous slope ahead, determining the lead vehicle includes: If the uphill section is significantly longer and / or steeper than the downhill section, the vehicle with the lightest load should be selected as the lead vehicle; or, if the downhill section is significantly longer and / or steeper than the uphill section, the vehicle with the heaviest load should be selected as the lead vehicle.

[0010] As can be seen, the method of this invention achieves more refined energy consumption management by intelligently identifying and predicting the dominant terrain features in continuous slopes. Specifically, when uphill is dominant, a lightly loaded vehicle leads the way, reducing core energy consumption during the climbing phase from the source; when downhill is dominant, a heavily loaded vehicle leads the way, fully utilizing its potential energy advantage to improve kinetic energy recovery efficiency. This strategy overcomes the limitations of decision-making for single slopes, ensuring optimal energy efficiency for mixed-load vehicle fleets in complex terrain by globally considering the energy balance of continuous slopes.

[0011] In one possible implementation, the method further includes: The total net energy consumption of different load vehicles in the truck convoy as lead vehicles as they pass through the continuous ramps is predicted, and the lead vehicle is determined based on the total net energy consumption.

[0012] In one possible implementation, determining the lead vehicle based on the net total energy consumption includes: The heavy-duty vehicles include a first heavy-duty vehicle and a second heavy-duty vehicle. A first net total energy consumption value is determined based on the first heavy-duty vehicle, and a second net total energy consumption value is determined based on the second heavy-duty vehicle. If the first net total energy consumption is less than the second net total energy consumption, then the second loaded vehicle is determined to be the lead vehicle.

[0013] As can be seen, the method of this invention achieves data-driven precise decision-making by quantitatively predicting the net total energy consumption of different navigation schemes on continuous slopes. Specifically, the system, based on a load-energy consumption model, accurately simulates and compares the global energy consumption of different candidate vehicles during navigation, ultimately selecting the optimal scheme with the lowest net total energy consumption. This strategy elevates energy-saving optimization from qualitative judgment to quantitative calculation, ensuring, through global optimization, that mixed-load convoys can achieve the theoretically maximum energy-saving effect on any type of continuous slope, reaching the highest level of refined control.

[0014] In one possible implementation, the lead vehicle or the following vehicle in the convoy is determined based on the thermal management status of the vehicles. This includes: in cold environments, instructing low-temperature-sensitive electric vehicles to move to the middle of the convoy, allowing less sensitive fuel or methanol vehicles to be positioned at the front and rear of the convoy to be exposed to the wind, providing "wind resistance protection" for the electric vehicles and reducing their battery heat dissipation and heating energy consumption. In high-temperature environments, instructing fuel cell vehicles to move to a better-ventilated position at the front of the convoy to optimize their heat dissipation efficiency. Before descending a long slope, instructing electric vehicles to pre-cool their batteries to prepare for the heat generated by high-power kinetic energy recovery.

[0015] In one possible implementation, the generation queue energy-saving control strategy further includes: Based on the real-time load difference between the lead vehicle and the following vehicles in the convoy, the expected following speed of the following vehicles is corrected.

[0016] As can be seen, the method of this invention effectively solves the control problem caused by the differences in vehicle dynamic characteristics in a mixed-load convoy by introducing a speed compensation mechanism based on the difference in load mass. Specifically, the system dynamically adjusts the desired speed of the following vehicles based on the load difference between the lead vehicle and the following vehicles, allowing the heavier vehicles to respond in advance. This significantly improves the consistency of convoy braking / acceleration, enhancing the smoothness and stability of convoy driving while ensuring a safe following distance.

[0017] In one possible implementation, the kinetic energy recovery model includes an instantaneous power model, comprising at least one of the following: a rolling resistance power term, a gradient resistance power term, and an acceleration resistance power term that are proportional to the real-time load mass of the vehicle.

[0018] In one possible implementation, the instantaneous power model is: P = (k1·M + k2·M + k3·M + P_air) - P_regen Where P is the instantaneous power required by the vehicle's drive wheels, M is the real-time load mass of the vehicle, k1·M represents the power consumed to overcome rolling resistance, k2·M represents the power consumed to overcome slope resistance, k3·M represents the power consumed to overcome acceleration resistance, Pair represents the power consumed to overcome air resistance, P_regen is the recovered power generated by the kinetic energy recovery system, and k1, k2, and k3 are coefficients related to vehicle speed, road slope, and acceleration.

