Clutch self-learning method and device and vehicle

Abstract

The embodiment of the invention relates to a self-learning method and device of a clutch and a vehicle. The method comprises the steps that whether the vehicle meets a preset vehicle state self-learning condition or not is recognized; when the vehicle meets the preset vehicle state self-learning condition, whether the vehicle meets a preset parking condition or not is recognized; when the preset parking condition is met, whether the vehicle meets a driver leaving condition or not is recognized; when the driver leaving condition is met, whether the vehicle meets a preset self-learning driving mode or not is recognized; when the preset self-learning driving mode is met, a clutch in the vehicle is controlled to conduct self-learning so as to obtain a self-learning result, and the self-learning result is used for conducting current compensation on control current of an electromagnetic valve of the clutch in the vehicle and / or conducting time compensation on oil charging time of the clutch in the vehicle. According to the technical scheme, the perception of the user on self-learning is reduced, and the user experience is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicle control, and particularly to a self-learning method and device for a clutch, and a vehicle. Background Art

[0002] With the development of current intelligent driving technology, users have an increasing demand for using and driving intelligent driving vehicles. Currently, vehicles with intelligent driving technology are mainly hybrid vehicles or new energy vehicles, and some traditional vehicles are also applying intelligent driving technology. As intelligent driving technology becomes more and more mature, the occupancy rate of vehicles equipped with intelligent driving functions is gradually increasing. Therefore, the workload of calibrating the clutch before the vehicle is put into use is also increasing. In order to shorten the time for the vehicle to be put into use, it has become more common to perform self-learning on the clutch after the vehicle is put into use.

[0003] In the related art, when the self-learning clutch performs pressure control, the whole vehicle may have a certain feeling, such as slight abnormal noise, etc., which reduces the user experience. Summary of the Invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a self-learning method and device for a clutch, and a vehicle.

[0005] An embodiment of the present disclosure provides a self-learning method for a clutch, the method including: identifying whether the vehicle meets a preset vehicle state self-learning condition; when the vehicle meets the preset vehicle state self-learning condition, identifying whether the vehicle meets a preset parking condition; when meeting the preset parking condition, identifying whether the vehicle meets a driver leaving condition; when meeting the driver leaving condition, identifying whether the vehicle meets a preset self-learning driving mode; when meeting the preset self-learning driving mode, controlling the clutch in the vehicle to perform self-learning to obtain a self-learning result, where the self-learning result is used to perform current compensation on the control current of the solenoid valve of the clutch in the vehicle, and / or, is used to perform time compensation on the oil filling time of the clutch in the vehicle.

[0006] The embodiments of the present disclosure also provide a self-learning device for a clutch. The device includes: a first identification module for identifying whether a vehicle meets a preset vehicle state self-learning condition; a second identification module for identifying whether the vehicle meets a preset parking condition when the vehicle meets the preset vehicle state self-learning condition; a third identification module for identifying whether the vehicle meets a driver leaving condition when the preset parking condition is met; a fourth identification module for identifying whether the vehicle meets a preset self-learning driving mode when the driver leaving condition is met; a self-learning control module for controlling the clutch in the vehicle to perform self-learning to obtain a self-learning result when the preset self-learning driving mode is met, wherein the self-learning result is used to perform current compensation on the control current of the solenoid valve of the clutch in the vehicle, and / or is used to perform time compensation on the oil filling time of the clutch in the vehicle.

[0007] The embodiments of the present disclosure also provide a vehicle, which includes: a processor; a memory for storing executable instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the self-learning method of the clutch provided by the embodiments of the present disclosure.

[0008] The technical solutions provided by the embodiments of the present disclosure have the following advantages compared with the prior art: The self-learning solution of the clutch provided by the embodiments of the present disclosure identifies whether a vehicle meets a preset vehicle state self-learning condition. When the vehicle meets the preset vehicle state self-learning condition, it identifies whether the vehicle meets a preset parking condition. When the preset parking condition is met, it identifies whether the vehicle meets a driver leaving condition. When the driver leaving condition is met, it identifies whether the vehicle meets a preset self-learning driving mode. Furthermore, when the preset self-learning driving mode is met, it controls the clutch in the vehicle to perform self-learning to obtain a self-learning result, wherein the self-learning result is used to perform current compensation on the control current of the solenoid valve of the clutch in the vehicle, and / or is used to perform time compensation on the oil filling time of the clutch in the vehicle. In this technical solution, the safety of the vehicle during self-learning is improved, the user's perception of self-learning is reduced, and the user experience is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Combined with the accompanying drawings and referring to the following specific embodiments, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more obvious. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the original components and elements are not necessarily drawn to scale.

[0010] Figure 1 It is a schematic flowchart of a self-learning method of a clutch provided by the embodiments of the present disclosure; Figure 2 Schematic flowchart of another clutch self - learning method provided by an embodiment of the present disclosure; Figure 3 Schematic flowchart of yet another clutch self - learning method provided by an embodiment of the present disclosure; Figure 4 Schematic structural diagram of a clutch self - learning device provided by an embodiment of the present disclosure; Figure 5 Schematic structural diagram of a vehicle provided by an embodiment of the present disclosure. Detailed implementation manners

[0011] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0012] It should be understood that the various steps recorded in the method embodiments of the present disclosure can be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0013] The term "including" and its variations used herein are open - ended, that is, "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0014] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependent relationships.

[0015] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be understood as "one or more".

[0016] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0017] To solve the above problems, an embodiment of the present disclosure provides a self-learning method for a clutch. The following introduces this method in combination with specific embodiments.

[0018] Figure 1 As shown in the flowchart of a self-learning method for a clutch provided by an embodiment of the present disclosure, this method can be executed by a self-learning device of the clutch, where the device can be implemented by software and / or hardware and is generally integrated in a vehicle. Figure 1 As shown, this method includes: Step 101, identify whether the vehicle meets the preset vehicle state self-learning conditions.

