QPV valve cut-off current self-learning method and system of clutch executing mechanism

Abstract

The problem that in an existing gearbox, a clutch is difficult to accurately control due to scattering difference of an electromagnetic valve is solved. According to the QPV valve cut-off current self-learning method and system of the clutch executing mechanism, repeated self-learning correction is carried out by combining the cut-off current of the electromagnetic valve with the displacement parameters of the clutch, the control precision of the clutch is improved, the whole vehicle drivability is guaranteed, the oil saving rate and comfort are improved, the robustness of software is guaranteed, and the control precision of the clutch is improved. And the control stability, the economical efficiency and the dynamic property of the whole vehicle are enhanced.

Description

Technical Field

[0001] This invention relates to the field of gearbox technology, and in particular to a self-learning method and system for the cut-off current of the QPV valve in a clutch actuator. Background Technology

[0002] Commercial vehicles, as a means of production, drive economic development. According to statistics from relevant professional institutions, the global heavy-duty truck AMT market reached US$2.517 billion in 2024 and is expected to increase to US$4.45 billion by 2031, with a compound annual growth rate of 8.6%. The domestic AMT penetration rate is expected to rise from 5% in 2020 to 50% in 2025, with heavy-duty trucks accounting for 80% of the target. From January to May 2024, Foton heavy-duty trucks achieved an AMT penetration rate of 65%, leading the industry by 33 percentage points. In 2025, China's light truck exports are projected to grow by 46%, with AMT models accounting for 56.4% of total exports.

[0003] Based on the mechanical structure of a traditional manual transmission, a hydraulic actuator, typically a QPV solenoid valve, is added to automate clutch engagement / disengagement and gear shifting, retaining the efficiency of gear transmission while improving operational convenience. Compared to AT / CVT transmissions, the improved hydraulic automated manual transmission (AMT) reduces manufacturing costs by 40% and achieves a transmission efficiency of over 95%, making it the preferred choice for economical automatic transmissions in commercial vehicles. This is illustrated in patents with publication numbers CN120941976A and CN121139620A.

[0004] Existing QPV solenoid valves in transmissions primarily operate in three phases: oil inlet, oil outlet, and cutoff. When the current exceeds the cutoff current, oil enters the clutch actuator, disengaging the clutch. When the current is less than the cutoff current, oil exits the clutch actuator, disengaging the clutch. When the current equals the cutoff current, the clutch actuator remains stable. Clutch control in an AMT (Automated Manual Transmission) directly impacts vehicle dynamics, fuel economy, and drivability. Clutch control precision depends entirely on the cutoff current of the QPV solenoid valve. However, in practice, different solenoid valves exhibit discrepancies in their cutoff currents, leading to deviations in control parameters and performance. This directly affects the accuracy of the solenoid valve's clutch operation, negatively impacting overall fuel consumption and the smoothness of transmission operation. Therefore, a self-learning method and system for cutoff currents that can adapt to the discrepancies of different solenoid valves is needed. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a self-learning method and system for the cut-off current of the QPV valve in a clutch actuator. This method repeatedly self-learns and corrects the cut-off current of the solenoid valve, improving the control accuracy of the clutch, ensuring the drivability of the vehicle, improving fuel efficiency and comfort, ensuring the robustness of the software, and enhancing the handling stability, economy, and power of the vehicle.

