Unmanned tractor DPF regeneration control method
By utilizing path prediction and key parameter models in unmanned tractors to intelligently determine the timing of regeneration, the problems of inaccurate carbon load models and passive regeneration timing in traditional DPF regeneration control are solved, thereby improving the stability of the regeneration process and fuel economy.
Patent Information
- Application Number
- CN202610383457.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-26
- Publication Date
- 2026-05-19
AI Technical Summary
Traditional DPF regeneration control strategies in unmanned tractors suffer from inaccurate carbon load model predictions and passive regeneration timing selection, leading to DPF clogging risks, deterioration of fuel economy, and runaway regeneration temperatures, thus affecting system stability.
By leveraging the path prediction capabilities of unmanned tractors, the system plans the work path through an automatic navigation module, combines key parameter prediction models, intelligently determines the timing of regeneration, corrects the carbon load model and key parameters, assesses working condition stability and fuel economy, and selects a suitable regeneration path window for DPF regeneration control.
It improves the accuracy and stability of regeneration timing, enhances fuel economy, and ensures the stability and efficiency of the regeneration process.
Smart Images
Figure CN122061885A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of agricultural machinery exhaust aftertreatment technology, specifically to a DPF regeneration control method for unmanned tractors. Background Technology
[0002] Traditional DPF regeneration control strategies primarily rely on model-based carbon load protection and differential pressure sensor-based flow resistance protection. However, tractors operate in a wide range of modes and their working conditions are characterized by strong transients and high dynamics, such as frequent turning and sudden load changes. This leads to: Inaccurate carbon loading model predictions: Under transient operating conditions, the model is prone to accumulating errors, causing the carbon loading model predictions to deviate from the actual values. Underestimating the carbon loading may lead to DPF clogging, while overestimating it may result in unnecessary and frequent regeneration.
[0003] The timing of regeneration is passive: Current technologies typically trigger regeneration immediately when the carbon load reaches a preset threshold and certain conditions are met, without considering the fuel economy and stability of the engine under current operating conditions. If regeneration is triggered when the engine is operating at low speed and low load, a large amount of fuel needs to be injected from far-injected fuel to raise the exhaust temperature to the required regeneration temperature, resulting in a significant deterioration in fuel economy during regeneration. If regeneration occurs under frequent changes in operating conditions, the exhaust temperature and air speed fluctuate drastically, which can easily lead to uncontrolled regeneration temperature (too high a temperature may cause DPF sintering, while too low a temperature may cause regeneration to be interrupted), affecting regeneration efficiency and system stability.
[0004] With the widespread adoption of autonomous driving technology in agricultural machinery, tractors have gained the ability to anticipate future operating paths and conditions. This capability provides a new technological approach to address the passive nature of traditional regeneration control. Therefore, there is an urgent need for an innovative method that can utilize navigation path information to achieve intelligent decision-making and forward-looking control of DPF regeneration timing. Summary of the Invention
[0005] To address the aforementioned technical problems, this invention provides a DPF regeneration control method for unmanned tractors, which can improve the accuracy of regeneration timing, enhance the stability of the regeneration process, and improve fuel economy.
[0006] To achieve the above technical objectives, the adopted technical solution is: a DPF regeneration control method for an unmanned tractor, comprising the following steps: Step S1: Before the operation begins, the automatic navigation module receives the target plot boundary information and plans the complete operation path. Then, it sends the planned operation path to the VCU. The VCU determines the path number of the operation cycle that needs to be collected based on the characteristics of the planned path. Step S2: After the operation begins, the VCU collects key parameters on the set operation path number according to different sampling frequencies used in actual work, and builds a key parameter prediction model on the navigation path. Step S3: During the operation, when the carbon loading model prediction value is higher than the upper limit of the carbon loading threshold window, the ECU will autonomously control the execution of the DPF regeneration process. When the carbon load model prediction value is lower than the lower limit of the carbon load threshold window, the carbon load model prediction value in the ECU and the prediction model of key parameters are corrected. When the carbon load model prediction value is within the carbon load threshold window range and the average load rate is greater than the set load rate threshold, the stability assessment condition is enabled; and when the stability index also meets the set threshold condition, the regeneration condition stability condition is enabled. Step S4: The VCU calculates the coordinates of key points suitable for entering the regeneration mode in the subsequent operation path. The coordinates of key points include the coordinates of the starting point of regeneration heating and the starting point of regeneration cooling. The operation path between the two points constitutes the regeneration path window. Step S5: The VCU evaluates the fuel economy index of the regeneration process within the regeneration path window. If the evaluated fuel economy index is less than the set threshold, the regeneration fuel economy condition is enabled. If the evaluated fuel economy index is greater than or equal to the set threshold, the VCU will no longer send DPF regeneration requests in the remaining planned operation paths. Step S6: When the actual position of the tractor reaches the calculated regeneration heating start point coordinates, and the fuel economy index also meets the set threshold conditions, the VCU sends a regeneration request command to the ECU. If the ECU meets the set regeneration conditions, the engine enters the regeneration mode and executes the corresponding regeneration actions. Step S7: When the ECU is in regeneration mode, if the regeneration is completed before the tractor reaches the starting point coordinate of the regeneration cooling point, the ECU will actively exit the regeneration mode and enter the normal working mode, and the VCU will no longer send the regeneration interruption command; if the regeneration process is not completed when the tractor reaches the starting point coordinate of the regeneration cooling point, the VCU will send the regeneration interruption command to the ECU. After receiving the regeneration interruption command, the ECU will exit the regeneration mode and return to the normal working mode.
