Wet clutch wear self-adaption method

Abstract

The invention discloses a wet clutch abrasion self-adaption method which comprises the steps that initial parameters within a certain time T before gear shifting and process parameters in the gear shifting process are obtained, whether a self-learning switch is turned on or not is judged according to the initial parameters and the process parameters, if yes, the second step is executed, and if not, the sixth step is executed; the initial parameters comprise an engine rotating speed, an input shaft rotating speed, an output shaft rotating speed, an accelerator pedal signal and an engine torque; current parameters and parameters in the gear shifting process are obtained to jointly judge whether a self-learning switch is turned on or not; the condition of false detection caused by road bumping before gear shifting, intention changing of a driver in the gear shifting process and the like is avoided, abnormal phenomena possibly occurring in the gear shifting process are classified, priority judgment is carried out, only the highest priority phenomenon is learned in each time of learning, and the condition of false learning is prevented; the technical problem that in the prior art, after a wet clutch gearbox operates for a certain mileage, the gear shifting quality becomes poor due to clutch abrasion is solved.

Description

Technical Field

[0001] The invention belongs to the field of automobile automatic transmissions, and in particular relates to a wet clutch wear self-adaptation method. Background Art

[0002] Wet clutches are a key component of automotive automatic transmissions and are widely used in products such as automatic transmissions (ATs), dual-clutch transmissions (DCTs), and construction machinery transmissions. They offer advantages such as smooth shifting, torque amplification, high driving comfort, and wide adaptability. However, wet clutches have drawbacks such as complex structure, high cost, high failure rate, high fuel consumption, and deteriorating shift quality over a certain period of mileage.

[0003] The present invention takes AT / DCT as an example. AT / DCT shifting mainly involves two clutch actions: an oncoming clutch (OC clutch) and an offgoing clutch (OG clutch).

[0004] During the gear shifting process, the clutch ( Figure 2 The main phases of the clutch phase (B1 to B10) are Fill Phase (oil filling phase B1 to B5), Torque Phase (torque phase B5 to B7), Speed ​​Phase (clutch inertia phase B7 to B8), Lockup (clutch pressing phase B8 to B10), and clutch separation ( Figure 2 Among them, A1~A8) mainly include ETG Phase (clutch preparation separation phase A1~A3), DTK Phase (separation clutch slip phase A3~A5), Speed ​​Phase (separation clutch inertia phase A5~A6), and DTN Phase (clutch complete separation A6~A8).

[0005] For the clutch engagement: Fill pulse: clutch filling state (FP, refers to the filling time, i.e., the time from B2 to B3); Fill stroke: clutch synovial point (KP, refers to the pressure of synovial point B4); TP is divided into TP1 stage (B5 to B6) and TP2 stage (B6 to B7); for the clutch disengagement: ETG is the pressure of A3, such as Figure 2 As shown;

[0006] Throughout the product's lifecycle, a wet clutch transmits torque through friction between a steel plate and a friction plate. Clutch wear is inevitable due to various factors, including frequent shifting, high-temperature operation, neglected maintenance, and aging clutch materials. Clutch wear alters the mechanical properties of the friction plate, resulting in inadequate initial off-line data and, consequently, poor shift quality.

[0007] Therefore, it is urgent to solve the problem that the shifting quality of the wet clutch transmission deteriorates due to clutch wear after running for a certain mileage. Summary of the Invention

[0008] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a wet clutch wear adaptive method to solve the technical problem in the existing technology that the shifting quality of the wet clutch transmission deteriorates due to clutch wear after running for a certain mileage.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0010] A wet clutch wear self-adaptive method specifically comprises the following steps:

[0011] Step 1: Obtain the initial parameters within a certain time T before the gear shift and the process parameters during the gear shift, and determine whether the self-learning switch is turned on based on the initial parameters and the process parameters. If so, proceed to step 2; if not, proceed to step 6;

[0012] The initial parameters include engine speed, input shaft speed, output shaft speed, accelerator pedal signal and engine torque;

[0013] The process parameters include engine speed, input shaft speed, output shaft speed, engine speed change rate, input shaft speed change rate, output shaft speed change rate, engine torque, accelerator pedal opening, accelerator pedal change rate, gear shift progress, gear shift progress change rate and duration of each gear shift stage;

[0014] Step 2: determine whether the process parameters of the shifting process obtained in step 1 are abnormal. If so, extract the abnormal parameters and identify the corresponding abnormal phenomenon, and then proceed to step 3. If not, proceed to step 6.