[0019] In an embodiment of the present disclosure, identifying whether the vehicle meets the preset vehicle state self-learning conditions means identifying whether the vehicle is in a state where self-learning can be performed.

[0020] It should be noted that in different application scenarios, the preset vehicle state self-learning conditions are different. In some possible embodiments, the preset vehicle state self-learning conditions include: (1) The preset self-learning switch is in the on state.

[0021] In this embodiment, a self-learning switch is preset. In this embodiment, the state of the preset self-learning switch can be based on the flg_AdptEnBaseAdsa flag. Different from the traditional self-learning mode, when the matching vehicle has an intelligent driving function and the current transmission mechanism requires a self-learning function, and according to the current calibration function requirements, this switch is set to flg_AdptEnBaseAdsa = 1. When flg_AdptEnBaseAdsa = 1, it indicates that the preset self-learning switch is in the on state. Among them, the switch of flg_AdptEnBaseAdsa can be turned off or on at any time according to the requirements of calibration testing, providing convenience and flexibility for testing.

[0022] (2) The intelligent driving function module in the vehicle is in a non-fault state.

[0023] Among them, the intelligent driving function module can include radars (such as lidar, millimeter-wave radar, etc.), camera modules, etc. In this embodiment, the state of the intelligent driving function module can be based on the flg_AdsaVaild flag. When flg_AdsaVaild = 1, it indicates that the intelligent driving function module in the vehicle is in a non-fault state.

[0024] (3) All door sensors, seat sensors, and seat belts in the vehicle are in a non-fault state.

[0025] In this embodiment, it is identified that all door sensors, seat sensors, and seat belts in the vehicle are in a non-fault state.

[0026] For example, when a vehicle includes four doors and the four doors of the vehicle are respectively identified based on flg_DriverDoorVaild, flg_FRDoorVaild, flg_RLDoorVaild, and flg_RRDoorVaild, when flg_DriverDoorVaild = 1, flg_FRDoorVaild = 1, flg_RLDoorVaild = 1, and flg_RRDoorVaild = 1, all the door sensors identifying the four doors of the vehicle are normal.

[0027] For example, when a vehicle includes five seats, generally there are two seat sensors in the front row and three seat sensors in the back row. The effectiveness of these five seat sensors indicates that the seat sensors are all in a non-fault state. Among them, when using flg_DriverSeatVaild, flg_FRSeatVaild, flg_FRSeatVaild, flg_RRSeatVaild, and flg_RMSeatVaild to identify the five seat sensors respectively, when flg_DriverSeatVaild = 1, flg_FRSeatVaild = 1, flg_RLSeatVaild = 1, flg_RRSeatVaild = 1, and flg_RMSeatVaild = 1, and the communication of the five seat sensors is effective, it is ensured that it can be timely detected whether there is someone on the seat.

[0028] In this embodiment, when a vehicle includes five seats and each seat includes a seat belt, when using flg_DriverBeltVaild, flg_FRBeltVaild, flg_RLBeltVaild, flg_RRBeltVaild, and flg_RRBeltVaild to identify the seat belts on the five seats respectively, when flg_DriverBeltVaild = 1, flg_FRBeltVaild = 1, flg_RLBeltVaild = 1, flg_RRBeltVaild = 1, and flg_RMBeltVaild = 1, it indicates that the signals of the seat belts are effective.

[0029] (4) Not all self-learning of the clutches in the vehicle has been completed.

[0030] In this embodiment, self-learning is required for each sensor. If the self-learning of the current clutch has been completed, there is no need to perform self-learning again, unless the self-learning value of a certain clutch is reset and re-self-learning is required, etc.

[0031] In this embodiment, when the self - learning of the clutch in the vehicle is not completed, it indicates that the self - learning of the clutch is required.

[0032] In an embodiment of the present disclosure, it is possible to determine whether the flg_AdptBaseAdsaAcTive flag meets the preset vehicle state self - learning condition. When the preset vehicle state self - learning condition = 1, it indicates that the preset vehicle state self - learning condition is met.

[0033] Step 102: When the vehicle meets the preset vehicle state self - learning condition, identify whether the vehicle meets the preset parking condition.

[0034] It can be understood that in order to reduce the user's perception of the self - learning process, the vehicle should be in a parked state. Therefore, in an embodiment of the present disclosure, when the vehicle meets the preset vehicle state self - learning condition, identify whether the vehicle meets the preset parking condition.

[0035] In different application scenarios, the preset parking conditions are different. In some possible embodiments, the preset parking conditions include: (1) The vehicle speed is 0.

[0036] In this embodiment, the vehicle speed can represent the current output shaft speed or the vehicle speed is 0. A vehicle speed of 0 can ensure that the current driver has the intention to stop and is also for the safety of the vehicle and personnel during the self - learning of the current vehicle.

[0037] (2) The vehicle is in a braking state or a parked state.

[0038] In this embodiment, the current driver's brake pedal is in a braking state, or the braking states of the Electronic Stability Program (ESP) and the Antilock Brake System (ABS) of the vehicle body are in a parked state. Or, the vehicle is equipped with an automatic parking function. After the vehicle stops, the ESP of the whole vehicle can automatically park. Or, when the driver opens the seat belt, leaves the seat, and opens the door, etc., the whole vehicle will automatically enter the parked state, which indicates that the vehicle is in a braking state or a parked state at this time. This condition is used to judge whether the current vehicle is in or is in the process of being in a braking state through the brake switch and the parking condition.

[0039] (3) The gear of the vehicle is in neutral or park.

[0040] In this embodiment, the gear requested by the current shift lever is in neutral or park. According to the driving habits of current drivers, some drivers are used to keeping the gear in neutral before getting out of the car, and some drivers generally control the gear in park before getting out of the car. Therefore, identify whether the gear of the vehicle is in neutral or park.

