阅读说明：本技术 车辆自动变速器挡位自学习方法及其相关设备 (Self-learning method for gears of automatic transmission of vehicle and related equipment thereof ) 是由 凌和平 高宏远 石明川 于 2020-03-31 设计创作，主要内容包括：本申请公开了一种车辆自动变速器挡位自学习方法及其相关设备,其中,方法包括：采集车辆在运行过程中的实际挡位信号；根据目标挡位信号和采集到的实际挡位信号,判断挡位是否发生改变；当判断挡位发生改变时,根据实时采集到的实际挡位信号和车辆的车速信号,确定挡位执行机构是否进入目标挡位；如果确定挡位执行机构进入目标挡位,则对目标挡位进行在线自学习。该方法可以得到准确的各挡位信息参数,准确度高,且基于实时采集到的实际挡位信号和车辆的车速信号来判断是否进入目标挡位,算法更加严谨。(The application discloses a self-learning method for gears of a vehicle automatic transmission and related equipment thereof, wherein the method comprises the following steps: acquiring an actual gear signal of a vehicle in the running process; judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal; when the gear is judged to be changed, whether the gear executing mechanism enters a target gear is determined according to an actual gear signal and a vehicle speed signal acquired in real time; and if the gear executing mechanism is determined to enter the target gear, performing online self-learning on the target gear. The method can obtain accurate information parameters of each gear, has high accuracy, judges whether the vehicle enters a target gear or not based on real-time collected actual gear signals and vehicle speed signals of the vehicle, and is more rigorous in algorithm.)

1. A self-learning method for gears of an automatic transmission of a vehicle is characterized by comprising the following steps:

acquiring an actual gear signal of a vehicle in a running process in real time;

judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal;

when the gear is judged to be changed, determining whether a gear executing mechanism enters a target gear or not according to an actual gear signal acquired in real time and a vehicle speed signal of the vehicle;

and if the gear executing mechanism is determined to enter a target gear, performing online self-learning on the target gear.

2. The method of claim 1, wherein determining whether a gear actuator enters a target gear according to the real-time collected actual gear signal and the vehicle speed signal of the vehicle comprises:

judging whether the difference value between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time meets a first preset condition or not;

calculating the shaft speed of an input shaft of the transmission according to the real-time acquired actual gear signal and the vehicle speed signal of the vehicle;

judging whether the calculated speed of the input shaft of the transmission meets a second preset condition or not;

and if the difference value between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time meets a first preset condition, and the shaft speed of the input shaft of the transmission obtained through calculation meets a second preset condition, determining that the gear executing mechanism enters a target gear.

3. The method according to claim 2, wherein the difference between the actual gear signals acquired at two adjacent sampling moments in the real-time acquired actual gear signals meets a first preset condition, which includes:

the difference value between the actual gear signals collected at two adjacent sampling moments in the actual gear signals collected in real time is smaller than a first threshold value, and the preset time is kept unchanged.

4. The method of claim 2, wherein calculating a transmission input shaft speed based on the real-time collected actual gear signal and the vehicle speed signal of the vehicle comprises:

when the real-time acquired actual gear signal is a power generation gear, calculating the shaft speed of the input shaft of the transmission according to the transmission ratio of the power generation gear of the transmission and the rotating speed of the motor;

and when the real-time acquired actual gear signal is a non-power generation gear, calculating the speed of the input shaft of the transmission according to the speed signal, the transmission gear transmission ratio of the transmission and the diameter of the wheel tire.

5. The method according to any one of claims 2 to 4, wherein determining whether the calculated transmission input shaft speed satisfies a second preset condition comprises:

acquiring an actual shaft speed of a transmission input shaft;

calculating a difference value between the calculated shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft, and judging whether the calculated difference value is smaller than a second threshold value;

if the difference value between the calculated shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft is smaller than the second threshold value, determining that the second preset condition is met;

and if the difference value between the calculated shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft is greater than or equal to the second threshold value, determining that the second preset condition is not met.

6. The method of claim 1, wherein self-learning the target gear on-line comprises:

determining a gear position parameter reference value and a target error value of the target gear online self-learning;

determining a sampling value screening range of the target gear online self-learning according to the gear position parameter reference value and the target error value of the target gear online self-learning;

screening the gear signal acquisition value in the online self-learning process of the target gear to acquire a real-time sampling value falling within the sampling value screening range;

and determining an online self-learning value learned by the target gear in the current online self-learning period according to the real-time sampling value falling in the sampling value screening range.

7. The method as claimed in claim 6, wherein the determining the online self-learning value learned by the target gear in the current online self-learning period according to the real-time sampling value falling within the sampling value filtering range comprises:

when the target gear is a 1 gear, a 2 gear, a 4 gear or an R gear, determining the minimum value of the real-time sampling values falling in the sampling value screening range as an online self-learning value learned by the target gear in the current online self-learning period;

and when the target gear is a 3-gear, a 5-gear, a G-gear or an E-gear, determining the maximum value of the real-time sampling values falling in the sampling value screening range as the online self-learning value learned by the target gear in the current online self-learning period.

8. The method according to claim 6 or 7, characterized in that if the target gear has been subjected to online self-learning, the reference value of the gear position parameter of the online self-learning of the target gear is the online self-learning value learned by the target gear in the last online self-learning period; and if the target gear is not subjected to online self-learning, the reference value of the gear position parameter of the online self-learning of the target gear is a self-learning value obtained by learning the target gear in offline self-learning.

9. The method of claim 8, wherein after determining the online self-learning value learned by the target gear in the current online self-learning period, the method further comprises:

and storing the on-line self-learning value learned by the target gear in the current on-line self-learning period so as to update the reference value of the gear position parameter learned by the target gear on-line self-learning.

10. The method of claim 8, wherein the offline self-learning of each gear of the transmission is achieved by:

when the vehicle is in a static working condition, determining an off-line gear shifting signal feedback value acquired by a gear sensor and the electromagnetic valve control current of each gear of the transmission;

and performing offline self-learning on each gear of the transmission according to an offline gear shifting signal feedback value acquired by the gear sensor and the electromagnetic valve control current of each gear of the transmission.