[0015] The abnormal phenomena include TP1 stage overspeed, TP2 stage overspeed, overcharging, speed regulation starting too late, speed regulation starting too early, speed regulation too fast and speed regulation too slow;

[0016] Step 3: Classify the abnormal phenomena in step 2 and sort the multiple abnormal phenomena that occur simultaneously according to the priority order to obtain the abnormal phenomenon ranking;

[0017] The priority order is: TP1 stage flying car > overcharging > TP2 stage flying car > speed regulation starts too late / speed regulation starts too early > speed regulation is too fast / speed regulation is too slow;

[0018] Step 4: Determine whether the abnormal phenomenon ranked in step 3 has the same abnormal phenomenon that occurs n times in succession in n gear shifts and has the highest priority in the abnormal phenomenon ranked in step 3. If so, the gear shift is considered abnormal, and the clutch parameters are corrected based on the correspondence between the abnormal phenomenon and the clutch parameter correction operation. If not, proceed to step 6.

[0019] The abnormal characteristic values ​​include the shifting process, the rate of change of the shifting process, the duration of each shifting stage and the value of the process parameter;

[0020] The corresponding relationship between the abnormal phenomenon and the correction operation of the clutch parameters is as follows:

[0021] TP1 stage flying car: increase the pressure of the clutch ETG point;

[0022] TP2 stage flying car: increase FP and KP with clutch;

[0023] Overcharge: Reduces FP of the engaged clutch;

[0024] Speed ​​regulation starts too early: reduce the KP of the clutch;

[0025] Speed ​​regulation starts too late: increase the FP and KP of the clutch;

[0026] Too fast speed regulation: reduce the slope of the clutch TP1 stage;

[0027] Too slow speed regulation: increase the slope of the clutch TP1 stage;

[0028] Step 5: Calculate the difference between the process parameter obtained in step 1 and the ideal value, use the difference to look up the calibration table, obtain the corresponding learning value, use linear interpolation to interpolate the learning value, and store the interpolated learning value in the memory NVRAM. In the next gear shift, map the value in the memory NVRAM to the memory RAM for compensation;

[0029] Step six, end.

[0030] The present invention also includes the following technical features

[0031] Step 1 specifically includes the following steps:

[0032] Step 1.1: Acquire and monitor the initial parameters within a certain time T before the gear shift, and determine whether the monitored initial parameters are stable. If they are stable, proceed to step 1.2; otherwise, proceed to step 6.

[0033] Step 1.2, obtaining and monitoring process parameters during the gear shifting process, and determining whether the monitored process parameters are stable. If they are stable, proceed to step 1.3; otherwise, proceed to step 6.

[0034] Step 1.3: Determine whether the self-learning switch is on based on the initial parameters and process parameters. If so, proceed to step 2; if not, proceed to step 6.

[0035] Step 2 specifically includes the following steps:

[0036] Step 2.1, determine whether formula (1) or formula (2) is satisfied. If so, it is considered that overcharging occurs and go to step 2.2. Otherwise, go directly to step 2.2.

[0037] ShiftProgress_max>SIPmax_Fill (1)

[0038] (InputSpdacc_max)-(InputSpdacc_min)>InputSpdDiff (2)

[0039] shift progression=SIP=(BC) / (BA)

[0040] A represents the target gear ratio;

[0041] B indicates the current gear ratio;

[0042] C represents the dynamic speed ratio;

[0043] ShiftProgress_max indicates the maximum value of the shift progress during the oil filling phase;

[0044] InputSpdacc_max represents the maximum acceleration of the input shaft during the oil filling phase;

[0045] InputSpdacc_min represents the minimum acceleration of the input shaft during the oil filling phase;

[0046] SIPmax_Fill represents the calibration value of the maximum value of the gear shift process in the ideal filling stage;

[0047] InputSpdDiff represents the calibration value of the input shaft acceleration difference during the ideal oil filling stage;

[0048] Step 2.2: Determine whether formula (3) is satisfied. If so, it is considered that the TP1 stage overrun phenomenon occurs and go to step 2.3. Otherwise, go directly to step 2.3.

