Clutch self-learning active correction method and equipment

Abstract

The invention provides a clutch self-learning active correction method and device, and the method comprises the steps: obtaining self-learning data of torque pressure conversion parameters under a gear shifting event in response to the gear shifting event of a clutch; according to the current gear shifting event, the role of the clutch in the current gear shifting event is judged, and the role is clutch combination or clutch separation; based on the role and the self-learning data of the torque pressure conversion parameters, whether a preset triggering condition is met or not is judged; if yes, a maturity value used for representing the credibility of the basic self-learning parameters is adjusted according to the triggering condition; when the maturity value is lower than a preset threshold value, basic self-learning parameters of the clutch are reset and re-learned; after re-learning, a torque pressure conversion parameter learning value associated with the clutch is reset. Based on the clutch self-learning active correction method, the invention further provides equipment. By distinguishing different roles of the clutch and setting differentiated trigger thresholds, accurate recognition of abnormal conditions is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of clutch intelligent correction, and particularly relates to a clutch self-learning active correction method and device. BACKGROUND

[0002] In the modern automatic transmission control strategy, the basic self-learning of the clutch is crucial, which is used to compensate for the difference between the transmission assembly and the matching power assembly, and is the basis for realizing the platform management of the calibrated parameters. On this basis, the self-learning of the corresponding clutch torque pressure conversion parameters for each shift process is used to ensure the accuracy of the individual shift quality. However, with the use of the transmission, the friction plates or other parts in the transmission reach the normal part tolerance range through running-in, or the parts are seriously worn through long-term use, resulting in differences in the characteristics of the original clutch state learning. On this basis, the self-learning parameters of the clutch torque pressure conversion parameters are no longer accurate. Since the self-learning of the clutch torque pressure conversion parameters is a compensation learning based on the basic self-learning parameters, the inaccuracy of the basic parameters will directly cause systematic deviation of the basic self-learning value, and further cause the shift quality to decrease. In severe cases, the clutch may be burned and the transmission may be damaged due to long-term slipping.

[0003] The existing technology mainly has two solutions: one is that when the shift quality problem occurs, the self-learning value is manually cleared by the maintenance personnel to trigger re-learning; the other is to automatically reset the self-learning in the software after setting a fixed mileage. The first solution is heavily dependent on manual work, has poor user experience and is not intelligent; the second solution has the risk of resetting not in time or blindly resetting due to the inability to sense the actual wear state of the clutch, which may cause mislearning or cover up potential faults. SUMMARY

[0004] In order to solve the above technical problems, the present application proposes a clutch self-learning active correction method and device. The clutch self-learning active correction strategy and device can monitor the clutch state in real time, intelligently judge and actively trigger the reset of the basic self-learning parameters, so as to improve the shift quality, the clutch life and the overall reliability of the transmission.

[0005] To achieve the above purpose, the present application adopts the following technical solutions: A clutch self-learning active correction method, comprising the following steps: In response to a clutch shift event, acquiring self-learning data of torque pressure conversion parameters of the clutch under the shift event; According to the current shift event, judging the role of the clutch in the current shift event, the role including being a combined clutch for torque transmission or a separated clutch for torque interruption; judging whether a preset trigger condition is met based on the role and self-learning data of the torque-pressure conversion parameter; If the trigger condition is met, adjusting a maturity value used to represent the credibility of the basic self-learning parameter according to the trigger condition; When the maturity value is lower than a preset threshold, resetting and relearning the basic self-learning parameter of the clutch; After the relearning is completed, resetting the torque-pressure conversion parameter learning value related to the clutch.

[0006] Further, when the clutch is judged to be a combination clutch in multiple shift gears, specifically comprising: Obtaining torque-pressure conversion parameter self-learning data of the clutch as a combination clutch in multiple shift gears; Analyzing the correction direction of the torque-pressure conversion parameter in multiple gear self-learning data; when it is determined that all analyzed gear correction directions are consistent and the absolute value of the correction ratio exceeds a first ratio threshold, it is determined that the trigger condition is met.