11. The method of claim 10, wherein the offline self-learning comprises neutral self-learning; the off-line self-learning of the neutral gear is realized through the following modes:

determining a reference value and an allowable error value of the neutral gear;

determining a gear range of the neutral gear according to the reference value and the allowable error value of the neutral gear;

on the basis of the time period of the neutral gear off-line self-learning, adjusting the current of a pressure regulating valve and a neutral gear electromagnetic valve to enable a gear executing mechanism to reciprocate in a gear range of a neutral gear;

when the gear executing mechanism stops, determining a signal feedback value acquired by the gear sensor, and determining the signal feedback value acquired by the gear sensor as an offline self-learning value of the neutral gear.

12. The method of claim 11, wherein the offline self-learning further comprises single-gear self-learning; the off-line self-learning method comprises the following steps of:

determining a target single-gear signal to be subjected to offline self-learning;

adjusting the current of a pressure regulating valve and a target single-gear electromagnetic valve to enable a gear executing mechanism to enter the target single gear based on the target single-gear signal and the time period of single-gear off-line self-learning;

and when the gear actuating mechanism is determined to enter the target single gear, determining a signal feedback value acquired by the gear sensor, and determining the signal feedback value acquired by the gear sensor as an offline self-learning value of the target single gear.

13. The method of claim 12, wherein the offline self-learning further comprises a full-process self-learning mode, the full-process self-learning mode being a mode in which the neutral self-learning is combined with the single-gear self-learning.

14. A gear self-learning device of a vehicle automatic transmission is characterized by comprising the following components:

the actual gear signal acquisition module is used for acquiring an actual gear signal of the vehicle in the running process;

the gear change judging module is used for judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal;

the gear engaging determination module is used for determining whether the gear actuating mechanism enters a target gear or not according to an actual gear signal acquired in real time and a vehicle speed signal of the vehicle when the gear is judged to be changed;

and the online self-learning module is used for performing online self-learning on the target gear when the gear executing mechanism is determined to enter the target gear.

15. A vehicle, characterized by comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing the vehicle automatic transmission gear self-learning method according to any of the claims 1 to 13.

16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for self-learning the gears of a vehicle automatic transmission according to any one of claims 1 to 13.

Technical Field

The application relates to the technical field of vehicle control, in particular to a self-learning method for gears of an automatic transmission of a vehicle and related equipment thereof.

Background

At present, the automatic transmission of the vehicle can automatically shift gears according to the speed and the opening degree of an accelerator pedal, learn the driving habit of a driver, reduce the hardware abrasion of the transmission and correct the hardware deviation. In the related technology, when a gear signal value of an automatic transmission enters a certain gear threshold range, the automatic transmission judges that the automatic transmission enters the gear, according to a certain time interval, subtraction is carried out on every two data to obtain the sum of absolute values, when the sum of the absolute values is smaller than a certain threshold and the value is minimum through iterative operation, the output signal of a gear sensor is stable, at the moment, a certain value or the average value of several numerical values in an array can be taken as a self-learning value of the gear, and finally, a certain value is added or subtracted according to the self-learning value of each gear to be taken as the gear threshold range.

However, in the above-mentioned technology, the transmission collects the gear signal value, and stores the collected gear signal value in the data set according to the preset storage rule, and the data set includes the address of the gear signal value and the corresponding gear signal value. To implement this process, a data register is needed to store the real-time data from a source. And a relatively complex storage rule needs to be established when the data is stored. In addition, when the acquired gear signal of the automatic transmission is within a threshold range of a certain gear, the automatic transmission judges that the gear is entered, wherein the threshold range is obtained by adding or subtracting a preset value from the last self-learning value, and the preset value needs to be set manually. If the preset value is not properly set, when the gear is engaged and the gear ejecting phenomenon occurs, although the acquired gear signal is within the threshold range, the gear is not actually combined, and the self-learning value is not accurate.

Disclosure of Invention

The object of the present application is to solve at least to some extent one of the above mentioned technical problems.

Therefore, the first purpose of the application is to provide a self-learning method for the gears of the automatic transmission of the vehicle, the method can obtain accurate information parameters of each gear, the accuracy is high, whether the vehicle enters a target gear or not is judged based on real-time collected actual gear signals and vehicle speed signals of the vehicle, and the algorithm is more rigorous.

A second object of the present application is to provide a gear self-learning apparatus of an automatic transmission for a vehicle.

A third object of the present application is to propose a vehicle.

A fourth object of the present application is to propose a computer readable storage medium.

To achieve the above object, an embodiment of the first aspect of the present application provides a method for self-learning gears of an automatic transmission of a vehicle, the method comprising: acquiring an actual gear signal of a vehicle in the running process; judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal; when the gear is judged to be changed, determining whether a gear executing mechanism enters a target gear or not according to an actual gear signal acquired in real time and a vehicle speed signal of the vehicle; and if the gear executing mechanism is determined to enter a target gear, performing online self-learning on the target gear.

According to the gear self-learning method of the automatic transmission of the vehicle, the actual gear signal of the vehicle in the running process is collected; judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal; when the gear is judged to be changed, determining whether a gear executing mechanism enters a target gear or not according to an actual gear signal acquired in real time and a vehicle speed signal of the vehicle; and if the gear executing mechanism is determined to enter a target gear, performing online self-learning on the target gear. The method can obtain accurate information parameters of each gear, has high accuracy, judges whether the vehicle enters a target gear or not based on real-time collected actual gear signals and vehicle speed signals of the vehicle, and is more rigorous in algorithm.

In order to achieve the above object, a second aspect of the present application provides a gear self-learning apparatus for a vehicle automatic transmission, the apparatus comprising: the actual gear signal acquisition module is used for acquiring an actual gear signal of the vehicle in the running process; the gear change judging module is used for judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal; the gear engaging determination module is used for determining whether the gear actuating mechanism enters a target gear or not according to an actual gear signal acquired in real time and a vehicle speed signal of the vehicle when the gear is judged to be changed; and the online self-learning module is used for performing online self-learning on the target gear when the gear executing mechanism is determined to enter the target gear.