[0041] In this embodiment, it is also necessary to ensure that the actual gear position of the transmission mechanism is consistent with the position of the shift lever. For example, when the current shift lever is in the neutral position, the actual gear position of the transmission mechanism is in the neutral position. If the gear position request of the current shift lever is in the parking position, the actual gear position of the transmission should also be in the parking position. If there is an inconsistency between the actual gear position of the transmission and the position of the shift lever, and the current inconsistency persists for a certain period of time (e.g., 2000 ms), self-learning is prohibited.

[0042] (4)The transmission mechanism in the vehicle is in a non-fault state.

[0043] Among them, the transmission mechanism may include a transmission, a clutch, gears, a speed sensor, etc.

[0044] In this embodiment, self-learning is only allowed when the transmission mechanism in the vehicle is in a non-fault state.

[0045] If there is a fault in the current transmission, such as a clutch, gears, or speed sensor, self-learning of the clutch by the transmission mechanism is prohibited. Because during the clutch process, it is necessary to ensure that all components and sensors of the transmission are in a fault-free state to enable normal clutch learning. For example, in the case of clutch ablation, the oil pressure control characteristics of the clutch change, which means the current clutch is in a diseased state. If self-learning is performed, the learning result will not be the state of a normal clutch. For example, if the speed sensor in the current transmission mechanism fails, it means that the current speed cannot be ensured to be real, and the current speed value will be used in self-learning, so self-learning cannot be performed. Therefore, when a fault occurs in the transmission mechanism, clutch self-learning cannot be carried out.

[0046] Step 103, when the preset parking condition is met, identify whether the vehicle meets the driver departure condition.

[0047] In order to reduce the driver's perception of self-learning, in an embodiment of the present disclosure, when the preset parking condition is met, identify whether the vehicle meets the driver departure condition.

[0048] It should be noted that in different application scenarios, the driver departure conditions are different. In some possible embodiments, the driver departure conditions may include: (1)The driver is in a seatbelt-fastened state before getting out of the vehicle. If the driver's seatbelt changes from the locked state to the open state, it indicates that the driver has a need to get out of the vehicle, and combined with other conditions below for judgment.

[0049] (2)The driver's seat sensor detects that the driver has left the driver's seat, that is, the seat changes from the state of having a driver sitting to the state of the driver leaving the seat, then it is confirmed that the driver has left.

[0050] In this embodiment, it is also possible to ensure that other users are not in the vehicle either, so as to further improve the user experience. For the detection of other seats, the same principle applies until no one is detected in all seat states.

[0051] (3) For a detection system that only detects the fatigue level of the driver in the vehicle, it can be based on the detection system of the driver's fatigue level to detect whether the driver is still in the vehicle. If the driver gets out of the vehicle, the driver will not be detected in the driver's seat.

[0052] If there is a camera or a detection system such as a rearview mirror in the vehicle to detect passengers, then it is detected whether the passengers on the corresponding seats are still on the seats. If there is no corresponding passenger detection system currently, the corresponding detection flag bit is always established (the flag bit being 0 indicates that there is no one on the corresponding seat), flg_DriverDet = 0, flg_FRPassengerDet = 0, flg_RLPassengerDet = 0, flg_RRPassengerDet = 0, flg_RMPassengerDet = 0. If the status is 0, then the status that the driver and passengers in the vehicle are not in the vehicle is established.

[0053] (4) In addition to detecting whether the driver and passengers in the vehicle are in the vehicle, it is also necessary to identify the driver or passengers in the vehicle through the external cameras of the intelligent driving system to determine whether the current vehicle occupants have already got out of the vehicle.

[0054] If there is a camera system in the vehicle to detect the driver, and there is also a corresponding camera detection system for the driver on the driver's side outside the vehicle, and the cameras inside and outside the vehicle identify the driver consistently and both are established, it can be determined that the driver has got out of the vehicle.

[0055] If there is no corresponding detection for the driver outside the vehicle, then this item does not need to be judged, and only whether the driver in the vehicle is still in the driver's seat needs to be judged. The judgment condition for the driver outside the vehicle is forced to be established.

[0056] For the detection of passengers, if there is a detection of whether the passengers are in their seats in the vehicle, and there is also an intelligent driving detection system for passengers outside the vehicle, then this condition is used for judgment. If there is no detection system in the vehicle and there is a corresponding camera outside the vehicle, there is no need to make a further judgment, and the condition for judging whether the passengers are outside the vehicle is forced to be established.

[0057] If there is only a detection system for passengers in the vehicle, then only the detection inside the vehicle needs to be judged, and the condition for detecting whether the passengers outside the vehicle have already got out of the vehicle is forced to be established.

[0058] (5) The door on the current driver's side is in the open state and the door state is normal. When the flg_DriverDoorVaild indicates the door state, when flg_DriverDoorVaild = 1, it indicates that the door state is normal.

[0059] When the above conditions (1)-(5) are all satisfied simultaneously, it can be determined that the current occupants in the vehicle are no longer in the vehicle.

[0060] Step 104, when the driver leaving condition is met, identify whether the vehicle meets the preset self-learning driving mode.

[0061] In an embodiment of the present disclosure, when the driver leaving condition is met, identify whether the vehicle meets the preset self-learning driving mode. Among them, in different application scenarios, the preset self-learning driving mode is different. In some possible embodiments, if the current vehicle is in pure electric mode, it is determined that the driver and passengers are already outside the vehicle, then the clutch of the normal transmission mechanism can be self-learned, and the motor speed can be controlled to the fixed speed value required for self-learning, and the required fixed speed value can be preset to 850 rpm.

[0062] In some possible embodiments, if the current vehicle is in hybrid mode, the preset self-learning driving mode may include: (1) The vehicle is not in the charging state.