[0019] As can be seen, the method of this invention provides a scientific decision-making basis for platoon energy-saving control by establishing an accurate quantitative relationship model between load and energy consumption. This instantaneous power model not only quantifies the direct impact of load mass on the three major resistances of rolling, gradient, and acceleration, but also innovatively introduces a kinetic energy recovery term that is positively correlated with load, fully characterizing the energy flow of the vehicle during driving. This enables the system to accurately predict the net energy consumption of vehicles with different loads under various operating conditions, thus providing a reliable mathematical basis for optimization strategies such as navigation decision-making and speed coordination, and realizing a leap from empirical control to model predictive control.

[0020] Secondly, the present invention provides a cooperative control device for a mixed-load truck platoon, comprising: The acquisition module is used to acquire the real-time load mass of each vehicle in the truck queue, and the load mass is used to construct a vehicle energy consumption model including kinetic energy recovery. The determination module is used to determine the lead vehicle based on the vehicle energy consumption model and the predicted road slope information; The control module is used to generate a queue energy-saving control strategy before the truck queue arrives at the ramp, the energy-saving control strategy being used to instruct the truck queue to travel in a coordinated manner.

[0021] In one possible implementation, the determining module is configured to: select the vehicle with the lightest load in the queue as the lead vehicle when the road gradient information indicates that the road ahead is an uphill section; and / or, When the road gradient information indicates that there is a downhill section ahead, the vehicle with the heaviest load in the queue is selected as the lead vehicle.

[0022] In one possible implementation, when the road slope information indicates that there is a continuous slope ahead, the determining module is configured to: select the vehicle with the lightest load as the lead vehicle if the uphill section is significantly longer and / or steeper than the downhill section; or select the vehicle with the heaviest load as the lead vehicle if the downhill section is significantly longer and / or steeper than the uphill section.

[0023] In one possible implementation, the device further includes a prediction module for: predicting the total net energy consumption of different load vehicles in the truck convoy as lead vehicles as they pass through the indicated continuous ramps, and determining the lead vehicle based on the total net energy consumption.

[0024] In one possible implementation, the determining module is configured to: determine a first net total energy consumption based on the first load vehicle and a second load vehicle; determine a second net total energy consumption based on the second load vehicle; and determine the second load vehicle as the lead vehicle if the first net total energy consumption is less than the second net total energy consumption.

[0025] In one possible implementation, the generation module is configured to: correct the expected following speed of the following vehicles based on the real-time load difference between the lead vehicle and the following vehicles in the convoy.

[0026] In one possible implementation, the kinetic energy recovery model includes an instantaneous power model, comprising at least one of the following: a rolling resistance power term, a gradient resistance power term, and an acceleration resistance power term that are proportional to the real-time load mass of the vehicle.

[0027] In one possible implementation, the instantaneous power model is: P = (k1·M + k2·M + k3·M + P_air) - P_regen Where P is the instantaneous power required by the vehicle's drive wheels, M is the real-time load mass of the vehicle, k1·M represents the power consumed to overcome rolling resistance, k2·M represents the power consumed to overcome slope resistance, k3·M represents the power consumed to overcome acceleration resistance, Pair represents the power consumed to overcome air resistance, P_regen is the recovered power generated by the kinetic energy recovery system, and k1, k2, and k3 are coefficients related to vehicle speed, road slope, and acceleration.

[0028] Thirdly, embodiments of this application also provide an electronic device, which includes: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores instructions that can be executed by at least one processor to cause the electronic device to perform a method corresponding to any embodiment of the first aspect of the present application.

[0029] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement any of the methods described in the first aspect of embodiments of this application.

[0030] Fifthly, this disclosure also provides a computer program product comprising computer-executable instructions, which, when executed by a processor, are used to implement the methods of any embodiment corresponding to the first aspect of this disclosure.

[0031] In summary, the heavy-duty vehicle platoon control method provided in this application achieves three core breakthroughs by establishing a precise energy consumption model that integrates load mass and kinetic energy recovery: First, a dynamic navigation strategy based on slope prediction and load matching prioritizes light-loaded vehicles leading the platoon uphill to reduce energy consumption, while heavy-loaded vehicles lead the platoon downhill to improve kinetic energy recovery efficiency; second, a global energy consumption prediction and optimization mechanism for continuous slopes ensures that the platoon maintains optimal energy efficiency in complex terrain; and finally, the introduction of speed compensation control based on load difference effectively eliminates the dynamic response differences of mixed heavy-duty truck platoons, significantly improving the smoothness and safety of platoon coordination. This method systematically solves the problem of insufficient energy consumption optimization caused by ignoring load differences, ultimately achieving refined and adaptive energy-saving control of mixed heavy-duty truck platoons across all scenarios. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0033] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of a mixed-load truck queue provided in an embodiment of this application; Figure 2 A schematic flowchart of a collaborative control method for a mixed-load truck platoon provided in this application embodiment; Figure 3 A schematic diagram of a collaborative control device for a mixed-load truck platoon provided in an embodiment of this application; Figure 4 This is a schematic diagram of a cooperative control electronic device for a mixed-load truck platoon, provided as an embodiment of this application. Detailed Implementation