[0049] minSftprogrssion1 <SIPmin (3)

[0050] minSftprogrssion1 represents the minimum value of the shift process in the TP1 stage;

[0051] SIPmin represents the calibration value of the minimum value of the TP1 or TP2 shift process;

[0052] Step 2.3: Determine whether formula (4) is satisfied. If so, it is considered that the TP2 stage overrun phenomenon occurs and go to step 2.4. Otherwise, go directly to step 2.4.

[0053] minSftprogrssion2 <SIPmin (4)

[0054] in:

[0055] minSftprogrssion2 represents the minimum value of the shift process in TP2 stage;

[0056] Step 2.4, determine whether formula (5) is satisfied. If so, it is considered that the speed regulation starts too early and the process goes to step 2.5. Otherwise, the process goes directly to step 2.5.

[0057] TP phase time <(T1-Δ t1 )ms (5)

[0058] in:

[0059] T1 represents the duration of the ideal TP phase;

[0060] Δ t1 represents the first time threshold;

[0061] Step 2.5, determine whether formula (6) is satisfied. If so, it is considered that the speed regulation starts too late and the process goes to step 2.6. Otherwise, the process goes directly to step 2.6.

[0062] TP phase time>(T1+Δ t1 )ms (6)

[0063] Step 2.6, determine whether formula (7) is satisfied. If so, it is considered that the speed regulation is too fast and go to step 2.7. Otherwise, go directly to step 2.7.

[0064] SP phase time<(T2-Δ t2 )ms (7)

[0065] in:

[0066] T2 represents the ideal shift SP phase time;

[0067] Δ t2 is the second time threshold;

[0068] Step 2.7, determine whether formula (8) is satisfied. If so, it is considered that the speed regulation is too slow and go to step 3. Otherwise, go directly to step 3.

[0069] SP phase time>(T2+Δ t2 )ms (8).

[0070] Compared with the prior art, the present invention has the following beneficial technical effects:

[0071] (I) The present invention obtains the current parameters and the parameters during the gear shifting process to jointly determine whether the self-learning switch is on; avoids the situation where the road is bumpy before the gear shifting and the driver changes his intention during the gear shifting, which may cause false detection, classifies the abnormal phenomena that may occur during the gear shifting process, and makes a priority judgment. Only the highest priority phenomenon is learned each time to prevent false learning; and solves the technical problem in the prior art that the gear shifting quality of the wet clutch gearbox deteriorates due to clutch wear after running for a certain mileage.

[0072] (II) The present invention provides seven detection methods for phenomena to ensure the correct identification of various abnormal phenomena, significantly improving the recognition accuracy; the interpolated learning value is stored in the memory NVRAM and written into the table corresponding to the current temperature, speed, and torque to ensure that the learned value can be correctly applied to the next gear shift. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flow chart of the present invention;

[0074] Figure 2 Control pressure curve for wet clutch shifting;

[0075] Figure 3 Adjust the KP calibration table at the beginning and end of speed regulation

[0076] The specific contents of the present invention are further explained in detail below with reference to the embodiments. DETAILED DESCRIPTION

[0077] It should be noted that, unless otherwise specified, all components in the present invention are components known in the art.

[0078] Specific embodiments of the present invention are given below. It should be noted that the present invention is not limited to the following specific embodiments, and all equivalent modifications made on the basis of the technical solution of this application fall within the protection scope of the present invention.

