Self-learning method and device, equipment and storage medium

Abstract

The invention discloses a self-learning method, device and equipment and a storage medium, which are used for improving the efficiency of vehicle-connected self-learning. The method comprises the steps of monitoring a vehicle, and if it is determined that garage shifting and gear shifting operation is triggered, obtaining parameter information corresponding to the garage shifting and gear shifting operation; determining a gear shifting rotation speed difference corresponding to the garage shifting and gear shifting operation based on the parameter information; determining whether self-learning of a target working condition corresponding to the library shifting and gear shifting operation is converged or not according to the gear shifting rotating speed difference; if not, a target oil charging pressure value corresponding to the vehicle is determined based on the gear shifting rotating speed difference; and updating the current oil charging pressure of the vehicle according to the target oil charging pressure value. In the embodiment of the invention, whether self-learning of the target working condition is converged or not is determined according to the gear shifting rotating speed difference, and under the condition that it is determined that self-learning of the target working condition is not converged, the current oil charging pressure of the vehicle is updated, so that the gear shifting time can be shortened, and the gear shifting process can be optimized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a self-learning method, device, equipment and storage medium. Background Art

[0002] In the related art, the shifting process of some multi-speed automatic transmission fuel vehicles equipped with multi-plate clutches requires the establishment of oil pressure to push the piston to move until the multi-plate clutch is fully attached to complete the shifting. Among them, the shifting process mainly refers to the process of shifting in place from neutral gear (Neutral) to forward gear (Drive), D gear to N gear, N gear to reverse gear (Reverse), R gear to N gear, etc. Among them, the non-dynamic gear entering the driving gear mainly involves the oil filling process, and the driving gear entering the non-driving gear mainly involves the oil unloading process.

[0003] During the oil filling process, the oil pressure pushes the piston to move. The clutch starts to work only when the oil fills the oil chamber. At this time, the turbine speed starts to drop from the steady-state speed until it becomes zero. The speed of the oil filling and oil pressure establishment process will directly affect the size of the speed fluctuation and the time of shifting gears, which in turn affects the perception of the entire vehicle.

[0004] In the related art, during the self-learning process of shifting gears, when the gear shifting time is too long, the basic oil filling pressure value will increase, and when the gear shifting time is too short or the speed fluctuates greatly, the basic oil filling pressure value will be reduced; however, the self-learning method has a long self-learning time, and converges slowly when there are more vehicle operating conditions, which leads to low self-learning efficiency. Summary of the invention

[0005] In view of this, the present application provides a self-learning method, device, equipment and storage medium to facilitate solving the above-mentioned technical problems.

[0006] In a first aspect, an embodiment of the present application provides a self-learning method, the method comprising: The vehicle is monitored, and if it is determined that a warehouse shifting and gear changing operation is triggered, parameter information corresponding to the warehouse shifting and gear changing operation is obtained; Determining a gear shift speed difference corresponding to the warehouse shift operation based on the parameter information; Determining whether the self-learning of the target working condition corresponding to the shift operation has converged according to the shift speed difference; If not converged, a target oil filling pressure value corresponding to the vehicle is determined based on the gear shift speed difference; and the current oil filling pressure of the vehicle is updated according to the target oil filling pressure value.

[0007] In some possible embodiments, the parameter information includes: current required torque, current gearbox temperature, accelerator pedal opening and closing degree, and current turbine speed. The determining of the target oil filling pressure value corresponding to the vehicle based on the gear shift speed difference includes: Determining a target operating condition corresponding to the warehouse shifting operation according to the parameter information; Determining a target acceleration factor and a cumulative oil filling pressure corresponding to the target operating condition; Obtaining a basic value of oil filling pressure according to the current required torque and the current gearbox temperature; Obtaining an oil filling ratio according to the target required torque, the current transmission temperature, and the accelerator pedal opening and closing degree; The target oil filling pressure value is obtained according to the target acceleration factor, the cumulative oil filling pressure, the oil filling pressure base value, and the oil filling ratio.

[0008] In some possible embodiments, obtaining the oil filling rate according to the target required torque, the current transmission temperature and the accelerator pedal opening degree includes: Obtaining a target shift time corresponding to the target working condition according to the current transmission temperature and the accelerator pedal opening / closing degree; Obtaining a target turbine speed according to the target required torque and the target shift time; Obtaining a turbine speed difference according to a difference between the target turbine speed and the current turbine speed; The oil filling ratio is obtained according to the turbine rotation speed difference.

[0009] In some possible embodiments, obtaining the target oil filling pressure value according to the target acceleration factor, the cumulative oil filling pressure, the oil filling pressure base value, and the oil filling ratio includes: Obtaining a first product value according to the oil filling ratio and the oil filling pressure basic value; The sum of the first product value, the target acceleration factor and the accumulated oil filling pressure is used as the target oil filling pressure value.

[0010] In some possible embodiments, determining whether the self-learning of the target working condition corresponding to the shift operation has converged according to the shift speed difference includes: determining whether the shift speed difference is greater than a calibrated shift speed difference corresponding to the target operating condition; If it is determined that the gear shift speed difference is greater than or equal to the calibrated gear shift speed difference, it is determined that the self-learning of the target operating condition has converged.

[0011] In some possible embodiments, after determining that the self-learning of the target operating condition converges, the method further includes: The convergence number of the target working condition is updated based on the historical convergence number to obtain the current convergence number.

[0012] In some possible embodiments, the method further includes: Determine whether the current convergence number is greater than a preset convergence number; If it is determined that the current convergence number is less than the preset convergence number, the process returns to the step of monitoring the vehicle.

