Variable spacing electric heavy truck platoon distributed predictive control method

Abstract

The application discloses a variable-interval electric heavy truck queue distributed prediction control method in the technical field of intelligent traffic control, and aims to solve the problem that the prior art fails to consider the influence of complex road topography and is difficult to realize the optimal kinetic energy control of electric heavy truck queues with different quality attributes on undulating pavements. The method comprises the following steps: acquiring the future road slope of a vehicle, and constructing a dynamics model according to the future road slope of the vehicle; wherein the vehicle comprises a leading vehicle and a plurality of following vehicles; according to the dynamics model, a leading vehicle optimization problem and a following vehicle optimization problem are established; the application establishes the leading vehicle optimization problem and the following vehicle optimization problem based on the future road slope, obtains the optimal state sequence and the optimal control sequence of each vehicle by solving the optimization problems, and ensures the traffic efficiency, safety and driving smoothness of the electric heavy truck queues with different quality attributes on the undulating pavement, so that the actual working effect is ensured.

Description

Technical Field

[0001] This invention relates to a distributed predictive control method for electric heavy-duty truck platoons with variable spacing, belonging to the field of intelligent transportation control technology. Background Technology

[0002] Energy-efficient driving strategies offer a crucial pathway to further improve energy efficiency by finely optimizing energy consumption at the kinematic level. For heavy-duty trucks, highway cruising is the most critical operational scenario, accounting for the majority of energy consumption during long-haul transportation. In this context, predictive kinetic management involves optimizing vehicle speed curves to utilize road terrain and reduce dissipative braking, thereby maximizing the conversion of potential energy and momentum. However, current research on single-vehicle energy-efficient driving is inherently limited by its "island-like" optimization characteristics. These individualized methods often fail to perceive the collaborative potential in connected vehicle environments or synchronize the motion states across multiple agents, making them difficult to apply to vehicle platoon control. Existing vehicle platooning utilizes vehicle-to-vehicle (V2V) communication to reduce following distance, thereby significantly reducing drag from following vehicles by leveraging aerodynamic wake effects. Distributed model predictive control (DMPC) has become a robust framework for such multi-agent systems, successfully decomposing the complex coupling of platooning into manageable single-vehicle subproblems.

[0003] Existing electric heavy-duty truck platoon control methods do not take into account the influence of complex road terrain, making it difficult to achieve optimal kinetic energy control for electric heavy-duty truck platoons with different mass attributes on undulating road surfaces, thus affecting actual work performance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a distributed predictive control method for electric heavy truck platoons with variable spacing. Based on the future road slope, a lead vehicle optimization problem and a follow vehicle optimization problem are established. By solving the optimization problems, the optimal state sequence and optimal control sequence of each vehicle are obtained, ensuring the traffic efficiency, safety and driving smoothness of vehicle platoons with different quality attributes on undulating roads, thereby ensuring the actual working effect.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a distributed predictive control method for electric heavy-duty truck platoons with variable spacing, comprising:

[0007] Step a: Obtain the future road slope of the vehicle and construct a dynamic model based on the future road slope of the vehicle; wherein, the vehicle includes a lead vehicle and multiple follower vehicles;

[0008] Step b: Based on the dynamic model, establish the optimization problems for the lead vehicle and the follower vehicle;

[0009] Step c: Obtain the current state. Based on the current state and the navigator optimization problem, obtain the optimal state sequence and optimal control sequence of the navigator. Control the navigator to execute the first control variable in the corresponding optimal control sequence, and transmit the optimal state sequence of the navigator to the nearest following vehicle behind the navigator. Then execute step d.

[0010] Step d: Based on the current state, the optimal state sequence of the lead vehicle, and the optimization problem of the follower vehicle, obtain the optimal state sequence and optimal control sequence of the follower vehicle, control the follower vehicle to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the follower vehicle to the next follower vehicle closest to the lead vehicle, and execute step e.

[0011] Step e: Replace the optimal state sequence of the lead vehicle in step d with the optimal state sequence of the following vehicle and repeat step d until all vehicles have obtained the corresponding optimal state sequence and executed the first control quantity in the corresponding optimal control sequence, then execute step f;

[0012] Step f: Repeat steps a, b, c, d and e to complete the distributed predictive control of the vehicle queue.