[0079] The present invention provides a wet clutch wear self-adaptive method, which specifically includes the following steps:

[0080] Step 1: Obtain the initial parameters within a certain time T before the gear shift and the process parameters during the gear shift, and determine whether the self-learning switch is turned on based on the initial parameters and the process parameters. If so, proceed to step 2; if not, proceed to step 6;

[0081] The initial parameters include engine speed, input shaft speed, output shaft speed, accelerator pedal signal and engine torque;

[0082] The process parameters include engine speed, input shaft speed, output shaft speed, engine speed change rate, input shaft speed change rate, output shaft speed change rate, engine torque, accelerator pedal opening, accelerator pedal change rate, gear shift progress, gear shift progress change rate and duration of each gear shift stage;

[0083] Step 2: determine whether the process parameters of the shifting process obtained in step 1 are abnormal. If so, extract the abnormal parameters and identify the corresponding abnormal phenomenon, and then proceed to step 3. If not, proceed to step 6.

[0084] Abnormal phenomena include TP1-stage overspeed, TP2-stage overspeed, overcharging, speed regulation starting too late, speed regulation starting too early, speed regulation too fast and speed regulation too slow;

[0085] Step 3: Classify the abnormal phenomena in step 2 and sort the multiple abnormal phenomena that occur simultaneously according to the priority order to obtain the abnormal phenomenon ranking;

[0086] Priority order: TP1 flying car > overcharging > TP2 flying car > speed regulation starts too late / speed regulation starts too early > speed regulation is too fast / speed regulation is too slow;

[0087] Step 4: Determine whether the abnormal phenomenon ranked in step 3 has the same abnormal phenomenon that occurs n times in succession in n gear shifts and has the highest priority in the abnormal phenomenon ranked in step 3. If so, the gear shift is considered abnormal, and the clutch parameters are corrected based on the correspondence between the abnormal phenomenon and the clutch parameter correction operation. If not, proceed to step 6.

[0088] Abnormal characteristic values ​​include the shift process, the rate of change of the shift process, the duration of each shift stage, and the values ​​of process parameters;

[0089] The corresponding relationship between abnormal phenomena and clutch parameter correction operations is as follows:

[0090] TP1 stage flying car: increase the pressure of the clutch ETG point;

[0091] TP2 stage flying car: increase FP and KP with clutch;

[0092] Overcharge: Reduces FP of the engaged clutch;

[0093] Speed ​​regulation starts too early: reduce the KP of the clutch;

[0094] Speed ​​regulation starts too late: increase the FP and KP of the clutch;

[0095] Too fast speed regulation: reduce the slope of the clutch TP1 stage;

[0096] Too slow speed regulation: increase the slope of the clutch TP1 stage;

[0097] Step 5: Calculate the difference between the process parameter obtained in step 1 and the ideal value, use the difference to look up the calibration table, obtain the corresponding learning value, use linear interpolation to interpolate the learning value, and store the interpolated learning value in the memory NVRAM. In the next gear shift, map the value in the memory NVRAM to the memory RAM for compensation;

[0098] Step six, end.

[0099] The specific meaning of the abnormal phenomenon in step 2 is as follows;

[0100] TP1 stage flying car: The flying car phenomenon occurs in the TP1 stage;

[0101] TP2 stage flying car: The flying car phenomenon occurs in the TP2 stage;

[0102] Overcharging: Too much oil filling causes gear shifting to be awkward;

[0103] Speed ​​regulation starts too late: SP phase starts too late when shifting;

[0104] Speed ​​regulation starts too early: SP phase starts too early when shifting;

[0105] Speed ​​regulation is too fast: SP time is too short;

[0106] Speed ​​regulation is too slow: SP time is too long;

[0107] In step 5, linear interpolation is used to interpolate and the calibration table is retrieved (the calibration table is a manually preset table. When the process parameters are different, the corresponding calibration type is also different. For example, the KP calibration table is adjusted early or late at the start of speed regulation, such as Figure 3 The values ​​under the current temperature, speed and torque are shown in the figure. For FP, the interpolation under the current temperature and speed is adopted, and for KP, the interpolation under the current temperature and torque is adopted. The values ​​under different temperature, speed and torque ranges do not interfere with each other.