[0007] The specific steps for correcting the carbon load model prediction value in the ECU are as follows: the VCU sends the carbon load model correction coefficient to the ECU based on the characteristics of the planned operation path and the numerical characteristics of key parameters, thereby correcting the carbon load prediction value.
[0008] The specific steps for correcting the key parameter prediction model are as follows: VCU continuously compares and analyzes the predicted and measured values of the key parameters. If the deviation exceeds a certain threshold and persists for a certain period of time, the corrected value of the key parameter prediction model is then corrected.
[0009] The beneficial effects of this invention are: 1. Improved accuracy of regeneration timing: Utilizing the path predictability of unmanned tractors, the carbon load prediction model is modified to expand the applicable operating conditions range of the carbon soot model and improve the accuracy of carbon soot model predictions, tailored to the characteristics of different operating paths; 2. Intelligent regeneration decision-making: By utilizing the path predictability of unmanned tractors, regeneration control is upgraded from "passive response" to "active planning." Within the carbon load threshold window, the key parameter prediction model is used to evaluate the stability of operating conditions and the fuel economy of the regeneration process, selecting a suitable operating path window for entering the regeneration mode, thereby improving the stability and fuel economy of the regeneration process. Attached Figure Description
[0010] Figure 1 for Figure 1 This is a schematic diagram illustrating the structural composition and information interaction of the control system of the present invention; Figure 2 This is a schematic diagram of the tractor plowing operation path planning and key coordinate points of the present invention. Figure 3 This is a flowchart illustrating the overall control method of the present invention. Detailed Implementation
[0011] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention. The core of the present invention lies in utilizing the path information planned by the unmanned driving system to collect key parameter information on the current operation path at different sampling frequencies, then analyzing the numerical characteristics of the collected key parameters, and finally constructing a key parameter prediction model on the future operation path, and intelligently deciding the optimal regeneration triggering time accordingly, significantly improving the stability and fuel economy of the regeneration process.
[0012] like Figure 1 As shown, an unmanned tractor DPF regeneration control system mainly includes a vehicle control unit (VCU) 110, an automatic navigation module 120, and an engine control unit (ECU) 130. The VCU 110 serves as the core decision-making unit, communicating bidirectionally with the automatic navigation module 120 and the ECU 130. The automatic navigation module 120 receives target plot boundary information, plans the work path, and provides real-time high-precision positioning information for the tractor. The ECU 130 responds to regeneration requests and regeneration interruption requests from the VCU 110 and is responsible for executing the DPF regeneration process.
[0013] like Figure 1 As shown, the VCU110 further integrates: a path condition prediction unit 111, which predicts key engine operating parameters on future operating paths based on the planned path information sent by the automatic navigation module 120 and the key parameters collected during the operation; a regeneration assessment and decision-making unit 112, which assesses the stability and economy of regeneration based on the predicted key engine operating parameters on future operating paths, calculates key coordinate points in the regeneration process, and intelligently decides whether to send a regeneration request to the ECU130; and a regeneration process monitoring unit 113, which monitors the regeneration progress and vehicle position during the regeneration process and issues a regeneration interruption command when necessary.
[0014] like Figures 2-3 As shown, a DPF regeneration control method for an unmanned tractor mainly includes the following steps: S1. Before the operation begins, the automatic navigation module 120 receives the target plot boundary information and plans the complete operation path, and then sends the planned operation path to the VCU110.
[0015] like Figure 2 As shown, the planned work path includes the work starting point. End of work 1. Operation sequence numbering and reversal mode.
[0016] The path condition prediction unit 111 in S2 and VCU110 analyzes the received planned path characteristics: number of operation cycles, U-turn mode, and proportion of straight operation sections, thereby determining the path number of the operation cycle to be collected. The collected operation cycle includes at least one complete operation cycle consisting of a straight driving section and a U-turn section.
