WO2022095445A1

WO2022095445A1 - Electronic parking gear position self-learning method and implementation method

Info

Publication number: WO2022095445A1
Application number: PCT/CN2021/098933
Authority: WO
Inventors: 王连新; 张彦霞; 张清路; 叶晓; 邢伟
Original assignee: 精进电动科技股份有限公司
Priority date: 2020-11-03
Filing date: 2021-06-08
Publication date: 2022-05-12
Also published as: CN112413118B; CN112413118A

Abstract

An electronic parking gear position self-learning method and an implementation method. The self-learning method comprises the following steps: setting respective position reference values of a driving motor and/or a pawl at an unlocking end and a parking end according to design parameters of a parking system (S110); when entering a self-learning proceeding state, performing position self-learning of the driving motor and/or the pawl when the driving motor and/or the pawl are/is at the unlocking end and the parking end, to obtain position self-learning values of the driving motor and/or the pawl when the driving motor and/or the pawl are/is at the unlocking end and the parking end (S120); and determining target positions of the driving motor and/or the pawl at the unlocking end and the parking end according to the position self-learning values and the position reference values (S130). According to the self-learning method, learning concerning the unlocking end and the parking end is performed at the same time, and a control position is bidirectionally determined according to a learning angle; the two ends share the problem of size chain error accumulation, such that the control accuracy is improved.

Description

Electronic parking gear position self-learning method and realization method

technical field

The invention belongs to the technical field of electronic parking control, and particularly relates to a self-learning method and an implementation method of an electronic parking gear position.

Background of the Invention

The electronic parking gear parking system is a technology that realizes the locking of the gearbox or deceleration mechanism by electronically controlling the parking motor. Compared with the complex structure, high technical difficulty and high cost of the hydraulic parking mechanism, the electric parking mechanism has a reliable and simple structure. , high efficiency, more and more applications in new energy vehicles.

The parking motor of the electronic parking gear parking system needs to be controlled and rotated according to the parking position and the unlocking position, but after the parking motor, it also includes the parking motor deceleration mechanism, guide shaft, parking pawl, parking ratchet and other components. Parts, due to manufacturing errors in the processing of parts, the parking position and unlocking position of each parking system are inconsistent. If the same set of control positions are used, the parking function may fail. If each system needs to be calibrated, the time The cost and after-sales cost are high, so it is necessary to provide a reasonable and effective location self-learning method. There is also a method of self-learning of the unlocking position in the prior art. The unlocking position is used to infer the parking position unidirectionally according to the mechanical angle. This method causes the parking position to shift due to the accumulation of dimensional chain errors, which affects the position control accuracy and causes mechanical problems. Collision damage, and the self-taught position may fail due to mechanical errors.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention discloses a self-learning method and implementation method for the position of an electronic parking gear, so as to overcome the above problems or at least partially solve the above problems.

In order to achieve the above object, the present invention adopts the following technical solutions:

One aspect of the embodiments of the present invention provides a self-learning method for the position of an electronic parking gear, and the self-learning method includes the following steps:

According to the design parameters of the parking system, set the position reference values of the drive motor and/or the pawl at the unlocking end and the parking end, respectively;

When entering the self-learning state, the position self-learning of the driving motor and/or the pawl at the unlocking end and the parking end is performed, and the driving motor and/or the pawl at the unlocking end and the parking end are obtained. The position self-learning value at the end of the car;

The target positions of the drive motor and/or the pawl at the unlocking end and the parking end are determined according to the position self-learning value and the position reference value.

Optionally, the position reference value specifically includes any one or more of the following: parking motor unlocking reference position and parking motor parking reference position, parking motor reference working angle, pawl unlocking position reference position, The angle between the pawl unlock position and the top tooth position, the angle between the pawl unlock position and the pawl park position.

Optionally, the self-learning of the positions of the drive motor and/or the pawl at the unlocking end and the parking end specifically includes:

The current position state is determined according to the driving motor and/or the reference position of the pawl. If the parking motor is currently in the parking state, the parking motor first performs the unlocking process, and before reaching the parking motor unlocking reference position, the current position is 70 % or more duty cycle output to ensure the output capacity of the parking motor, and output with a duty cycle below 40% after reaching the parking motor unlocking reference position to ensure that the actuator of the parking system reaches the limit point smoothly , the parking motor position after the unlocking process is completed is the parking motor unlocking self-learning position, and the pawl position is the pawl unlocking self-learning position. After the unlocking position self-learning is completed, it enters the parking position self-learning;

If it is currently in an unlocked state, the parking motor first executes the parking process to perform self-learning of the parking position, and then performs the unlocking process to perform the self-learning of the unlocked position.