[0108] The learned clutch parameters are stored in NVRAM. For the next gear shift at the same temperature, speed or torque, the values ​​in NVRAM are mapped to RAM for compensation. The value acquisition logic and interpolation logic are the same.

[0109] The current parameters and the parameters during the gear shifting process are obtained to jointly determine whether the self-learning switch is on; it avoids false detections caused by bumpy roads before gear shifting and changes in driver intention during gear shifting, classifies abnormal phenomena that may occur during gear shifting, and makes priority judgments. Only the highest priority phenomenon is learned each time to prevent false learning; it solves the technical problem in the prior art that the gear shifting quality of the wet clutch gearbox deteriorates due to clutch wear after running for a certain mileage.

[0110] Step 1 specifically includes the following steps:

[0111] Step 1.1: Acquire and monitor the initial parameters within a certain time T before the gear shift, and determine whether the monitored initial parameters are stable. If they are stable, proceed to step 1.2; otherwise, proceed to step 6.

[0112] Step 1.2, obtaining and monitoring process parameters during the gear shifting process, and determining whether the monitored process parameters are stable. If they are stable, proceed to step 1.3; otherwise, proceed to step 6.

[0113] Step 1.3: Determine whether the self-learning switch is on based on the initial parameters and process parameters. If so, proceed to step 2; if not, proceed to step 6.

[0114] In the above technical solution, the engine speed, input shaft speed, output shaft speed, accelerator pedal signal, and engine torque are obtained a certain time before the gear shift to determine whether the state before the gear shift is stable, so as to eliminate the phenomenon of abnormal gear shift data caused by road bumps, abnormal engine torque fluctuations, accelerator pedal fluctuations before gear shifting, and resonance.

[0115] Specifically, if both the initial parameters and the process parameters meet the conditions for turning on the self-learning switch, the self-learning switch is turned on;

[0116] The initial parameters satisfy the self-learning switch opening conditions:

[0117] Maximum time T before shifting Minimum time T before shifting Maximum-Minimum Allowable fluctuation range Engine speed / rpm Engspd_max Engspd_min ΔEngspd 50 Input shaft speed / rpm Inpspd_max Inpspd_min ΔInpspd 50 Output shaft speed / rpm Outspd_max Outspd_min ΔOutspd 50 Accelerator pedal opening / % AccPedal_max AccPedal_min ΔAccpedal 20 Engine torque / Nm Engtrq_max Engtrq_min Δengtrq 100

[0118] The process parameters meet the self-learning switch opening conditions:

[0119] Maximum time T before shifting Minimum time T before shifting Maximum-Minimum Allowable fluctuation range Accelerator pedal opening / % AccPedal_max AccPedal_min ΔAccpedal 20 Engine torque / Nm Engtrq_max Engtrq_min Δengtrq 200

[0120] Step 2 specifically includes the following steps:

[0121] Step 2.1, determine whether formula (1) or formula (2) is satisfied. If so, it is considered that overcharging occurs and go to step 2.2. Otherwise, go directly to step 2.2.

[0122] ShiftProgress_max>SIPmax_Fill (1)

[0123] (InputSpdacc_max)-(InputSpdacc_min)>InputSpdDiff (2)

[0124] shift progression=SIP=(BC) / (BA)

[0125] A represents the target gear ratio;

[0126] B indicates the current gear ratio;

[0127] C represents the dynamic speed ratio;

[0128] ShiftProgress_max indicates the maximum value of the shift progress during the oil filling phase;

[0129] InputSpdacc_max represents the maximum acceleration of the input shaft during the oil filling phase;

[0130] InputSpdacc_min represents the minimum acceleration of the input shaft during the oil filling phase;

[0131] SIPmax_Fill represents the calibration value of the maximum value of the gear shift process in the ideal filling stage;

[0132] InputSpdDiff represents the calibration value of the input shaft acceleration difference during the ideal oil filling stage;

[0133] Step 2.2: Determine whether formula (3) is satisfied. If so, it is considered that the TP1 stage overrun phenomenon occurs and go to step 2.3. Otherwise, go directly to step 2.3.