[0013] Furthermore, the specific expression of the dynamic model is as follows:

[0014]

[0015]

[0016] In the formula: For a specific kinetic energy, v is the vehicle speed, and s is the distance traveled. To increase the drag coefficient, For vehicle quality, This is the motor torque. This is the discrete gear ratio. Main reduction ratio, For the wheel radius, For mechanical braking force, It is the acceleration due to gravity. The future road gradient is given by f, where f is the rolling resistance coefficient. This is the aerodynamic drag coefficient when the vehicle is traveling alone. For the windward area of ​​heavy trucks t represents the air density coefficient, and t represents time.

[0017] Furthermore, the specific expression for the navigator optimization problem is as follows:

[0018]

[0019] st

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] In the formula: u is the control input, This indicates that the control input u is used as the decision variable to find the minimum value, and i is the distance from the walk point. To predict the number of steps, This is the instantaneous power of the motor. Let be the motor torque at a distance i from the walking point. Let be the rotational speed at a distance i from the walking point. Let i be the specific kinetic energy at a distance i from the walking point. Let i be the specific kinetic energy at a distance i+1 from the walking point. It represents the distance difference between two adjacent walking points. Let the single-step energy consumption cost function be... Let i be the current state at a distance i from the walking point. This is the control input at a distance i from the walking point. Let i be the distance traveled from the walking point i. Let i be the time i hours away from the walking point. Let i be the time i+1 minutes away from the walking point. For minimum acceleration, For maximum acceleration, For the minimum specific kinetic energy, For the maximum specific kinetic energy, To achieve a rotational speed of The maximum motor torque at that time, Let be the mechanical braking force at a distance i from the walking point.

[0026] Furthermore, the specific expression for the following vehicle optimization problem is as follows:

[0027]

[0028] st

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] In the formula: Let i be the distance between the car and the walking point. For the middle vehicle at a distance of aerodynamic drag coefficient at that time Let i be the time of synchronous arrival at a distance i from the walking point. This is the minimum allowable distance.

[0036] Furthermore, the construction of the dynamic model based on the future road slope of the vehicle includes:

[0037] The kinematic relationship between vehicle speed and engine speed is obtained, and the specific expression is as follows:

[0038]

[0039] In the formula: Rotational speed;

[0040] The output shaft torque is calculated based on the future road gradient, as shown in the following expression:

[0041]

[0042] In the formula: For output shaft torque, To accelerate the vehicle, This is the aerodynamic drag coefficient;

[0043] For following vehicles, considering the wake effect, a distance-dependent attenuation model is adopted, the specific expression of which is as follows:

[0044]

[0045] In the formula: Let be the aerodynamic drag coefficient of the middle vehicle at a distance of d. To minimize the proportion, It is a natural constant. Where L is the attenuation coefficient and L is the characteristic length;

[0046] Introducing specific kinetic energy As a state variable, to eliminate nonlinearity, the specific expression is as follows:

[0047]

[0048] A dynamic model is constructed by combining the kinematic relationship between vehicle speed and rotational speed, the output shaft torque and the distance-related attenuation model.

[0049] Furthermore, the specific expression for the instantaneous power of the motor is as follows:

[0050]

[0051] In the formula: This is the instantaneous power of the motor. This is the motor torque. For rotational speed, , , , , and All of these are calibration parameters.

[0052] Furthermore, The specific expression is as follows:

[0053]

[0054] In the formula: and All of these are calibration parameters.

[0055] Secondly, the present invention provides a distributed predictive control system for electric heavy-duty truck queues with variable spacing, comprising:

[0056] The first processing module is used to obtain the future road slope of the vehicle and construct a dynamic model based on the future road slope of the vehicle; wherein, the vehicle includes a lead vehicle and multiple follower vehicles;

[0057] The second processing module is used to establish the optimization problems of the lead vehicle and the follower vehicle based on the dynamic model;

[0058] The first calculation module is used to obtain the current state, obtain the optimal state sequence and optimal control sequence of the navigator based on the current state and the navigator optimization problem, control the navigator to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the navigator to the nearest following vehicle behind the navigator, and execute the second calculation module.