Optionally, after the position self-learning of the drive motor and/or the pawl at the unlocking end and the parking end is performed, the position self-learning method further includes:

The position of the driving motor and/or the pawl is checked to determine whether the position self-learning value satisfies a preset condition. When the preset condition is met, the position self-learning succeeds, otherwise the position self-learning fails.

Optionally, the preset conditions are as follows: the parking motor unlocking self-learning position value is greater than the parking motor unlocking reference position value; the parking motor parking self-learning position value is less than the parking motor parking reference position value; the pawl is unlocked The self-learning position value is greater than the reference position value of the pawl unlocking position; the angle difference between the pawl unlocking self-learning position and the current position of the pawl in the parking state is within the preset range of the angle between the pawl unlocking position and the top tooth position, or The angle difference between the self-learning position of the pawl unlocking and the current position of the pawl in the parking state is greater than the preset range of the unlocking position of the pawl and the parking angle of the pawl.

Optionally, determining the target positions of the drive motor and/or the pawl at the unlocking end and the parking end according to the position self-learning value and the position reference value includes:

Set the parking motor control unlocking target position value as Pmde_target, the parking motor control parking target position value as Pme_target, the pawl control unlocking target position value as Ppde_target, the pawl control top tooth target position value as Ppt_target, and the pawl control parking target position value as Ppt_target. The target position value is Ppe_target, the parking motor control unlocking self-learning position value is Pmde_study, the parking motor control parking self-learning position value is Pme_study, the pawl control unlocking self-learning position value is Ppde_study, and the parking motor reference working angle is Pm_valid , the reference angle between the pawl unlocking position and the top tooth position is Ppt_valid, the angle between the pawl unlocking position and the pawl parking position is Pp_valid, then:

Pmde_target=Pmde_study-(Pmde_study-Pme_study-Pm_valid)/2;

Pme_target=Pme_study+(Pmde_study-Pme_study-Pm_valid)/2;

Ppde_target=Ppde_study;

Ppt_target=Ppde_study–Ppt_valid;

Ppe_target=Ppde_study-Pp_valid.

Optionally, the position self-learning method further includes:

The parking controller judges the current system state after receiving the self-learning control command. If there is a position sensor failure or a system failure that affects the realization of the function, the controller enters the system failure state;

If the controller can return to the initial state in the system failure state, self-learning success and self-learning failure state after receiving the reset command, it will not respond during self-learning.

Another aspect of the embodiments of the present invention provides a method for implementing self-learning of an electronic parking gear position, characterized in that the implementation method includes the following steps:

The controller of the electronic parking receives the position self-learning control command sent by the host computer, and the position self-learning control command includes the self-learning command and the function reset command. If the controller receives the self-learning command, execute any of the above. According to the position self-learning method described above, if the function reset command is received, it will return to the initial state in the system failure state, self-learning success state and self-learning failure state;

After the position self-learning is successful, the controller receives the time recording instruction and the time marker value sent by the host computer, and after receiving the time recording instruction, the controller stores the time marker value to the data table, and the data table also saves the data with the value of the time marker. The position value of the drive motor and/or the pawl corresponding to the time marker value;

After receiving the information reading instruction, the controller reads the time stamp value and the position value recorded during the position self-learning process from the data table.

Optionally, before the controller of the electronic parking receives the position self-learning control command sent by the host computer, the implementation method further includes the step of confirming the service connection:

The host computer sends a random code to the controller through CAN communication, and generates the first secret key data through an encryption algorithm. The controller generates and returns the second secret key data through the same encryption algorithm as the host computer, and sends it back to the host computer through CAN communication. , the host computer detects whether the first key data and the second key data are consistent; if they are consistent, the service connection is successful; if they are inconsistent, the connection fails, and a connection failure failure is fed back.

Optionally, the implementation method is performed using a CAN diagnostic protocol and diagnostic determination software, and/or the implementation method is performed in a factory test phase or an after-sales maintenance phase of the electronic parking gear.