[0007] Further, when the clutch is judged to be a combination clutch in only one shift gear, specifically comprising: Obtaining torque-pressure conversion parameter self-learning data of a single gear and its correction ratio; When the absolute value of the correction ratio exceeds a second ratio threshold, it is determined that the trigger condition is met; wherein the numerical value of the second ratio threshold is higher than the first ratio threshold.

[0008] Further, when the clutch is identified as a separation clutch in shifting, specifically comprising: Monitoring the shift process in which the clutch is involved as a separation clutch; When the fly-off flag is set in the shift process, it is determined that the trigger condition is met.

[0009] Further, when judging whether the preset trigger condition is met, it also includes judging the stability of the torque-pressure conversion parameter self-learning, specifically: Recording the torque-pressure conversion parameter self-learning value of the clutch in consecutive multiple shift events; Calculating and analyzing the change direction and amplitude between consecutive learning values; When the reciprocating floating mode of opposite direction and amplitude change less than a third ratio threshold is identified in the consecutive learning values, it is determined that the trigger condition is met.

[0010] Further, adjusting the maturity value used to represent the credibility of the basic self-learning parameter according to the trigger condition, specifically: Identifying the abnormal severity level corresponding to the trigger condition; Based on the identified abnormal severity level, one of the predefined adjustment operations is selected for execution, wherein the predefined adjustment operations include at least a first adjustment operation and a second adjustment operation, the first adjustment operation is to quantitatively reduce the maturity value once, and the second adjustment operation is to reset the maturity value directly to a specified initial value.

[0011] Further, the torque pressure conversion parameter learning value related to the clutch is reset, specifically: Access the memory to obtain the historical learning value of each gear torque pressure conversion parameter related to the clutch stored before the current trigger-based self-learning reset; The real-time learning value of each gear torque pressure conversion parameter related to the clutch currently used by the controller is updated to the obtained pre-reset value.

[0012] Further, the updating method of the maturity value is: After the clutch successfully completes the basic self-learning each time, it is judged whether the current maturity value is lower than the stability threshold value; if so, the maturity value is increased by a first fixed step; if equal to or higher than the stability threshold value, it is further judged whether the deviation between the current self-learning result and the historical learning result is less than a convergence threshold value; if so, the maturity value is increased by a second fixed step, wherein the second fixed step is much larger than the first fixed step.

[0013] Further, the basic self-learning parameter includes an oil filling time parameter and a torque gradient parameter obtained by self-learning, the oil filling time parameter is used to describe the oil filling dynamic characteristic of the clutch, and the torque gradient parameter is used to describe the torque transmission capability of the clutch.

[0014] The application further provides a clutch self-learning active correction device, including at least one processor and a memory, the memory stores a computer program, and the computer program is implemented when the at least one processor is executed to realize the clutch self-learning active correction method.

[0015] The effects provided in the summary are only the effects of the embodiments, not all the effects of the application, and one of the above technical solutions has the following advantages or beneficial effects: The application provides a clutch self-learning active correction method and device, the method comprises the following steps: in response to a clutch shift event, acquiring self-learning data of torque pressure conversion parameters of the clutch under the shift event; judging the role of the clutch in the current shift event according to the current shift event, the role including a combined clutch as torque transmission or a separated clutch as torque interruption; judging whether a preset trigger condition is met based on the role and the self-learning data of the torque pressure conversion parameters; if the trigger condition is met, adjusting a maturity value for representing the reliability of the basic self-learning parameters according to the trigger condition; when the maturity value is lower than a preset threshold, resetting and relearning the basic self-learning parameters of the clutch; after the relearning is completed, resetting the torque pressure conversion parameter learning value related to the clutch. Based on the clutch self-learning active correction method, a clutch self-learning active correction device is also provided. By distinguishing different roles of the clutch and setting differential trigger thresholds, the application realizes accurate identification of abnormal conditions, effectively prevents misjudgment and omission, and ensures the reliability of the correction action.