[0059] The second calculation module is used to obtain the optimal state sequence and optimal control sequence of the following vehicle based on the current state, the optimal state sequence of the navigating vehicle, and the optimization problem of the following vehicle, control the following vehicle to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the following vehicle to the next following vehicle closest to the following vehicle, and execute the third calculation module.

[0060] The third calculation module is used to replace the optimal state sequence of the lead vehicle in the second calculation module with the optimal state sequence of the following vehicle and repeat the second calculation module until all vehicles have obtained the corresponding optimal state sequence and executed the first control quantity in the corresponding optimal control sequence, and then execute the loop module.

[0061] The loop module is used to repeatedly execute the first processing module, the second processing module, the first calculation module, the second calculation module, and the third calculation module to complete the distributed predictive control of the vehicle queue.

[0062] Thirdly, the present invention provides a terminal, including a processor and a storage medium;

[0063] The storage medium is used to store instructions;

[0064] The processor is configured to operate according to the instructions to perform the steps of the method according to the first aspect.

[0065] Fourthly, a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect.

[0066] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0067] This distributed predictive control method for electric heavy-duty truck platoons with variable spacing establishes optimization problems for the lead vehicle and the follower vehicle based on the future road gradient. By solving the optimization problems, the optimal state sequence and optimal control sequence of each vehicle are obtained, ensuring the traffic efficiency, safety and smoothness of the platoons with different quality attributes on undulating roads, thereby ensuring the actual working effect. Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating a distributed predictive control method for a variable-spacing electric heavy-duty truck queue according to an embodiment of the present invention.

[0069] Figure 2 This is a schematic diagram illustrating the time-position evolution between the lead vehicle and the follower vehicle according to an embodiment of the present invention;

[0070] Figure 3 This is a schematic diagram illustrating the tracking performance and aerodynamic coupling of a following vehicle according to an embodiment of the present invention;

[0071] Figure 4 This is a schematic diagram of energy consumption statistics for three strategies provided in embodiments of the present invention. Detailed Implementation

[0072] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0073] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0074] Example 1:

[0075] like Figure 1 As shown, this invention provides a distributed predictive control method for electric heavy-duty truck queues with variable spacing, comprising:

[0076] Step a: Obtain the future road slope of the vehicle and construct a dynamic model based on the future road slope of the vehicle; wherein, the vehicle includes a lead vehicle and multiple follower vehicles;

[0077] Step b: Based on the dynamic model, establish the optimization problems for the lead vehicle and the follower vehicle;

[0078] Step c: Obtain the current state. Based on the current state and the navigator optimization problem, obtain the optimal state sequence and optimal control sequence of the navigator. Control the navigator to execute the first control variable in the corresponding optimal control sequence, and transmit the optimal state sequence of the navigator to the nearest following vehicle behind the navigator. Then execute step d.

[0079] Step d: Based on the current state, the optimal state sequence of the lead vehicle, and the optimization problem of the follower vehicle, obtain the optimal state sequence and optimal control sequence of the follower vehicle, control the follower vehicle to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the follower vehicle to the next follower vehicle closest to the lead vehicle, and execute step e.

[0080] Step e: Replace the optimal state sequence of the lead vehicle in step d with the optimal state sequence of the following vehicle and repeat step d until all vehicles have obtained the corresponding optimal state sequence and executed the first control quantity in the corresponding optimal control sequence, then execute step f;

[0081] Step f: Repeat steps a, b, c, d and e to complete the distributed predictive control of the vehicle queue.

[0082] Specifically, this invention employs a Sequential Distributed Model Predictive Control (DMPC) architecture with a leading vehicle following topology, where trajectory information propagates unidirectionally downstream;

[0083] First: Navigator (A1): By solving the navigator optimization problem, its energy-optimal spatial domain trajectory is calculated, and the optimal state sequence of the navigator is obtained. and optimal control sequence Take the optimal control sequence The first control variable is Execution, resulting in the optimal state sequence The data is transmitted to the following vehicle via the vehicle-to-vehicle communication network.

