Ramp energy-saving control method based on off-line speed planning and dual-mode model prediction tracking

Abstract

The invention provides a ramp energy-saving control method based on off-line speed planning and dual-mode model prediction tracking, and aims to solve the problems of insufficient precision of ramp driving energy consumption and safety cooperative control and frequent mode switching of an automatic driving vehicle. The method comprises the following steps: firstly, identifying a key road section based on high-precision road gradient data, and generating an optimal energy-saving reference speed matched with gradient characteristics in an off-line manner by adopting a dynamic planning and sequential quadratic planning hybrid optimization strategy; secondly, when the vehicle runs, relevant information is loaded after confirmation of a driver, the system takes over the longitudinal control right, two control modes are dynamically switched based on the front vehicle distance, and frequent mode switching is avoided through a hysteresis threshold value; and finally, taking the slope-fused vehicle longitudinal dynamics model as a prediction model, solving an optimal control instruction online by adopting a model prediction control algorithm, performing closed-loop execution, monitoring intervention operation of a driver, and immediately returning the driving right after the intervention operation is detected. According to the method, offline planning and online dual-mode tracking technologies are integrated, the method has the advantages of high energy consumption optimization precision, stable mode switching and good safety adaptability, and energy-saving and safe driving of the automatic driving vehicle in a ramp scene is realized.

Description

Technical Field

[0001] This invention belongs to the field of automotive energy-saving control, specifically a slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking. Background Technology

[0002] Hill-start control technology for autonomous vehicles, through precise speed planning and tracking control, reduces overall vehicle energy consumption on inclines while ensuring following stability. This is currently a research hotspot and technical challenge in the field of intelligent driving. Optimization and upgrades to this technology can effectively improve the range and driving safety of autonomous vehicles, laying a core technological foundation for the widespread adoption of intelligent transportation systems.

[0003] Some existing related patents, such as the invention patent with patent number CN202411591312.4, design a slope-adaptive predictive energy-saving control method for heavy-duty trucks. Based on neural networks, it achieves long-scale slope adaptation and dynamically adjusts controller parameters to save fuel. However, this patent mainly relies on a single neural network algorithm to achieve slope adaptation, which is difficult to accurately match the complex changing characteristics of different slopes. This can easily lead to a disconnect between the reference vehicle speed and the actual slope conditions, limiting the accuracy of energy consumption control. Another example is the invention patent with patent number CN201810963489.0, which proposes a vehicle control method and system based on the slope information ahead. According to the physical laws followed by the vehicle during driving, it determines the vehicle's energy-saving parameters and reduces the vehicle's energy consumption when crossing slopes. However, this method lacks dynamic scenario adaptation capabilities. Its preset three-stage acceleration control mode is fixed and does not consider real-time traffic interference such as fluctuations in the distance to the vehicle in front and sudden deceleration during slope driving, which can easily cause safety hazards related to following other vehicles.

[0004] However, existing energy-saving control methods still have many problems in real-world slope driving scenarios. First, the energy-saving control methods are not well adapted to slope characteristics. Due to insufficient integration of high-precision slope data or the use of a single planning algorithm, it is difficult to match different slope variations, resulting in a disconnect between the reference speed and the actual slope conditions, and low energy consumption control accuracy. At the same time, the safety and collaborative control in slope scenarios is poor. The predictive model does not fully integrate slope factors, resulting in insufficient accuracy of control commands, and there is a lack of a rapid response mechanism for driver intervention, making it difficult to achieve a dynamic balance between energy saving and tracking safety during slope driving. Therefore, how to achieve collaborative control of global energy consumption optimization, real-time traffic adaptation, and stable response has become a key bottleneck restricting the large-scale application of autonomous vehicles in slope scenarios. Summary of the Invention

[0005] This invention proposes a slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking to address the problem of insufficient accuracy in energy consumption optimization and safety coordination control of existing autonomous vehicles on slopes. By integrating offline speed planning technology that combines dynamic programming and sequential quadratic programming with vehicle longitudinal dynamics model prediction and control algorithm that incorporates slope characteristics, a slope energy-saving control scheme is provided that balances energy consumption optimization, mode stability, and safety adaptation, effectively improving the energy efficiency and safety of autonomous vehicles on slopes.