[0016] By monitoring the self-learning data of the clutch torque pressure conversion parameters in real time and introducing a maturity value mechanism, the application realizes intelligent diagnosis and adaptive resetting of the state of the clutch basic parameters, and completely eliminates the dependence on fixed mileage or manual intervention.

[0017] By resetting the self-learning value of the clutch torque pressure conversion parameters, the application ensures that the entire learning system can quickly and smoothly switch to a new correct state after the basic parameters are corrected, avoids secondary deterioration of the shift quality, and forms a repair closed loop.

[0018] The application can timely find and correct potential problems, effectively prevent wear and ablation caused by continuous abnormal slipping of the clutch, significantly improve the durability of the transmission, and improve user satisfaction by maintaining excellent shift quality. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A clutch self-learning active correction method flowchart is provided for the application embodiment 1; Figure 2 A clutch self-learning active correction device schematic diagram is provided for the application embodiment 2. DETAILED DESCRIPTION

[0020] For purposes of clarity of the technical features of the present application, the present application is described in detail below with specific reference to the drawings. The following disclosure provides many different embodiments, or examples, for implementing different structures of the present application. For the purpose of simplicity and clarity of the present application, the description of the specific examples in the following text is described. In addition, the present application can repeatedly refer to the same numbers and / or letters in different examples. Such repetition is for the purpose of simplicity and clarity, and does not indicate the relationship between the various embodiments and / or settings being discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. The present application omits the description of well-known components and processing techniques and processes to avoid unnecessarily limiting the present application.

[0021] Embodiment 1 Embodiment 1 of the present application proposes a clutch self-learning active correction method to solve the problems of shift quality degradation, clutch wear and ablation, and even transmission damage caused by the inability to timely detect changes in clutch characteristics in the prior art.

[0022] According to different T2P self-learning, different situations occur, wherein T2P self-learning is torque pressure conversion parameter self-learning, and the roles of some clutches in the shift process are different, such as C4 clutch only as OG clutch, i.e. separation clutch, in all upshift and downshift processes, and there is no role of OC clutch, wherein OC clutch is combination clutch, therefore the strategy for triggering automatic clearing of basic self-learning is classified.

[0023] T2P self-learning mainly detects whether the clutch appears in the shift process, the time of the spdstart stage, and the ratio of the Spdchg time to the target time.

[0024] Among them, spdstart is the torque phase end point; Spdchg is the entire process of engine speed change to synchronization.

[0025] Generally speaking, the occurrence of flying car and the time of Spdstart and Spdchg stage are too long, and the T2P value is increased based on different torques through T2P self-learning; if there is no flying car, and the time of Spdstart and Spdchg stage is too short, or the time is 0, the T2P value is decreased based on different torques through self-learning.

[0026] Figure 1 A flowchart of a clutch self-learning active correction method is proposed for embodiment 1 of the present application; When the clutch is judged as a combination clutch in multiple shift gears, it specifically includes: Obtaining torque pressure conversion parameter self-learning data of the clutch as a combination clutch in multiple shift gears; The correction direction of the torque-pressure conversion parameter in the multiple-gear self-learning data is analyzed; when it is determined that the correction directions of all the analyzed gears are consistent and the absolute value of the correction ratio exceeds the first ratio threshold, it is determined that the trigger condition is met.

[0027] When there are more than two shift gears for the clutch as the OC clutch, for example, the C3 clutch is used as the OC clutch when shifting gears in DD12, DD56, and DD78. In T2P self-learning, it is found that the self-learning correction directions of the three gears are consistent, and the learned results T2P are all increasing pct_ClutchPlus or all decreasing pct_ClutchMinus, and the proportion increased through self-learning is pct_ClutchPlus>50% or the proportion decreased is pct_ClutchMinus>60%. Therefore, it can be judged that after the calibration before mass production and a period of driving, the clutch characteristic deviation occurs, leading to consistent self-learning deviation of related gears. Therefore, it can be judged that the current clutch may be due to part wear or long driving mileage of the whole vehicle, and the basic parameters of the clutch need to be corrected. Therefore, the maturity value of the clutch basic self-learning can be reduced by 1.