[0063] In this embodiment, if the current is in the charging state, in order to ensure the power balance of the power battery or meet the driver's demand for the power target value of pure electric driving, the charging strategy is preferentially executed. Therefore, when the vehicle is in the charging state, self-learning is not performed.

[0064] (2) The vehicle does not include a device to be cooled.

[0065] In this embodiment, if the vehicle uses a mechanical compressor and the driver has a refrigeration requirement, or there is a cooling requirement for high-voltage components such as the power battery and the motor, the refrigeration strategy for the driver and high-voltage components is preferentially adopted. Unless the temperature inside the vehicle has reached the refrigeration temperature condition set by the driver, or the cooling temperature of the high-voltage components has met the safety requirements of the components, the engine is allowed to stop, and then the motor is used to perform clutch self-learning at a fixed speed.

[0066] However, if after the compressor stops working, the temperature inside the vehicle rises and is more than 10°C above the temperature set by the driver, or the temperature of the high-voltage components rises to the safety temperature +10°C, or reaches the upper limit value of the components, the current clutch self-learning is stopped and the engine is started to continue the refrigeration work to refrigerate the inside of the vehicle or the high-voltage components.

[0067] Therefore, in this embodiment, it is also necessary to determine that the vehicle does not include the device to be cooled. Among them, the device to be cooled is a component that needs to be cooled and whose temperature is not within the preset safe temperature range.

[0068] (3) The vehicle is in the engine direct drive mode.

[0069] In this embodiment, when the current driving mode is the engine direct drive mode, in this mode, when the vehicle occupants leave, the engine can be shut down and fixed to a certain speed by the motor for learning. The engine shutdown in this case will not cause complaints from the driver because there is an engine automatic start-stop mode in the engine direct drive mode, and the driver in the vehicle has left the vehicle, so the engine can be automatically shut down, and a certain amount of fuel can be saved, improving fuel economy. However, because of the shutdown for self-learning in this case, even after the self-learning is completed, the engine does not need to be restarted unless the driver needs to shift gears to start driving.

[0070] (4) The vehicle is not in the preset driving condition.

[0071] In this embodiment, during self-learning, to ensure vehicle safety, it is also necessary to determine that the vehicle is not in the preset driving condition.

[0072] In this embodiment, if the current driving mode of the whole vehicle is in the four-wheel drive driving mode of 4L or 4H, or the driving mode is in harsh driving modes such as sand, mud, mountain, snow, etc., then the self-learning of the clutch is prohibited. It can also be considered that stopping the currently started engine is prohibited. In this case, generally, the driver has a high demand for the power of the engine and motor of the whole vehicle, and it is also possible that the current vehicle is in a rescue situation, etc. If the engine power is shut down due to self-learning, it is very likely to cause inconvenience to the driver.

[0073] And in such harsh working conditions, when the whole vehicle performs self-learning and the clutch is in the engagement process, if the clutch consistency is poor, the impact on the whole vehicle will be very large, and if the whole vehicle is on a mountain or in the snow at this time, it will bring danger to the whole vehicle.

[0074] Step 105, when the preset self-learning drive mode is satisfied, control the clutch in the vehicle to perform self-learning to obtain a self-learning result, where the self-learning result is used to perform current compensation on the control current of the solenoid valve of the clutch in the vehicle, and / or, is used to perform time compensation on the oil filling time of the clutch in the vehicle.

[0075] When the preset self - learning driving mode is satisfied, the clutch in the vehicle is controlled to perform self - learning to obtain a self - learning result. Thus, through the above logic, it can be determined that the driver and passengers in the vehicle have left the vehicle, and the vehicle is also in a state suitable for self - learning. Performing self - learning at this time will not bring any perception to the passengers, and the clutch of the transmission mechanism will complete self - learning imperceptibly. This improves the complaints about clutch self - learning that the driver may have during previous self - learning processes.

[0076] In the embodiments of the present disclosure, the self - learning result is used to perform current compensation on the control current of the solenoid valve of the clutch in the vehicle, and / or to perform time compensation on the oil - filling time of the clutch in the vehicle. Thus, in subsequent vehicle control, current compensation can be performed on the control current of the solenoid valve of the clutch in the vehicle based on the self - learning result, and / or time compensation can be performed on the oil - filling time of the clutch in the vehicle.

[0077] In summary, the self - learning method of the clutch in the embodiments of the present disclosure identifies whether the vehicle meets the preset vehicle - state self - learning conditions. When the vehicle meets the preset vehicle - state self - learning conditions, it identifies whether the vehicle meets the preset parking conditions. When the preset parking conditions are met, it identifies whether the driver - leaving conditions are met. When the driver - leaving conditions are met, it identifies whether the preset self - learning driving mode is satisfied. Furthermore, when the preset self - learning driving mode is satisfied, the clutch in the vehicle is controlled to perform self - learning to obtain a self - learning result, where the self - learning result is used to perform current compensation on the control current of the solenoid valve of the clutch in the vehicle, and / or to perform time compensation on the oil - filling time of the clutch in the vehicle. In this technical solution, the safety of the vehicle during self - learning is improved, the user's perception of self - learning is reduced, and the user experience is enhanced.

[0078] The following describes the self - learning process of the clutch in the embodiments of the present disclosure with specific embodiments.

[0079] Figure 2 is a flowchart of the self - learning method of the clutch according to another embodiment of the present disclosure, as Figure 2 shown. The method includes: Step 201, determine whether the vehicle meets the entry conditions for the preset parameter calibration self - learning stage.