[0006] A slope energy-saving control method based on offline speed planning and dual-mode model predictive tracking, the control method comprising: 1. A slope energy-saving control method based on offline speed planning and dual-mode model predictive tracking, characterized by comprising the following steps: S1: Based on high-precision road slope data, identify key road sections with slopes; for each road section, with road slope as the core input, adopt a hybrid optimization strategy combining dynamic programming and sequential quadratic programming to solve the optimization problem with minimizing vehicle energy consumption as the objective, and generate the optimal energy-saving reference speed that matches the slope characteristics of the road section offline; S2: When the vehicle is driving, it determines whether it has entered a road segment that has been optimized offline based on real-time positioning information; if so, it provides an activation suggestion to the driver through the human-machine interface, and after obtaining the driver's confirmation, it loads the optimal energy-saving reference speed and slope information of the corresponding road segment from the cloud or local cache. S3: After receiving driver confirmation, the system takes over longitudinal control of the vehicle and dynamically switches control modes based on real-time perception of the traffic conditions ahead: when no vehicle is detected ahead or the distance to the vehicle ahead is low... When an interference-free curve tracking mode is detected, it enters the mode; when a vehicle is detected ahead and At that time, it enters traffic coordination and tracking mode; among which, and The preset hysteresis handover threshold is satisfied. To avoid frequent mode switching; S4: Based on the current control mode, the vehicle longitudinal dynamics model that integrates road slope is used as the prediction model, and the model predictive control algorithm is used to solve the optimal control command online for tracking control; in the non-interference curve tracking mode, the optimization objective is to track the optimal energy-saving reference speed that matches the slope characteristics; in the traffic coordination tracking mode, an additional safe following term is introduced into the optimization objective to achieve a trade-off between energy saving and safety. S5: Based on the current control mode, output the first acceleration command in the optimal control command sequence obtained by the corresponding model predictive control algorithm to the vehicle drive system or braking system for execution to achieve closed-loop control; monitor the driver's active intervention operations in real time, and when the operation is detected, exit the current control mode and fully return the driving power to the driver.

[0007] 2. The slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking according to claim 1, characterized in that the optimal energy-saving reference speed calculation process in step S1 is as follows: S11: Objective function Taking into account both the driving energy consumption during uphill driving and the braking loss during downhill driving, the specific form is as follows: (1) In the formula, and These represent the velocity and acceleration at discrete positions, respectively. This refers to the road slope at the corresponding location. For instantaneous fuel consumption model, For instantaneous power consumption model, For the braking energy loss model, , These are the weighting coefficients.

[0008] S12: Use dynamic programming algorithm for global initial optimization and establish a state grid. Define the state transition cost function. The initial vehicle speed curve is obtained through forward recursion and backward solution. S13: Using the initial vehicle speed curve as the initial value, and under the conditions of satisfying vehicle dynamics constraints, slope speed constraints, and acceleration continuity constraints, sequential quadratic programming is used to perform fine optimization of local vehicle speed.

[0009] 3. The slope energy-saving control method based on offline speed planning and dual-mode model predictive tracking according to claim 1, characterized in that the specific implementation of the model predictive control algorithm in step S4 is as follows: S41: The basic model used is a vehicle longitudinal dynamics model that incorporates road slope. (2) In the formula, For road slope, For vehicle quality, It is the acceleration due to gravity. For vehicle driving force, For braking force, For rolling resistance, For air resistance; S42: In the interference-free curve tracking mode, the prediction model is: (3) Optimize objective function for: (4) The corresponding constraints are: .

[0010] S43: Under the traffic coordination tracking mode, the prediction model is: (5) In the formula, the subscript Relative value Value for the vehicle in front.

[0011] The safe distance model is as follows: (6) In the formula, For time interval, This is the minimum static distance.

[0012] Optimize objective function for: (7) In the formula, To predict vehicle speed, To control acceleration, To predict vehicle distance, For a safe distance, These are the weighting coefficients.

[0013] The new security constraints are as follows: .

[0014] Compared with the prior art, the advantages of this invention are: 1. The present invention proposes a slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking, which solves the problem of insufficient accuracy in the coordinated control of energy consumption and safety of autonomous vehicles on slopes, and provides a new solution for energy-saving and safe driving of autonomous vehicles on slopes.

[0015] 2. The slope energy-saving control method based on offline speed planning and dual-mode model prediction tracking described in this invention adopts a hybrid optimization strategy combining dynamic programming and sequential quadratic programming to generate the optimal energy-saving reference speed matching the slope characteristics offline. By combining the vehicle longitudinal dynamics model that integrates road slope with the model prediction control algorithm, the optimal control command can be accurately solved, which significantly improves the energy consumption optimization accuracy and control stability of slope driving.