[0134] minSftprogrssion1 <SIPmin (3)

[0135] minSftprogrssion1 represents the minimum value of the shift process in the TP1 phase;

[0136] SIPmin represents the calibration value of the minimum value of the TP1 or TP2 shift process;

[0137] Step 2.3: Determine whether formula (4) is satisfied. If so, it is considered that the TP2 stage overrun phenomenon occurs and go to step 2.4. Otherwise, go directly to step 2.4.

[0138] minSftprogrssion2 <SIPmin (4)

[0139] in:

[0140] minSftprogrssion2 represents the minimum value of the shift process in TP2 stage;

[0141] Step 2.4, determine whether formula (5) is satisfied. If so, it is considered that the speed regulation starts too early and the process goes to step 2.5. Otherwise, the process goes directly to step 2.5.

[0142] TP phase time <(T1-Δ t1 )ms (5)

[0143] in:

[0144] T1 represents the duration of the ideal TP phase;

[0145] Δ t1 represents the first time threshold;

[0146] Step 2.5, determine whether formula (6) is satisfied. If so, it is considered that the speed regulation starts too late and the process goes to step 2.6. Otherwise, the process goes directly to step 2.6.

[0147] TP phase time>(T1+Δ t1 )ms (6)

[0148] Step 2.6, determine whether formula (7) is satisfied. If so, it is considered that the speed regulation is too fast and go to step 2.7. Otherwise, go directly to step 2.7.

[0149] SP phase time<(T2-Δ t2 )ms (7)

[0150] in:

[0151] T2 represents the ideal shift SP phase time;

[0152] Δ t2 is the second time threshold;

[0153] Step 2.7, determine whether formula (8) is satisfied. If so, it is considered that the speed regulation is too slow and go to step 3. Otherwise, go directly to step 3.

[0154] SP phase time>(T2+Δ t2 )ms (8).

Claims

1. A wet clutch wear adaptive method, characterized in that: The specific steps include: Step 1: Obtain the initial parameters within a certain time T before the gear shift and the process parameters during the gear shift, and determine whether the self-learning switch is turned on based on the initial parameters and the process parameters. If so, proceed to step 2; if not, proceed to step 6; The initial parameters include engine speed, input shaft speed, output shaft speed, accelerator pedal signal and engine torque; The process parameters include engine speed, input shaft speed, output shaft speed, engine speed change rate, input shaft speed change rate, output shaft speed change rate, engine torque, accelerator pedal opening, accelerator pedal change rate, gear shift progress, gear shift progress change rate and duration of each gear shift stage; Step 2: determine whether the process parameters of the shifting process obtained in step 1 are abnormal. If so, extract the abnormal parameters and identify the corresponding abnormal phenomenon, and then proceed to step 3. If not, proceed to step 6. The abnormal phenomena include TP1 stage overspeed, TP2 stage overspeed, overcharging, speed regulation starting too late, speed regulation starting too early, speed regulation too fast and speed regulation too slow; Step 3: Classify the abnormal phenomena in step 2 and sort the multiple abnormal phenomena that occur simultaneously according to the priority order to obtain the abnormal phenomenon ranking; The priority order is: TP1 stage flying car > overcharging > TP2 stage flying car > speed regulation starts too late / speed regulation starts too early > speed regulation is too fast / speed regulation is too slow; Step 4: Determine whether the abnormal phenomenon ranked in step 3 has the same abnormal phenomenon that occurs n times in succession in n gear shifts and has the highest priority in the abnormal phenomenon ranked in step 3. If so, the gear shift is considered abnormal, and the clutch parameters are corrected based on the correspondence between the abnormal phenomenon and the clutch parameter correction operation. If not, proceed to step 6. The abnormal characteristic values ​​include the shifting process, the rate of change of the shifting process, the duration of each shifting stage and the value of the process parameter; The corresponding relationship between the abnormal phenomenon and the correction operation of the clutch parameters is as follows: TP1 stage flying car: increase the pressure of the clutch ETG point; TP2 stage flying car: increase FP and KP with clutch; Overcharge: Reduces FP of the engaged clutch; Speed ​​regulation starts too early: reduce the KP of the clutch; Speed ​​regulation starts too late: increase the FP and KP of the clutch; Too fast speed regulation: reduce the slope of the clutch TP1 stage; Too slow speed regulation: increase the slope of the clutch TP1 stage; Step 5: Calculate the difference between the process parameter obtained in step 1 and the ideal value, use the difference to look up the calibration table, obtain the corresponding learning value, use linear interpolation to interpolate the learning value, and store the interpolated learning value in the memory NVRAM. In the next gear shift, map the value in the memory NVRAM to the memory RAM for compensation; Step six, end.