[0080] In this embodiment, the state bits of the state machine can be used to identify the various states of the vehicle self-learning stage (also referred to as the P2C stage). Among them, the P2C self-learning mainly focuses on learning the solenoid valve P2C characteristics of the clutch. By controlling the current of the solenoid valve, the pressure of the clutch command is controlled. Due to the inconsistencies of factors such as the valve plate, solenoid valve, oil pipeline, and clutch characteristics in the transmission, the current for controlling the solenoid valve in different transmissions cannot meet the pressure requirements of the clutch. Therefore, after learning, the control current of the solenoid valve of the transmission of the currently assembled vehicle is corrected to meet the corresponding pressure requirements of the clutch. Among them, the state machine can include three main states: Idle, Work, and Post. Among them, the Work stage can include the Pre stage and the P2Cadpt stage, etc. Among them, the Pre stage is the initialization stage of parameter calibration, and the P2Cadpt stage is the preset parameter calibration self-learning stage. POST is the self-learning exit stage, and Idle is the self-learning initial stage.

[0081] In this embodiment, the conditions for Idle to enter the Work stage can include: (1) The enabling condition for self-learning to enter is established, and this enabling condition can include Figure 1 each of the conditions shown in

[0082] (2) The current gear is in neutral, and the shift lever condition remains in neutral for a certain period of time. Here, the time can be preset to 2000 ms, etc.

[0083] (3) The self-learning clutch selection is not empty. The selection of the self-learning clutch generally follows the order of C1, C2, C3, C4, B1. If the current clutch has completed learning, this clutch is skipped for the learning of other clutches.

[0084] Among them, the conditions for the Work stage to enter the POST stage can include one of the following: (1) There is a current shift requirement, whether it is the driver's or the transmission's own shift requirement. Then, stop self-learning and enter the POST stage, and give priority to performing the shift according to the driver's shift requirement, etc.

[0085] (2) The current gear is not in neutral.

[0086] Among them, it should be noted that the completion of the preset parameter calibration self-learning stage indicates that the self-learning of this stage is completed (Completed) or self-learning fails (Failed).

[0087] Step 202, when the conditions for entering the preset parameter calibration self-learning stage are met, determine the first current offset of the target clutch, where the target clutch is the clutch to be self-learned in the vehicle.

[0088] In an embodiment of the present disclosure, when the condition for entering the preset parameter calibration self - learning stage is met, determine the first current offset of the target clutch, where the target clutch is the clutch to be self - learned in the vehicle. For example, the target clutch is the self - learned clutch among C1, C2, C3, C4, and B1.

[0089] Among them, when the target clutch first enters the preset parameter calibration self - learning stage currently, the first current offset is the initially calibrated value. This current offset can be positive or negative. Generally, it is negative, aiming to set the target clutch to have a relatively small slip and force self - learning. Here, the slip refers to the difference between the engine input shaft speed and the transmission input shaft speed.

[0090] Step 203: Calculate the first sum value of the first current offset and the current control current of the solenoid valve of the target clutch, and control the target clutch according to the first sum value.

[0091] Step 204: Determine whether the slip of the target clutch belongs to the corresponding first preset slip range. When it belongs to the first preset slip range, complete the self - learning of the target clutch in the preset parameter calibration self - learning stage.

[0092] Step 205: When it does not belong to the corresponding first preset slip range, query the first preset table according to the slip to obtain the second current offset, calculate the second sum value of the second current offset and the current control current of the solenoid valve, and control the target clutch according to the second sum value until the self - learning of the target clutch in the preset parameter calibration self - learning stage is completed.

[0093] In this embodiment, calculate the first sum value of the first current offset and the current control current of the solenoid valve of the target clutch, control the target clutch according to the first sum value, determine whether the slip of the target clutch belongs to the corresponding first preset slip range, and when it belongs to the first preset slip range, complete the self - learning of the target clutch in the preset parameter calibration self - learning stage.

[0094] When it does not belong to the corresponding first preset slip range, query the first preset table according to the slip to obtain the second current offset, that is, query the table according to the result of the last self-learning to obtain the second current offset, calculate the second sum value of the second current offset and the current control current of the solenoid valve, and control the target clutch according to the second sum value until the self-learning of the target clutch in the preset parameter calibration self-learning stage is completed. That is, for example, control the target clutch according to the second sum value, and judge whether the slip of the target clutch belongs to the corresponding first preset slip range. When it belongs to the first preset slip range, the self-learning of the target clutch in the preset parameter calibration self-learning stage is completed. When it does not belong to the first preset slip range, query the first preset table based on this slip to obtain the third current offset... In this embodiment, there may be two special cases, and the above method may not be used to judge whether the self-learning of the target clutch is completed: (1) When the first learning result (slip) is marked as RangeUnderKP, the second learning result is marked as RangeOverKP, and the third learning result is marked as RangeUnderKP. Judge whether the current offset corresponding to RangeOverKP is less than the preset lower limit value, and the current offset corresponding to RangeUnderKP in the third time is greater than the preset lower limit value, then directly determine that the self-learning in the preset parameter calibration self-learning stage of the target clutch is successful, and complete the self-learning of the target clutch.

[0095] (2) Calculate the difference between each adjacent two learning results. When the difference is greater than the preset difference threshold, the average value of the two current offsets corresponding to the adjacent two learning results can be directly used as the self-learning result to determine that the self-learning of the target clutch is completed.

[0096] In an embodiment of the present disclosure, the self-learning stage may further include a preset oil filling self-learning stage, that is, after P2Cadpt in the above Work stage, there is also a BoostAdpt stage. BoostAdpt identifies the preset oil filling self-learning stage. In the oil filling stage, if the oil filling amount of the clutch is insufficient, the pressure in the pipeline related to the clutch is not fully filled, resulting in insufficient pressure during the engagement of the clutch. Therefore, in this embodiment, the self-learning of the preset oil filling stage is also performed.

[0097] Among them, as Figure 3 shown, the method further includes: Step 301, determine whether the vehicle meets the entry conditions for the preset oil filling self-learning stage.