[0016] The present invention describes a slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking. It dynamically switches between two control modes based on the distance to the vehicle in front, avoids frequent mode switching through a hysteresis threshold, and monitors driver intervention in real time and immediately returns driving control. It effectively balances the comfort, stability and human-machine interaction safety during the driving process, and provides important guidance for the development of longitudinal control technology for autonomous vehicles on slopes. Attached Figure Description

[0017] Figure 1 The flowchart of the slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking used in the embodiment is shown below. Figure 2 The logic diagram of the dynamic switching control mode used in the embodiment; Figure 3 The flowchart for the offline calculation of the optimal energy-saving reference speed used in the embodiment is shown below. Figure 4 The flowchart illustrates the basic principles and optimization steps of the model predictive control algorithm used in this embodiment. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described examples are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example

[0019] A slope energy-saving control method based on offline speed planning and dual-mode model predictive tracking is illustrated in the flowchart below. Figure 1 As shown. First, key road sections are identified using high-precision road slope data. A hybrid optimization strategy combining dynamic programming and sequential quadratic programming is employed to generate an optimal energy-saving reference speed curve matching the slope characteristics offline. Then, during vehicle operation, relevant planning information is loaded after driver confirmation, and the system takes over longitudinal control. Two control modes are dynamically switched based on the distance to the vehicle ahead, and a hysteresis threshold is used to avoid frequent mode switching. Finally, a vehicle longitudinal dynamics prediction model incorporating slope is constructed. A model predictive control algorithm is used to solve for the optimal control command online and execute it in a closed loop. Simultaneously, driver intervention is monitored in real time, and driving control is immediately returned upon detection. This energy-saving control method specifically includes: 1. A slope energy-saving control method based on offline speed planning and dual-mode model predictive tracking, characterized by comprising the following steps: S1: Based on high-precision road slope data, identify key road sections with slopes; for each road section, with road slope as the core input, adopt a hybrid optimization strategy combining dynamic programming and sequential quadratic programming to solve the optimization problem with minimizing vehicle energy consumption as the objective, and generate the optimal energy-saving reference speed that matches the slope characteristics of the road section offline; S2: When the vehicle is driving, it determines whether it has entered a road segment that has been optimized offline based on real-time positioning information; if so, it provides an activation suggestion to the driver through the human-machine interface, and after obtaining the driver's confirmation, it loads the optimal energy-saving reference speed and slope information of the corresponding road segment from the cloud or local cache. S3: After receiving confirmation from the driver, the system takes over longitudinal control of the vehicle and dynamically switches control modes based on real-time perception of the traffic conditions ahead, such as... Figure 2 As shown. When no vehicle is detected ahead or the distance to the vehicle ahead is... When an interference-free curve tracking mode is detected, it enters the mode; when a vehicle is detected ahead and At that time, it enters traffic coordination and tracking mode; among which, and The preset hysteresis handover threshold is satisfied. To avoid frequent mode switching; S4: Based on the current control mode, the vehicle longitudinal dynamics model that integrates road slope is used as the prediction model, and the model predictive control algorithm is used to solve the optimal control command online for tracking control; in the non-interference curve tracking mode, the optimization objective is to track the optimal energy-saving reference speed that matches the slope characteristics; in the traffic coordination tracking mode, an additional safe following term is introduced into the optimization objective to achieve a trade-off between energy saving and safety. S5: Based on the current control mode, output the first acceleration command in the optimal control command sequence obtained by the corresponding model predictive control algorithm to the vehicle drive system or braking system for execution to achieve closed-loop control; monitor the driver's active intervention operations in real time, and when the operation is detected, exit the current control mode and fully return the driving power to the driver.

[0020] 2. The slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking according to claim 1, characterized in that the optimal energy-saving reference speed calculation process in step S1 is as follows: Figure 3 As shown, specifically: S11: Objective function Taking into account both the driving energy consumption during uphill driving and the braking loss during downhill driving, the specific form is as follows: (8) In the formula, and These represent the velocity and acceleration at discrete positions, respectively. This refers to the road slope at the corresponding location. For instantaneous fuel consumption model, For instantaneous power consumption model, For the braking energy loss model, , These are the weighting coefficients.

[0021] S12: Use dynamic programming algorithm for global initial optimization and establish a state grid. Define the state transition cost function. The initial vehicle speed curve is obtained through forward recursion and backward solution. S13: Using the initial vehicle speed curve as the initial value, and under the conditions of satisfying vehicle dynamics constraints, slope speed constraints, and acceleration continuity constraints, sequential quadratic programming is used to perform fine optimization of local vehicle speed.

[0022] 3. The slope energy-saving control method based on offline speed planning and dual-mode model predictive tracking according to claim 1, characterized in that the specific implementation of the model predictive control algorithm in step S4 is as follows: S41: The basic model used is a vehicle longitudinal dynamics model that incorporates road slope. (9) In the formula, For road slope, For vehicle quality, It is the acceleration due to gravity. For vehicle driving force, For braking force, For rolling resistance, For air resistance; S42: In the interference-free curve tracking mode, the prediction model is: (10) Optimize objective function for: (11) The corresponding constraints are: .