[0084] Then: Following vehicle (Aj, j≥2): The j-th vehicle receives the optimal state sequence planned by the preceding vehicle. Using this trajectory as a deterministic reference sequence, the following vehicle extracts arrival time parameters through a V2V information spatiotemporal synchronization algorithm, and then solves the following vehicle optimization problem to obtain its own optimal state sequence. and optimal control sequence Take the optimal control sequence The first control variable is Execute, and Further broadcast to vehicles Repeat this step until all vehicles have obtained the optimal state sequence and the optimal control sequence, and execute the corresponding control variables.

[0085] This invention establishes a lead vehicle optimization problem and a follower vehicle optimization problem based on the future road slope. By solving the optimization problem, the optimal state sequence and optimal control sequence of each vehicle are obtained, ensuring the traffic efficiency, safety and driving smoothness of vehicle platoons with different quality attributes on undulating roads, thereby ensuring the actual working effect.

[0086] In some possible embodiments, the energy storage system of the vehicle in this application consists of a lithium-ion battery pack, and is modeled using a first-order resistor-capacitor (RC) equivalent circuit to balance computational efficiency and transient accuracy; the evolution expression of the state of charge (SOC) is as follows:

[0087]

[0088] Among them, open circuit voltage With internal resistance All are nonlinear functions of the charged state, obtained through empirical characterization. It is the battery's output power. This is the battery's rated capacity.

[0089] In this embodiment, the step of constructing a dynamic model based on the future road slope of the vehicle includes:

[0090] Obtain the kinematic relationship between vehicle speed and engine speed, including:

[0091] The drive motor is the core actuator for speed regulation, and its mechanical power output... , defined as motor torque With rotational speed The function, specifically the expression:

[0092]

[0093] In the formula: To determine the overall efficiency of the motor and inverter, the kinematic relationship between vehicle speed and engine speed is obtained as follows:

[0094]

[0095] In the formula: v is the vehicle speed, and r is the wheel radius. Main reduction ratio, This represents the discrete gear ratio.

[0096] The vehicle's motion is governed by a balance between traction force and various resistances (rolling resistance, gradient resistance, and aerodynamic drag). The output shaft torque is calculated based on the vehicle's future road gradient, as shown in the following expression:

[0097]

[0098] In the formula: For output shaft torque, To accelerate the vehicle, This is the aerodynamic drag coefficient. To increase the drag coefficient, For vehicle quality, It is the acceleration due to gravity. The future road gradient is given by f, where f is the rolling resistance coefficient. For the windward area of ​​heavy trucks It is the air density coefficient;

[0099] For following vehicles, the aerodynamic drag coefficient Instead of static parameters, considering the wake effect, a distance-dependent attenuation model is adopted, with the specific expression as follows:

[0100]

[0101] In the formula: Let be the aerodynamic drag coefficient of the middle vehicle at a distance of d. This is the aerodynamic drag coefficient when the vehicle is traveling alone. To minimize the proportion, It is a natural constant. Where L is the attenuation coefficient and L is the characteristic length;

[0102] To decouple the complex multi-vehicle optimization problem, this invention proposes a sequential distributed model predictive control (DMPC) framework. First, the lead vehicle constructs a speed optimization problem based on spatial domain preview information. Then, the following vehicle optimizes its own speed curve by comprehensively considering the following safety and the air resistance reduction caused by sharing the trajectory with the lead vehicle.

[0103] To handle location-dependent road terrain, the optimal control problem is constructed in the spatial domain; by using the driving distance s as the independent variable, the road slope can be accurately previewed, avoiding the time-domain problem caused by vehicle speed fluctuations in traditional time-domain models.

[0104] Introducing specific kinetic energy As a state variable, to eliminate the nonlinear characteristics caused by the aerodynamic drag term, the specific expression is as follows:

[0105]

[0106] In the formula, t is time, v is vehicle speed;

[0107] A dynamic model is constructed by further combining the kinematic relationship between vehicle speed and rotational speed, the output shaft torque and the distance-related attenuation model.