[0028] The maturity value is a number that represents the reliability of the clutch basic self-learning result. It is determined whether repeated learning is needed by comparing the deviation of the self-learning results. If the results of the second learning are similar to those of the first learning, it means that the reliability of the two learning is high, Assuming that the initial maturity value is 1, if the result converges after successful learning, the maturity value is +10, i.e. 11, num_ClutchBaseAdpt=11. When the maturity value is greater than 10, it means that the clutch learning is complete. If the clutch maturity value is less than or equal to 10, the clutch needs to be relearned until the clutch maturity value is greater than 10. num_ClutchBaseAdpt is an intelligent counter or confidence index, which is used to quantify and manage the reliability of each clutch basic self-learning parameter (filling time, KP value).

[0029] When it is determined through T2P that the clutch basic parameters need to be relearned, the maturity value is reduced by 1. If the previous clutch maturity value is 11, the value is not greater than 10 after being reduced by 1, and the clutch needs to be relearned. If the previous clutch maturity value is greater than 12, it needs to be determined again whether the T2P self-learning meets the basic self-learning reset. If it continues to be true, the maturity value is continuously reduced until the maturity value is not greater than 10, which meets the condition that the clutch needs to be relearned.

[0030] But if all the gear T2P self-learning of this clutch increases or decreases the proportion is greater than 100%, the maturity value of this clutch is directly assigned to 0, to ensure that the clutch resets the basic self-learning immediately in the case of large deviation, to ensure that the basic parameters of the clutch can be adjusted immediately in the case of large deviation of the clutch, to ensure the shift quality, to prevent the transmission from slipping and damaging the transmission for a long time under incorrect basic parameters, and to further protect the transmission.

[0031] When the clutch basic self-learning reset is reset to learn again, in order to quickly ensure the accuracy of the T2P self-learning result and the shift quality and clutch protection, the gear T2P learning result of the corresponding clutch is restored to the last learning result, to ensure that the learning result is restored before the learning deviation. Then the shift T2P self-learning of the clutch learns and corrects the parameters again based on this basis.

[0032] When the clutch is judged to be only in one shift gear as a combined clutch, specifically including: Obtaining single-gear torque pressure conversion parameter self-learning data and its correction proportion; When the absolute value of the correction proportion exceeds the second proportion threshold, it is determined that the trigger condition is met; wherein the numerical value of the second proportion threshold is higher than the first proportion threshold.

[0033] When a certain clutch is only used as an OC clutch of a certain gear, the clutch basic self-learning can only be reset by self-learning of the gear, and the judgment condition is different from that of multiple gears.

[0034] The frequency of upward correction of the gear T2P self-learning freq_LearnPlus>80% or the frequency of downward correction freq_LearnMinus>90%, then the maturity value of the clutch basic self-learning is reduced by 1. If the upward or downward correction amplitude exceeds 120% of the calibration range, the maturity value of the clutch is directly assigned to 0, and the T2P learning result is restored to the state before the abnormal deviation.

[0035] When the clutch is identified as a separating clutch in shifting, specifically including: Monitoring the shift process in which the clutch is used as a separating clutch; When the overrun flag is set in the shift process, it is determined that the trigger condition is met.

[0036] When a certain clutch does not work as an OC clutch for upshift, but this clutch only works as an OG clutch for a certain fixed gear. For example, the C4 clutch only works as an OG clutch for the DD56 upshift. If the gear self-learning result is abnormal and only appears in this shift process, and the OC-related gear self-learning of the shift clutch does not appear abnormal, therefore only the OG clutch in this shift needs to be reset, that is, the maturity value of this clutch is reduced by 1.