[0023] S43: Under the traffic coordination tracking mode, the prediction model is: (12) In the formula, the subscript Relative value Value for the vehicle in front.

[0024] The safe distance model is as follows: (13) In the formula, For time interval, This is the minimum static distance.

[0025] Optimize objective function for: (14) In the formula, To predict vehicle speed, To control acceleration, To predict vehicle distance, For a safe distance, These are the weighting coefficients.

[0026] The new security constraints are as follows: .

[0027] The basic principles and optimization steps of the model predictive control algorithm in both modes are as follows: Figure 4 As shown, a unified rolling optimization framework is adopted, and the prediction time domain is... Control time domain is The control cycle is Both models ultimately transform into a standard quadratic programming problem: (15) In the formula, For decision-making, Let be the coefficient matrix of the quadratic term of the objective function. The gradient vector, For the constraint matrix, To constrain the lower bound, To constrain the upper bound.

[0028] The optimal acceleration sequence is calculated online using a QP solver and then output to the actuator.

[0029] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A slope energy-saving control method based on offline speed planning and dual-mode model predictive tracking, characterized in that, Includes the following steps: S1: Based on high-precision road slope data, identify key road sections with slopes; for each road section, with road slope as the core input, adopt a hybrid optimization strategy combining dynamic programming and sequential quadratic programming to solve the optimization problem with minimizing vehicle energy consumption as the objective, and generate the optimal energy-saving reference speed that matches the slope characteristics of the road section offline; S2: When the vehicle is driving, it determines whether it has entered a road section that has been optimized offline based on real-time location information; If so, the driver is given an activation suggestion through the human-machine interface, and after the driver confirms, the optimal energy-saving reference speed and gradient information for the corresponding road segment are loaded from the cloud or local cache. S3: After receiving driver confirmation, the system takes over longitudinal control of the vehicle and dynamically switches control modes based on real-time perception of the traffic conditions ahead: when no vehicle is detected ahead or the distance to the vehicle ahead is low... When an interference-free curve tracking mode is detected, it enters the mode; when a vehicle is detected ahead and At that time, it enters traffic coordination and tracking mode; among which, and The preset hysteresis handover threshold is satisfied. To avoid frequent mode switching; S4: Based on the current control mode, the vehicle longitudinal dynamics model that integrates road slope is used as the prediction model, and the model predictive control algorithm is used to solve the optimal control command online for tracking control; in the non-interference curve tracking mode, the optimization objective is to track the optimal energy-saving reference speed that matches the slope characteristics; in the traffic coordination tracking mode, an additional safe following term is introduced into the optimization objective to achieve a trade-off between energy saving and safety. S5: Based on the current control mode, output the first acceleration command in the optimal control command sequence obtained by the corresponding model predictive control algorithm to the vehicle drive system or braking system for execution to achieve closed-loop control; monitor the driver's active intervention operations in real time, and when the operation is detected, exit the current control mode and fully return the driving power to the driver.

2. The slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking according to claim 1, characterized in that, The calculation process for the optimal energy-saving reference speed in step S1 is as follows: S11: Objective function Taking into account both the driving energy consumption during uphill driving and the braking loss during downhill driving, the specific form is as follows: (1) In the formula, and These represent the velocity and acceleration at discrete positions, respectively. This refers to the road slope at the corresponding location. For instantaneous fuel consumption model, For instantaneous power consumption model, For the braking energy loss model, , These are the weighting coefficients; S12: Use dynamic programming algorithm for global initial optimization and establish a state grid. Define the state transition cost function. The initial vehicle speed curve is obtained through forward recursion and backward solution; S13: Using the initial vehicle speed curve as the initial value, and under the conditions of satisfying vehicle dynamics constraints, slope speed constraints, and acceleration continuity constraints, sequential quadratic programming is used to perform fine optimization of local vehicle speed.

3. The slope energy-saving control method based on offline speed planning and dual-mode model prediction and tracking according to claim 1, characterized in that, The specific implementation of the model predictive control algorithm in step S4 is as follows: S41: The basic model used is a vehicle longitudinal dynamics model that incorporates road slope. (2) In the formula, For road slope, For vehicle quality, It is the acceleration due to gravity. For vehicle driving force, For braking force, For rolling resistance, For air resistance; S42: In the interference-free curve tracking mode, the prediction model is: (3) Optimize objective function for: (4) The corresponding constraints are: ; S43: Under the traffic coordination tracking mode, the prediction model is: (5) In the formula, the subscript Relative value Value for the vehicle in front; The safe distance model is as follows: (6) In the formula, For time interval, Minimum static distance; Optimize objective function for: (7) In the formula, To predict vehicle speed, To control acceleration, To predict vehicle distance, For a safe distance, These are the weighting coefficients; The new security constraints are as follows: .

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