[0013] In some possible embodiments, the method further includes: Determining whether a driving cycle duration of the vehicle is greater than a preset duration; If it is determined that the driving cycle duration of the vehicle is greater than the preset duration, obtaining all self-learning converged working conditions from the start time of the vehicle driving cycle to the current time; Determining a first operating condition and a second operating condition from the operating conditions converged by the self-learning; Determining a target oil filling pressure value for an unlearned operating condition based on the first operating condition and the second operating condition; the unlearned operating condition is an operating condition that has not been monitored from the start time of the vehicle driving cycle to the current time; The target oil filling pressure value of the unlearned working condition is used to update the oil filling pressure value of the unlearned working condition.

[0014] In some possible embodiments, determining the target oil filling pressure value of the unlearned operating condition based on the first operating condition and the second operating condition includes: Based on the acquired current demand torque and target oil filling pressure value corresponding to the first working condition, the current demand torque and target oil filling pressure value corresponding to the second working condition, and the calibrated demand torque and calibrated oil filling pressure value corresponding to the unlearned working condition, a self-learning offset corresponding to the unlearned working condition is obtained; A current oil filling pressure value of the unlearned operating condition is obtained based on the self-learning offset and the calibrated oil filling pressure value corresponding to the unlearned operating condition.

[0015] In a second aspect, an embodiment of the present application provides a self-learning device, the device comprising: A monitoring module is used to monitor the vehicle, and if it is determined that a shift operation is triggered, obtain parameter information corresponding to the shift operation; A gear shift speed difference determination module, used to determine the gear shift speed difference corresponding to the warehouse shift operation based on the parameter information; A self-learning module, used to determine whether the self-learning of the target working condition corresponding to the shift operation has converged according to the shift speed difference; The oil filling pressure updating module is used to determine a target oil filling pressure value corresponding to the vehicle based on the gear shift speed difference if convergence has not occurred; and to update the current oil filling pressure of the vehicle according to the target oil filling pressure value.

[0016] In some possible embodiments, the parameter information includes: current required torque, current gearbox temperature, accelerator pedal opening and closing degree, current turbine speed, and the oil filling pressure update module is specifically used to: Determining a target operating condition corresponding to the warehouse shifting operation according to the parameter information; Determining a target acceleration factor and a cumulative oil filling pressure corresponding to the target operating condition; Obtaining a basic value of oil filling pressure according to the current required torque and the current gearbox temperature; Obtaining an oil filling ratio according to the target required torque, the current transmission temperature, and the accelerator pedal opening and closing degree; The target oil filling pressure value is obtained according to the target acceleration factor, the cumulative oil filling pressure, the oil filling pressure base value, and the oil filling ratio.

[0017] In some possible embodiments, the oil filling pressure updating module is specifically used to: Obtaining a target shift time corresponding to the target working condition according to the current transmission temperature and the accelerator pedal opening / closing degree; Obtaining a target turbine speed according to the target required torque and the target shift time; Obtaining a turbine speed difference according to a difference between the target turbine speed and the current turbine speed; The oil filling ratio is obtained according to the turbine rotation speed difference.

[0018] In some possible embodiments, the oil filling pressure updating module is specifically used to: Obtaining a first product value according to the oil filling ratio and the oil filling pressure basic value; The sum of the first product value, the target acceleration factor and the accumulated oil filling pressure is used as the target oil filling pressure value.

[0019] In some possible embodiments, the self-learning module is specifically used to: determining whether the shift speed difference is greater than a calibrated shift speed difference corresponding to the target operating condition; If it is determined that the gear shift speed difference is greater than or equal to the calibrated gear shift speed difference, it is determined that the self-learning of the target operating condition has converged.

[0020] In some possible embodiments, the self-learning module is further used to: The convergence number of the target working condition is updated based on the historical convergence number to obtain the current convergence number.

[0021] In some possible embodiments, the self-learning module is further used to: Determine whether the current convergence number is greater than a preset convergence number; If it is determined that the current convergence number is less than the preset convergence number, the process returns to the step of monitoring the vehicle.

[0022] In some possible embodiments, the self-learning module is further used to: Determining whether a driving cycle duration of the vehicle is greater than a preset duration; If it is determined that the driving cycle duration of the vehicle is greater than the preset duration, obtaining all self-learning converged working conditions from the start time of the driving cycle on the vehicle to the current time; Determining a first operating condition and a second operating condition from the operating conditions converged by the self-learning; Determining a target oil filling pressure value for an unlearned operating condition based on the first operating condition and the second operating condition; the unlearned operating condition is an operating condition that has not been monitored from the start time of the vehicle driving cycle to the current time; The target oil filling pressure value of the unlearned working condition is used to update the oil filling pressure value of the unlearned working condition.

[0023] In some possible embodiments, the self-learning module is further used to: Based on the acquired current demand torque and target oil filling pressure value corresponding to the first working condition, the current demand torque and target oil filling pressure value corresponding to the second working condition, and the calibrated demand torque and calibrated oil filling pressure value corresponding to the unlearned working condition, a self-learning offset corresponding to the unlearned working condition is obtained; A current oil filling pressure value of the unlearned operating condition is obtained based on the self-learning offset and the calibrated oil filling pressure value corresponding to the unlearned operating condition.

[0024] In the third aspect, another embodiment of the present application also provides an electronic device, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any method provided in the embodiment of the first aspect of the present application.

[0025] In a fourth aspect, another embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable a computer to execute any method provided in the embodiment of the first aspect of the present application.