According to the gear self-learning device of the automatic transmission of the vehicle, the actual gear signal of the vehicle in the running process is collected; judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal; when the gear is judged to be changed, determining whether a gear executing mechanism enters a target gear or not according to an actual gear signal acquired in real time and a vehicle speed signal of the vehicle; if the gear executing mechanism is confirmed to enter the target gear, the target gear is subjected to online self-learning, accurate gear information parameters can be obtained by the device, accuracy is high, whether the target gear is entered or not is judged based on real-time collected actual gear signals and vehicle speed signals of the vehicle, and an algorithm is more rigorous.

To achieve the above object, an embodiment of a third aspect of the present application proposes a vehicle including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the vehicle automatic transmission gear self-learning method according to the embodiment of the first aspect of the application.

To achieve the above object, a fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for self-learning a gear of an automatic transmission of a vehicle according to an embodiment of the first aspect of the present application.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

FIG. 1 is a schematic flow diagram of a method for self-learning a gear of an automatic transmission of a vehicle according to one embodiment of the present application;

FIG. 2 is a schematic diagram of a vehicle automatic transmission gear according to one embodiment of the present application;

FIG. 3 is a schematic structural diagram of a shift actuator of a vehicle automatic transmission according to an embodiment of the present application;

FIG. 4 is a control schematic diagram of a vehicle automatic transmission shift electro-hydraulic module according to one embodiment of the present application

FIG. 5 is a schematic illustration of an on-line self-learning range for a vehicle automatic transmission gear according to an embodiment of the present application;

FIG. 6 is a schematic flow chart diagram of a method for self-learning a gear of an automatic transmission for a vehicle according to another embodiment of the present application;

FIG. 7 is a schematic structural diagram of an offline self-learning of a vehicle automatic transmission according to an embodiment of the present application;

FIG. 8 is a schematic flow chart of neutral self-learning for a vehicle automatic transmission according to one embodiment of the present application;

FIG. 9 is a schematic flow chart of a single gear self-learning for a vehicle automatic transmission according to one embodiment of the present application;

FIG. 10 is a schematic structural diagram of a vehicle automatic transmission range self-learning device according to an embodiment of the present application;

FIG. 11 is a schematic structural diagram of a vehicle according to one embodiment of the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.

FIG. 1 is a flow chart illustrating a method for self-learning a gear of an automatic transmission of a vehicle according to one embodiment of the present application.

Step 101, acquiring an actual gear signal of a vehicle in a running process in real time.

In the embodiment of the application, the gear sensor can be used for acquiring the gear signal of the transmission of the vehicle in the running process in real time, and the gear signal is used as the actual gear signal of the vehicle in the running process. Wherein, the vehicle can be the new forms of energy vehicle. The actual gear signals of the vehicle during operation may include, but are not limited to, gear 1, gear 2, gear 3, gear 4, gear 5, gear R, gear G, and gear E, where gear 1, gear 2, gear 3, gear 4, and gear 5 are forward gears, gear R is reverse gear, gear G is power generation gear, and gear E is EV gear, i.e., a gear in which the pure electric vehicle is driven without the engine.

And 102, judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal.

In the embodiment of the application, the gear action is monitored by using the target gear signal and the actual gear signal, and whether the gear is changed or not is judged. For example, if it is determined that a target gear signal (which may be understood as a gear signal to be engaged) of the transmission is 3, and an actual gear signal acquired at this time is 2, it may be determined that the gear signal has changed, that is, a gear change is required. For another example, if it is determined that the target gear signal of the transmission is gear 2 and the actual gear signal collected at this time is gear 2, it is determined that the gear has not changed.

And 103, when the gear is judged to be changed, determining whether the gear executing mechanism enters a target gear according to the real gear signal and the vehicle speed signal acquired in real time.

In the embodiment of the application, in order to make the algorithm for judging whether the gear actuator enters the target gear more rigorous, two requirements must be met at the same time to be considered as entering the target gear. Some of the requirements are: the difference value is kept within an error allowable range for a period of time and is not changed any more; wherein another part of the requirements is: and calculating the rotating speed of the input shaft of the transmission according to the transmission ratio of each gear of the gearbox and the diameter of the tire of the wheel by using the vehicle speed signal and the gear signal, and making a difference with the shaft speed of the input shaft of the transmission acquired by the sensor, wherein the absolute value of the difference is within an error allowable range. For details, see the description of the following embodiments.

And 104, if the gear executing mechanism is determined to enter the target gear, performing online self-learning on the target gear.

Before describing the on-line self-learning of the target gear in detail, the transmission gear structure, the transmission gear executing mechanism and the transmission gear electrohydraulic module in the application can be described in detail.

FIG. 2 is a schematic diagram of a shift configuration for a vehicle automatic transmission according to one embodiment of the present application. As shown in fig. 2, the automatic transmission is composed of 4 shafts, each shaft is provided with a gear actuator for two coaxial gears, and a shifting fork is driven by a target hydraulic pressure obtained by controlling the opening degree of an electromagnetic valve through current to perform shifting action. Fig. 3 is a schematic structural diagram of a shift actuator of a vehicle automatic transmission according to an embodiment of the application. The position of the steel ball is the neutral position in fig. 3. Because the structure setting of neutral gear, the recess inner wall slope of neutral structure is greater than the outside slope, can know according to mechanics principle that neutral engaging force is less than neutral groove and takes off the gear resistance, in case the steel ball gets into the neutral groove, because of the shift fork engaging control force of neutral is unchangeable and is less than the resistance that breaks off from the neutral, ensures that the neutral can not break away from current position.