[0017] like Figure 2 The diagram shows the planned work path during tractor plowing operations. The paths with key parameters to be collected based on the path characteristics are numbered 1, 12_1, 12_2, and 12_3. Path number 1 is the straight driving section, path number 12_1 is the deceleration and deviation process at the end of the field, path number 12_2 is the reversing process at the end of the field, and path number 12_3 is the acceleration and straightening process at the end of the field.
[0018] After the operation begins, the path condition prediction unit 111 collects key parameters on the set operation path number at different sampling frequencies according to the characteristics of the changes of various sampling parameters in actual operation. These parameters include, but are not limited to: sampling point coordinates, tractor speed, gear, engine speed, throttle opening, engine torque, engine load rate, engine fuel injection quantity, DOC inlet temperature, DPF inlet temperature, and DPF carbon load model prediction value.
[0019] After the work cycle data collection is completed, the path condition prediction unit 111 calculates the numerical characteristics of the collected key parameters in segments according to the path number: mean, rate of change, standard deviation, coefficient of variation, etc. Then, based on these parameters and in combination with the planned work path, a prediction model for the key parameters on the work path is constructed.
[0020] Furthermore, the carbon loading threshold window is an interval range, which consists of the lower limit and the upper limit of the carbon loading threshold window.
[0021] During operation, when the predicted carbon load is higher than the upper limit of the carbon load threshold window, the regeneration process is autonomously decided and executed by ECU130.
[0022] S3. During operation, when the predicted carbon load value is lower than the lower limit of the carbon load threshold window, the path condition prediction unit 111 sends a carbon load correction coefficient to the ECU 130 based on the characteristics of the planned operation path and the numerical characteristics of key parameters to correct the predicted carbon load value.
[0023] During operation, when the predicted carbon load is lower than the lower limit of the carbon load threshold window, the path condition prediction unit 111 continuously compares and analyzes the predicted and measured values of key parameters. If the deviation exceeds a certain threshold and continues for a certain period of time, the key parameter prediction model is corrected.
[0024] S4. During operation, when the predicted carbon load value is within the carbon load threshold window, the regeneration assessment and decision-making unit 112 first assesses the average load rate during subsequent operations. If the average load rate is greater than the set load rate threshold, the operating condition stability assessment conditions are enabled.
[0025] After the stability assessment conditions are met, the stability of the working conditions during subsequent operations is assessed. Based on the current actual working position, the standard deviation of the corresponding key parameters in the future path is calculated according to the proportion of straight working segments in the remaining working path and the key parameter prediction model. and coefficient of variation (CV). If the standard deviation σ is less than the set threshold for each parameter... And the coefficient of variation (CV) is less than the set threshold. If the current operating condition is considered to be a stable operating condition, the regeneration assessment and decision-making unit 112 enables the stability condition of the regeneration operating condition.
[0026] S5. After the stability conditions of the regeneration operation are met, the regeneration assessment and decision-making unit 112 calculates the coordinates of key points in the regeneration process. The coordinates of key points include the coordinates of the starting point of regeneration heating and the starting point of regeneration cooling.
[0027] Regeneration heating start point Select the starting point of the straight-line travel segment closest to the current work point as the regeneration starting point to ensure that the post-treatment temperature can be raised stably and quickly in the initial stage of regeneration heating.
[0028] like Figure 2 As shown, during the operation of route number 2, the tractor... If the carbon load is found to be within the set threshold range during the inspection, and the operating stability conditions are met, the starting point of the nearest straight driving segment 3 is selected as the regeneration heating start point. .
[0029] Regeneration cooling start point Based on the key parameter model, the time required for the cooling process is calculated. Then, combined with the predicted vehicle speed, the required work path for the cooling process is predicted. Finally, the endpoint of the work is determined. The starting point for regeneration cooling was calculated for reference. This ensures that the DPF has sufficient time and distance to cool to normal operating temperature before the tractor finishes its work.
[0030] Regeneration path window: and The planned operation path segment between them is the regeneration path window.
[0031] S6. Based on the key parameter model and regeneration path window, the regeneration assessment and decision-making unit 112 evaluates the fuel economy of entering the regeneration mode from a fuel economy perspective, mainly through regenerated fuel economy indicators. The calculation is done using the following formula: in, This represents the total amount of fuel injected far behind the regeneration path window. and These are the carbon soot mass before and after regeneration predicted by the carbon soot model in the ECU. The model mainly calculates the carbon soot burning rate by exhaust temperature and then predicts the carbon soot mass removed within the regeneration window.
[0032] The lower the value, the less fuel is needed to remove a unit mass of soot, resulting in better fuel economy. When the η value is greater than the set threshold... If the current operating condition indicates poor fuel economy and is unsuitable for regeneration mode, no further regeneration requests will be sent to the remaining planned operating paths. When the η value is less than the set threshold... When the current operating conditions are good for fuel economy in regeneration mode, it indicates that the VCU enables regeneration fuel economy conditions.