[0026] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0028] Figure 1 A schematic diagram of the overall process of a self-learning method provided in an embodiment of the present application; Figure 2 A schematic diagram of the relationship between the gear shifting direction and the gear shifting speed difference of a self-learning method provided in an embodiment of the present application; Figure 3 A schematic diagram of a process of determining a target oil filling pressure value corresponding to a vehicle based on an oil filling rate, an acceleration factor, a cumulative oil filling pressure and an oil filling pressure base value according to a self-learning method provided in an embodiment of the present application; Figure 4 A schematic diagram showing the corresponding relationship between the basic value of the oil filling pressure and the current required torque and the current gearbox temperature of a self-learning method provided in an embodiment of the present application; Figure 5 A schematic diagram of a flow chart of obtaining an oil filling rate according to a target required torque, a current gearbox temperature and an accelerator pedal opening and closing degree in a self-learning method provided in an embodiment of the present application; Figure 6 A schematic diagram showing the relationship between the turbine speed difference and the oil filling ratio in a self-learning method provided in an embodiment of the present application; Figure 7 A schematic diagram of a process flow of determining self-learning convergence of a target working condition in a self-learning method provided in an embodiment of the present application; Figure 8 A schematic diagram of a flow chart of updating the oil filling pressure value of an unlearned working condition in a self-learning method provided in an embodiment of the present application; Fig. 9 A schematic diagram of determining a target oil filling pressure value for an unlearned operating condition based on a first operating condition and a second operating condition in a self-learning method provided in an embodiment of the present application; Fig.10 A schematic diagram of a device for a self-learning method provided in an embodiment of the present application; Fig.11A schematic diagram of an electronic device for a self-learning method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0030] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0031] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0032] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0033] The inventors have found that in the related art, the shifting process of some multi-speed automatic transmission fuel vehicles equipped with multi-plate clutches requires the establishment of oil pressure to push the piston to move until the multi-plate clutch is completely close to complete the shifting. Among them, the shifting process mainly refers to the process of shifting in place from N gear to D gear, D gear to N gear, N gear to R gear, and R gear to N gear. Among them, the non-dynamic gear entering the driving gear mainly involves the oil filling process, and the driving gear entering the non-driving gear mainly involves the oil unloading process.

[0034] During the oil filling process, the oil pressure pushes the piston to move. The clutch starts to work only when the oil fills the oil chamber. At this time, the turbine speed starts to drop from the steady-state speed until it becomes zero. The speed of the oil filling and oil pressure establishment process will directly affect the size of the speed fluctuation and the time of shifting gears, which in turn affects the perception of the entire vehicle.

[0035] In the related art, during the self-learning process of shifting gears, when the gear shifting time is too long, the basic oil filling pressure value will increase, and when the gear shifting time is too short or the speed fluctuates greatly, the basic oil filling pressure value will be reduced; however, the self-learning method has a long self-learning time, and converges slowly when there are more vehicle operating conditions, which leads to low self-learning efficiency.

[0036] In view of the above problems, the embodiments of the present application provide a self-learning method, device, equipment and storage medium for solving the above problems. The inventive concept of the present application can be summarized as follows: monitoring the vehicle, and if it is determined that a warehouse shifting operation is triggered, obtaining parameter information corresponding to the warehouse shifting operation; determining the shifting speed difference corresponding to the warehouse shifting operation based on the parameter information; determining whether the self-learning of the target working condition corresponding to the warehouse shifting operation converges according to the shifting speed difference; if not, determining the target oil filling pressure value corresponding to the vehicle based on the shifting speed difference; and updating the current oil filling pressure of the vehicle according to the target oil filling pressure value.

[0037] In an embodiment of the present application, whether the self-learning of the target operating condition has converged is determined based on the gear shift speed difference. When it is determined that the self-learning of the target operating condition has not converged, the gear shifting time can be shortened by updating the current oil filling pressure of the vehicle.

[0038] For ease of understanding, a self-learning method provided in an embodiment of the present application is described in detail below with reference to the accompanying drawings: like Figure 1 FIG. 1 is a schematic diagram of the overall process of a self-learning method provided in an embodiment of the present application, wherein: In step 101: the vehicle is monitored, and if it is determined that a warehouse shifting operation is triggered, parameter information corresponding to the warehouse shifting operation is obtained.

[0039] In the embodiment of the present application, when the user triggers a garage shifting operation, parameter information in the vehicle when the garage shifting operation occurs will be obtained.

[0040] In some possible embodiments, the parameter information in the vehicle includes but is not limited to: current required torque, current gearbox temperature, accelerator pedal opening, current turbine speed, and current engine speed. Among them, the current required torque is calculated based on the opening of the accelerator pedal, and the calculation process is the same as in the relevant technology, which will not be repeated here. The current gearbox sensor, accelerator pedal opening, current turbine speed, and current engine speed are all directly obtained through their corresponding sensors.

[0041] In step 102: the gear shift speed difference corresponding to the shift operation of shifting the warehouse is determined based on the parameter information.

[0042] In the embodiment of the present application, it is considered that the vehicle will fill or unload oil during the shifting operation, and the filling pressure will affect the turbine speed of the vehicle. When the filling pressure is different, the turbine speed of the vehicle is different. Therefore, the shift speed difference of the vehicle can be determined according to the current turbine speed in the parameter information, and then the self-learning of the vehicle can be determined according to the shift speed difference. When the vehicle is self-learning converged, the filling pressure of the target working condition corresponding to the shifting operation will be at an optimal value, that is, the filling pressure when the self-learning converges will reduce the fluctuation of the turbine speed of the vehicle, reduce the overall perception of the vehicle, and shorten the shifting time.

[0043] In some possible embodiments, the gear shift speed difference corresponding to the shift operation is determined based on the parameter information, which can be specifically implemented as follows: the difference between the current engine speed and the current turbine speed is used as the gear shift speed difference. The current engine speed is in revolutions per minute (rpm), and the current turbine speed is also in rpm, so the difference can be directly taken, and the difference is the gear shift speed difference.

[0044] In step 103: determining whether the self-learning of the target operating condition corresponding to the shift operation is converged according to the shift speed difference.

[0045] In an embodiment of the present application, the gear shift speed difference may be compared with a calibrated gear shift speed difference corresponding to the warehouse shift operation to determine whether the self-learning of the target operating condition has converged.

