CN112413118B - Self-learning method and implementation method for electronic parking gear position - Google Patents
Self-learning method and implementation method for electronic parking gear position Download PDFInfo
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- CN112413118B CN112413118B CN202011211039.XA CN202011211039A CN112413118B CN 112413118 B CN112413118 B CN 112413118B CN 202011211039 A CN202011211039 A CN 202011211039A CN 112413118 B CN112413118 B CN 112413118B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H63/00—Control outputs from the control unit to change-speed- or reversing-gearings for conveying rotary motion or to other devices than the final output mechanism
- F16H63/02—Final output mechanisms therefor; Actuating means for the final output mechanisms
- F16H63/30—Constructional features of the final output mechanisms
- F16H63/34—Locking or disabling mechanisms
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H63/00—Control outputs from the control unit to change-speed- or reversing-gearings for conveying rotary motion or to other devices than the final output mechanism
- F16H63/02—Final output mechanisms therefor; Actuating means for the final output mechanisms
- F16H63/30—Constructional features of the final output mechanisms
- F16H63/34—Locking or disabling mechanisms
- F16H63/3416—Parking lock mechanisms or brakes in the transmission
- F16H63/3425—Parking lock mechanisms or brakes in the transmission characterised by pawls or wheels
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H63/00—Control outputs from the control unit to change-speed- or reversing-gearings for conveying rotary motion or to other devices than the final output mechanism
- F16H63/02—Final output mechanisms therefor; Actuating means for the final output mechanisms
- F16H63/30—Constructional features of the final output mechanisms
- F16H63/34—Locking or disabling mechanisms
- F16H63/3416—Parking lock mechanisms or brakes in the transmission
- F16H63/3458—Parking lock mechanisms or brakes in the transmission with electric actuating means, e.g. shift by wire
- F16H63/3466—Parking lock mechanisms or brakes in the transmission with electric actuating means, e.g. shift by wire using electric motors
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H63/00—Control outputs from the control unit to change-speed- or reversing-gearings for conveying rotary motion or to other devices than the final output mechanism
- F16H63/40—Control outputs from the control unit to change-speed- or reversing-gearings for conveying rotary motion or to other devices than the final output mechanism comprising signals other than signals for actuating the final output mechanisms
- F16H63/48—Signals to a parking brake or parking lock; Control of parking locks or brakes being part of the transmission
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
- F16H2061/0075—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by a particular control method
- F16H2061/0087—Adaptive control, e.g. the control parameters adapted by learning
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- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Gear-Shifting Mechanisms (AREA)
Abstract
The invention discloses a self-learning method and an implementation method for electronic parking gear position. The self-learning method comprises the following steps: setting position reference values of a driving motor and/or a pawl at an unlocking end and a parking end respectively according to design parameters of a parking system; when entering a self-learning state, carrying out self-learning of the positions of the driving motor and/or the pawl at the unlocking end and the parking end to obtain self-learning values of the positions of the driving motor and/or the pawl at the unlocking end and the parking end; and determining the target positions of the driving 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. According to the technical scheme, the unlocking end and the parking end are simultaneously learned, the control position is determined in a two-way mode according to the learning angle, and the problem of error accumulation caused by a size chain is shared by the two ends, so that the control precision is improved; and the self-learning position is corrected according to the position reference value, so that the method is more reasonable and accurate.
Description
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 for electronic parking gear position.
Background
The electronic parking gear parking system is a technology for locking a gearbox or a speed reducing mechanism by electronically controlling a parking motor, and compared with a hydraulic parking mechanism, the electronic parking gear parking system is complex in structure, high in technical difficulty and high in cost, and the electronic parking mechanism is reliable, simple and efficient in structure and is increasingly applied to new energy vehicles.
The parking motor of the electronic parking gear parking system needs to control rotation according to a parking position and an unlocking position, but the parking motor also comprises a parking motor speed reducing mechanism, a guide shaft, a parking pawl, a parking ratchet wheel and other parts behind the parking motor, because the parts have manufacturing errors in processing, the parking position and the unlocking position of each set of parking system are not consistent, if the same set of control positions are used, the parking function can be failed, if each set of system needs to be calibrated, the time cost and the after-sale cost are high, and therefore a reasonable and effective position self-learning method needs to be provided. In the prior art, a method for self-learning the unlocking position also exists, the parking position is deduced unidirectionally according to the mechanical angle through the unlocking position, the parking position is deviated due to the problem of size chain error accumulation, the position control precision is influenced, mechanical collision damage is caused, and the self-learning position may fail due to the occurrence of errors in the mechanical problem.
Disclosure of Invention
In view of the above problems, the present invention discloses an electronic parking position self-learning method and an implementation method to overcome the above problems or at least partially solve the above problems.