[0006] The present invention specifically adopts the following technical solution: A self-learning method for the cut-off current of the QPV valve in a clutch actuator, characterized by comprising the following steps: Step 1: The system receives the vehicle status signal and determines whether the conditions for triggering self-learning are met based on the vehicle status; if the conditions for self-learning are met, the system enters the self-learning state. Step 2: After entering the self-learning state, first control the clutch to be in the initial state of self-learning, triggering the Fast self-learning process; Step 3: Determine if there is a cutoff current from the previous self-learning moment; if there is a cutoff current from the previous self-learning moment, control the solenoid valve to work for a certain period of time according to the cutoff current; otherwise, control the solenoid valve to work for a certain period of time according to the initially preset cutoff current I1; and record the displacement of the clutch and calculate the displacement change rate. Step 4: Look up the recorded displacement change rate against the first update table and compare them to obtain the updated self-learning cutoff current I2; Step 5: Control the clutch to return to the initial state of self-learning, and control the solenoid valve to work for a certain period of time according to the newly obtained cutoff current of self-learning, drive the clutch to act, record the displacement of the clutch, and calculate the displacement change rate. Step 6: Based on the displacement change rate calculated in Step 5, look up the updated cutoff current I3 in the second update table. Step 7: The system determines whether the self-learning process is complete based on the last recorded rate of change of clutch displacement; if the recorded rate of change of displacement does not meet the set conditions, it returns to step 5; if the recorded rate of change of displacement meets the set conditions, it is considered that the self-learning process of cutoff current is complete, and the process ends.

[0007] Furthermore, the vehicle status in step 1 includes signals for braking status, engine speed, target gear, actual gear, transmission oil temperature, vehicle speed, and throttle opening.

[0008] Furthermore, in step 2, the initial state of the clutch's self-learning is that the clutch is in the middle displacement between the fully open displacement and the fully closed displacement.

[0009] Furthermore, the clutch displacement and displacement change rate recorded in steps 3 and 5 are both recorded in E squared.

[0010] Furthermore, the update step size in the first update table is different from that in the second update table, wherein the update step size in the first update table is larger than that in the second update table.

[0011] Furthermore, in step 7, when determining whether self-learning is complete, if the recorded displacement change rate is less than the set value, it is considered that the cut-off current can control the solenoid valve to drive the clutch to move smoothly, and the self-learning process ends.

[0012] A self-learning system for the cut-off current of a QPV valve in a clutch actuator, based on the aforementioned self-learning method; wherein the self-learning system includes a self-learning function triggering module, a signal processing module, a self-learning function state coordination module, a self-learning preparation module, a Fast self-learning module, a Slow self-learning module, a solenoid valve current coordination module, a solenoid valve drive module, and an E-square module. Self-learning function trigger module: This module determines in real time whether to perform self-learning based on the input signal. When the judgment condition is met, it sends a self-learning request and the status is "request self-learning". Signal processing module: This module is responsible for calculating the rate of change of the actual clutch displacement and the difference between the target displacement and the actual displacement in real time. The self-learning function state coordination module determines which sub-state of the self-learning process is in based on the self-learning request status, actual clutch displacement, and actual displacement change rate. This includes states such as self-learning not triggered, Fast self-learning, Slow self-learning, and self-learning completed.

[0013] Self-learning preparation module: This module controls the clutch displacement to a certain range when the self-learning conditions are just met, preparing for subsequent Fast self-learning, Slow self-learning, or the state after self-learning is completed.

[0014] Fast self-learning module: This module queries the first update table based on the average change rate of the actual clutch displacement and the deviation between the target displacement and the actual displacement at the end of the Fast self-learning cycle. Slow self-learning module: This module queries the second update table based on the average rate of change of the actual clutch displacement and the deviation between the target displacement and the actual displacement at the end of the Slow self-learning cycle. Self-learning completion module: This module updates the cutoff current after self-learning and outputs it to E squared for storage; Solenoid valve current coordination module: This module coordinates the target current of the clutch under different states and outputs it to E squared for storage; Solenoid valve drive module: This module has a BSW function and controls the solenoid valve through the target current to make it perform the corresponding action; E-square module: This module is for storing and reading / writing E-square.