The advantages and beneficial effects of the present invention are:

The first is the technical solution disclosed in the embodiment of the present invention. The parking position and the unlocking position are calibrated at the same time, and the control position is determined in both directions according to the learning angle. Since both ends of the parking position and the unlocking position share the problem of accumulation of errors in the dimension chain, so It can improve the control accuracy; and the self-learning position is corrected according to the position reference value, which is more accurate;

Second, the embodiments of the present invention can perform correctness verification on the position of the parking motor or the pawl, add a reasonable determination method to the self-learning position, and verify the reasonable validity of the self-learning position;

Third, for the above position self-learning method, an implementation method of the above-mentioned position self-learning online control is proposed, so as to realize the effective control of position self-learning, repeatable learning and multiple verification, especially suitable for the parking system after leaving the factory. Line testing and after-sales maintenance stage;

Fourth, the CAN diagnosis protocol and software tools are used for position self-learning and verification, which is safer and more stable for the vehicle system and conforms to the current vehicle diagnosis trend.

Brief Description of Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for the purpose of illustrating preferred embodiments only and are not to be considered limiting of the invention. Also, the same components are denoted by the same reference numerals throughout the drawings. In the attached image:

1 is a schematic flowchart of a method for self-learning of the position of an electronic parking gear in an embodiment of the present invention;

2 is a schematic diagram of the unlocking and parking process and the positional relationship of the parking motor (pawl) in an embodiment of the present invention;

3 is a schematic diagram of the transition of the position self-learning state of the parking system controller in an embodiment of the present invention;

FIG. 4 is a schematic flowchart of a method for implementing self-learning of the position of an electronic parking gear in an embodiment of the present invention.

MODE OF IMPLEMENTING THIS APPLICATION

In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The technical solutions provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

Example 1

A schematic flowchart of the method for self-learning of the position of the electronic parking gear shown in FIG. 1 . The self-learning method includes the following steps:

S110, according to the design parameters of the mechanical mechanism in the parking system, such as the size and matching requirements of the parking motor deceleration mechanism, guide shaft, parking pawl, parking ratchet and other components, set the driving motor and pawl respectively in the The position reference value of the unlocking end and the parking end, and sensors are installed in the parking system to detect the rotation angle of the drive motor and the real-time position of the pawl respectively.

The above parking motor and pawl positions take the position when parking as the lower limit position, and the unlocked position is the upper limit position range. For example, the parking motor and pawl can be set to 0 at the parking angle or position, and then rotate with the angle of rotation. or the value of the position increases. The above-mentioned position reference value may preferably be the position from the moment when the vehicle is unlocked and parked from the moment when the vehicle is parked and when the vehicle is unlocked. This position is used as a reference to finally determine the target position of the control to be implemented.

S120, when the software operation state in the controller, such as the controller, is in the self-learning in-progress state, perform self-learning of the positions of the drive motor and/or the pawl at the unlocking end and the parking end, to obtain the drive The position of the motor and/or the pawl at the unlocking end and the parking end is self-learning value.

As can be seen from Figure 2, the self-learning position is preferably the limit position that can be reached by testing the drive motor or the pawl during the factory or after-sales maintenance and diagnosis. In the above preferred situation, the motor parking self-learning (lower limit) is performed. The position value is smaller than the reference position value of the execution motor parking, smaller than the reference position value of the execution motor unlocking, and smaller than the position value of the execution motor unlocking self-learning (upper limit). Of course, other intermediate test positions or real-time positions are also within the protection scope of this embodiment.

S130: Determine the target positions of the drive motor and/or the pawl at the unlocking end and the parking end according to the position self-learning value and the position reference value.

Since the selection of the self-learning value and the reference value are respectively close to the two sides of the final control target position, the final target position to be controlled and realized is obtained according to the above positional relationship.

To sum up, according to the technical solutions described in the above embodiments, by performing self-learning at the unlocking end and the parking end respectively, the simultaneous calibration of the parking position and the unlocking position can be realized, and the control target to be realized can be determined bidirectionally according to the learned position or angle. Since the two ends share the problem of accumulation of errors in the dimension chain, the control accuracy is improved and mechanical collision damage is avoided; and the self-learning position is corrected according to the position reference value, which is more reasonable and accurate.