[0037] When the runaway flag is detected in the shift process in which the split clutch is involved, we regard it as an important indication that the split clutch may have a deviation in the basic parameters, and accordingly determine that the trigger condition is met. Therefore, only the OG clutch needs to be detected for the runaway problem. And directly assign the maturity value of this clutch to 0, the clutch basic self-learning detects that the maturity of this clutch is not greater than 10, therefore when the basic self-learning condition is met, directly reset the basic self-learning parameters and relearn. After the clutch basic self-learning reset and relearning, in order to quickly ensure the accuracy of the T2P self-learning result and the shift quality and clutch protection, the corresponding clutch gear T2P learning result is restored to the last learning result, ensuring that the learning result is restored before the learning abnormal deviation. Then the shift T2P self-learning of the clutch learns and corrects the parameters again based on this basis.

[0038] When determining whether the preset trigger condition is met, it also includes judging the stability of the torque pressure conversion parameter self-learning, specifically: Record the torque pressure conversion parameter self-learning value of the clutch in continuous multiple shift events; Calculate and analyze the change direction and amplitude between the continuous learning values; When the reciprocating floating mode of opposite direction and amplitude change less than the third proportional threshold is identified, it is determined that the trigger condition is met.

[0039] The T2P self-learning logic is relatively stable based on the calibration parameters and vehicle parameters, but there may be differences in the basic self-learning parameters due to abnormal calibration parameters, resulting in obvious up and down floating of the self-learning value due to wear and tear of the clutch.

[0040] First, the possible reason for this phenomenon is that the compensation step size of each learning is not reasonable, and the compensation is large after each learning, resulting in that when the T2P of the clutch needs to be increased, the increase is too much due to the large step size, resulting in overcompensation, and the next time the learning finds that the T2P of the clutch needs to be reduced, thus the up and down floating phenomenon occurs. But the reason is not limited to this.

[0041] When two reciprocating learning phenomena occur continuously during T2P self-learning at a certain shift, and the deviation of the two times is within 10%, it is considered that the basic self-learning function of the clutch needs to be triggered, and the maturity value of the clutch is -1.

[0042] The application adjusts the maturity value for characterizing the reliability of the basic self-learning parameter according to a trigger condition, specifically: identifying an abnormal severity level corresponding to the trigger condition; based on the identified abnormal severity level, selecting one from the predefined adjustment operations; wherein the predefined adjustment operations at least include: a first adjustment operation and a second adjustment operation; the first adjustment operation is to quantitatively reduce the maturity value once; the second adjustment operation is to reset the maturity value directly to a specified initial value.

[0043] Resetting the torque pressure conversion parameter learning value related to the clutch, specifically: accessing the memory to obtain the historical learning value of each gear torque pressure conversion parameter related to the clutch stored before the current trigger of the basic self-learning reset; updating the real-time learning value of each gear torque pressure conversion parameter related to the clutch currently used by the controller to the obtained pre-reset value.

[0044] The updating method of the maturity value is: after the clutch successfully completes the basic self-learning each time, judging whether the current maturity value is lower than a stability threshold; if it is lower, then increasing the maturity value by a first fixed step; if it is equal to or higher than the stability threshold, further judging whether the deviation between the current self-learning result and the historical learning result is less than a convergence threshold; if it is less, then increasing the maturity value by a second fixed step, wherein the second fixed step is much larger than the first fixed step.

[0045] The basic self-learning parameter includes an oil filling time parameter and a torque gradient parameter obtained through self-learning; the oil filling time parameter is used to describe the oil filling dynamic characteristic of the clutch; and the torque gradient parameter is used to describe the torque transmission ability of the clutch.

[0046] The active correction method for clutch self-learning proposed in embodiment 1 of the application distinguishes different roles of the clutch and sets differentiated trigger thresholds, realizes accurate identification of abnormal conditions, effectively prevents misjudgment and omission, and ensures the reliability of the correction action.