[0035] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0036] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0037] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0038] Pacified vehicles are most widely used in the logistics and transportation industry, primarily for transporting goods. However, during transportation, they may pass through different cargo stations, and the load capacity of the platooned vehicles may change due to variations in cargo type and transportation needs. In such cases, adjustments to the platooning control strategy are necessary to better ensure driving safety.

[0039] Figure 1 This is a schematic diagram of the structure of a mixed-load truck platoon provided in an embodiment of this application. This embodiment uses a platoon containing three trucks as an example for illustration. During actual operation, this platoon will pass through different cargo stations to perform loading and unloading operations, causing the load status of each vehicle to be constantly and dynamically changing. In the platoon architecture design, all three trucks are configured to have navigation authority, supporting dynamic switching of the navigation role according to control commands during the journey.

[0040] exist Figure 1In this context, vehicles can communicate directly with each other via V2V. Communication methods include, but are not limited to, Long Range Radio (LoRa) modules, Narrow Band Internet of Things (NB-IoT) modules, Enhanced Machine-Type Communication (eMTC) modules, mobile communication, LTE-V (LTE-Vehicle-to-Everything), Dedicated Short-Range Communication (DSRC), mobile communication, Cellular Vehicle-to-Everything (C-V2X), and Vehicle-to-Everything (V2X) wireless communication technologies. Understandably, in certain special scenarios, such as in remote areas without base station signals, installing communication modules on transport vehicles creates a local area network (LAN) between convoy vehicles. This LAN allows the lead vehicle and following vehicles to communicate, ensuring safe driving.

[0041] In this embodiment of the disclosure, the vehicles in the vehicle platoon may be, but are not limited to: multiple wheeled mobile robots, wheeled mobile robots, mobile robots, passenger cars, commercial vehicles (e.g., trucks, buses, vans, etc.), special purpose vehicles (e.g., ambulances, fire trucks, engineering vehicles, rescue vehicles, etc.), agricultural and industrial vehicles (e.g., harvesters, forklifts, etc.), transportation and logistics vehicles (e.g., container trucks, refrigerated trucks, etc.), new energy vehicles (e.g., electric vehicles, hybrid vehicles), and special vehicles (e.g., garbage trucks, water trucks, etc.).

[0042] Figure 2 This is a schematic flowchart illustrating a collaborative control method for a mixed-load truck queue, provided in an embodiment of this application. It includes steps S201, S202, and S203. Each step is described in detail below.

[0043] Step S201: Obtain the real-time load mass of each vehicle in the truck queue. The load mass is used to construct a vehicle energy consumption model that includes kinetic energy recovery.

[0044] In this embodiment of the application, each vehicle obtains or estimates its own load capacity in real time through at least one of the following methods: Method 1: By measuring the load pressure between the vehicle chassis and axle using pressure sensors installed on the vehicle's suspension system (such as air springs), and combining this with a pre-calibrated vehicle tare weight, the current gross vehicle weight is directly calculated. A polling measurement is performed after each destination is reached to ensure the model is updated in real time.

[0045] Method 2: Based on the vehicle's longitudinal dynamics model, the engine / motor output torque (or driving force) and longitudinal acceleration are acquired in real time via the vehicle's CAN bus. The total vehicle mass is then estimated in real time using the formula M = F / a (where F is the driving force minus the estimated value of rolling resistance and air resistance, and a is the longitudinal acceleration). After obtaining the real-time load mass, each vehicle broadcasts and shares its own mass data, speed, location, and other information within the queue through the V2X communication module.

[0046] After obtaining the real-time load mass, a vehicle energy consumption model incorporating kinetic energy recovery is constructed. In one possible implementation, the kinetic energy recovery model includes an instantaneous power model, comprising at least one of the following: a rolling resistance power term, a gradient resistance power term, and an acceleration resistance power term proportional to the real-time load mass of the vehicle.