[0033] Furthermore, the threshold for fuel economy is set. The value is positively correlated with the current carbon load level; the higher the carbon load, the lower the fuel economy requirement. The set threshold... The higher the value, the higher the fuel economy requirement; conversely, the lower the carbon load, the higher the set threshold. The smaller the value.
[0034] S7 and VCU regeneration assessment and decision-making unit 112 monitor the actual position of the tractor in real time. When the actual position of the tractor reaches the set regeneration heating start point position... If the regeneration stability condition and the regeneration fuel economy condition are both met, then the VCU sends a regeneration command to the ECU.
[0035] S8. After receiving the VCU regeneration request, the ECU checks the regeneration conditions set in the ECU. If the conditions are met, the engine enters the regeneration mode and performs the corresponding regeneration actions.
[0036] S9 and VCU regeneration process monitoring unit 113 monitor the regeneration process and the actual working position of the tractor in real time. When the tractor completes regeneration before reaching the regeneration cooling start point coordinate Pcool, the ECU actively exits the regeneration mode and enters the normal working mode.
[0037] When the tractor reaches the coordinates of the starting point of regeneration cooling If regeneration is not completed, the VCU regeneration process monitoring unit 113 sends a regeneration interruption command to the ECU. After receiving the regeneration interruption command, the ECU exits the regeneration mode and resumes normal operation. Before stopping operation, the DPF system is cooled to normal operating temperature by utilizing the relatively low exhaust temperature in normal operation mode.
[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A DPF regeneration control method for an unmanned tractor, characterized in that, Includes the following steps: Step S1: Before the operation begins, the automatic navigation module receives the target plot boundary information and plans the complete operation path. Then, it sends the planned operation path to the VCU. The VCU determines the path number of the operation cycle that needs to be collected based on the characteristics of the planned path. Step S2: After the operation begins, the VCU collects key parameters on the set operation path number according to different sampling frequencies used in actual work, and builds a key parameter prediction model on the navigation path. Step S3: During the operation, when the carbon loading model prediction value is higher than the upper limit of the carbon loading threshold window, the ECU will autonomously control the execution of the DPF regeneration process. When the carbon load model prediction value is lower than the lower limit of the carbon load threshold window, the carbon load model prediction value in the ECU and the prediction model of key parameters are corrected. When the carbon load model prediction value is within the carbon load threshold window range and the average load rate is greater than the set load rate threshold, the stability assessment condition is enabled; and when the stability index also meets the set threshold condition, the regeneration condition stability condition is enabled. Step S4: The VCU calculates the coordinates of key points suitable for entering the regeneration mode in the subsequent operation path. The coordinates of key points include the coordinates of the starting point of regeneration heating and the starting point of regeneration cooling. The operation path between the two points constitutes the regeneration path window. Step S5: The VCU evaluates the fuel economy index of the regeneration process within the regeneration path window. If the evaluated fuel economy index is less than the set threshold, the regeneration fuel economy condition is enabled. If the evaluated fuel economy index is greater than or equal to the set threshold, the VCU will no longer send DPF regeneration requests in the remaining planned operation paths. Step S6: When the actual position of the tractor reaches the calculated regeneration heating start point coordinates, and the fuel economy index also meets the set threshold conditions, the VCU sends a regeneration request command to the ECU. If the ECU meets the set regeneration conditions, the engine enters the regeneration mode and executes the corresponding regeneration actions. Step S7: When the ECU is in regeneration mode, if the regeneration is completed before the tractor reaches the starting point coordinate of the regeneration cooling point, the ECU will actively exit the regeneration mode and enter the normal working mode, and the VCU will no longer send the regeneration interruption command; if the regeneration process is not completed when the tractor reaches the starting point coordinate of the regeneration cooling point, the VCU will send the regeneration interruption command to the ECU. After receiving the regeneration interruption command, the ECU will exit the regeneration mode and return to the normal working mode.
2. The DPF regeneration control method for an unmanned tractor as described in claim 1, characterized in that: The specific steps for correcting the carbon load model prediction value in the ECU are as follows: the VCU sends the carbon load model correction coefficient to the ECU based on the characteristics of the planned operation path and the numerical characteristics of key parameters, thereby correcting the carbon load prediction value.
3. The DPF regeneration control method for an unmanned tractor as described in claim 1, characterized in that: The specific steps for correcting the key parameter prediction model are as follows: VCU continuously compares and analyzes the predicted and measured values of the key parameters. If the deviation exceeds the set threshold and continues for a set time, the corrected value of the key parameter prediction model is then corrected.