[0047] The active correction method for clutch self-learning proposed in embodiment 1 of the application realizes intelligent diagnosis and adaptive reset of the state of the clutch basic parameter by monitoring the clutch torque pressure conversion parameter self-learning data in real time and introducing the maturity value mechanism, and completely gets rid of the dependence on fixed mileage or manual intervention.

[0048] The clutch self-learning active correction method proposed in Embodiment 1 of this invention, through the matching mechanism of resetting the self-learning value of clutch torque pressure conversion parameters, ensures that after the basic parameters are corrected, the entire learning system can quickly and smoothly switch to the new correct state, avoid secondary deterioration of shifting quality, and form a repair closed loop.

[0049] The clutch self-learning active correction method proposed in Embodiment 1 of this invention can promptly detect and correct potential problems, effectively prevent wear and burning caused by continuous abnormal clutch slippage, significantly improve the durability of the transmission, and enhance user satisfaction by maintaining excellent shifting quality.

[0050] Example 2 Based on the clutch self-learning active correction method proposed in Embodiment 1 of the present invention, Embodiment 2 of the present invention also proposes a clutch self-learning active correction device. Figure 2 This is a schematic diagram of a clutch self-learning active correction device proposed in Embodiment 2 of the present invention. The device has at least one processor and a memory. The memory stores a computer program, which, when executed by the at least one processor, implements a clutch self-learning active correction method.

[0051] In response to a clutch shift event, self-learning data of the torque pressure conversion parameters of the clutch under the shift event is acquired; Based on the current shift event, determine the role of the clutch in the current shift event, the role including an engaging clutch as a torque transmission clutch or a disengaging clutch as a torque interruption clutch; Based on the self-learning data of the role and torque pressure conversion parameters, determine whether the preset triggering conditions are met; If the conditions are met, the maturity value used to characterize the credibility of the basic self-learning parameters is adjusted according to the triggering conditions. When the maturity value is lower than a preset threshold, the basic self-learning parameters of the clutch are reset and relearned. After completing the relearning, reset the learned values ​​of the torque pressure conversion parameters related to the clutch.

[0052] When the clutch is determined to be engaged in multiple gear positions, this application specifically includes: Acquire self-learning data of torque-pressure conversion parameters when the clutch is engaged in multiple gear positions; analyze the correction direction of torque-pressure conversion parameters in the self-learning data of multiple gear positions; when it is determined that the correction direction of all analyzed gear positions is consistent and the absolute value of the correction ratio exceeds the first ratio threshold, it is determined that the trigger condition is met.

[0053] The application specifically comprises the following steps when the clutch is determined to be a coupling clutch in only one shift gear: obtaining single-gear torque pressure conversion parameter self-learning data and a correction ratio thereof; determining that the trigger condition is met when the absolute value of the correction ratio exceeds a second ratio threshold; and the numerical value of the second ratio threshold is higher than that of the first ratio threshold.

[0054] The application specifically comprises the following steps when the clutch is determined to be a coupling clutch in only one shift gear: obtaining single-gear torque pressure conversion parameter self-learning data and a correction ratio thereof; determining that the trigger condition is met when the absolute value of the correction ratio exceeds a second ratio threshold; and the numerical value of the second ratio threshold is higher than that of the first ratio threshold.

[0055] The application further comprises the following steps when determining whether the preset trigger condition is met: recording torque pressure conversion parameter self-learning values of the clutch in continuous shift events; calculating and analyzing the change direction and amplitude between the continuous learning values; and determining that the trigger condition is met when a reciprocating floating mode in which the continuous learning values have opposite directions and the amplitude changes are both less than a third ratio threshold is identified.