[0046] It is understandable that different warehouse shifting operations correspond to different calibrated shift speed differences, and the calibrated shift speed difference can be obtained by looking up a table according to the shift direction corresponding to the warehouse shifting operation.

[0047] For example: Figure 2 As shown, if the shifting operation of shifting the warehouse is from N gear to D gear, the shifting direction is 0, and the calibrated shifting speed difference is xx; if the shifting operation of shifting the warehouse is from D gear to N gear, the shifting direction is 1, and the calibrated shifting speed difference is yy; if the shifting operation of shifting the warehouse is from N gear to R gear, the shifting direction is 2, and the calibrated shifting speed difference is zz; if the shifting operation of shifting the warehouse is from R gear to N gear, the shifting direction is 3, and the calibrated shifting speed difference is rr.

[0048] In some possible embodiments, whether the self-learning of the target operating condition corresponding to the library shifting operation has converged is determined based on the shift speed difference. Specifically, it can be implemented as follows: determining whether the shift speed difference is greater than a calibrated shift speed difference corresponding to the target operating condition; if it is determined that the shift speed difference is greater than or equal to the calibrated shift speed difference, then it is determined that the self-learning of the target operating condition has converged.

[0049] That is, in the embodiment of the present application, when it is determined that the gear shift speed difference is greater than the calibrated gear shift speed difference corresponding to the target operating condition, it means that the oil filling pressure value of the vehicle at this time is sufficient to overcome the inertia force of the vehicle. At this time, the clutch reaches the kiss point, that is, the clutch begins to gradually engage with the gear that needs to be shifted, and the self-learning convergence of the target operating condition is determined. When it is determined that the gear shift speed difference is less than or equal to the calibrated gear shift speed difference corresponding to the target operating condition, it is determined that the self-learning of the target operating condition has not converged, and it is necessary to continue to execute subsequent steps to update the current oil filling pressure of the vehicle, thereby reducing the gear shifting time.

[0050] In step 104 : if it has not converged, a target oil filling pressure value corresponding to the vehicle is determined based on the gear shift speed difference, and the current oil filling pressure of the vehicle is updated according to the target oil filling pressure value.

[0051] In an embodiment of the present application, when it is determined that the target operating condition self-learning has not converged, it is necessary to determine the target oil filling pressure value corresponding to the vehicle and update the current oil filling pressure value so as to shorten the shifting time the next time the gear is shifted under the same operating condition.

[0052] In some possible embodiments, the target oil filling pressure value corresponding to the vehicle is determined based on the gear shift speed difference, which can be specifically implemented as follows: Figure 3 The steps shown, where: In step 301: the target working condition corresponding to the warehouse shifting operation is determined according to the parameter information.

[0053] In the embodiment of the present application, the calibration values ​​and historical data corresponding to different working conditions are different, so the target working condition corresponding to the warehouse shifting operation is first determined.

[0054] In some possible embodiments, the specific values ​​of parameter information corresponding to different working conditions are different, so different parameter information ranges can be set for different working conditions. When determining the working condition, the target working condition corresponding to the shifting operation can be determined according to the range within which the parameter information falls.

[0055] For example, the parameter information includes: current required torque, current gearbox temperature, current engine speed. The parameter range corresponding to working condition 1 includes: required torque range of 150 Nm-200 Nm, gearbox temperature range of 30 degrees Celsius (℃)-40℃, engine speed range of 2200 rpm-2500 rpm; the parameter range corresponding to working condition 2 includes: required torque range of 201 Nm-250 Nm, gearbox temperature range of 41℃-50℃, engine speed range of 2501rpm-3000rpm; If it is determined that the current required torque is 175 Nm, the current transmission temperature range is 35°C, and the current engine speed is 2400 rpm, then the target operating condition can be determined to be operating condition 1.

[0056] In step 302: a target acceleration factor and a cumulative oil filling pressure corresponding to the target operating condition are determined.

[0057] In the embodiment of the present application, in order to speed up the convergence speed of self-learning, an acceleration factor is introduced. In the case of self-learning, if the basic oil filling pressure is insufficient, it means that the oil filling piston is not enough to overcome the empty stroke at this time, that is, the clutch is not fully engaged. After multiple self-learning, if there is still a turbine speed difference, it is necessary to set the acceleration factor to prevent the entire self-learning learning event from being too long. The present application takes into account that before convergence, that is, before the clutch reaches the kiss-point, since the values ​​obtained by looking up the table in each previous learning process are all relative values, and the kiss-point is not reached after each learning (that is, characterized by insufficient oil filling pressure), it is necessary to use the previously learned values ​​as a basis and add them together to the current target basic pressure value. By setting the cumulative oil filling pressure, the self-learning process can be made faster. It can be understood that the methods for determining the target acceleration factor and the cumulative oil filling pressure value corresponding to different working conditions are the same, which will be explained below.

[0058] In some possible embodiments, determining a target acceleration factor corresponding to a target operating condition may be specifically implemented as follows: obtaining historical acceleration factors corresponding to the target operating condition; and accumulating the historical acceleration factors to obtain the target acceleration factor.

[0059] In the embodiment of the present application, the historical acceleration factors corresponding to the target operating condition are target acceleration factors corresponding to the operating conditions that are the same as the target operating condition and before the target operating condition.