[0098] In this embodiment, after the self-learning in the preset parameter calibration self-learning stage is completed, it can be directly determined that the vehicle meets the entry conditions for the preset oil filling self-learning stage.

[0099] The completion conditions for the preset parameter calibration self - learning stage may include one of the following: (1) The slip of all target clutches to be learned belongs to the corresponding preset slip range.

[0100] That is, all target clutches to be learned have been successfully learned.

[0101] (2) The self - learning coefficients of all target clutches to be learned are greater than or equal to the preset coefficient threshold.

[0102] In this embodiment, after the first self - learning in the preset parameter calibration self - learning stage, the self - learning coefficient of the preset parameter calibration self - learning stage is set to 1. After the non - first self - learning in the preset parameter calibration self - learning stage, calculate the absolute value of the difference between the slip of the target clutch after the self - learning in the current preset parameter calibration self - learning stage and the slip of the target clutch after the self - learning in the previous preset parameter calibration self - learning stage. When the absolute value of the difference is less than the preset slip threshold, update the self - learning coefficient to the sum of the first preset coefficient value and the self - learning coefficient. When the absolute value of the difference is not less than the preset slip threshold, update the self - learning coefficient to the sum of the second preset coefficient value and the self - learning coefficient, where the first preset coefficient value is greater than the second preset coefficient value. In some possible embodiments, the first preset coefficient value may be 10, the second preset coefficient value may be 1, and the preset coefficient threshold may be 11.

[0103] If the learning result of the target clutch in this stage is Failed, it means that the target clutch fails in this learning. For the self - learning coefficient, since it is not successful this time, no value is assigned. For the learned value (current offset) of P2C after self - learning, since the learning is not successful this time, the result (current offset) of the previous successful learning is continued to be kept unchanged. Among them, if the self - learning status of each stage is completed (including Idle, Pre, P2CAdpt, etc.), it is considered that the self - learning is successful at this time, otherwise, it is considered a failure.

[0104] Step 302, when the condition for entering the preset oil - filling self - learning stage is met, determine the first oil - filling time offset of the target clutch.

[0105] In this embodiment, when the condition for entering the preset oil - filling self - learning stage is met, determine the first oil - filling time offset of the target clutch. Among them, when the target clutch first enters the preset oil - filling self - learning stage, the first oil - filling time offset of the preset oil - filling self - learning stage is a preset initial value.

[0106] Step 303, calculate the third sum value of the first oil - filling time offset and the current oil - filling time of the target clutch, and control the oil - filling of the target clutch according to the third sum value.

[0107] In this embodiment, a third sum value of the first oil filling time offset and the current oil filling time of the target clutch is calculated, and the oil filling of the target clutch is controlled according to the third sum value.

[0108] Step 304: When the target clutch enters the engaged state, determine the reference slip of the target clutch in the engaged state and / or the reference time for entering the engaged state.

[0109] In this embodiment, when the target clutch enters the engaged state, that is, when the target clutch enters the KP state, determine the reference slip of the target clutch in the engaged state and / or the reference time for entering the engaged state (the time from the oil filling stage to KP).

[0110] Step 305: Determine whether the preset oil filling self-learning stage is successfully learned according to the reference slip and / or the reference time.

[0111] In this embodiment, determine whether the preset oil filling self-learning stage is successfully learned according to the reference slip and / or the reference time.

[0112] That is, determine whether the reference slip belongs to the second preset slip range and / or whether the time difference (for example, the time difference between the reference time and the preset target time (the target time is the ideal time preset when entering the KP state)) belongs to the preset time range. When the reference slip belongs to the second preset slip range and / or the time difference belongs to the preset time range, determine that the preset oil filling self-learning stage is successfully learned.

[0113] Step 306: When the preset oil filling self-learning stage fails to be learned, calculate the time difference between the reference time and the preset target time.

[0114] In this embodiment, when the preset oil filling self-learning stage fails to be learned, calculate the time difference between the reference time and the preset target time.

[0115] Step 307: Query the second preset table according to the time difference to obtain the second oil filling time offset, calculate a fourth sum value of the second oil filling time offset and the current oil filling time of the target clutch, and control the oil filling of the target clutch according to the fourth sum value until the self-learning of the target clutch in the preset oil filling self-learning stage is successful.

[0116] In this embodiment, the second preset table is queried according to the time difference to obtain the second oil filling time offset, the fourth sum value of the second oil filling time offset and the current oil filling time of the target clutch is calculated, and the target clutch is controlled to fill oil according to the fourth sum value until the self-learning of the target clutch in the preset oil filling self-learning stage is successful. That is, the target clutch is controlled to fill oil according to the fourth sum value; when the target clutch enters the engaged state, the reference slip of the target clutch in the engaged state and / or the reference time to enter the engaged state are determined; whether the preset oil filling self-learning stage is successfully learned is determined according to the reference slip and / or the reference time. When the learning is not successful, the above steps are continued until the learning is successful.

[0117] Of course, in an embodiment of the present disclosure, it is not necessarily considered that the preset oil filling self-learning stage is completed only when the learning is successful. In the following two cases, it is also considered that the preset oil filling self-learning stage is completed: (1) When the first learning result (for example, the reference slip) is marked as RangeUnderFill, the second learning result is marked as RangeOverFill, and the third learning result is marked as RangeUnderFill. It is judged whether the reference slip corresponding to RangeOverFill is less than the preset lower limit value, and the reference slip corresponding to RangeUnderKP for the third time is greater than the preset lower limit value, then it is directly determined that the preset oil filling self-learning stage is successfully learned, and the self-learning of the target clutch is completed.

[0118] (2) Calculate the slip difference between every two adjacent learning results. When the slip difference is greater than the preset difference threshold, the average value of the two oil filling time offsets corresponding to the two adjacent learning results can be directly used as the self-learning result, and it is determined that the self-learning of the target clutch is completed.