[0015] The beneficial effects of this invention are as follows: By performing multiple self-learning operations on the solenoid valve's cutoff current based on the clutch's displacement change rate, the clutch's control precision is improved, hardware variance is adapted, system stability is enhanced, overall vehicle drivability, fuel efficiency, and comfort are guaranteed, software robustness is ensured, and overall vehicle handling stability, economy, and power are improved. By setting up a first update table and a second update table with different step sizes, the self-learning process can be helped to converge quickly, resulting in more accurate self-learning results. Attached Figure Description

[0016] Figure 1 Here is a flowchart of the self-learning method in Example 1; Figure 2 This is a block diagram of the overall structure of the system in Example 1. Detailed Implementation

[0017] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0018] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the figures only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0019] Example 1: like Figure 1 As shown, a self-learning method for the cut-off current of the QPV valve in a clutch actuator is characterized by the following steps: Step 1: The system receives the vehicle status signal and determines whether the conditions for triggering self-learning are met based on the vehicle status; if the conditions for self-learning are met, the system enters the self-learning state. Step 2: After entering the self-learning state, first control the clutch to be in the initial state of self-learning, triggering the Fast self-learning process; Step 3: Determine if there is a cutoff current from the previous self-learning moment; if there is a cutoff current from the previous self-learning moment, control the solenoid valve to work for a certain duration based on this cutoff current. In this example, the working duration is set to 2s, and the working duration of the solenoid valve is also controlled to be 2s during the subsequent self-learning process; otherwise, control the solenoid valve to work for a certain duration based on the initially preset cutoff current I1; and record the displacement of the clutch and calculate the displacement change rate. Step 4: Look up the recorded displacement change rate and compare it with the first update table to obtain the updated self-learning cutoff current I2; the first update table records the relationship between the displacement change rate and the changing current. Step 5: Control the clutch to return to the initial state of self-learning, and control the solenoid valve to work for a certain period of time according to the newly obtained cutoff current of self-learning, drive the clutch to act, record the displacement of the clutch, and calculate the displacement change rate. Step 6: Based on the displacement change rate calculated in Step 5, look up the second update table to obtain the updated cutoff current I3; it should be noted that the update step size in the first update table is larger than the update step size in the second update table. Step 7: The system determines whether the self-learning process is complete based on the last recorded rate of change of clutch displacement; if the recorded rate of change of displacement does not meet the set conditions, it returns to step 5; if the recorded rate of change of displacement meets the set conditions, it is considered that the self-learning process of cutoff current is complete, and the process ends.

[0020] The vehicle status in step 1 specifically includes signals for braking status, engine speed, target gear, actual gear, transmission oil temperature, vehicle speed, and throttle opening. In this example, when the braking status is triggered, the engine speed is less than 900 rpm, the actual gear is any gear other than N, the target gear is N, the vehicle speed is 0, and the throttle opening and transmission oil temperature are both within the set range, then the conditions for self-learning are considered met, and the self-learning state is automatically triggered. Here, "any gear other than N" and "target gear N" indicate the moment of switching from a non-N gear to N gear. That is to say, every time the vehicle switches to N gear, a check of the self-learning conditions is triggered.

[0021] In step 2, the initial state of clutch self-learning is when the clutch is at an intermediate displacement between fully open and fully closed. For example, if the clutch displacement is 0mm when fully closed and 10mm when fully open, then in the self-learning state, the target clutch displacement is controlled to be 5mm. After the clutch reaches the target displacement, the solenoid valve is controlled to stop the clutch at the 5mm position according to the cutoff current of the previous self-learning or a preset cutoff current. It should be noted that the preset cutoff current is obtained based on the solenoid valve model and a preset parameter table.

[0022] The clutch displacement and displacement change rate recorded in steps 3 and 5 are both recorded in E squared; this storage method ensures that the data will not be lost after power failure, so that parameters such as the cutoff current stored previously can be read during the next startup.

[0023] In step 7, when determining whether self-learning is complete, if the recorded displacement change rate is less than the set value, it is considered that the cutoff current can control the solenoid valve to drive the clutch to move smoothly, and the self-learning process ends. It should be noted that each time self-learning in steps 5 and 6 is repeated, a count is performed. If the set number of cycles is exceeded, it is considered that the self-learning process cannot converge, the self-learning process ends, and the cutoff current of the last self-learning is recorded as the initial cutoff current in the next self-learning process.