[0047] In one possible implementation, the instantaneous power model is: P = (k1·M + k2·M + k3·M + P_air) - P_regen In one possible implementation, the instantaneous power model is: P = (k1·M + k2·M +P_air)-P_regen In one possible implementation, the instantaneous power model is: P = (k1·M +k3·M + P_air)-P_regen Where P is the instantaneous power required by the vehicle's drive wheels, M is the real-time load mass of the vehicle, k1·M represents the power consumed to overcome rolling resistance, k2·M represents the power consumed to overcome slope resistance, k3·M represents the power consumed to overcome acceleration resistance, Pair represents the power consumed to overcome air resistance, P_regen is the recovered power generated by the kinetic energy recovery system, and k1, k2, and k3 are coefficients related to vehicle speed, road slope, and acceleration.

[0048] As can be seen, the method of this invention provides a scientific decision-making basis for platoon energy-saving control by establishing an accurate quantitative relationship model between load and energy consumption. This instantaneous power model not only quantifies the direct impact of load mass on the three major resistances of rolling, gradient, and acceleration, but also innovatively introduces a kinetic energy recovery term that is positively correlated with load, fully characterizing the energy flow of the vehicle during driving. This enables the system to accurately predict the net energy consumption of vehicles with different loads under various operating conditions, thus providing a reliable mathematical basis for optimization strategies such as navigation decision-making and speed coordination, and realizing a leap from empirical control to model predictive control.

[0049] Step S202: Based on the vehicle energy consumption model and predicted road slope information, determine the lead vehicle.

[0050] In this embodiment, the system can obtain road slope profile information θ(s) within a preset distance (e.g., 5-10 kilometers) ahead in real time through an in-vehicle high-precision map and a positioning system (such as GPS / IMU fusion positioning), where s is the longitudinal coordinate of the road. In this way, the system can predict upcoming uphill, downhill, or continuous slope road conditions in advance.

[0051] In one possible implementation, determining the lead vehicle includes: When the road gradient information indicates that the road ahead is an uphill section, the vehicle with the lightest load in the queue is selected as the lead vehicle; and / or, when the road gradient information indicates that the road ahead is a downhill section, the vehicle with the heaviest load in the queue is selected as the lead vehicle.

[0052] For example, taking a convoy of three autonomous trucks as an example, their load states are as follows: Vehicle V1 (empty): total mass 8 tons, Vehicle V2 (half-loaded): total mass 16 tons, Vehicle V3 (fully loaded): total mass 32 tons.

[0053] When the convoy reaches a mountainous highway and a high-precision map detects a 3-kilometer uphill section with an average gradient of 5% starting 2 kilometers ahead, the decision-making system performs calculations and analyses based on the established energy consumption model. In the uphill scenario, the gradient resistance power term becomes the primary energy consumption factor, and its value is directly proportional to the vehicle's mass. The lightest vehicle in the convoy, V1 (8 tons), is selected as the lead vehicle. When V1 leads, the power required to overcome gradient resistance is lower than other vehicles, significantly reducing the overall energy consumption of the convoy on the uphill section. In the downhill scenario, when a continuous 4-kilometer downhill section with a 4% gradient is detected ahead, the system re-evaluates the leading strategy. Based on the kinetic energy recovery model analysis, it is determined that heavily loaded vehicles have greater kinetic energy recovery potential on downhill sections. Therefore, the system selects the heaviest vehicle in the convoy, V3 (32 tons), as the lead vehicle. When V3 leads, its greater inertia helps maintain a stable speed, while the energy recoverable by the kinetic energy recovery system is significantly increased.

[0054] As can be seen, the method of this invention achieves significant energy-saving effects by intelligently matching load characteristics with road gradient. Specifically, when going uphill, lightly loaded vehicles lead the way, effectively reducing the core energy consumption required to overcome gradient resistance; when going downhill, heavily loaded vehicles lead the way, making full use of their greater inertia to maintain speed while maximizing kinetic energy recovery efficiency. This strategy enables the convoy to adaptively adjust its formation structure according to real-time road conditions, significantly improving the overall energy efficiency of mixed-load convoys in complex terrain while ensuring driving safety.

[0055] In one possible implementation, when the road gradient information indicates that there is a continuous slope ahead, determining the lead vehicle includes: If the uphill section is significantly longer and / or steeper than the downhill section, the vehicle with the lightest load should be selected as the lead vehicle; or, if the downhill section is significantly longer and / or steeper than the uphill section, the vehicle with the heaviest load should be selected as the lead vehicle.

[0056] For example, consider a convoy of three autonomous trucks with the following load conditions: Vehicle V1 (light load): 10 tons total mass, Vehicle V2 (medium load): 15 tons total mass, and Vehicle V3 (heavy load): 25 tons total mass.