2. The wet clutch wear adaptive method according to claim 1, characterized in that: Step 1 specifically includes the following steps: Step 1.1: Acquire and monitor the initial parameters within a certain time T before the gear shift, and determine whether the monitored initial parameters are stable. If they are stable, proceed to step 1.2; otherwise, proceed to step 6. Step 1.2, obtaining and monitoring process parameters during the gear shifting process, and determining whether the monitored process parameters are stable. If they are stable, proceed to step 1.3; otherwise, proceed to step 6. Step 1.3: Determine whether the self-learning switch is on based on the initial parameters and process parameters. If so, proceed to step 2; if not, proceed to step 6.

3. The wet clutch wear adaptive method according to claim 1, characterized in that: Step 2 specifically includes the following steps: Step 2.1, determine whether formula (1) or formula (2) is satisfied. If so, it is considered that overcharging occurs and go to step 2.

2. Otherwise, go directly to step 2.

2. ShiftProgress_max>SIPmax_Fill (1) (InputSpdacc_max)-(InputSpdacc_min)>InputSpdDiff (2) shift progression=SIP=(BC) / (BA) A represents the target gear ratio; B indicates the current gear ratio; C represents the dynamic speed ratio; ShiftProgress_max indicates the maximum value of the shift progress during the oil filling phase; InputSpdacc_max represents the maximum acceleration of the input shaft during the oil filling phase; InputSpdacc_min represents the minimum acceleration of the input shaft during the oil filling phase; SIPmax_Fill represents the calibration value of the maximum value of the gear shift process in the ideal filling stage; InputSpdDiff represents the calibration value of the input shaft acceleration difference during the ideal oil filling stage; Step 2.2: Determine whether formula (3) is satisfied. If so, it is considered that the TP1 stage overrun phenomenon occurs and go to step 2.

3. Otherwise, go directly to step 2.

3. minSftprogrssion1 <SIPmin (3) minSftprogrssion1 represents the minimum value of the shift process in the TP1 stage; SIPmin represents the calibration value of the minimum value of the TP1 or TP2 shift process; Step 2.3: Determine whether formula (4) is satisfied. If so, it is considered that the TP2 stage overrun phenomenon occurs and go to step 2.

4. Otherwise, go directly to step 2.

4. minSftprogrssion2 <SIPmin (4) in: minSftprogrssion2 represents the minimum value of the shift process in TP2 stage; Step 2.4, determine whether formula (5) is satisfied. If so, it is considered that the speed regulation starts too early and the process goes to step 2.

5. Otherwise, the process goes directly to step 2.

5. TP phase time <(T1-Δ t1 )ms (5) in: T1 represents the duration of the ideal TP phase; Δ t1 represents the first time threshold; Step 2.5, determine whether formula (6) is satisfied. If so, it is considered that the speed regulation starts too late and the process goes to step 2.

6. Otherwise, the process goes directly to step 2.

6. TP phase time>(T1+Δ t1 )ms (6) Step 2.6, determine whether formula (7) is satisfied. If so, it is considered that the speed regulation is too fast and go to step 2.

7. Otherwise, go directly to step 2.

7. SP phase time<(T2-Δ t2 )ms (7) in: T2 represents the ideal shift SP phase time; Δ t2 is the second time threshold; Step 2.7, determine whether formula (8) is satisfied. If so, it is considered that the speed regulation is too slow and go to step 3. Otherwise, go directly to step 3. SP phase time>(T2+Δ t2 )ms (8).

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