In the embodiment of the application, the gear executing mechanism of the automatic transmission can obtain the target hydraulic pressure through the opening degree of the current control solenoid valve to drive the shifting fork to perform the gear shifting action. FIG. 4 is a control schematic diagram of a vehicle automatic transmission range electro-hydraulic module according to one embodiment of the present application. As shown in FIG. 4, the electro-hydraulic control module is provided with oil pressure required under all working conditions by a main oil circuit. The module has two independent oil paths. The high-pressure oil regulated by the 134G pressure regulating valve is supplied to the 13 electromagnetic valve and the 4G electromagnetic valve, and the 13 electromagnetic valve and the 4G electromagnetic valve respectively control the corresponding gear shifting fork. The high-pressure oil regulated by the 25ER pressure regulating valve is supplied to a 25 electromagnetic valve and an ER electromagnetic valve, and the 25 electromagnetic valve and the ER electromagnetic valve respectively control corresponding gear shifting forks. The 134G pressure regulating valve and the 25RE pressure regulating valve both use current to control the output pressure, and when the control current is the maximum current (such as 0xCC0), the oil pressure in the pipeline is enough to meet the requirements of the 13 solenoid valve, the 4G solenoid valve, the 25 solenoid valve and the ER solenoid valve on different pressures. When the control current is the minimum current (e.g., 0x800), the line pressure is 0 and there is no pressure to move the gear actuator. It should be noted that the oil pressure of the main oil line is monitored by a pressure sensor, and the start and stop of the oil pressure motor are controlled in real time, so that the oil pressure of the main oil line is always kept in a reasonable range.

Similarly, the solenoid valves are also controlled using current, as shown in the lower half of fig. 4, and the shift cylinders are of a double-acting differential type structure. The solenoid valve controls the opening of A-P and A-T with current. When the A-T is switched on, the high-pressure oil pressures on the two sides of the cylinder body are equal in strength, but the action areas on the two sides in the gear shifting hydraulic cylinder are different, the pressures on the two sides are different, so that a piston rod in the hydraulic cylinder moves rightwards, and a 25ER gear is engaged by the gear executing mechanism. When the A-P is switched on, one side of a rodless cavity in the hydraulic cylinder is connected with the oil pan at the moment, the oil pressure is 0, pressure exists in a rod cavity, and the piston rod pushes the gear actuating mechanism to move leftwards to engage in a 134G gear.

In the embodiment of the application, the online self-learning of the target gear can be realized by the following steps: determining a gear position parameter reference value and a target error value of the target gear on-line self-learning; determining a sampling value screening range of the target gear online self-learning according to the gear position parameter reference value and the target error value of the target gear online self-learning; screening a gear signal acquisition value in the target gear online self-learning process to acquire a real-time sampling value falling within a sampling value screening range; and determining an online self-learning value learned by the target gear in the current online self-learning period according to the real-time sampling value falling in the sampling value screening range.

That is to say, the sampling value screening range of the target gear online self-learning can be determined through the gear position parameter reference value and the target error value of the target gear online self-learning; and then, screening the gear signal acquisition value in the target gear online self-learning process according to the sampling value screening range of the target gear online self-learning, and determining the real-time sampling value falling in the sampling value screening range as the online self-learning value learned by the target gear in the current online self-learning period. Therefore, after the gear executing mechanism is determined to enter the target gear, the target gear is self-learned on line, accuracy of a self-learning exercise sample can be guaranteed, and the condition that accuracy is affected due to inaccurate learning result values caused by self-learning under the condition that a tooth jacking phenomenon or the gear is not hung correctly is eliminated.

It should be noted that, when different gears perform online self-learning, the sampling value screening ranges used when the real-time sampling values are screened may be different, and the selection modes of the online self-learning results may also be different. In the embodiment of the application, an online self-learning value a learned in a last online self-learning period of a gear is assumed to be used as a gear position parameter reference value of the gear online self-learning at this time, a target error value is preset to be b, the sampling value screening ranges of 1 gear, 2 gear, 4 gear and R gear can be determined to be [ a-b, a ], and the sampling value screening ranges of 3 gear, 5 gear, G gear and E gear are determined to be [ a, a + b ]. As shown in FIG. 5, in order to prevent gear disengagement or even gear non-upshift caused by phenomena such as abrasion of a transmission structure, when a real-time sampling value falling within a sampling value screening range is obtained, self-learning results of 1-gear, 2-gear, 4-gear and R-gear can take the minimum value within the sampling value screening range [ a-b, a ], self-learning results of 3-gear, 5-gear, G-gear and E-gear can take the maximum value within the sampling value screening range [ a, a + b ], and the obtained online self-learning value is a limit value after parameters of each gear change after the transmission is used for a period of time.

In the embodiment of the application, after the online self-learning value learned by the target gear in the current online self-learning period is determined, the online self-learning value learned by the target gear in the current online self-learning period is stored, so that the gear position parameter reference value learned by the target gear online self-learning is updated, and the gear position parameter reference value is used as the gear position parameter reference value of the target gear when the target gear is subjected to the next online self-learning. Therefore, the learning result value of each self-learning is stored as the latest reference value so as to be referred to in the next self-learning, so that a register is not required to be arranged to store a large amount of data, and various complicated storage rules are not required to be established.

The method comprises the following steps that if a target gear is subjected to online self-learning, a gear position parameter reference value of the target gear in online self-learning can be an online self-learning value learned by the target gear in the last online self-learning period; and if the target gear is not subjected to online self-learning, the reference value of the gear position parameter of the online self-learning of the target gear can be a self-learning value obtained by learning the target gear in the offline self-learning process. That is to say, the offline self-learning can provide the initial value of the first online self-learning for the online self-learning function, and the initial value is used as the reference value of the gear position parameter of the first online self-learning. And the online self-learning value learned by the target gear in the current online self-learning period is stored to update the gear position parameter reference value learned by the target gear online self-learning, so that after the target gear is subjected to online self-learning, the gear position parameter reference value learned by the target gear online self-learning can be found from the stored gear position parameter reference value, and the found gear position parameter reference value is the online self-learning value learned by the target gear in the last online self-learning period.