[0056] The application adjusts the maturity value used to represent the reliability of the basic self-learning parameter according to the trigger condition, specifically: identifying the abnormal severity level corresponding to the trigger condition; selecting one from the predefined adjustment operations based on the identified abnormal severity level; wherein the predefined adjustment operations at least include: a first adjustment operation and a second adjustment operation; the first adjustment operation is to quantitatively reduce the maturity value once; and the second adjustment operation is to reset the maturity value directly to a specified initial value.

[0057] The application resets the torque pressure conversion parameter learning value related to the clutch, specifically: accessing the memory to obtain the historical learning value of each gear torque pressure conversion parameter related to the clutch stored before the current trigger of the basic self-learning reset; and updating the real-time learning value of each gear torque pressure conversion parameter related to the clutch currently used by the controller to the obtained value before the reset.

[0058] The updating method of the maturity value of the application is: after the clutch successfully completes the basic self-learning each time, determining whether the current maturity value is lower than a stability threshold; if so, increasing the maturity value by a first fixed step; if equal to or higher than the stability threshold, further determining whether the deviation between the current self-learning result and the historical learning result is less than a convergence threshold; if so, increasing the maturity value by a second fixed step, wherein the second fixed step is much larger than the first fixed step.

[0059] The self-learning basic parameters of the application include an oil filling time parameter and a torque gradient parameter obtained through self-learning; the oil filling time parameter is used to describe the clutch oil filling dynamic characteristic; and the torque gradient parameter is used to describe the clutch torque transmission capability.

[0060] The clutch self-learning active correction device provided in Embodiment 2 of the application realizes accurate identification of abnormal conditions by distinguishing different roles of the clutch and setting differentiated trigger thresholds, effectively prevents misjudgment and missed judgment, and guarantees the reliability of correction actions.

[0061] The clutch self-learning active correction device provided in Embodiment 2 of the application realizes intelligent diagnosis and adaptive resetting of the state of clutch basic parameters by real-time monitoring of clutch torque pressure conversion parameter self-learning data and introducing a maturity value mechanism, and completely gets rid of the dependence on fixed mileage or manual intervention.

[0062] The clutch self-learning active correction device provided in Embodiment 2 of the application ensures that, after the correction of the basic parameters, the entire learning system can quickly and smoothly switch to a new correct state, avoids the secondary deterioration of gear shifting quality, and forms a repair closed loop.

[0063] The clutch self-learning active correction device provided in Embodiment 2 of the application can timely find and correct potential problems, effectively prevents wear and ablation caused by continuous abnormal slipping of the clutch, significantly improves the durability of the transmission, and at the same time improves user satisfaction by maintaining excellent gear shifting quality.

[0064] The description of the related parts in the clutch self-learning active correction device provided in Embodiment 2 of the application can refer to the detailed description of the corresponding parts in the clutch self-learning active correction method provided in Embodiment 1 of the application, and will not be repeated here.

[0065] It should be noted that, in this document, relationship terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device inherently includes a series of elements. Without more limitations, the element defined by the statement "includes a" does not exclude the presence of another identical element in the process, method, article or device including the element. In addition, the above technical solutions provided in the embodiments of the application have not been described in detail, as excessive repetition.

[0066] The above describes the specific embodiments of the present application in conjunction with the drawings, but is not intended to limit the scope of protection of the present application. Based on the above description, those skilled in the art can make other different forms of modifications or changes. Here, it is not necessary and impossible to exhaust all the embodiments. Various modifications or changes made by those skilled in the art without creative labor on the basis of the technical solutions of the present application are still within the scope of protection of the present application.

Claims

1. A self-learning active correction method for a clutch, characterized in that, Includes the following steps: In response to a clutch shift event, self-learning data of the torque pressure conversion parameters of the clutch under the shift event is acquired; Based on the current shift event, determine the role of the clutch in the current shift event, wherein the role is either an engaging clutch for torque transmission or a disengaging clutch for torque interruption. Based on the self-learning data of the role and torque pressure conversion parameters, determine whether the preset triggering conditions are met; If the conditions are met, the maturity value used to characterize the credibility of the basic self-learning parameters is adjusted according to the triggering conditions. When the maturity value is lower than a preset threshold, the basic self-learning parameters of the clutch are reset and relearned. After completing the relearning, reset the learned values ​​of the torque pressure conversion parameters related to the clutch.