[0057] When the convoy reached the mountain highway, the high-precision map detected that the uphill section (2km) was significantly longer than the downhill section (1km), and the uphill gradient (6%) was significantly steeper. The system determined that the uphill section dominated the entire continuous slope and selected the lightest vehicle, V1 (10 tons), as the lead vehicle. While V1's leadership saved some energy on the uphill section, the energy recovery benefits were slightly reduced on the downhill section, but the overall energy saving effect was optimal. Then, the system detected that the downhill section (3km) was significantly longer than the uphill section (1km), and the downhill gradient (5%) was steeper. The system determined that the downhill section dominated the entire continuous slope and selected the heaviest vehicle, V3 (30 tons), as the lead vehicle. V3's leadership fully utilized the energy recovery advantages of heavy-duty vehicles on the downhill section, improving overall energy recovery efficiency and effectively compensating for the extra energy consumption on the uphill section.

[0058] In this embodiment, by comprehensively evaluating the slope angle (which determines the intensity of energy expenditure) and the slope length (which determines the duration of energy expenditure), the system can accurately predict the total net energy of the convoy throughout the entire route under different navigation strategies. This ensures that the selected strategy is the optimal solution for the entire journey, rather than the optimal solution for a certain local segment, thereby achieving maximum energy saving. Considering only the slope angle may be misleading (for example, a very short but very steep slope may not have as much impact as a long and gentle slope). Taking both length and angle into account makes the decision-making basis more comprehensive and accurate. This enables the system to intelligently distinguish between "nominal dominant slopes" and "actual energy-consuming dominant slopes," making reasonable and efficient decisions even in complex situations where the uphill and downhill parameters are similar, enhancing the system's adaptability and robustness under different terrains.

[0059] As can be seen, the method of this invention achieves more refined energy consumption management by intelligently identifying and predicting the dominant terrain features in continuous slopes. Specifically, when uphill is dominant, a lightly loaded vehicle leads the way, reducing core energy consumption during the climbing phase from the source; when downhill is dominant, a heavily loaded vehicle leads the way, fully utilizing its potential energy advantage to improve kinetic energy recovery efficiency. This strategy overcomes the limitations of decision-making for single slopes, ensuring optimal energy efficiency for mixed-load vehicle fleets in complex terrain by globally considering the energy balance of continuous slopes.

[0060] In one possible implementation, the method further includes: The total net energy consumption of different load vehicles in the truck convoy as lead vehicles as they pass through the continuous ramps is predicted, and the lead vehicle is determined based on the total net energy consumption.

[0061] In one possible implementation, determining the lead vehicle based on the net total energy consumption includes: The heavy-duty vehicles include a first heavy-duty vehicle and a second heavy-duty vehicle. A first net total energy consumption value is determined based on the first heavy-duty vehicle, and a second net total energy consumption value is determined based on the second heavy-duty vehicle. If the first net total energy consumption is less than the second net total energy consumption, then the second loaded vehicle is determined to be the lead vehicle.

[0062] For example, three trucks in a platoon (V1: 12 tons, V2: 20 tons, V3: 28 tons) travel up a continuous slope (2 km uphill + 1 km downhill). The system calculates the net total energy consumption for the three navigation schemes using an energy consumption model: when vehicle V1 is leading, it consumes 180 MJ uphill and recovers 35 MJ downhill, resulting in a net value of -145 MJ; when vehicle V2 is leading, it consumes 240 MJ uphill and recovers 52 MJ downhill, resulting in a net value of -188 MJ; and when vehicle V3 is leading, it consumes 295 MJ uphill and recovers 68 MJ downhill, resulting in a net value of -227 MJ. The comparison shows that vehicle V1 has the lowest net total energy consumption (-145 MJ), and the system therefore determines vehicle V1 as the optimal lead vehicle. This embodiment demonstrates through precise quantitative calculations that, in certain continuous slope scenarios, leading with a lightly loaded vehicle can achieve optimal global energy consumption. In the embodiments of this application, the actual values ​​may vary depending on the specific vehicle parameters, control strategies and road conditions. The values ​​of uphill consumption and downhill recovery involved in this application are all exemplary illustrations to help to better understand this method.

[0063] As can be seen, the method of this invention achieves data-driven precise decision-making by quantitatively predicting the net total energy consumption of different navigation schemes on continuous slopes. Specifically, the system, based on a load-energy consumption model, accurately simulates and compares the global energy consumption of different candidate vehicles during navigation, ultimately selecting the optimal scheme with the lowest net total energy consumption. This strategy elevates energy-saving optimization from qualitative judgment to quantitative calculation, ensuring, through global optimization, that mixed-load convoys can achieve the theoretically maximum energy-saving effect on any type of continuous slope, reaching the highest level of refined control.