[0060] For example: the target operating condition is operating condition 1, and operating condition 1 appears for the fifth time. The target acceleration factor corresponding to the first occurrence of operating condition 1 is the acceleration factor calibration value, recorded as A. The target acceleration factor corresponding to the second occurrence of operating condition 1 is: acceleration factor calibration value + target acceleration factor corresponding to the first operating condition 1, that is, A+A=2A; the target acceleration factor corresponding to the third occurrence of operating condition 1 is: acceleration factor calibration value + target acceleration factor corresponding to the first operating condition 1 + target acceleration factor corresponding to the second operating condition = A+A+2A=4A; the target acceleration factor corresponding to the fourth occurrence of operating condition 1 is: acceleration factor calibration value + target acceleration factor corresponding to the first operating condition 1 + target acceleration factor corresponding to the second operating condition 1 + target acceleration factor corresponding to the third operating condition 1 = A+A+2A+4A=8A, and the target acceleration factor corresponding to the fifth occurrence of operating condition 1 (target operating condition) is: acceleration factor calibration value + target acceleration factor corresponding to the first operating condition 1 + target acceleration factor corresponding to the second operating condition 1 + target acceleration factor corresponding to the third operating condition 1 + target acceleration factor corresponding to the fourth operating condition 1 = A+A+2A+4A+8A=16A.

[0061] In some possible embodiments, determining a target oil filling pressure base value corresponding to a target operating condition may be specifically implemented as follows: obtaining historical oil filling pressure values ​​corresponding to the target operating condition; and accumulating the historical oil filling pressure values ​​to obtain a target oil filling pressure base value.

[0062] In the embodiment of the present application, the historical oil filling pressure values ​​corresponding to the target operating condition are the target oil filling pressure values ​​corresponding to the same operating condition as the target operating condition before the target operating condition.

[0063] For example: the target operating condition is operating condition 1, and operating condition 1 occurs for the fifth time. The corresponding target oil filling pressure basic value when operating condition 1 occurs for the first time is recorded as the oil filling pressure calibration value B. The corresponding target oil filling pressure basic value when operating condition 1 occurs for the second time is: oil filling pressure calibration value + target oil filling pressure basic value corresponding to the first operating condition 1, that is, B+B=2B; the corresponding target oil filling pressure basic value when operating condition 1 occurs for the third time is: oil filling pressure calibration value + target oil filling pressure basic value corresponding to the first operating condition 1 + target oil filling pressure basic value corresponding to the second operating condition = B+B+2B=4B; the fourth time operating condition 1 occurs The corresponding target acceleration factor is: oil filling pressure calibration value + target oil filling pressure basic value corresponding to the first operating condition 1 + target oil filling pressure basic value corresponding to the second operating condition 1 + target oil filling pressure basic value corresponding to the third operating condition 1 = B+B+2B+4B=8B, the target oil filling pressure basic value corresponding to the fifth occurrence of operating condition 1 (target operating condition) is: oil filling pressure calibration value + target oil filling pressure basic value corresponding to the first operating condition 1 + target oil filling pressure basic value corresponding to the second operating condition 1 + target oil filling pressure basic value corresponding to the third operating condition 1 + target oil filling pressure basic value corresponding to the fourth operating condition 1 = B+B+2B+4B+8B=16B.

[0064] In step 303: the basic value of the oil filling pressure is obtained according to the current required torque and the current transmission temperature.

[0065] In the embodiment of the present application, in order to ensure that the determined target oil filling pressure value is more accurate, different oil filling pressure basic values ​​are set for different situations. When obtaining the oil filling pressure basic value, it can be obtained by looking up a table according to the current required torque and the current gearbox temperature.

[0066] For example: The table constructed by the basic value of oil filling pressure, current required torque, and current gearbox temperature is as follows: Figure 4 As shown, it is determined that the current required torque obtained this time is 175 Nm, and the current transmission temperature range is 35°C, then the basic value of the filling oil pressure can be determined to be P0.

[0067] In step 304: the oil filling ratio is obtained according to the target required torque, the current transmission temperature and the accelerator pedal opening and closing degree.

[0068] In the embodiment of the present application, in order to speed up the first self-learning process, the oil filling ratio is set. For example, if the difference of the first gear shift speed difference is large, the obtained oil filling ratio is large, and thus a relatively large oil filling pressure value can be generated. By setting the oil filling ratio, the oil filling pressure value can quickly approach the convergence point, thereby shortening the self-learning time. In some possible embodiments, the oil filling rate is obtained according to the target required torque, the current transmission temperature and the accelerator pedal opening and closing degree, which can be specifically implemented as follows: Figure 5The steps shown, where: In step 501: a target shift time corresponding to a target working condition is obtained according to the current transmission temperature and the accelerator pedal opening / closing degree.

[0069] In the embodiment of the present application, the specific implementation method of obtaining the target shift time according to the current transmission temperature and the accelerator pedal opening and closing degree is the same as that in the related art and will not be repeated here.

[0070] In step 502: a target turbine speed is obtained according to the target required torque and the target shift time.

[0071] In the embodiment of the present application, after the target required torque and the target shift time are obtained, the ratio between the target required torque and the target shift time is used as the target turbine speed.

[0072] In step 503: the turbine speed difference is obtained according to the difference between the target turbine speed and the current turbine speed.

[0073] That is, in the embodiment of the present application, the turbine speed difference=|target turbine speed−current turbine speed|.

[0074] In step 504: the oil filling ratio is obtained according to the turbine speed difference.

[0075] In an embodiment of the present application, a relationship table between the turbine speed difference and the oil filling ratio is pre-constructed. After the oil filling ratio is obtained, the oil filling ratio can be determined by looking up the relationship table.

[0076] For example: The relationship between the turbine speed difference and the oil filling rate is shown in the table below: Figure 6 As shown, if the turbine speed difference is determined to be 100 rpm, then the oil filling ratio can be determined to be M1.

[0077] In step 305: a target oil filling pressure value is obtained according to the target acceleration factor, the accumulated oil filling pressure, the oil filling pressure base value, and the oil filling ratio.

[0078] In some possible embodiments, the target oil filling pressure value is obtained according to the target acceleration factor, the cumulative oil filling pressure, the oil filling pressure base value, and the oil filling ratio. Specifically, it can be implemented as follows: a first product value is obtained according to the oil filling ratio and the oil filling pressure base value; and the sum of the first product value, the target acceleration factor, and the cumulative oil filling pressure is used as the target oil filling pressure value.