[0108] The specific expression of the dynamic model is as follows:

[0109]

[0110]

[0111] In the formula: It is a mechanical braking force.

[0112] Let the current state be denoted as Control input It represents; where T is the transpose, and the current state is the initial value of the optimal state sequence.

[0113] In this embodiment, to achieve computationally efficient energy assessment, a multinomial response surface model was trained to estimate the instantaneous power of the motor, and the specific expression is as follows:

[0114]

[0115] In the formula: This is the instantaneous power of the motor. , , , , and All of these are calibration parameters.

[0116] The problem of minimizing the predicted energy consumption of the pilot vehicle is transformed into the following nonlinear programming (NLP) problem, namely the pilot vehicle optimization problem, with the specific expression as follows:

[0117]

[0118] st

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] In the formula: u is the control input, This indicates that the control input u is used as the decision variable to find the minimum value, and i is the distance from the walk point. To predict the number of steps, This is the instantaneous power of the motor. Let be the motor torque at a distance i from the walking point. Let be the rotational speed at a distance i from the walking point. Let i be the specific kinetic energy at a distance i from the walking point. Let i be the specific kinetic energy at a distance i+1 from the walking point. It represents the distance difference between two adjacent walking points. Let the single-step energy consumption cost function be... Let i be the current state at a distance i from the walking point. This is the control input at a distance i from the walking point. Let i be the distance traveled from the walking point i. Let i be the time i hours away from the walking point. Let i be the time i+1 minutes away from the walking point. For minimum acceleration, For maximum acceleration, For the minimum specific kinetic energy, For the maximum specific kinetic energy, To achieve a rotational speed of The maximum motor torque at that time, Let be the mechanical braking force at a distance i from the walking point.

[0125] in, The corresponding continuous spatial derivatives defined in the dynamic model ensure that the requested torque remains strictly within the motor's operating envelope, and this is achieved through empirical square root boundary modeling. , and All are calibration parameters; in addition, the navigator optimization problem limits the vehicle acceleration 'a', which is crucial for maintaining cargo stability and avoiding excessive torque transients in the transmission system. Optionally, calibration parameters can be obtained from the calibration parameter table.

[0126] Table 1 Calibration Parameter Table

[0127]

[0128] For following vehicles in a platoon (such as the second and third trucks), their optimal speed trajectory requires careful consideration of the trade-off between energy efficiency and strict following safety. Unlike the static aerodynamics of the lead vehicle, the drag coefficient experienced by following vehicles varies with the real-time vehicle spacing. Because of the dynamic changes, this dynamic aerodynamic coupling must be explicitly embedded in the predictive state-space model.

[0129] To quantify the distance between the two vehicles in the spatial domain, we map the time trajectories of the two vehicles to a common spatial coordinate system, such as... Figure 2 The diagram shows the time-position evolution between the lead vehicle and the following vehicles. The current position of the vehicle in front. Let H be the current position difference between the vehicle in front and the vehicle behind, and let H be the predicted field of view. For time span;

[0130] At any given node At this point, the longitudinal difference between the trajectory of the following vehicle and the trajectory of the lead vehicle is the time interval. By using this time difference and combining it with the instantaneous speed of the following vehicle, the actual physical distance can be calculated. The specific expression is as follows:

[0131]

[0132] In the formula: Spatial coordinates of the vehicle in front The absolute timestamp of time, For a given node The time spent together, For a given node The speed of the vehicle at that location For a given node The specific kinetic energy at a given location; this transformation from the time domain to the spatial domain can be achieved by means of... Enables online updating of the wake-induced drag coefficient.

[0133] To solve this optimization problem, the preceding vehicle's time-indexed speed curve, seamlessly acquired through vehicle-to-vehicle communication, needs to be converted into a spatially indexed arrival time vector. The synchronization process can generate a deterministic parameter sequence on the preview window. The specific operation process of this domain conversion is implemented through the spatiotemporal synchronization algorithm of V2V information.