[0064] In one possible implementation, the lead vehicle or the following vehicle in the convoy is determined based on the thermal management status of the vehicles. This includes: in cold environments, instructing low-temperature-sensitive electric vehicles to move to the middle of the convoy, allowing less sensitive fuel or methanol vehicles to be positioned at the front and rear of the convoy to be exposed to the wind, providing "wind resistance protection" for the electric vehicles and reducing their battery heat dissipation and heating energy consumption. In high-temperature environments, instructing fuel cell vehicles to move to a better-ventilated position at the front of the convoy to optimize their heat dissipation efficiency. Before descending a long slope, instructing electric vehicles to pre-cool their batteries to prepare for the heat generated by high-power kinetic energy recovery.

[0065] For example, when the ambient temperature is below a first threshold, vehicles of the first category that are sensitive to low temperatures are instructed to move to the middle of the queue, while vehicles of the second category that are not sensitive to low temperatures are instructed to move to the front and rear of the queue. When the ambient temperature is above a second threshold, vehicles of the third category that have high heat dissipation requirements are instructed to move to the designated position in the queue with the best ventilation. When it is anticipated that there is a long downhill section ahead, vehicles of the fourth category with kinetic energy recovery function are instructed to initiate the pre-cooling operation of their power batteries. The first category of vehicles includes pure electric vehicles, the second category of vehicles includes internal combustion engine vehicles or methanol fuel vehicles, the third category of vehicles includes fuel cell vehicles, and the fourth category of vehicles includes pure electric vehicles.

[0066] Step S203: Before the truck convoy arrives at the ramp, generate a convoy energy-saving control strategy, which is used to instruct the truck convoy to travel in a coordinated manner.

[0067] In this embodiment, a safe and smooth lane-changing trajectory is planned for vehicles that need to change positions (especially a newly appointed lead vehicle). This trajectory must take into account the current traffic environment, ensuring that the lane-changing operation does not affect other vehicles outside the queue and meets the larger turning radius requirements of heavily loaded vehicles. Based on the lane-changing plan, the queue coordinates and adjusts the speed of each vehicle to create sufficient safe space within the target lane for merging vehicles. For example, vehicles behind can slightly decelerate to increase the distance, providing conditions for vehicles ahead to merge.

[0068] After the convoy stabilizes in the new configuration, to eliminate the differences in dynamic response caused by the mixed load, the system initiates refined speed coordination control. This involves adjusting the expected following speed of the following vehicles based on the real-time load difference between the lead vehicle and the following vehicles in the convoy, including: (1) Load difference calculation: The load mass difference (ΔM) between the lead vehicle and each following vehicle is calculated in real time. (2) Feedforward compensation: Based on ΔM, the expected following speed of the following vehicles is corrected by feedforward compensation. When the lead vehicle brakes, the system predicts that the braking deceleration of the heavily loaded following vehicle (ΔM>0) will be less than that of the lead vehicle. Therefore, a deeper braking command will be output to the heavily loaded following vehicle in advance or the braking will start earlier to compensate for its longer braking distance and prevent rear-end collisions. When the lead vehicle accelerates, the system predicts that the acceleration response of the heavily loaded following vehicle will be slower. Therefore, a larger driving torque command will be output to the heavily loaded following vehicle to help it catch up with the acceleration rhythm of the lead vehicle more quickly and maintain the compactness of the convoy.

[0069] Once the convoy completes its configuration switch and enters a stable following state, the lead vehicle uses an optimized speed curve for cruising based on global path and road condition information. For example, it accelerates appropriately at the bottom of the slope to utilize inertia to climb, and releases the accelerator in advance at the top of the slope to utilize gravity for coasting, thereby guiding the entire convoy to achieve further energy savings.

[0070] In incline driving conditions, this invention significantly improves the driving stability of mixed-load convoys by introducing a speed compensation mechanism based on real-time mass difference. Specifically, the system continuously monitors the mass difference (ΔM) between the lead vehicle and each following vehicle. When the convoy accelerates or decelerates on an incline, this mass difference directly leads to unexpected speed differences between vehicles—such as delayed acceleration of heavily loaded vehicles uphill and delayed braking downhill. To solve this problem, the control system calculates the speed compensation amount in real time based on ΔM: when ΔM>0 (i.e., the following vehicle is heavier), the drive torque is dynamically increased for that vehicle in uphill scenarios to match acceleration performance, and braking force is applied in advance in downhill scenarios to compensate for its longer braking distance. Through this feedforward compensation control, the differences in dynamic response caused by different loads are effectively eliminated, and the speed fluctuations between vehicles in the convoy are controlled within ±0.5km / h. This ensures that the compactness and stability of the convoy can still be maintained under complex incline conditions, providing a crucial foundation for energy-saving control.