In order to achieve the purpose, the invention adopts the following technical scheme:
the embodiment of the invention provides a self-learning method of an electronic parking gear position on one hand, and the self-learning method comprises the following steps:
setting position reference values of a driving motor and/or a pawl at an unlocking end and a parking end respectively according to design parameters of a parking system;
when entering a self-learning state, performing self-learning of the positions of the driving motor and/or the pawl at an unlocking end and a parking end to obtain self-learning values of the positions of the driving motor and/or the pawl at the unlocking end and the parking end;
and determining the target positions of the driving 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.
Optionally, the position reference value specifically includes any one or more of: the device comprises a parking motor unlocking reference position, a parking motor parking reference position, a parking motor reference working angle, a pawl unlocking position reference position, an angle between a pawl unlocking position and a top tooth position, and an angle between a pawl unlocking position and a pawl parking position.
Optionally, said performing self-learning of the position of said drive motor and/or said pawl at the unlocking end and the parking end specifically comprises:
judging the current position state according to the driving motor and/or the pawl reference position, if the current position state is in a parking state, firstly executing an unlocking process by the parking motor, outputting the parking motor at a duty ratio of more than 70% before reaching the parking motor unlocking reference position to ensure the output capacity of the parking motor, outputting the parking motor at a duty ratio of less than 40% after reaching the parking motor unlocking reference position to ensure that an executing mechanism of a parking system smoothly reaches a limit point, wherein the parking motor position after the unlocking process is the parking motor unlocking self-learning position, the pawl position is the pawl unlocking self-learning position at the moment, and the parking motor position self-learning is entered after the unlocking position self-learning is completed;
and if the parking motor is in the unlocking state, the parking motor firstly executes the parking process to perform self-learning of the parking position, and then executes the unlocking process to perform self-learning of the unlocking position.
Optionally, after performing the position self-learning of the drive motor and/or the pawl at the unlocking end and the parking end, the position self-learning method further comprises:
and checking the positions of the driving motor and/or the pawl, judging whether the position self-learning values meet preset conditions or not, wherein when the preset conditions are met, the position self-learning is successful, and otherwise, the position self-learning is failed.
Optionally, the preset conditions are as follows: the unlocking self-learning position value of the parking motor is larger than the unlocking reference position value of the parking motor; the parking self-learning position value of the parking motor is smaller than the parking reference position value of the parking motor; the pawl unlocking self-learning position value is greater than the pawl unlocking position reference position value; the angle difference value between the unlocking self-learning position of the pawl and the current position of the parking state pawl is located in a preset range of angles between the unlocking position of the pawl and the position of the top tooth, or the angle difference value between the unlocking self-learning position of the pawl and the current position of the parking state pawl is larger 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 driving 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:
assuming that a parking motor control unlocking target position value is PmdeTarget, a parking motor control parking target position value is PmdeTarget, a pawl control unlocking target position value is PdpeTarget, a pawl control top tooth target position value is Pptt _ Target, a pawl control parking target position value is Ppe _ Target, a parking motor control unlocking self-learning position value is PmdeWhudy, a parking motor control parking self-learning position value is PmdeWhudy, a pawl control unlocking self-learning position value is PdpeWhudy, a parking motor reference working angle is Pmdvalid, a reference angle between a pawl unlocking position and a top tooth position is PdpyValid, and an angle between an unlocking position and a pawl parking position is PpVvalid, 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 location self-learning method further includes:
the parking controller judges the current system state after receiving the self-learning control instruction, and if a position sensor fault or a system fault influencing function realization exists, the controller enters the system fault state;
if the controller receives the reset instruction, the controller can return to an initial state in a system fault state, a self-learning success state and a self-learning failure state, and the self-learning does not respond in the process of going.
The embodiment of the invention provides a method for realizing self-learning of the position of the electronic parking gear, which is characterized by comprising the following steps:
the method comprises the steps that a controller of the electronic parking receives a position self-learning control instruction sent by an upper computer, wherein the position self-learning control instruction comprises a self-learning command and a function resetting instruction, if the controller receives the self-learning command, the position self-learning method is executed, and if the function resetting instruction is received, the position self-learning method returns to an initial state under a system fault state, a self-learning success state and a self-learning failure state;
after the position self-learning succeeds, the controller receives a time recording instruction and a time mark numerical value sent by an upper computer, the controller stores the time mark numerical value to a data table after receiving the time recording instruction, and the data table also stores the position numerical value of the driving motor and/or the pawl corresponding to the time mark numerical value;
and after receiving an information reading instruction, the controller reads the time mark numerical value and the position numerical value recorded in the position self-learning process from the data table.