[0079] That is, in the embodiment of the present application, the target oil filling pressure value can be determined by using Formula 1: Target filling pressure value = ratio Filling pressure base value + target acceleration factor + cumulative filling pressure, (Formula 1) In an embodiment of the present application, after obtaining the target oil filling pressure, the current oil filling pressure of the vehicle can be updated according to the target oil filling pressure to ensure the stability of the vehicle during subsequent shifting and shifting, and to shorten the duration of the shifting and shifting.

[0080] In some possible embodiments, it is determined that the self-learning for the target working condition has not converged. After the target oil filling pressure value is determined and the current oil filling pressure value is updated, it is determined that the self-learning is completed once, but the self-learning for the target working condition is not completed, and it is necessary to return to step 101. During the process of monitoring the vehicle, if it is determined that the shifting operation corresponding to the target working condition is triggered again, the self-learning is performed again according to the Figure 1 The steps in the above table determine whether the self-learning of the target working condition has converged. If not, it is necessary to perform the self-learning again according to Figure 3 The current oil filling pressure value is updated according to the steps in, and the process continues to return to step 101 until the self-learning for the target working condition converges.

[0081] In some other possible embodiments, if the self-learning convergence of the target working condition is determined, it is necessary to implement the following Figure 7 The steps shown, where: In step 701: the convergence number of the target working condition is updated based on the historical convergence number to obtain the current convergence number.

[0082] In the embodiment of the present application, the historical convergence number is the number of times the self-learning converged last time under the target working condition. The current convergence number can be obtained by adding one based on the historical convergence number.

[0083] For example, if the current convergence number corresponding to the last self-learning convergence of the target working condition is 8 times, then for this target working condition, the historical convergence number is 8 times and the current convergence number is 9 times.

[0084] In step 702: determine whether the current convergence number is greater than the preset convergence number.

[0085] In step 703: if it is determined that the current convergence number is less than the preset convergence number, then return to the step of monitoring the vehicle.

[0086] In the embodiment of the present application, in order to ensure the accuracy of the target oil filling pressure value corresponding to the target working condition, the convergence number is set. When the convergence number exceeds the preset convergence number, the self-learning of the target working condition is stopped, otherwise it continues to execute. Figure 1 Steps shown.

[0087] In some possible embodiments, considering that some operating conditions may not be triggered during a driving cycle of the vehicle, there may be operating conditions that are not self-learned during a driving cycle, that is, unlearned operating conditions. In order to cover all operating conditions, it is possible to use Figure 8 The method shown is used to update the oil filling pressure value of the unlearned working condition, where: In step 801: determine whether the driving cycle duration of the vehicle is greater than a preset duration.

[0088] In an embodiment of the present application, in order to improve the accuracy of the oil filling pressure value corresponding to each operating condition, when the driving cycle duration is greater than a preset duration, a converged operating condition can be used to calculate the oil filling pressure value of an unlearned operating condition.

[0089] Among them, the preset duration is set by technical personnel based on experience.

[0090] In step 802: if it is determined that the driving cycle duration of the vehicle is greater than the preset duration, all self-learning convergent working conditions from the start time of the vehicle driving cycle to the current time are obtained.

[0091] In step 803: a first operating condition and a second operating condition are determined from the operating conditions of self-learning convergence.

[0092] In an embodiment of the present application, in order to ensure the accuracy of the oil filling pressure value of the determined unlearned operating condition, all the operating conditions that have been self-learned and converged from the start of the driving cycle to the present can be obtained, and the number of convergences of each self-learned and converged operating condition can be sorted from high to low, and the first one in the sequence is selected as the first operating condition, and the second one in the sequence is selected as the second operating condition.

[0093] For example: determine that the operating conditions in which the vehicle has self-learned and converged from the start of the driving cycle to the current moment include: operating condition 1, operating condition 2, operating condition 3, operating condition 4, operating condition 5, and operating condition 6, where the number of self-learning convergences for operating condition 1 is 5 times, the number of self-learning convergences for operating condition 2 is 7 times, the number of self-learning convergences for operating condition 3 is 3 times, the number of self-learning convergences for operating condition 4 is 4 times, the number of self-learning convergences for operating condition 5 is 3 times, and the number of self-learning convergences for operating condition 6 is 2 times; then according to the number of convergences of each operating condition, the sequence obtained by sorting from high to low is: operating condition 2, operating condition 1, operating condition 4, operating condition 2, operating condition 5, and operating condition 6, where the first operating condition is operating condition 2 and the second operating condition is operating condition 1.

[0094] In step 804: a target oil filling pressure value of an unlearned operating condition is determined based on the first operating condition and the second operating condition; the unlearned operating condition is an operating condition that has not been monitored from the start of the vehicle driving cycle to the current time.

[0095] In the embodiment of the present application, after the first operating condition and the second operating condition are obtained, a two-point self-learning method can be used to obtain the current oil filling pressure value of the unlearned operating condition.

[0096] In some possible embodiments, the target oil filling pressure value of the unlearned working condition is determined based on the first working condition and the second working condition, which can be specifically implemented as follows: Fig. 9 The steps shown, where: In step 901: based on the current required torque and target oil filling pressure value corresponding to the first operating condition, the current required torque and target oil filling pressure value corresponding to the second operating condition, and the calibrated required torque and calibrated oil filling pressure value corresponding to the unlearned operating condition, the self-learning offset corresponding to the unlearned operating condition is obtained.

[0097] Among them, the above-mentioned target oil filling pressure value is the oil filling pressure value corresponding to the first working condition and the second working condition when the self-learning converges.