[0071] As can be seen, the method of this invention effectively solves the control problem caused by the differences in vehicle dynamic characteristics in a mixed-load convoy by introducing a speed compensation mechanism based on the difference in load mass. Specifically, the system dynamically adjusts the desired speed of the following vehicles based on the load difference between the lead vehicle and the following vehicles, allowing the heavier vehicles to respond in advance. This significantly improves the consistency of convoy braking / acceleration, enhancing the smoothness and stability of convoy driving while ensuring a safe following distance.

[0072] In summary, the heavy-duty vehicle platoon control method provided in this application establishes an accurate energy consumption model that integrates load mass and kinetic energy recovery. First, based on a dynamic navigation strategy of slope prediction and load matching, the platoon is led by lightly loaded vehicles when going uphill to reduce energy consumption, and by heavily loaded vehicles when going downhill to improve kinetic energy recovery efficiency. Second, through a global energy consumption prediction and optimization mechanism for continuous slopes, the platoon maintains optimal energy efficiency in complex terrain. Finally, speed compensation control based on load difference is introduced to effectively eliminate the dynamic response differences of mixed heavy-duty truck platoons, significantly improving the smoothness and safety of platoon coordination. This method systematically solves the problem of insufficient energy consumption optimization caused by ignoring load differences, ultimately achieving refined and adaptive energy-saving control of mixed heavy-duty truck platoons in all scenarios.

[0073] Figure 3 This is a schematic diagram of a collaborative control device for a mixed-load truck queue, provided as an embodiment of this application. It includes an acquisition module 301, a determination module 302, and a control module 303. The acquisition module 301 is used to acquire the real-time load mass of each vehicle in the truck queue, and the load mass is used to construct a vehicle energy consumption model including kinetic energy recovery. The determination module 302 is used to determine the lead vehicle based on the vehicle energy consumption model and the predicted road slope information; The control module 303 is used to generate a queue energy-saving control strategy before the truck queue arrives at the ramp, the energy-saving control strategy being used to instruct the truck queue to travel in a coordinated manner.

[0074] In one possible implementation, the determining module 302 is configured to: select the vehicle with the lightest load in the queue as the lead vehicle when the road gradient information indicates that the road ahead is an uphill section; and / or, When the road gradient information indicates that there is a downhill section ahead, the vehicle with the heaviest load in the queue is selected as the lead vehicle.

[0075] In one possible implementation, when the road slope information indicates that there is a continuous slope ahead, the determining module 302 is configured to: select the vehicle with the lightest load as the lead vehicle if the uphill section is significantly longer and / or steeper than the downhill section; or select the vehicle with the heaviest load as the lead vehicle if the downhill section is significantly longer and / or steeper than the uphill section.

[0076] In one possible implementation, the device further includes a prediction module 304 for: predicting the total net energy consumption of different load vehicles in the truck convoy as lead vehicles as they pass through the indicated continuous ramps, and determining the lead vehicle based on the total net energy consumption.

[0077] In one possible implementation, the determining module 302 is configured to: determine a first net total energy consumption based on the first load vehicle and a second load vehicle based on the second load vehicle; and determine a second net total energy consumption based on the second load vehicle if the first net total energy consumption is less than the second net total energy consumption, thereby determining the second load vehicle as the lead vehicle.

[0078] In one possible implementation, the generation module 305 is used to: correct the expected following speed of the following vehicles based on the real-time load difference between the lead vehicle and the following vehicles in the convoy.

[0079] In one possible implementation, the kinetic energy recovery model includes an instantaneous power model, comprising at least one of the following: a rolling resistance power term, a gradient resistance power term, and an acceleration resistance power term that are proportional to the real-time load mass of the vehicle.

[0080] In one possible implementation, the instantaneous power model is: P = (k1·M + k2·M + k3·M + P_air) - P_regen Where P is the instantaneous power required by the vehicle's drive wheels, M is the real-time load mass of the vehicle, k1·M represents the power consumed to overcome rolling resistance, k2·M represents the power consumed to overcome slope resistance, k3·M represents the power consumed to overcome acceleration resistance, Pair represents the power consumed to overcome air resistance, P_regen is the recovered power generated by the kinetic energy recovery system, and k1, k2, and k3 are coefficients related to vehicle speed, road slope, and acceleration.