[0134] The synchronous arrival time at a distance of i from the walking point With the aiming parameters incorporated, the optimization problem for following vehicles can be constructed, and the specific expression is as follows:

[0135]

[0136] st

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] In the formula: Let i be the distance between the car and the walking point. For the middle vehicle at a distance of aerodynamic drag coefficient at that time Let i be the time of synchronous arrival at a distance i from the walking point. This is the minimum allowable distance.

[0144] in, It is intentionally constrained to a conservative value of 10 meters; this physical buffer has a dual function: it can prevent catastrophic rear-end collisions during emergency braking, and it can also ensure that the radiator receives sufficient front air intake, thereby avoiding the severe degradation of heat dissipation performance that is usually caused by the thermal exhaust of the vehicle in front.

[0145] Specifically, the spatiotemporal synchronization algorithm for V2V information includes:

[0146] Input: Time-domain velocity sequence of the preceding vehicle Current location of the vehicle Current time of the vehicle Current vehicle spacing ;

[0147] Output: Arrival time series of the preceding vehicle at the spatial prediction node ;

[0148] First, the time-domain speed of the preceding vehicle within the predicted time window is integrated to obtain the cumulative travel distance sequence of the preceding vehicle;

[0149] Secondly, starting from the current vehicle position, according to a fixed spatial step size... Constructing spatial prediction nodes for autonomous vehicles ;

[0150] Will Interpolation is mapped to the spatial domain to obtain the corresponding node. Forward vehicle space speed distribution ;

[0151] Next, for each spatial segment, the time increment for the preceding vehicle to pass through that segment is calculated based on the spatial velocities of adjacent nodes. The relative arrival times of the preceding vehicle to each spatial node are obtained through recursive accumulation; finally, the current vehicle time is combined with this. By synchronizing this time series, the absolute arrival time distribution of the preceding vehicle at each spatial prediction node can be obtained. .

[0152] In some possible embodiments, three strategies are compared: fixed-distance cruise control, fixed-distance DMPC (in sequential distributed model predictive control), and variable-distance DMPC proposed in this invention.

[0153] like Figure 3 As shown, this illustrates the tracking performance and aerodynamic coupling of the following vehicle; as... Figure 3 As shown in (a), even under significant changes in road gradient, both following vehicles A2 and A3 can achieve accurate speed tracking relative to the lead vehicle A1; Figure 3 (b) and (c) highlight the unique behavior of the variable spacing DMPC of the present invention. Unlike the fixed spacing strategy, the proposed controller dynamically adjusts the following distance according to the vehicle's kinetic energy state and road terrain. Specifically, during high-speed cruising, the following distance is compressed to maximize air resistance reduction. During transient conditions, the following distance is appropriately widened to avoid unnecessary braking and energy dissipation. As a result, the drag coefficient of the following vehicle fluctuates within an optimized range.

[0154] Energy consumption statistics for the three strategies are summarized in Figure 4 See Table 2; the results show that the DMPC framework brings significant energy savings to the entire platoon. Compared with the fixed-distance cruise control strategy, the fixed-distance DMPC reduces the total net energy consumption from 17.92 × 10⁻⁶. 7 J decreased to 13.43 × 10 7J, achieving a 25.06% energy saving; particularly noteworthy is that the variable-pitch DMPC proposed in this invention further enhances the energy-saving potential to 27.96%, with a total net energy consumption of 12.91 × 10⁻⁶. 7 J; For a single vehicle, compared with a fixed-spacing DMPC, the DMPC proposed in this invention reduces energy consumption by 5.94% and 6.47% for A2 and A3, respectively; This additional gain is attributed to the optimal balance between air resistance reduction and momentum utilization, verifying the necessity of dynamic spacing adjustment in heterogeneous heavy truck platoons.

[0155] Table 2: Energy Consumption Statistics for Three Strategies (10 7 J)

[0156]

[0157] Example 2:

[0158] Based on the same inventive concept as Embodiment 1, the present invention provides a distributed predictive control system for electric heavy-duty truck queues with variable spacing, comprising:

[0159] The first processing module is used to obtain the future road slope of the vehicle and construct a dynamic model based on the future road slope of the vehicle; wherein, the vehicle includes a lead vehicle and multiple follower vehicles;

[0160] The second processing module is used to establish the optimization problems of the lead vehicle and the follower vehicle based on the dynamic model;

[0161] The first calculation module is used to obtain the current state, obtain the optimal state sequence and optimal control sequence of the navigator based on the current state and the navigator optimization problem, control the navigator to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the navigator to the nearest following vehicle behind the navigator, and execute the second calculation module.