[0119] The completion conditions of the preset oil filling self-learning stage may include one of the following: (1) All target clutches to be learned have been successfully learned.

[0120] (2) The self-learning coefficients of all target clutches to be learned are greater than or equal to the preset coefficient threshold.

[0121] In this embodiment, after the self-learning in the preset oil filling self-learning stage for the first time, the self-learning coefficient in the preset parameter calibration self-learning stage is set to 1. After the self-learning in the preset parameter calibration self-learning stage for non-first time, the absolute value of the difference between the reference slip of the target clutch after the self-learning in the preset oil filling self-learning stage this time and the reference slip of the target clutch after the self-learning in the preset oil filling self-learning stage last time is calculated. When the absolute value of the difference is less than the preset slip threshold, the self-learning coefficient is updated to the sum of the first preset coefficient value and the self-learning coefficient. When the absolute value of the difference is not less than the preset slip threshold, the self-learning coefficient is updated to the sum of the second preset coefficient value and the self-learning coefficient, where the first preset coefficient value is greater than the second preset coefficient value. In some possible embodiments, the first preset coefficient value can be 10, the second preset coefficient value can be 1, and the preset coefficient threshold can be 11.

[0122] If the learning result of the target clutch in this stage is Failed, it means that the target clutch fails to learn this time. For the self-learning coefficient, since it fails this time, no value is assigned. For the learned value (oil filling time offset) after the self-learning of BoostAdpt, since the learning fails this time, the result (oil filling time offset) of the last successful learning is continued to be kept unchanged. Among them, if the self-learning status of each stage is completed (including Idle, Pre, P2Cadpt, BoostAdpt, etc.), it is considered that the self-learning is successful at this time, otherwise, it is considered a failure.

[0123] In summary, in the embodiments of the present disclosure, while ensuring that the user's perception of self-learning is reduced, the self-learning of the target clutch is performed, improving the driving experience.

[0124] To implement the above embodiments, the present disclosure also proposes a self-learning device for a clutch.

[0125] Figure 4 is a schematic structural diagram of a self-learning device for a clutch according to an embodiment of the present disclosure, as Figure 4 shown. The self-learning device for the clutch includes: a first recognition module 410, a second recognition module 420, a third recognition module 430, a fourth recognition module 440, and a self-learning control module 450, where The first recognition module 410 is configured to recognize whether the vehicle meets the preset vehicle state self-learning condition; The second recognition module 420 is configured to recognize whether the vehicle meets the preset parking condition when the vehicle meets the preset vehicle state self-learning condition; The third recognition module 430 is configured to recognize whether the vehicle meets the driver departure condition when the preset parking condition is met; A fourth recognition module 440, configured to recognize whether the vehicle meets a preset self-learning driving mode when the driver leaving condition is satisfied; A self-learning control module 450, configured to control a clutch in the vehicle to perform self-learning to obtain a self-learning result when the preset self-learning driving mode is satisfied, where the self-learning result is used to perform current compensation on a control current of a solenoid valve of the clutch in the vehicle, and / or is used to perform time compensation on an oil filling time of the clutch in the vehicle.

[0126] The self-learning device of the clutch provided by the embodiments of the present disclosure may execute the self-learning method of the clutch provided by any embodiment of the present disclosure, and has corresponding functional modules and beneficial effects for executing the method.

[0127] To implement the above embodiments, the present disclosure also proposes a computer program product, including a computer program / instructions, which when executed by a processor, implements the self-learning method of the clutch in the above embodiments.

[0128] Figure 5 It is a schematic structural diagram of a vehicle provided by an embodiment of the present disclosure. Exemplarily, as Figure 5 shown, the vehicle 500 includes a memory 501 and a processor 502, where the memory is used to store executable instructions 5011 that can be executed by the processor, and the processor is used to read the executable instructions 5011 from the memory and execute the executable instructions to implement the above method.

[0129] In this embodiment, the vehicle may be divided into functional modules according to the above method example. For example, each functional module may be corresponding, or two or more functions may be integrated into one processing module. The above integrated module may be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, and is only a logical function division. There may be other division methods in actual implementation.

[0130] In the case of dividing each functional module corresponding to each function, the vehicle may include: a first recognition module, a second recognition module, a third recognition module, a fourth recognition module, and a self-learning control module. It should be noted that all relevant contents of each step involved in the above method embodiment may be cited in the function description of the corresponding functional module, and will not be repeated here.

[0131] The vehicle provided by this embodiment is used to execute the above self-learning method of a clutch, and thus can achieve the same effect as the above implementation method.

[0132] In the case of adopting an integrated unit, the vehicle may include a processing module and a storage module. Among them, the processing module may be used to control and manage the actions of the vehicle. The storage module may be used to support the vehicle to execute mutual program codes and data, etc.

[0133] Among them, the processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the present disclosure. The processor may also be a combination that realizes computing functions, such as including a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0134] This embodiment also provides a computer-readable storage medium. Computer program codes are stored in the computer-readable storage medium (including but not limited to disk memories, CD-ROMs, optical memories, etc.). When the computer program codes run on a computer, the computer is enabled to execute the above-related method steps to implement a self-learning method of a clutch provided in the above embodiment.

[0135] Among them, the beneficial effects of the above embodiment can refer to the beneficial effects in the corresponding method provided above, and will not be elaborated here.

[0136] Through the description of the above embodiments, those skilled in the art can understand that, for the convenience and conciseness of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0137] In the embodiments provided by the present disclosure, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms. The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the present disclosure.

[0138] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments can also be implemented combinatorially in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0139] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are merely example forms for implementing the claims.