[0098] In the embodiment of the present application, after obtaining the current required torque and the current oil filling pressure values ​​corresponding to the first working condition and the second working condition respectively, the offset of the unlearned working condition can be obtained by using Formula 2, where: , (Formula 2) in, is the offset of the unlearned condition, For unlearned conditions The corresponding calibrated oil filling pressure value, is the target oil filling pressure value corresponding to the first working condition, is the target oil filling pressure value corresponding to the second working condition, is the current required torque corresponding to the second working condition, is the current required torque corresponding to the first working condition, For unlearned conditions The corresponding calibrated required torque.

[0099] In step 902: a target oil filling pressure value of the unlearned operating condition is obtained based on the self-learning offset and the calibrated oil filling pressure value corresponding to the unlearned operating condition.

[0100] In the embodiment of the present application, after obtaining the self-learning offset corresponding to the unlearned working condition, the sum of the self-learning offset and the calibrated oil filling pressure value can be used as the target oil filling pressure value of the unlearned working condition.

[0101] In step 805: the current oil filling pressure value of the unlearned working condition is updated using the target oil filling pressure value of the unlearned working condition.

[0102] In the embodiment of the present application, after the target oil filling pressure value of the unlearned working condition is obtained, the target oil filling pressure value is used to update the current oil filling pressure value.

[0103] In some possible embodiments, in order to further improve the accuracy of the target oil filling pressure value of the unlearned working condition, after obtaining the target oil filling pressure value, a preset basic oil filling pressure safety margin value can be added to the target oil filling pressure value to obtain the final oil filling pressure value.

[0104] Based on the same inventive concept, after introducing a self-learning method provided by an embodiment of the present application, as shown in FIG. Fig.10 As shown, a self-learning device 1000 provided in an embodiment of the present application is described below, and the device includes: The monitoring module 10001 is used to monitor the vehicle, and if it is determined that the shifting operation is triggered, obtain parameter information corresponding to the shifting operation; A gear shift speed difference determination module 10002 is used to determine the gear shift speed difference corresponding to the warehouse shift operation based on the parameter information; A self-learning module 10003, used to determine whether the self-learning of the target working condition corresponding to the shift operation has converged according to the shift speed difference; The oil filling pressure updating module 10004 is used to determine the target oil filling pressure value corresponding to the vehicle based on the gear shift speed difference if convergence has not occurred; and update the current oil filling pressure of the vehicle according to the target oil filling pressure value.

[0105] In some possible embodiments, the parameter information includes: current required torque, current gearbox temperature, accelerator pedal opening and closing degree, current turbine speed, and the oil filling pressure updating module 10004 is specifically used for: Determining a target operating condition corresponding to the warehouse shifting operation according to the parameter information; Determining a target acceleration factor and a cumulative oil filling pressure corresponding to the target operating condition; Obtaining a basic value of oil filling pressure according to the current required torque and the current gearbox temperature; Obtaining an oil filling ratio according to the target required torque, the current transmission temperature, and the accelerator pedal opening and closing degree; The target oil filling pressure value is obtained according to the target acceleration factor, the cumulative oil filling pressure, the oil filling pressure base value, and the oil filling ratio.

[0106] In some possible embodiments, the oil filling pressure updating module 10004 is specifically used to: Obtaining a target shift time corresponding to the target working condition according to the current transmission temperature and the accelerator pedal opening / closing degree; Obtaining a target turbine speed according to the target required torque and the target shift time; Obtaining a turbine speed difference according to a difference between the target turbine speed and the current turbine speed; The oil filling ratio is obtained according to the turbine rotation speed difference.

[0107] In some possible embodiments, the oil filling pressure updating module 10004 is specifically used to: Obtaining a first product value according to the oil filling ratio and the oil filling pressure basic value; The sum of the first product value, the target acceleration factor and the accumulated oil filling pressure is used as the target oil filling pressure value.

[0108] In some possible embodiments, the self-learning module 10003 is specifically used to: determining whether the shift speed difference is greater than a calibrated shift speed difference corresponding to the target operating condition; If it is determined that the gear shift speed difference is greater than or equal to the calibrated gear shift speed difference, it is determined that the self-learning of the target operating condition has converged.

[0109] In some possible embodiments, the self-learning module 10003 is further used to: The convergence number of the target working condition is updated based on the historical convergence number to obtain the current convergence number.

[0110] In some possible embodiments, the self-learning module 10003 is further used to: Determine whether the current convergence number is greater than a preset convergence number; If it is determined that the current convergence number is less than the preset convergence number, the process returns to the step of monitoring the vehicle.

[0111] In some possible embodiments, the self-learning module 10003 is further used to: Determining whether a driving cycle duration of the vehicle is greater than a preset duration; If it is determined that the driving cycle duration of the vehicle is greater than the preset duration, obtaining all self-learning converged working conditions from the start time of the vehicle driving cycle to the current time; Determining a first operating condition and a second operating condition from the operating conditions converged by the self-learning; Determining a target oil filling pressure value for an unlearned operating condition based on the first operating condition and the second operating condition; the unlearned operating condition is an operating condition that has not been monitored from the start time of the vehicle driving cycle to the current time; The target oil filling pressure value of the unlearned working condition is used to update the oil filling pressure value of the unlearned working condition.

[0112] In some possible embodiments, the self-learning module 10003 is further used to: Based on the acquired current demand torque and target oil filling pressure value corresponding to the first working condition, the current demand torque and target oil filling pressure value corresponding to the second working condition, and the calibrated demand torque and calibrated oil filling pressure value corresponding to the unlearned working condition, a self-learning offset corresponding to the unlearned working condition is obtained; A current oil filling pressure value of the unlearned operating condition is obtained based on the self-learning offset and the calibrated oil filling pressure value corresponding to the unlearned operating condition.

[0113] Corresponding to the above embodiments, the present application also provides an electronic device. Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, the electronic device 1100 may include: a processor 1101, a memory 1102 and a communication unit 1103. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiment of the present invention, it can be a bus structure or a star structure, and can also include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0114] The communication unit 1103 is used to establish a communication channel so that the electronic device can communicate with other devices, receive user data sent by other devices or send user data to other devices.