[0024] In this example, the purpose of controlling the update step size between the first and second update tables is to achieve multiple cutoff current corrections. The first correction uses a longer step size in the first update table, which means the difference in the first correction is usually larger. This reduces the data comparison range when looking up the data in the second update table, allowing the updated cutoff current in step 6 to be closer to the target cutoff current. Without the self-learning process of looking up the first update table, relying solely on the second update table might result in the self-learning process failing to converge due to excessively large differences. Furthermore, in this example, by iterating through steps 5 and 6, the difference between the self-learned updated cutoff current and the target cutoff current can be further reduced until the self-learned cutoff current meets the requirements, accurately controlling the solenoid valve to be in the cutoff state.

[0025] like Figure 2 As shown, a self-learning system for the QPV valve cut-off current of a clutch actuator is based on the above-mentioned self-learning method; wherein the self-learning system includes a self-learning function triggering module, a signal processing module, a self-learning function state coordination module, a self-learning preparation module, a Fast self-learning module, a Slow self-learning module, a solenoid valve current coordination module, a solenoid valve drive module, and an E-square module.

[0026] Among them, the self-learning function triggering module: This module determines in real time whether to perform the self-learning function based on the input signal. When the judgment condition is met, it sends a self-learning request and the status is "requesting self-learning". Signal processing module: This module is responsible for calculating the rate of change of the actual clutch displacement and the difference between the target displacement and the actual displacement in real time. The self-learning function state coordination module determines which sub-state of the self-learning process is in based on the self-learning request status, actual clutch displacement, and actual displacement change rate. This includes states such as self-learning not triggered, Fast self-learning, Slow self-learning, and self-learning completed.

[0027] Self-learning preparation module: This module controls the clutch displacement to a certain range when the self-learning conditions are just met, preparing for subsequent Fast self-learning, Slow self-learning, or the state after self-learning is completed.

[0028] Fast self-learning module: This module queries the first update table based on the average rate of change of the actual clutch displacement and the deviation between the target displacement and the actual displacement at the end of the Fast self-learning cycle, which helps to achieve rapid and stable convergence of the clutch displacement during subsequent self-learning. Slow self-learning module: This module queries the second update table based on the average change rate of the actual clutch displacement and the deviation between the target displacement and the actual displacement at the end of the Slow self-learning cycle, to help achieve rapid and stable convergence of the clutch displacement in the next cycle or the next self-learning. Self-learning completion module: This module updates the cutoff current after self-learning and outputs it to E squared for storage; Solenoid valve current coordination module: This module coordinates the target current of the clutch under different states and outputs it to E squared for storage; Solenoid valve drive module: This module has a BSW function and controls the solenoid valve through the target current to make it perform the corresponding action; E-square module: This module is for storing and reading / writing E-square.

[0029] The above description is merely a specific example of the present invention and does not constitute any limitation on the present invention. Obviously, those skilled in the art, after understanding the content and principles of the present invention, may make various modifications and changes in form and detail without departing from the principles and structure of the present invention; however, these modifications and changes based on the spirit of the present invention are still within the scope of protection of the claims of the present invention.