In the embodiment of the application, the offline self-learning of each gear of the transmission can be realized by the following modes: when the vehicle is in a static working condition, determining an off-line gear shifting signal feedback value acquired by a gear sensor and the electromagnetic valve control current of each gear of the transmission; and performing offline self-learning on each gear of the transmission according to an offline gear shifting signal feedback value acquired by the gear sensor and the electromagnetic valve control current of each gear of the transmission. For example, an offline gear shifting signal feedback value and electromagnetic valve control current can be acquired through a gear sensor, and under a static working condition (vehicle is static and unpowered output) of a vehicle, self-learning is performed on each gear of the transmission through a controller program instruction according to the offline gear shifting signal feedback value acquired by the gear sensor and the electromagnetic valve control current of each gear of the transmission, so that manual calibration is replaced, the condition that gear information parameters of manual calibration during offline of the transmission are inconsistent with those after actual loading can be avoided, the time of manual calibration is saved, and the efficiency and the accuracy are improved. The offline self-learning can be divided into three modes, namely neutral self-learning, single-gear self-learning and full-process self-learning, and the detailed description of the following embodiments is provided.

For example, assuming that the target gear is 3 gears, taking an online self-learning value a learned in a last online self-learning period of the 3 gears as a reference value of a gear position parameter of the 3 gears in the current online self-learning, and presetting a target error value b as an example, a gear signal acquisition value in the 3 gears self-learning process is screened to obtain a real-time sampling value falling within a sampling value screening range [ a, a + b ] of the online self-learning of the gear. And then, determining the maximum value of the real-time sampling values falling in the sampling value screening range [ a, a + b ] as the online self-learning value learned by the gear in the current online self-learning period. And then, storing the on-line self-learning value learned by the gear 3 in the on-line self-learning period, and performing real-time correction and compensation on the gear through bottom control to realize accurate control on the gear.

It should be noted that, in an embodiment of the present application, in the process of performing self-learning on the target gear, if none of the collected values falls within the sampling value screening range of the target gear, or the online self-learning of the target gear is overtime, it may be determined that the online self-learning of the target gear fails, a failure flag is sent, the transmission is used to process, the online self-learning value learned in the previous online self-learning period of the gear is continuously used, the gear is changed into the next time, the self-learning is performed, and the information parameter of the gear is updated.

In summary, whether the gears are changed or not is judged by combining the target gear signals and the collected actual gear signals, and when the gears are changed, whether the gear executing mechanism enters the target gears or not is judged according to the real gear signals and the vehicle speed signals collected in real time, so that whether the gear executing mechanism enters the target gears or not is judged more strictly, the self-learning process is ensured, the gear and the gear ring are completely meshed, the gears really enter the gears, and the self-learning value is more accurate. Meanwhile, a register is not required to be arranged for storing a large amount of data, various complex storage rules are not required to be formulated, algorithm complexity is reduced, and the method is simple and practical.

In the embodiment of the application, as shown in fig. 6, according to whether a difference value between two actual gear signals acquired at two adjacent sampling times in the actual gear signals acquired in real time meets a first preset condition and whether the calculated speed of the input shaft of the transmission meets a second preset condition, it is determined whether the gear actuator enters the target gear. The specific implementation process is as follows:

step 601, judging whether a difference value between actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time meets a first preset condition.

Optionally, a difference between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time is smaller than a first threshold and remains unchanged for a preset time, which may be understood as: the difference value between the actual gear signals collected at two adjacent sampling moments in the actual gear signals collected in real time is smaller than a first threshold value, and the preset time is kept unchanged.

That is to say, a difference value can be made between an actual gear signal value acquired by a gear sensor in real time and an actual gear signal value acquired by delaying the actual gear signal value by one step, and when the difference value is smaller than a first threshold value and the preset time is not changed, the difference value between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time meets a first preset condition; and when the difference value is greater than or equal to the first threshold value and/or the preset time is not kept unchanged, the difference value between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time does not meet a first preset condition.

Step 602, calculating the speed of the input shaft of the transmission according to the real-time collected actual gear signal and the vehicle speed signal of the vehicle.

It will be appreciated that the manner in which the speed of the transmission input shaft is calculated will vary with the gear position.

As an example, when the actual gear signal acquired in real time is the power generation gear, the shaft speed of the input shaft of the transmission is calculated according to the transmission ratio of the power generation gear of the transmission and the rotating speed of the motor. In the embodiment of the present application, the transmission input shaft speed may be calculated according to the following formula:

η_{input terminal}＝η_{Electric power}/i

Wherein eta is_{Input terminal}Representing transmission input shaft speed (in revolutions per minute), η_{Electric power}The motor speed (in revolutions per minute) is shown, and i represents the transmission power generation gear transmission ratio.

As an example, when the real-time collected actual gear signal is a non-power generation gear, the speed of the input shaft of the transmission is calculated according to the vehicle speed signal, the transmission gear transmission ratio and the diameter of the wheel tire. In the embodiment of the present application, the transmission input shaft speed may be calculated according to the following formula:

η_{input terminal}＝1000×v×i/60×π×D

Wherein eta is_{Input terminal}Representing the speed of the input shaft of the transmission (in revolutions per minute), v representing the speed of the vehicle (in kilometers per hour), i representing the transmission power generation gear transmission ratio, pi representing the circumferential ratio, and D representing the diameter of the wheel tyre (in meters).

And step 603, judging whether the calculated speed changer input shaft speed meets a second preset condition.

Optionally, obtaining an actual shaft speed of the transmission input shaft; calculating a difference value between the calculated shaft speed of the input shaft of the transmission and the actual shaft speed of the input shaft of the transmission, and judging whether the calculated difference value is smaller than a second threshold value; if the calculated difference value between the shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft is smaller than a second threshold value, judging that a second preset condition is met; and if the calculated difference value between the shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft is greater than or equal to the second threshold value, determining that the second preset condition is not met.

That is to say, the actual shaft speed of the transmission input shaft is acquired through the gear sensor, the calculated shaft speed of the transmission input shaft is different from the actual shaft speed of the transmission input shaft acquired by the gear sensor, the difference value is compared with a second threshold value, and if the calculated difference value between the shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft acquired by the gear sensor is smaller than the second threshold value, the calculated shaft speed of the transmission input shaft meets a second preset condition; and if the difference value between the calculated shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft acquired by the gear sensor is greater than or equal to a second threshold value, the calculated shaft speed of the transmission input shaft does not meet a second preset condition.