Specifically, the position reference value specifically includes: the parking motor unlocking reference position and the parking motor parking reference position, the parking motor reference working angle at the above two reference positions, and the maximum control angle that the parking motor can achieve; The reference position of the pawl unlocking position and the reference position of the pawl parking position can also be the angle formed by the pawl when the reference position is unlocked and the top tooth position, and can also be the angle formed between the reference position of the pawl unlocking and the reference position of the pawl parking The angle of the top tooth is the position where the pawl and the ratchet or cam are in contact with the teeth of the ratchet or cam or even get stuck, and the probability of the pawl being at the top tooth position during the parking process is also relatively large.

One of the above positions or angles can be selected, and self-learning can be performed at the unlocking end and the parking end respectively according to any of the above. Of course, it is preferable to test all of the above in order to obtain the best results.

Further, the specific implementation process of S120 is as follows: the current position state is determined according to the driving motor and/or the reference position of the pawl, and if the current parking gear is in the parking state, the parking motor needs to first perform the unlocking process, and when it arrives Before the parking motor unlocks the reference position, it is output with a large duty ratio of 70% or more, preferably 100%, to ensure the output capacity and speed of the parking motor. After reaching the parking motor unlocking reference position, the output is 40% or less, preferably 30%. The output of the duty cycle ensures that the actuator of the parking system reaches the limit point smoothly. The limit position of the parking motor after the unlocking process is completed can be recorded as the parking motor unlocking self-learning position, and the pawl position at this time is the pawl unlocking. Self-learning position, unlocking position self-learning is completed after entering the parking position self-learning, such a cycle, the entire parking or unlocking process can be completed within 1 second.

On the contrary, if the electronic parking gear is in the unlocked state at the beginning, the parking motor first performs the parking process, performs the self-learning of the parking position, and then performs the unlocking process, and performs the self-learning of the unlocking position. Are the same.

In a preferred embodiment of the present invention, after the step of S120 is started, the self-learning method further includes a step of position verification, judging the state of the current self-learning according to a preset condition, and when the preset condition is satisfied, the position self-learning is successful , otherwise the position self-learning fails. Then reset according to the judgment result and then self-learn.

Specifically, referring to FIG. 2 , the preset condition is a condition determined according to the self-learning position and the ideal position, preferably all preset conditions are satisfied, so as to obtain a correct position result. If the self-learning position is correct, there are: 1. The parking motor unlocking self-learning position value is greater than the parking motor unlocking reference position value; 2. The parking motor parking self-learning position value is smaller than the parking motor parking reference position Position value; 3. The pawl unlocking self-learning position value is greater than the reference position value of the pawl unlocking position; 4. The difference between the pawl unlocking self-learning position value and the current position of the pawl in the parking state is between the pawl unlocking position and the top tooth position Within the preset range of the angle between the pawls, or, the difference between the pawl unlocking self-learning position and the current position of the pawl in the parking state is greater than the preset range of the pawl unlocking position and the pawl parking angle, wherein the preset range is preferably the above-mentioned angle. An error range of positive and negative θ, the specific value can be set according to the actual situation of the parking system.

Through the above verification methods, the accuracy and reasonable validity of the self-learning are improved, and errors in the self-learning of the parking system are avoided.

In one embodiment, referring also to the relationship between the positions shown in FIG. 2 , S130 specifically includes: assuming that the parking motor control unlocking target position value is Pmde_target, the parking motor control parking target position value is Pme_target, The pawl control unlocking target position value is Ppde_target, the pawl control top tooth target position value is Ppt_target, the pawl control parking target position value is Ppe_target, the parking motor control unlocking self-learning position value is Pmde_study, the parking motor control parking auto The learning position value is Pme_study, the pawl control unlocking self-learning position value is Ppde_study, the reference working angle of the parking motor is Pm_valid, the reference angle between the pawl unlocking position and the top tooth position is Ppt_valid, the pawl unlocking position and the pawl parking The angle Pp_valid between the positions, the above values should satisfy the following relationship:

Pmde_target=Pmde_study-(Pmde_study-Pme_study-Pm_valid)/2;

Pme_target=Pme_study+(Pmde_study-Pme_study-Pm_valid)/2;

Ppde_target=Ppde_study;

Ppt_target=Ppde_study–Ppt_valid;

Ppe_target=Ppde_study-Pp_valid.

Therefore, according to the above formula, each target value of the parking motor and the pawl at the unlocking end and the parking end can be calculated, and then the ideal position of each parking system can be set according to the target value.