[0162] The second calculation module is used to obtain the optimal state sequence and optimal control sequence of the following vehicle based on the current state, the optimal state sequence of the navigating vehicle, and the optimization problem of the following vehicle, control the following vehicle to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the following vehicle to the next following vehicle closest to the following vehicle, and execute the third calculation module.

[0163] The third calculation module is used to replace the optimal state sequence of the lead vehicle in the second calculation module with the optimal state sequence of the following vehicle and repeat the second calculation module until all vehicles have obtained the corresponding optimal state sequence and executed the first control quantity in the corresponding optimal control sequence, and then execute the loop module.

[0164] The loop module is used to repeatedly execute the first processing module, the second processing module, the first calculation module, the second calculation module, and the third calculation module to complete the distributed predictive control of the vehicle queue.

[0165] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0166] Example 3:

[0167] This invention also provides a terminal, including a processor and a storage medium;

[0168] The storage medium is used to store instructions;

[0169] The processor is configured to operate according to the instructions to execute the steps of the method described in Embodiment 1.

[0170] Example 4:

[0171] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0172] Since the storage medium provided in this embodiment of the invention can execute the method provided in embodiment 1 of the invention, it has the corresponding functional modules and beneficial effects for executing the method.

[0173] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0174] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0175] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0176] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0177] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A distributed predictive control method for electric heavy-duty truck platoons with variable spacing, characterized in that, include: Step a: Obtain the future road slope of the vehicle and construct a dynamic model based on the future road slope of the vehicle; wherein, the vehicle includes a lead vehicle and multiple follower vehicles; Step b: Based on the dynamic model, establish the optimization problems for the lead vehicle and the follower vehicle; Step c: Obtain the current state. Based on the current state and the navigator optimization problem, obtain the optimal state sequence and optimal control sequence of the navigator. Control the navigator to execute the first control variable in the corresponding optimal control sequence, and transmit the optimal state sequence of the navigator to the nearest following vehicle behind the navigator. Then execute step d. Step d: Based on the current state, the optimal state sequence of the lead vehicle, and the optimization problem of the follower vehicle, obtain the optimal state sequence and optimal control sequence of the follower vehicle, control the follower vehicle to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the follower vehicle to the next follower vehicle closest to the lead vehicle, and execute step e. Step e: Replace the optimal state sequence of the lead vehicle in step d with the optimal state sequence of the following vehicle and repeat step d until all vehicles have obtained the corresponding optimal state sequence and executed the first control quantity in the corresponding optimal control sequence, then execute step f; Step f: Repeat steps a, b, c, d and e to complete the distributed predictive control of the vehicle queue.

2. The distributed predictive control method for variable-spacing electric heavy-duty truck queues according to claim 1, characterized in that, The specific expression of the dynamic model is as follows: In the formula: For a specific kinetic energy, v is the vehicle speed, and s is the distance traveled. To increase the drag coefficient, For vehicle quality, This is the motor torque. This is the discrete gear ratio. Main reduction ratio, For the wheel radius, For mechanical braking force, It is the acceleration due to gravity. The future road gradient is given by f, where f is the rolling resistance coefficient. This is the aerodynamic drag coefficient when the vehicle is traveling alone. For the windward area of ​​heavy trucks t represents the air density coefficient, and t represents time.