And step 604, if the difference value between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time meets a first preset condition and the calculated speed of the input shaft of the transmission meets a second preset condition, determining that the gear executing mechanism enters a target gear.

Further, in the embodiment of the application, if a difference value between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time does not satisfy a first preset condition, and/or the calculated speed of the input shaft of the transmission does not satisfy a second preset condition, the gear executing mechanism does not enter the target gear.

Therefore, the gear execution mechanism is determined to enter the target gear according to the fact that the difference value between the actual gear signals collected at two adjacent sampling moments in the actual gear signals collected in real time meets a first preset condition, and the calculated speed of the input shaft of the transmission meets a second preset condition. After the gear executing mechanism is determined to enter the target gear, the transmission performs gear self-learning again, accuracy of self-learning exercise samples can be guaranteed, and the condition that accuracy is affected due to the fact that learning result values are inaccurate due to self-learning under the condition that a gear ejecting phenomenon or gears are not correctly hung is eliminated.

In the embodiment of the application, if the target gear is not subjected to online self-learning, the reference value of the gear position parameter of the target gear subjected to online self-learning is a self-learning value obtained by learning the target gear in offline self-learning. Namely, offline self-learning provides initial values of first online self-learning for online self-learning, and the initial values are used as reference values of gear position parameters for judging result accuracy after the first online self-learning succeeds. As shown in fig. 7, the offline self-learning can be divided into three modes, namely, neutral self-learning, single-gear self-learning and overall-process self-learning. The whole process self-learning mode is a mode combining neutral self-learning and single-gear self-learning.

In one embodiment of the present application, the offline self-learning of the neutral gear may be achieved by: determining a reference value and an allowable error value of a neutral gear, determining a gear range of the neutral gear according to the reference value and the allowable error value of the neutral gear, then adjusting the current of a pressure regulating valve and a neutral electromagnetic valve based on a time period of offline self-learning of the neutral gear to enable a gear executing mechanism to reciprocate in the gear range of the neutral gear, determining a signal feedback value acquired by a gear sensor when the gear executing mechanism stops, and determining the signal feedback value acquired by the gear sensor as the offline self-learning value of the neutral gear.

For example, as shown in fig. 8, the neutral self-learning may first calibrate a reference value a and a reference value allowable error b for a given neutral (the values of a and b determine the approximate position of the neutral) according to experiments, and the current of the pressure regulating valve and the target gear solenoid valve is adjusted to enable the gear actuator to reciprocate within the range of a +/-b. It should be noted that the principle of neutral self-learning is shown in fig. 3, and the position of the steel ball in the diagram is the neutral position. Because the structure setting of neutral gear, the recess inner wall slope of neutral structure is greater than the outside slope, can know according to mechanics principle that neutral engaging force is less than neutral groove and takes off the gear resistance, in case the steel ball gets into the neutral groove, because of the shift fork engaging control force of neutral is unchangeable and is less than the resistance that breaks off from the neutral, ensures that the neutral can not break away from current position.

In the embodiment of the application, the time period of the neutral self-learning is that the current of the pressure regulating valve is reduced from the maximum current (such as 0xCC0) to the minimum current (such as 0x800), namely the hydraulic pressure controlled by the pressure regulating valve is reduced from the maximum hydraulic pressure to the minimum. Taking a time period T as an example, in the process of reducing the current of the pressure regulating valve from the maximum current (such as 0xCC0) to the minimum current (such as 0x800), the gear executing mechanism reciprocates in a neutral gear range [ a +/-b ], when the current of the pressure regulating valve is reduced to the minimum current (such as 0x800), the current is minimum, the hydraulic pressure controlled by the pressure regulating valve is minimum (0), the hydraulic pressure does not push the gear executing mechanism to move any more, the gear executing mechanism stops, the signal feedback value collected by the gear sensor is the neutral self-learning result, and the self-learning result is the final stop position of the gear executing mechanism in the neutral gear range [ a +/-b ] in one current period T. And continuously carrying out three times of neutral self-learning, recording corresponding learning values, and if the three times of neutral self-learning results are mutually different and the difference values are within an allowable error range (for example, +/-50), successfully carrying out the neutral self-learning. And if the neutral self-learning is successful, taking the average value of the three self-learning values as a neutral self-learning result, and sending a self-learning success flag bit. And if the neutral self-learning fails, sending a failure flag bit, and re-performing the neutral self-learning. In order to prevent the self-learning process from being circulated endlessly without result output all the time, if the whole neutral self-learning process is overtime (such as 300s), the neutral self-learning is judged to be failed, and the neutral self-learning is waited to be carried out again.

In one embodiment of the present application, the offline self-learning of each single gear may be performed by: determining a target single-gear signal to be subjected to offline self-learning; adjusting the current of a pressure regulating valve and a target single-gear electromagnetic valve to enable a gear executing mechanism to enter a target single gear based on a target single-gear signal and a time period of single-gear off-line self-learning; and when the gear actuating mechanism is determined to enter the target single gear, determining a signal feedback value acquired by the gear sensor, and determining the signal feedback value acquired by the gear sensor as an offline self-learning value of the target single gear.

For example, as shown in fig. 9, after the system is initialized, a minimum current (e.g., 0x800) is applied to all solenoid valves, so that the gear actuator is positioned at the leftmost side, and a maximum current (e.g., 0xCC0) is applied to all pressure regulating valves to obtain a maximum hydraulic pressure. The single-gear self-learning principle is based on a physical structure of the transmission, and the gear executing mechanism pushes the piston to move leftwards to be engaged with the gear 1 and moves rightwards to be engaged with the gear 3 by controlling the current of the electromagnetic valve and using hydraulic pressure. It should be noted that the transmission is provided with a gear protection device, and even if the gear reaches the limit position and continues to apply force, the gear execution machine is not damaged.