It should be noted that, according to the position self-learning state transition diagram shown in FIG. 3 , the self-learning method can also be reset under appropriate conditions according to the state transition needs, thereby increasing the maneuverability. The details are as follows: the parking controller judges the current system state after receiving the self-learning control command. If there is a position sensor failure or a system failure that affects the function realization, the controller enters the system failure state; if the controller receives the reset command, it can The state, self-learning success and self-learning failure state return to the initial state, and the self-learning is in progress and does not respond.

Through the above method, the controllability of the self-learning can be realized, and the repeated learning and multiple verifications can be performed to ensure the smooth implementation of the self-learning.

Example 2

Referring to a schematic flowchart of a method for implementing self-learning of an electronic parking gear position shown in FIG. 4 , the implementation method includes the following steps:

Control and implementation of position self-learning: The electronic parking controller receives the position self-learning control command sent by the host computer. The position self-learning control command includes self-learning command and function reset command. If the controller receives a self-learning command, Then execute the preset position self-learning algorithm as above, firstly carry out the system fault self-check, if the self-check successfully enters the self-learning state, of course, the self-check may also feed back the initial state and the system fault state, and it may be necessary to restart the system. set.

If the function reset command is received, it will return to the initial state in the system failure state, self-learning success state and self-learning failure state;

After the position self-learning is successful, the controller receives the time recording instruction and the time marker value sent by the host computer, and the controller stores the time marker value in the data table after receiving the sending time recording instruction sent by the host computer. The position value of the drive motor and/or the pawl corresponding to the time stamp value is also stored.

The above-mentioned numerical values specifically include: the unlocking position of the parking actuator, the parking position of the parking actuator, the unlocking position of the pawl, the position of the top tooth of the pawl, the parking position of the pawl, and the time mark value.

The above implementation method conforms to the current vehicle diagnosis trend, and can be repeatedly learned and verified many times.

In a preferred embodiment, in order to ensure that the position self-learning can only be triggered in a controllable and safe situation, the following "handshake verification" link is also set: that is, the host computer sends a random code to the controller through CAN communication, and The secret key data is generated by the encryption algorithm, the controller generates the return secret key data through the same encryption algorithm as the host computer, and sends it back to the host computer through CAN communication. The host computer detects whether the two secret key data are consistent; if they are consistent, the service is connected. If it is successful, if it is inconsistent, the connection fails, and a connection failure failure is reported.

The implementation method adopts CAN diagnosis protocol and diagnosis judgment software to carry out the electronic parking gear factory test phase or after-sales maintenance phase, which is safer and more stable for the vehicle system, and the self-learning position ensures accurate and avoids functional failure.

The above descriptions are merely embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, expansion, etc. made within the spirit and principle of the present invention are all included in the protection scope of the present invention.

Claims

An electronic parking gear position self-learning method is characterized in that, the position self-learning method comprises the following steps:

According to the design parameters of the parking system, set the position reference values of the drive motor and/or the pawl at the unlocking end and the parking end, respectively;

When entering the self-learning state, the position self-learning of the driving motor and/or the pawl at the unlocking end and the parking end is performed, and the driving motor and/or the pawl at the unlocking end and the parking end are obtained. The position self-learning value at the end of the car;

The target positions of the drive motor and/or the pawl at the unlocking end and the parking end are determined according to the position self-learning value and the position reference value.
The position self-learning method according to claim 1, wherein the position reference value specifically includes any one or more of the following: a parking motor unlocking reference position, a parking motor parking reference position, a parking motor Reference working angle, reference position of pawl unlocking position, angle between pawl unlocking position and top tooth position, angle between pawl unlocking position and pawl parking position.
The position self-learning method according to claim 2, wherein the performing the position self-learning of the drive motor and/or the pawl at the unlocking end and the parking end specifically includes:

The current position state is determined according to the driving motor and/or the reference position of the pawl. If the parking motor is currently in the parking state, the parking motor first performs the unlocking process, and before reaching the parking motor unlocking reference position, the current position is 70 % or more duty cycle output to ensure the output capacity of the parking motor, and output with a duty cycle below 40% after reaching the parking motor unlocking reference position to ensure that the actuator of the parking system reaches the limit point smoothly , the parking motor position after the unlocking process is completed is the parking motor unlocking self-learning position, and the pawl position is the pawl unlocking self-learning position. After the unlocking position self-learning is completed, it enters the parking position self-learning;