3. The distributed predictive control method for variable-spacing electric heavy-duty truck queues according to claim 2, characterized in that, The specific expression for the navigator optimization problem is as follows: s.t. In the formula: u is the control input, This indicates that the control input u is used as the decision variable to find the minimum value, and i is the distance from the walk point. To predict the number of steps, This is the instantaneous power of the motor. Let be the motor torque at a distance i from the walking point. Let be the rotational speed at a distance i from the walking point. Let i be the specific kinetic energy at a distance i from the walking point. Let i be the specific kinetic energy at a distance i+1 from the walking point. It represents the distance difference between two adjacent walking points. Let the single-step energy consumption cost function be... Let i be the current state at a distance i from the walking point. This is the control input at a distance i from the walking point. Let i be the distance traveled from the walking point i. Let i be the time i hours away from the walking point. Let i be the time i+1 minutes away from the walking point. For minimum acceleration, For maximum acceleration, For the minimum specific kinetic energy, For the maximum specific kinetic energy, To achieve a rotational speed of The maximum motor torque at that time, Let be the mechanical braking force at a distance i from the walking point.

4. The distributed predictive control method for variable-spacing electric heavy-duty truck queues according to claim 3, characterized in that, The specific expression for the following vehicle optimization problem is as follows: s.t. In the formula: Let i be the distance between the car and the walking point. For the middle vehicle at a distance of aerodynamic drag coefficient at that time Let i be the time of synchronous arrival at a distance i from the walking point. This is the minimum allowable distance.

5. The distributed predictive control method for variable-spacing electric heavy-duty truck queues according to claim 2, characterized in that, The construction of a dynamic model based on the future road gradient of the vehicle includes: The kinematic relationship between vehicle speed and engine speed is obtained, and the specific expression is as follows: In the formula: Rotational speed; The output shaft torque is calculated based on the future road gradient, as shown in the following expression: In the formula: For output shaft torque, To accelerate the vehicle, This is the aerodynamic drag coefficient; For following vehicles, considering the wake effect, a distance-dependent attenuation model is adopted, the specific expression of which is as follows: In the formula: Let be the aerodynamic drag coefficient of the middle vehicle at a distance of d. To minimize the proportion, It is a natural constant. Where L is the attenuation coefficient and L is the characteristic length; Introducing specific kinetic energy As a state variable, to eliminate nonlinearity, the specific expression is as follows: A dynamic model is constructed by combining the kinematic relationship between vehicle speed and rotational speed, the output shaft torque and the distance-related attenuation model.

6. The distributed predictive control method for variable-spacing electric heavy-duty truck queues according to claim 3, characterized in that, The specific expression for the instantaneous power of the motor is as follows: In the formula: This is the instantaneous power of the motor. This is the motor torque. For rotational speed, , , , , and All of these are calibration parameters.

7. The distributed predictive control method for variable-spacing electric heavy-duty truck queues according to claim 3, characterized in that, The specific expression is as follows: In the formula: and All of these are calibration parameters.

8. A distributed predictive control system for electric heavy-duty truck platoons with variable spacing, characterized in that, include: The first processing module is used to obtain the future road slope of the vehicle and construct a dynamic model based on the future road slope of the vehicle; wherein, the vehicle includes a lead vehicle and multiple follower vehicles; The second processing module is used to establish the optimization problems of the lead vehicle and the follower vehicle based on the dynamic model; The first calculation module is used to obtain the current state, obtain the optimal state sequence and optimal control sequence of the navigator based on the current state and the navigator optimization problem, control the navigator to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the navigator to the nearest following vehicle behind the navigator, and execute the second calculation module. The second calculation module is used to obtain the optimal state sequence and optimal control sequence of the following vehicle based on the current state, the optimal state sequence of the navigating vehicle, and the optimization problem of the following vehicle, control the following vehicle to execute the first control quantity in the corresponding optimal control sequence, and transmit the optimal state sequence of the following vehicle to the next following vehicle closest to the following vehicle, and execute the third calculation module. The third calculation module is used to replace the optimal state sequence of the lead vehicle in the second calculation module with the optimal state sequence of the following vehicle and repeat the second calculation module until all vehicles have obtained the corresponding optimal state sequence and executed the first control quantity in the corresponding optimal control sequence, and then execute the loop module. The loop module is used to repeatedly execute the first processing module, the second processing module, the first calculation module, the second calculation module, and the third calculation module to complete the distributed predictive control of the vehicle queue.

9. A terminal, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 7.

Images