Claims

1. A self-learning method for the cut-off current of the QPV valve in a clutch actuator, characterized in that, Includes the following steps: Step 1: The system receives the vehicle status signal and determines whether the conditions for triggering self-learning are met based on the vehicle status; if the conditions for self-learning are met, the system enters the self-learning state. Step 2: After entering the self-learning state, first control the clutch to be in the initial state of self-learning, triggering the Fast self-learning process; Step 3: Determine if the cutoff current from the previous self-learning moment exists; If the cutoff current learned in the previous moment exists, the solenoid valve is controlled to work for a certain period of time according to the cutoff current; otherwise, the solenoid valve is controlled to work for a certain period of time according to the initially preset cutoff current I1; and the displacement of the clutch is recorded and the displacement change rate is calculated. Step 4: Look up the recorded displacement change rate against the first update table and compare them to obtain the updated self-learning cutoff current I2; Step 5: Control the clutch to return to the initial state of self-learning, and control the solenoid valve to work for a certain period of time according to the newly obtained cutoff current of self-learning, drive the clutch to act, record the displacement of the clutch, and calculate the displacement change rate. Step 6: Based on the displacement change rate calculated in Step 5, look up the updated cutoff current I3 in the second update table. Step 7: The system determines whether the self-learning process is complete based on the last recorded rate of change of clutch displacement; if the recorded rate of change of displacement does not meet the set conditions, it returns to step 5; if the recorded rate of change of displacement meets the set conditions, it is considered that the self-learning process of cutoff current is complete, and the process ends.

2. The self-learning method for the QPV valve cut-off current of a clutch actuator according to claim 1, characterized in that, The vehicle status in step 1 includes signals for braking status, engine speed, target gear, actual gear, transmission oil temperature, vehicle speed, and throttle opening.

3. The self-learning method for the QPV valve cutoff current of a clutch actuator according to claim 1, characterized in that, In step 2, the initial state of the clutch's self-learning is that the clutch is in the middle displacement between the fully open displacement and the fully closed displacement.

4. The self-learning method for the QPV valve cutoff current of a clutch actuator according to claim 1, characterized in that, The clutch displacement and displacement change rate recorded in steps 3 and 5 are both recorded in E squared.

5. The self-learning method for the QPV valve cutoff current of a clutch actuator according to claim 1, characterized in that, The update step size in the first update table is different from that in the second update table, where the update step size in the first update table is larger than that in the second update table.

6. The self-learning method for the QPV valve cut-off current of a clutch actuator according to claim 1, characterized in that, In step 7, when determining whether self-learning is complete, if the recorded displacement change rate is less than the set value, it is considered that the cut-off current can control the solenoid valve to drive the clutch to move smoothly, and the self-learning process ends.

7. A self-learning system for the cut-off current of a QPV valve in a clutch actuator, characterized in that, The self-learning method is based on any one of claims 1 to 6; wherein the self-learning system includes a self-learning function triggering module, a signal processing module, a self-learning function state coordination module, a self-learning preparation module, a Fast self-learning module, a Slow self-learning module, a solenoid valve current coordination module, a solenoid valve drive module, and an E-square module. Self-learning function trigger module: This module determines in real time whether to perform self-learning based on the input signal. When the judgment condition is met, it sends a self-learning request and the status is "request self-learning". Signal processing module: This module is responsible for calculating the rate of change of the actual clutch displacement and the difference between the target displacement and the actual displacement in real time. Self-learning function state coordination module: Based on the self-learning request status, actual clutch displacement, and actual displacement change rate, determine which state within the self-learning sub-state is being in, including the states of self-learning not triggered, Fast self-learning, Slow self-learning, and self-learning completed. Self-learning preparation module: This module controls the clutch displacement to a certain range when the self-learning conditions are just met, in preparation for subsequent Fast self-learning, Slow self-learning, or the state of self-learning completion. Fast self-learning module: This module queries the first update table based on the average change rate of the actual clutch displacement and the deviation between the target displacement and the actual displacement at the end of the Fast self-learning cycle. Slow self-learning module: This module queries the second update table based on the average rate of change of the actual clutch displacement and the deviation between the target displacement and the actual displacement at the end of the Slow self-learning cycle. Self-learning completion module: This module updates the cutoff current after self-learning and outputs it to E squared for storage; Solenoid valve current coordination module: This module coordinates the target current of the clutch under different states and outputs it to E squared for storage; Solenoid valve drive module: This module has a BSW function and controls the solenoid valve through the target current to make it perform the corresponding action; E-square module: This module is for storing and reading / writing E-square.

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