Optionally, before the controller for electronic parking receives a position self-learning control instruction sent by an upper computer, the implementation method further includes a step of service connection confirmation:
the upper computer sends a random code to the controller through CAN communication, first secret key data are generated through an encryption algorithm, second secret key data are generated and transmitted back by the controller through the same encryption algorithm as the upper computer and transmitted back to the upper computer through CAN communication, and the upper computer detects whether the first secret key data and the second secret key data are consistent; if the service connection is consistent with the service connection failure, the service connection is successful, and if the service connection is inconsistent with the service connection failure, the service connection failure is fed back.
Optionally, the implementation method is performed by using a CAN diagnostic protocol and diagnostic decision software, and/or the implementation method is performed in a factory test stage or an after-sales maintenance stage of the electronic parking lot.
The invention has the advantages and beneficial effects that:
according to the technical scheme disclosed by the embodiment of the invention, the control position is determined bidirectionally according to the learning angle through simultaneous calibration of the parking position and the unlocking position, and the problem of error accumulation caused by a size chain is shared by the two ends of the parking position and the unlocking position, so that the control precision can be improved; the self-learning position is corrected according to the position reference value, so that the method is more accurate;
secondly, the embodiment of the invention can carry out correctness verification on the position of the parking motor or the pawl, adds a reasonable judging method for the self-learning position and verifies the reasonable effectiveness of the self-learning position;
thirdly, aiming at the position self-learning method, the method for realizing the position self-learning online control is provided, so that the effective control of the position self-learning is realized, the learning can be repeated, the verification is carried out for multiple times, and the method is particularly suitable for the delivery offline test and the after-sales maintenance stage of the parking system;
and fourthly, the position self-learning and the verification are carried out by adopting a CAN (controller area network) diagnosis protocol, a software tool and the like, so that the vehicle system is safer and more stably accords with the current vehicle diagnosis trend.
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 only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic flow chart of a method for self-learning an electronic parking position in accordance with an embodiment of the present invention;
FIG. 2 is a schematic illustration of the unlocking and parking process and the positional relationship of the parking motor (pawl) in one embodiment of the present invention;
FIG. 3 is a schematic illustration of a transition of a position self-learning state of a park system controller in an embodiment of the present invention;
fig. 4 is a flowchart illustrating a method for implementing self-learning of an electronic parking position according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and fully with reference to the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solutions provided by the embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Example 1
The method for self-learning the electronic parking position is shown in fig. 1. The self-learning method comprises the following steps:
and S110, setting position reference values of a driving motor and a pawl at an unlocking end and a parking end respectively according to design parameters of a mechanical mechanism in the parking system, such as the sizes and the matching requirements of parts of a parking motor reducing mechanism, a guide shaft, a parking pawl, a parking ratchet wheel and the like, and installing sensors in the parking system for detecting the rotating angle of the driving motor and the real-time position of the pawl respectively.
The parking motor and the pawl position are set to be a lower limit position when the vehicle is parked, and are set to be an unlocked position, namely an upper limit position range, for example, the parking motor and the pawl can be set to be 0 at a parking angle or position and then rise along with the value of the rotation angle or position. The position reference value may preferably be a position immediately after the unlocking and the parking and immediately after the unlocking, and the target position of the control to be achieved is finally determined with reference to the position.
And S120, when the operation state of software in a controller such as the controller is in a self-learning ongoing state, carrying out self-learning on the positions of the driving motor and/or the pawl at the unlocking end and the parking end to obtain self-learning values of the positions of the driving motor and/or the pawl at the unlocking end and the parking end.
As can be seen from fig. 2, the self-learned position is preferably the extreme position which can be reached by the drive motor or the pawl during factory or after-market service diagnostics, in which case the value of the actuating motor parking self-learning (lower extreme) position is smaller than the value of the actuating motor parking reference position, smaller than the value of the actuating motor unlocking reference position and smaller than the value of the actuating motor unlocking self-learning (upper extreme) position. Of course, other intermediate test positions or real-time positions are within the scope of the present embodiment.
And S130, determining the target positions of the driving 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.
The self-learning value and the reference value are respectively selected to be close to two sides of the final control target position, so that the final target position to be controlled and realized is obtained according to the position relation.
In summary, according to the technical scheme described in the above embodiment, the parking position and the unlocking position can be calibrated at the same time by self-learning at the unlocking end and the parking end respectively, the control target position to be realized is determined bidirectionally according to the learned position or angle, and the problem of error accumulation caused by a size chain is shared at both ends, so that the control precision is improved, and the mechanical collision damage is avoided; and the self-learning position is corrected according to the position reference value, so that the method is more reasonable and accurate.