In the embodiment of the application, for the self-learning of the 1 st gear and the 3 rd gear single gear, the maximum current (such as 0xCC0) is applied to the solenoid valve, so that the gear actuating mechanism is positioned at the rightmost side, the gear actuating mechanism moves to the left with the maximum hydraulic pressure to shift into the 1 st gear, and the gear actuating mechanism moves to the left with the maximum hydraulic pressure from the rightmost side to shift into the 1 st gear, namely, the gear actuating mechanism moves to the left with the maximum hydraulic pressure from the rightmost side. And continuously judging the position of the gear executing mechanism, continuously judging the difference value of the current position of the gear executing mechanism and the sampling value delayed by one step length, and finishing a gear self-learning period after the difference value is met and a preset condition is allowed for a period of time. Applying minimum current (0x800) to the electromagnetic valve, enabling the gear executing mechanism to be located at the leftmost side, enabling the gear executing mechanism to move rightwards with maximum hydraulic pressure to be engaged in a gear 3, namely enabling the gear executing mechanism to move rightwards with maximum hydraulic pressure from the leftmost side, continuously judging the position of the gear executing mechanism, continuously judging the difference value between the current position of the gear executing mechanism and the sampling value delayed by one step length of the current position of the gear executing mechanism, and finishing a gear self-learning period after the difference value is met and a preset condition is allowed for a period of time. In a similar way, the self-learning principle of the 2-gear and 5-gear, the 4-gear and G-gear, the R-gear and E-gear is completely the same as that of the 1-gear and 3-gear single-gear.

In the embodiment of the application, each gear repeats 10 self-learning actions, and the self-learning result of each single gear is recorded. And (3) carrying out data processing on the 10 self-learning results, mutually calculating a difference value of the 10 self-learning results, and if the difference value is within a preset allowable error range, determining the difference value as an effective value. If not, the self-learning result is an invalid value, then the invalid value is removed, and if the self-learning result has at least 8 valid values, the self-learning is judged to be successful. And if the effective value of the self-learning result is less than 8 or the self-learning process is overtime (such as 300s), judging that the single-gear self-learning fails. If the single-gear self-learning succeeds, the single-gear self-learning result is stored by the transmission according to the principle that the gears 1, 2, 4 and R output the maximum value in the self-learning results of each gear (the value with the largest numerical value in the self-learning public effective values is the limit position which can be learned by the target gear), and the gears 3, 5, G and E output the minimum value in the self-learning results of each gear (the value with the smallest numerical value in the self-learning public effective values is the limit position which can be learned by the target gear), and a single-gear self-learning success mark is sent. And if the self-learning of the single gear is failed, sending a self-learning failure mark and recording the self-learning result of the single gear as 0, and after the transmission receives the self-learning failure mark, continuing to use the self-learning result of the single gear of the last time.

The full process self-learning mode in the offline self-learning may include: neutral self-learning and single-gear self-learning. The neutral self-learning may refer to the description of the embodiment shown in fig. 8, and the single-gear self-learning may refer to the description of the embodiment shown in fig. 9, which is not repeated herein.

In the embodiment of the application, the offline self-learning can provide an initial value of the first online self-learning for the online self-learning function, and the initial value is used as a reference value for judging the accuracy of the result after the first online self-learning is successful. After the vehicle runs for a period of time, the vehicle is periodically checked and maintained, an offline self-learning function can be started, the actual condition of the transmission is judged to determine whether to replace or return to the factory for maintenance, specifically, offline self-learning can be started during vehicle maintenance, the result of the offline self-learning during maintenance can be compared with the result value of the first offline self-learning when the vehicle leaves a factory, and the damage state of the transmission can be obtained. When certain functions of the vehicle need to use the static gear parameters, the offline self-learning function can be called at any time to obtain corresponding parameters.

According to the gear self-learning method of the automatic transmission of the vehicle, the actual gear signal of the vehicle in the running process is collected; judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal; when the gear is judged to be changed, determining whether a gear executing mechanism enters a target gear or not according to an actual gear signal acquired in real time and a vehicle speed signal of the vehicle; and if the target gear is determined to enter the target gear, the target gear is self-learned on line, whether the gear is changed is judged by combining the target gear signal and the acquired actual gear signal, and whether the gear actuating mechanism enters the target gear is judged according to the actual gear signal and the vehicle speed signal acquired in real time when the gear is changed, so that whether the gear actuating mechanism enters the target gear is judged more strictly, the self-learning process is ensured, the gear ring is completely meshed, the gear is really entered, and the self-learning value is more accurate. Meanwhile, a register is not required to be arranged for storing a large amount of data, various complex storage rules are not required to be formulated, algorithm complexity is reduced, and the method is simple and practical.

In accordance with the vehicle automatic transmission gear self-learning method provided in the above-mentioned embodiments, an embodiment of the present application further provides a vehicle automatic transmission gear self-learning device, and since the vehicle automatic transmission gear self-learning device provided in the embodiment of the present application corresponds to the vehicle automatic transmission gear self-learning method provided in the above-mentioned embodiments, the foregoing embodiment of the vehicle automatic transmission gear self-learning method is also applicable to the vehicle automatic transmission gear self-learning device provided in the embodiment, and will not be described in detail in the embodiment. FIG. 10 is a schematic structural diagram of a gear self-learning device of a vehicle automatic transmission according to an embodiment of the application. As shown in fig. 10, the shift self-learning apparatus for the automatic transmission of the vehicle includes: the automatic transmission gear shifting system comprises an actual gear signal acquisition module 1010, a gear change judgment module 1020, a gear engaging determination module 1030 and an online self-learning module 1040.

The actual gear signal acquisition module 1010 is used for acquiring an actual gear signal of the vehicle in the running process; the gear change judging module 1020 is used for judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal; the gear engaging determination module 1030 is configured to determine whether the gear actuator enters a target gear according to an actual gear signal and a vehicle speed signal acquired in real time when it is determined that the gear is changed; and the online self-learning module 1040 is configured to perform online self-learning on the target gear when it is determined that the gear executing mechanism enters the target gear.