Claims

1. A self-learning method for a clutch, characterized in that, Including: Identifying whether the vehicle meets the preset vehicle state self - learning conditions; When the vehicle meets the preset vehicle state self - learning conditions, identifying whether the vehicle meets the preset parking conditions; When meeting the preset parking conditions, identifying whether the vehicle meets the driver - leaving conditions; When meeting the driver - leaving conditions, identifying whether the vehicle meets the preset self - learning driving mode; When meeting the preset self - learning driving mode, controlling the clutch in the vehicle to perform self - learning to obtain a self - learning result, wherein the self - learning result is used to perform current compensation on the control current of the solenoid valve of the clutch in the vehicle, and / or is used to perform time compensation on the oil - filling time of the clutch in the vehicle.

2. The method according to claim 1, characterized in that, The preset vehicle state self - learning conditions include: The preset self - learning switch is in the on state; The intelligent driving function module in the vehicle is in a non - fault state; All door sensors, seat sensors and seat belts in the vehicle are in a non - fault state; All self - learning of the clutch in the vehicle has not been completed.

3. The method according to claim 1, characterized in that, The preset parking conditions include: The vehicle speed of the vehicle is 0; The vehicle is in a braking state or a parking state; The gear of the vehicle is in neutral or park; The transmission mechanism in the vehicle is in a non - fault state.

4. The method according to claim 1, wherein When the vehicle is a hybrid - mode vehicle, the preset self - learning driving mode includes: The vehicle is not in a charging state; The vehicle does not include a device to be cooled; The vehicle is in the engine direct - drive mode; The vehicle is not in a preset driving condition.

5. The method according to any one of claims 1-4, characterized in that The controlling the clutch in the vehicle to perform self - learning to obtain a self - learning result includes: Determining whether the vehicle meets the preset parameter calibration self - learning stage entry conditions; When meeting the preset parameter calibration self - learning stage entry conditions, determining the first current offset of the target clutch, wherein the target clutch is the clutch to be self - learned in the vehicle; Calculating the first sum value of the first current offset and the current control current of the solenoid valve of the target clutch, and controlling the target clutch according to the first sum value; Judging whether the slip of the target clutch belongs to the corresponding first preset slip range, and when it belongs to the first preset slip range, completing the self - learning of the target clutch in the preset parameter calibration self - learning stage; When it does not belong to the corresponding first preset slip range, querying a first preset table according to the slip to obtain a second current offset, calculating the second sum value of the second current offset and the current control current of the solenoid valve, and controlling the target clutch according to the second sum value until the self - learning of the target clutch in the preset parameter calibration self - learning stage is completed.

6. The method according to claim 5, characterized in that, It also includes: Determining whether the vehicle meets the preset oil - filling self - learning stage entry conditions; When meeting the preset oil - filling self - learning stage entry conditions, determining the first oil - filling time offset of the target clutch; Calculating the third sum value of the first oil - filling time offset and the current oil - filling time of the target clutch, and controlling the oil - filling of the target clutch according to the third sum value; When the target clutch enters the engaged state, determine the reference slip of the target clutch in the engaged state and / or the reference time for entering the engaged state; Determine whether the preset oil filling self-learning stage is successfully learned according to the reference slip and / or the reference time; When the preset oil filling self-learning stage fails to be learned, calculate the time difference between the reference time and the preset target time; Query a second preset table according to the time difference to obtain a second oil filling time offset, calculate a fourth sum value of the second oil filling time offset and the current oil filling time of the target clutch, and control the oil filling of the target clutch according to the fourth sum value until the self-learning of the target clutch in the preset oil filling self-learning stage is completed.

7. The method according to claim 6, characterized in that The determining whether the preset oil filling self-learning stage is successfully learned according to the reference slip and / or the reference time includes: Determine whether the reference slip belongs to a second preset slip range, and / or determine whether the time difference belongs to a preset time range; When the reference slip belongs to the second preset slip range, and / or the time difference belongs to the preset time range, determine that the preset oil filling self-learning stage is successfully learned.

8. The method according to claim 5, wherein It further includes: After the self-learning of the preset parameter calibration self-learning stage is performed for the first time, set the self-learning coefficient of the preset parameter calibration self-learning stage to 1; After the self-learning of the preset parameter calibration self-learning stage is performed non-first time, calculate the absolute value of the difference between the slip of the target clutch after the self-learning of the preset parameter calibration self-learning stage this time and the slip of the target clutch after the self-learning of the preset parameter calibration self-learning stage last time; When the absolute value of the difference is less than the preset slip threshold, update the self-learning coefficient to the sum of the first preset coefficient value and the self-learning coefficient; When the absolute value of the difference is not less than the preset slip threshold, update the self-learning coefficient to the sum of the second preset coefficient value and the self-learning coefficient, where the first preset coefficient value is greater than the second preset coefficient value; The completion of the self-learning of the target clutch in the preset parameter calibration self-learning stage includes: The slips of all target clutches to be learned belong to the corresponding preset slip ranges; and / or, The self-learning coefficients of all target clutches to be learned are greater than or equal to the preset coefficient threshold.

9. A self-learning device for a clutch, characterized in that, It includes: A first recognition module for recognizing whether the vehicle meets the preset vehicle state self-learning condition; A second recognition module for recognizing whether the vehicle meets the preset parking condition when the vehicle meets the preset vehicle state self-learning condition; A third recognition module for recognizing whether the vehicle meets the driver leaving condition when the preset parking condition is met; A fourth recognition module for recognizing whether the vehicle meets the preset self-learning driving mode when the driver leaving condition is met; A self-learning control module, configured to control a clutch in the vehicle to perform self-learning to obtain a self-learning result when a preset self-learning driving mode is satisfied, wherein the self-learning result is used to perform current compensation on a control current of a solenoid valve of the clutch in the vehicle, and / or is used to perform time compensation on an oil filling time of the clutch in the vehicle.

10. A vehicle, characterized in that, The vehicle includes: a processor; a memory for storing executable instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the self-learning method of the clutch according to any one of claims 1-8 above.

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