If it is currently in an unlocked state, the parking motor first executes the parking process to perform self-learning of the parking position, and then performs the unlocking process to perform the self-learning of the unlocked position.
The position self-learning method according to claim 1, wherein after the position self-learning of the drive motor and/or the pawl at the unlocking end and the parking end is performed, the position self-learning method further comprises: include:

The position of the driving motor and/or the pawl is checked to determine whether the position self-learning value satisfies a preset condition. When the preset condition is met, the position self-learning succeeds, otherwise the position self-learning fails.
The position self-learning method according to claim 4, wherein the preset conditions are as follows: the parking motor unlocking self-learning position value is greater than the parking motor unlocking reference position value; the parking motor parking self-learning position value is less than The parking reference position value of the parking motor; the value of the pawl unlocking self-learning position is greater than the reference position value of the pawl unlocking position; the angle difference between the pawl unlocking self-learning position and the current position of the pawl in the parking state is between the pawl unlocking position and the top. Within the preset range of the angle between the tooth positions, or the angle difference between the pawl unlocking self-learning position and the current position of the pawl in the parking state is greater than the preset range of the pawl unlocking position and the pawl parking angle.
The position self-learning method according to claim 4, wherein the determining the target positions of the drive motor and/or the pawl at the unlocking end and the parking end according to the position self-learning value and the position reference value comprises:

Set the parking motor control unlocking target position value as Pmde_target, the parking motor control parking target position value as Pme_target, the pawl control unlocking target position value as Ppde_target, the pawl control top tooth target position value as Ppt_target, and the pawl control parking target position value as Ppt_target. The target position value is Ppe_target, the parking motor control unlocking self-learning position value is Pmde_study, the parking motor control parking self-learning position value is Pme_study, the pawl control unlocking self-learning position value is Ppde_study, and the parking motor reference working angle is Pm_valid , the reference angle between the pawl unlocking position and the top tooth position is Ppt_valid, the angle between the pawl unlocking position and the pawl parking position is Pp_valid, then:

Pmde_target=Pmde_study-(Pmde_study-Pme_study-Pm_valid)/2;

Pme_target=Pme_study+(Pmde_study-Pme_study-Pm_valid)/2;

Ppde_target=Ppde_study;

Ppt_target=Ppde_study–Ppt_valid;

Ppe_target=Ppde_study-Pp_valid.
The position self-learning method according to claim 4, wherein the position self-learning method further comprises:

The parking controller judges the current system state after receiving the self-learning control command. If there is a position sensor failure or a system failure that affects the realization of the function, the controller enters the system failure state;

If the controller returns to the initial state after receiving the reset command in the state of system failure, successful self-learning and failure of self-learning, it will not respond to the reset command during self-learning.
A method for realizing self-learning of the position of an electronic parking gear, characterized in that the realization method comprises the following steps:

The controller of the electronic parking receives the position self-learning control command sent by the host computer, and the position self-learning control command includes the self-learning command and the function reset command. If the controller receives the self-learning command, the claims 1-7 are executed. In any one of the position self-learning methods, if a function reset instruction is received, it returns to the initial state in the system failure state, the self-learning success state and the self-learning failure state;

After the position self-learning is successful, the controller receives the time recording instruction and the time marker value sent by the host computer, and after receiving the time recording instruction, the controller stores the time marker value to the data table, and the data table also saves the data with the value of the time marker. The position value of the drive motor and/or the pawl corresponding to the time marker value;

After receiving the information reading instruction, the controller reads the time stamp value and the position value recorded during the position self-learning process from the data table.
The implementation method according to claim 8, wherein before the controller of the electronic parking receives the position self-learning control command sent by the upper computer, the implementation method further comprises the step of confirming the service connection:

The host computer sends a random code to the controller through CAN communication, and generates the first secret key data through an encryption algorithm. The controller generates and returns the second secret key data through the same encryption algorithm as the host computer, and sends it back to the host computer through CAN communication. , the host computer detects whether the first key data and the second key data are consistent; if they are consistent, the service connection is successful; if they are inconsistent, the connection fails, and a connection failure failure is fed back.
The implementation method according to claim 8, characterized in that, the implementation method is performed using CAN diagnostic protocol and diagnostic determination software, and/or the implementation method is performed in the electronic parking gear factory testing stage or after-sales maintenance stage.