2. The method according to claim 1, characterized in that, When the clutch is determined to be engaged in multiple gear positions, the specific situations include: Acquire self-learning data of torque-pressure conversion parameters of the clutch when it is engaged in multiple shift gears; The correction direction of torque-pressure conversion parameters in the self-learning data of multiple gears is analyzed; when it is determined that the correction direction of all analyzed gears is consistent and the absolute value of the correction ratio exceeds the first ratio threshold, the trigger condition is determined to be met.

3. The method according to claim 1, characterized in that, When a clutch is determined to be engaged only in one shift gear, the specific criteria include: Acquire self-learning data and correction ratios of torque-pressure conversion parameters for a single gear; When the absolute value of the correction ratio exceeds the second ratio threshold, the triggering condition is determined to be met; wherein the value of the second ratio threshold is higher than the first ratio threshold.

4. The method according to claim 1, characterized in that, When a clutch is identified as a disengaged clutch during gear shifting, specifically including: Monitor the gear shifting process in which the clutch acts as the disengaged clutch; When the flying car icon is detected to be set during gear shifting, the triggering condition is determined to be met.

5. The method according to claim 1, characterized in that, When determining whether the preset triggering conditions are met, the process also includes assessing the self-learning stability of the torque-pressure conversion parameters, specifically: Record the self-learning values ​​of the torque-pressure conversion parameters of the clutch during multiple consecutive gear shifting events; Calculate and analyze the direction and magnitude of change between consecutive learning values; When a reciprocating floating pattern is detected in which consecutive learning values ​​show opposite directions and the magnitude changes are all less than the third proportional threshold, it is determined that the triggering condition is met.

6. The method according to any one of claims 2 to 5, characterized in that, The maturity value used to characterize the credibility of the basic self-learning parameters is adjusted according to the aforementioned triggering conditions, specifically as follows: Identify the severity level of the anomaly corresponding to the triggering condition; Based on the identified severity level of the anomaly, one of the predefined adjustment operations is selected for execution; wherein the predefined adjustment operations include at least: a first adjustment operation and a second adjustment operation; the first adjustment operation is to quantitatively reduce the maturity value; the second adjustment operation is to directly reset the maturity value to the specified initial value.

7. The method according to claim 1, characterized in that, Reset the learned values ​​of the clutch-related torque-pressure conversion parameters, specifically: Access the memory to retrieve the historical learning values ​​of the torque-pressure conversion parameters of each gear related to the clutch stored before this triggering of the basic self-learning reset; The real-time learned values ​​of the torque and pressure conversion parameters of each gear related to the clutch, currently used by the controller, are updated to the values ​​obtained before the reset.

8. The method according to claim 1, characterized in that, The method for updating maturity values ​​is as follows: After the clutch successfully completes basic self-learning each time, it is determined whether the current maturity value is lower than the stability threshold. If it is lower, the maturity value is increased by a first fixed step size. If it is equal to or higher than the stability threshold, it is further determined whether the deviation between the current self-learning result and the historical learning result is less than a convergence threshold. If it is less, the maturity value is increased by a second fixed step size, wherein the second fixed step size is much larger than the first fixed step size.

9. The method according to claim 1, characterized in that, The basic self-learning parameters include the filling time parameter and torque gradient parameter obtained through self-learning; the filling time parameter is used to describe the clutch filling dynamic characteristics; the torque gradient parameter is used to describe the clutch torque transmission capability.

10. A clutch self-learning active correction device, comprising at least one processor and a memory, the memory storing a computer program, characterized in that, When the computer program is executed by the at least one processor, it implements a clutch self-learning active correction method as described in any one of claims 1 to 9.

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