[0081] Figure 4 This is a schematic diagram of a cooperative control electronic device for a mixed-load truck platoon, provided as an embodiment of this application. Figure 4 As shown, the electronic device 400 includes a memory 410 and a processor 420.

[0082] The memory 410 stores a computer program that can be executed by at least one processor 420. This computer program is executed by at least one processor 420 to cause the electronic device to implement the methods provided in any of the above embodiments.

[0083] The memory 410 and the processor 420 can be connected via a bus 430.

[0084] The relevant explanations can be understood by referring to the corresponding descriptions and effects in the method embodiments, and will not be repeated here.

[0085] One embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to perform the following: Figure 1 The method provided in any of the corresponding embodiments.

[0086] The computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0087] One embodiment of this application provides a computer program product comprising computer execution instructions that, when executed by a processor, are used to implement the method provided in any embodiment as shown in 1.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0089] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope of this application is indicated by the claims.

Claims

1. A method of cooperative control of a mixed load truck platoon, characterized by, include: The real-time load mass of each vehicle in the truck queue is obtained, and the load mass is used to construct a vehicle energy consumption model that includes kinetic energy recovery. Based on the vehicle energy consumption model and predicted road slope information, the lead vehicle is determined; Before the truck convoy arrives at the ramp, a convoy energy-saving control strategy is generated, which is used to instruct the truck convoy to travel in a coordinated manner.

2. The method of claim 1, wherein, The determination of the lead vehicle includes: When the road gradient information indicates an uphill section ahead, the vehicle with the lightest load in the queue is selected as the lead vehicle; and / or, When the road gradient information indicates that there is a downhill section ahead, the vehicle with the heaviest load in the queue is selected as the lead vehicle.

3. The method of claim 2, wherein, When the road gradient information indicates that there is a continuous slope ahead, determining the lead vehicle includes: If the uphill section is significantly longer and / or steeper than the downhill section, select the vehicle with the lightest load as the lead vehicle; or, If the downhill section is significantly longer and / or steeper than the uphill section, the vehicle with the heaviest load capacity should be selected as the lead vehicle.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: The total net energy consumption of different load vehicles in the truck convoy as lead vehicles as they pass through the continuous ramps is predicted, and the lead vehicle is determined based on the total net energy consumption.

5. The method of claim 4, wherein, The process of determining the lead vehicle based on the net total energy consumption includes: The heavy-duty vehicles include a first heavy-duty vehicle and a second heavy-duty vehicle. A first net total energy consumption value is determined based on the first heavy-duty vehicle, and a second net total energy consumption value is determined based on the second heavy-duty vehicle. If the first net total energy consumption is less than the second net total energy consumption, then the second loaded vehicle is determined to be the lead vehicle.

6. The method according to any one of claims 1-5, characterized in that, The generation queue energy-saving control strategy also includes: Based on the real-time load difference between the lead vehicle and the following vehicles in the convoy, the expected following speed of the following vehicles is corrected.

7. The method according to any one of claims 1-6, characterized in that, The kinetic energy recovery model includes an instantaneous power model, comprising at least one of the following: The rolling resistance power term, the gradient resistance power term, and the acceleration resistance power term are proportional to the vehicle's real-time load mass.

8. The method according to claim 7, characterized in that, The instantaneous power model is as follows: P = (k1·M + k2·M + k3·M + P_air) - P_regen Where P is the instantaneous power required by the vehicle's drive wheels, M is the real-time load mass of the vehicle, k1·M represents the power consumed to overcome rolling resistance, k2·M represents the power consumed to overcome slope resistance, k3·M represents the power consumed to overcome acceleration resistance, Pair represents the power consumed to overcome air resistance, P_regen is the recovered power generated by the kinetic energy recovery system, and k1, k2, and k3 are coefficients related to vehicle speed, road slope, and acceleration.

9. A collaborative control device for a mixed-load truck platoon, characterized in that, include: The acquisition module is used to acquire the real-time load mass of each vehicle in the truck queue, and the load mass is used to construct a vehicle energy consumption model including kinetic energy recovery. The determination module is used to determine the lead vehicle based on the vehicle energy consumption model and the predicted road slope information; The control module is used to generate a queue energy-saving control strategy before the truck queue arrives at the ramp, the energy-saving control strategy being used to instruct the truck queue to travel in a coordinated manner.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

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