As a possible implementation manner of the embodiment of the present application, the gear shift determination module 1030 is specifically configured to: judging whether the difference value between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time meets a first preset condition or not; calculating the shaft speed of an input shaft of the transmission according to the real-time acquired actual gear signal and the vehicle speed signal of the vehicle; judging whether the calculated speed of the input shaft of the transmission meets a second preset condition or not; and if the difference value between the actual gear signals acquired at two adjacent sampling moments in the actual gear signals acquired in real time meets a first preset condition and the calculated speed of the input shaft of the transmission meets a second preset condition, determining that the gear actuating mechanism enters a target gear.

As a possible implementation manner of the embodiment of the present application, the gear shift determination module 1030 is specifically configured to: the difference value between the actual gear signals collected at two adjacent sampling moments in the actual gear signals collected in real time is smaller than a first threshold value, and the preset time is kept unchanged.

As a possible implementation manner of the embodiment of the present application, the gear shift determination module 1030 is specifically configured to: when the actual gear signal acquired in real time is a power generation gear, calculating the shaft speed of the input shaft of the transmission according to the transmission ratio of the power generation gear of the transmission and the rotating speed of the motor; and when the real-time acquired actual gear signal is a non-power generation gear, calculating the speed of the input shaft of the transmission according to the speed signal, the transmission gear transmission ratio of the transmission and the diameter of the wheel tire.

As a possible implementation manner of the embodiment of the present application, the gear shift determination module 1030 is specifically configured to: acquiring an actual shaft speed of a transmission input shaft; calculating a difference value between the calculated shaft speed of the input shaft of the transmission and the actual shaft speed of the input shaft of the transmission, and judging whether the calculated difference value is smaller than a second threshold value; if the calculated difference value between the shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft is smaller than the second threshold value, judging that a second preset condition is met; and if the calculated difference value between the shaft speed of the transmission input shaft and the actual shaft speed of the transmission input shaft is greater than or equal to a second threshold value, determining that a second preset condition is not met.

As a possible implementation manner of the embodiment of the present application, the online self-learning module 1040 is specifically configured to: determining a gear position parameter reference value and a target error value of the target gear on-line self-learning; determining a sampling value screening range of the target gear online self-learning according to the gear position parameter reference value and the target error value of the target gear online self-learning; screening a gear signal acquisition value in the target gear online self-learning process to acquire a real-time sampling value falling within a sampling value screening range; and determining an online self-learning value learned by the target gear in the current online self-learning period according to the real-time sampling value falling in the sampling value screening range.

As a possible implementation manner of the embodiment of the application, if the target gear has already been subjected to online self-learning, the reference value of the gear position parameter of the online self-learning of the target gear is the online self-learning value learned by the target gear in the last online self-learning period; and if the target gear is not subjected to online self-learning, the reference value of the gear position parameter of the online self-learning of the target gear is a self-learning value obtained by learning the target gear in the offline self-learning process.

As a possible implementation manner of the embodiment of the present application, the online self-learning module 1040 further includes: after the online self-learning value learned by the target gear in the current online self-learning period is determined, the online self-learning value learned by the target gear in the current online self-learning period is stored, so that the gear position parameter reference value learned by the target gear online self-learning is updated.

As a possible implementation manner of the embodiment of the present application, the gear self-learning apparatus of the vehicle automatic transmission may further include: and an offline self-learning module. The offline self-learning module is used for: when the vehicle is in a static working condition, determining an off-line gear shifting signal feedback value acquired by a gear sensor and the electromagnetic valve control current of each gear of the transmission; and performing offline self-learning on each gear of the transmission according to an offline gear shifting signal feedback value acquired by the gear sensor and the electromagnetic valve control current of each gear of the transmission.

According to the gear self-learning device of the automatic transmission of the vehicle, the actual gear signal of the vehicle in the running process is collected; judging whether the gear is changed or not according to the target gear signal and the acquired actual gear signal; when the gear is judged to be changed, determining whether a gear executing mechanism enters a target gear or not according to an actual gear signal acquired in real time and a vehicle speed signal of the vehicle; if the gear executing mechanism is confirmed to enter a target gear, the target gear is subjected to online self-learning, whether the gear is changed or not is judged by combining a target gear signal and an acquired actual gear signal, and when the gear is changed, whether the gear executing mechanism enters the target gear or not is judged according to the actual gear signal and a vehicle speed signal acquired in real time, so that whether the gear executing mechanism enters the target gear or not is judged more strictly, the self-learning process is ensured, the gear ring is completely meshed, the gear is really entered, and the self-learning value is more accurate. Meanwhile, a register is not required to be arranged for storing a large amount of data, various complex storage rules are not required to be formulated, algorithm complexity is reduced, and the method is simple and practical.

In order to achieve the above embodiments, the present application also proposes a vehicle, as shown in fig. 11, which may include: memory 1001, processor 1002, and computer programs stored on memory 1001 and executable on processor 1002. The processor 1002, when executing the program, implements the vehicle automatic transmission range self-learning method provided in the above embodiments.

Further, the vehicle further includes: a communication interface 1003 for communicating between the memory 1001 and the processor 1002. A memory 1001 for storing computer programs that may be run on the processor 1002. Memory 1001 may include high-speed RAM memory and may also include non-volatile memory (e.g., at least one disk memory). The processor 1002 is used for implementing the self-learning method for the gears of the automatic transmission of the vehicle according to the above embodiments when executing the program. If the memory 1001, the processor 1002, and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001, and the processor 1002 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 11, but this is not intended to represent only one bus or type of bus.

Optionally, in a specific implementation, if the memory 1001, the processor 1002, and the communication interface 1003 are integrated on one chip, the memory 1001, the processor 1002, and the communication interface 1003 may complete communication with each other through an internal interface.

The processor 1002 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present invention.

In order to achieve the above embodiments, the present application further proposes a computer readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the method for self-learning the gears of the automatic transmission of the vehicle as described in the above embodiments. In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.