[0115] The processor 1101 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 1102, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 1101 can only include a central processing unit (CPU). In an embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0116] The memory 1102 is used to store the execution instructions of the processor 1101. The memory 1102 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0117] When the execution instructions in the memory 1102 are executed by the processor 1101, the electronic device 1100 can execute Figure 1 Some or all of the steps in the illustrated embodiments.

[0118] In a specific implementation, the present invention further provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment of the self-learning method provided by the present invention. The storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0119] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0120] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the device embodiment and the terminal embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

Claims

1. A self-learning method, characterized in that: Applied to a vehicle, the method comprises: The vehicle is monitored, and if it is determined that a warehouse shifting and gear changing operation is triggered, parameter information corresponding to the warehouse shifting and gear changing operation is obtained; Determining a gear shift speed difference corresponding to the warehouse shift operation based on the parameter information; Determining whether the self-learning of the target working condition corresponding to the shift operation has converged according to the shift speed difference; If not converged, a target oil filling pressure value corresponding to the vehicle is determined based on the gear shift speed difference; and the current oil filling pressure of the vehicle is updated according to the target oil filling pressure value.

2. The method according to claim 1, characterized in that The parameter information includes: current required torque, current gearbox temperature, accelerator pedal opening and closing degree, and current turbine speed. The target oil filling pressure value corresponding to the vehicle is determined based on the gear shift speed difference, including: Determining a target operating condition corresponding to the warehouse shifting operation according to the parameter information; Determining a target acceleration factor and a cumulative oil filling pressure corresponding to the target operating condition; Obtaining a basic value of oil filling pressure according to the current required torque and the current gearbox temperature; Obtaining an oil filling ratio according to the target required torque, the current transmission temperature, and the accelerator pedal opening and closing degree; The target oil filling pressure value is obtained according to the target acceleration factor, the cumulative oil filling pressure, the oil filling pressure base value, and the oil filling ratio.

3. The method according to claim 2, characterized in that The obtaining of the oil filling rate according to the target required torque, the current transmission temperature and the accelerator pedal opening degree includes: Obtaining a target shift time corresponding to the target working condition according to the current transmission temperature and the accelerator pedal opening / closing degree; Obtaining a target turbine speed according to the target required torque and the target shift time; Obtaining a turbine speed difference according to a difference between the target turbine speed and the current turbine speed; The oil filling ratio is obtained according to the turbine rotation speed difference.

4. The method according to claim 2, characterized in that: The step of obtaining the target oil filling pressure value according to the target acceleration factor, the accumulated oil filling pressure, the oil filling pressure base value, and the oil filling ratio includes: Obtaining a first product value according to the oil filling ratio and the oil filling pressure basic value; The sum of the first product value, the target acceleration factor and the accumulated oil filling pressure is used as the target oil filling pressure value.

5. The method according to claim 1, characterized in that The step of determining whether the self-learning of the target working condition corresponding to the shifting operation has converged according to the shifting speed difference includes: determining whether the shift speed difference is greater than a calibrated shift speed difference corresponding to the target operating condition; If it is determined that the gear shift speed difference is greater than or equal to the calibrated gear shift speed difference, it is determined that the self-learning of the target operating condition has converged.

6. The method according to claim 5, characterized in that After determining that the self-learning of the target operating condition has converged, the method further includes: The convergence number of the target working condition is updated based on the historical convergence number to obtain the current convergence number.

7. The method according to claim 6, characterized in that The method further comprises: Determine whether the current convergence number is greater than a preset convergence number; If it is determined that the current convergence number is less than the preset convergence number, the process returns to the step of monitoring the vehicle.

8. The method according to claim 7, characterized in that The method further comprises: Determining whether a driving cycle duration of the vehicle is greater than a preset duration; If it is determined that the driving cycle duration of the vehicle is greater than the preset duration, obtaining all self-learning converged working conditions from the start time of the vehicle driving cycle to the current time; Determining a first operating condition and a second operating condition from the operating conditions converged by the self-learning; Determining a target oil filling pressure value for an unlearned operating condition based on the first operating condition and the second operating condition; the unlearned operating condition is an operating condition that has not been monitored from the start time of the vehicle driving cycle to the current time; The target oil filling pressure value of the unlearned working condition is used to update the oil filling pressure value of the unlearned working condition.

9. The method according to claim 8, characterized in that The determining of the target oil filling pressure value of the unlearned operating condition based on the first operating condition and the second operating condition includes: Based on the acquired current demand torque and target oil filling pressure value corresponding to the first working condition, the current demand torque and target oil filling pressure value corresponding to the second working condition, and the calibrated demand torque and calibrated oil filling pressure value corresponding to the unlearned working condition, a self-learning offset corresponding to the unlearned working condition is obtained; A current oil filling pressure value of the unlearned operating condition is obtained based on the self-learning offset and the calibrated oil filling pressure value corresponding to the unlearned operating condition.

10. A self-learning device, characterized in that: Applied to a vehicle, the device comprises: A monitoring module is used to monitor the vehicle, and if it is determined that a shift operation is triggered, obtain parameter information corresponding to the shift operation; A gear shift speed difference determination module, used to determine the gear shift speed difference corresponding to the warehouse shift operation based on the parameter information; A self-learning module, used to determine whether the self-learning of the target working condition corresponding to the shift operation has converged according to the shift speed difference; The oil filling pressure updating module is used to determine a target oil filling pressure value corresponding to the vehicle based on the gear shift speed difference if convergence has not occurred; and to update the current oil filling pressure of the vehicle according to the target oil filling pressure value.

11. An electronic device, characterized in that: The device comprises a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 9.

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