Specifically, the position reference value specifically includes: the parking control method comprises the following steps of (1) unlocking a parking motor reference position and a parking motor parking reference position, referring to a working angle of the parking motor at the two reference positions, and realizing a maximum control angle of the parking motor; the pawl unlocking position reference position and the pawl parking position reference position can be angles formed by the pawl when the pawl is in the unlocking reference position and the top tooth position, and can also be angles formed between the pawl unlocking reference position and the pawl parking reference position, wherein the top tooth position is a position where the pawl and a ratchet wheel or a cam are contacted or even clamped with teeth of the ratchet wheel or the cam when the pawl and the ratchet wheel or the cam are contacted, and the probability that the pawl is in the top tooth position during parking is also relatively high.
One of the positions or angles can be selected, and self-learning at the unlocking end and the parking end can be carried out according to any one of the positions or angles. It is, of course, preferred to test each of the above to obtain the best results.
Further, the specific implementation process of S120 is as follows: judging the current position state according to the driving motor and/or the pawl reference position, if the current parking gear is in the parking state, the parking motor needs to firstly execute an unlocking process, outputting the parking motor with a large duty ratio of more than 70% and preferably 100% before reaching the parking motor unlocking reference position to ensure the output capacity and speed of the parking motor, outputting the parking motor with a duty ratio of less than 40% and preferably 30% after reaching the parking motor unlocking reference position to ensure that an executing mechanism of the parking system smoothly reaches a limit point, marking the parking motor limit position after the unlocking process is finished as a parking motor unlocking self-learning position, setting the pawl position as a pawl unlocking self-learning position at the moment, entering the parking position self-learning after the unlocking position self-learning is finished, and finishing the whole parking or unlocking process within 1 second in the circulation.
On the contrary, if the electronic parking gear is in the unlocking state at the beginning, the parking motor firstly executes the parking process, performs self-learning of the parking position, then executes the unlocking process, and performs self-learning of the unlocking position, and the essential processes are the same.
In a preferred embodiment of the present invention, after the step S120 is started, the self-learning method further includes a step of location checking, where the self-learning status of this time is judged according to a preset condition, and when the preset condition is met, the location self-learning is successful, otherwise, the location self-learning fails. And then resetting and self-learning are carried out according to the judgment result.
Specifically, referring to fig. 2, the preset condition is a condition determined according to the self-learned position and the ideal position, and preferably all the preset conditions are satisfied, so that a correct position result is obtained. If the self-learning position is correct, the following steps are performed: 1. the unlocking self-learning position value of the parking motor is larger than the unlocking reference position value of the parking motor; 2. the parking self-learning position value of the parking motor is smaller than the parking reference position value of the parking motor; 3. the pawl unlocking self-learning position value is greater than the pawl unlocking position reference position value; 4. the difference value between the unlocking self-learning position value of the pawl and the current position of the parking state pawl is located in a preset range of an angle between the unlocking position of the pawl and the position of the top tooth, or the difference value between the unlocking self-learning position of the pawl and the current position of the parking state pawl is larger than the preset range of the unlocking position of the pawl and the parking angle of the pawl, wherein the preset range is preferably positive and negative theta of an error range of the angle, and specific numerical values can be set according to the actual condition of a parking system.
Through the verification mode, the accuracy and the reasonable effectiveness of self-learning are improved, and self-learning errors of the parking system are avoided.
In one embodiment, also referring to the relationship between the positions shown in fig. 2, S130 specifically includes: assuming that a parking motor control unlocking target position value is PmdeTarget, a parking motor control parking target position value is PmdeTarget, a pawl control unlocking target position value is PdpeTarget, a pawl control top tooth target position value is Pptt _ Target, a pawl control parking target position value is Ppe _ Target, a parking motor control unlocking self-learning position value is PmdeWhityPmdy, a parking motor control parking self-learning position value is PmdeWhityPmd, a pawl control unlocking self-learning position value is PdpeWhitudy, a parking motor reference working angle is Pmdvalid, a reference angle between a pawl unlocking position and a top tooth position is Pptt _ valid, and an angle between a pawl unlocking position and a pawl parking position is PpdyValid, the values should satisfy the following relations:
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 formula, the target values 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 values.
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 requirement, thereby increasing the controllability. The method comprises the following specific steps: the parking controller judges the current system state after receiving the self-learning control instruction, and if a position sensor fault or a system fault influencing function realization exists, the controller enters the system fault state; if the controller receives the reset instruction, the controller can return to an initial state in a system fault state, a self-learning success state and a self-learning failure state, and the self-learning does not respond in the process of going.
By the aid of the mode, controllability of self-learning can be achieved, repeated learning and repeated verification can be achieved, and smooth implementation of self-learning is guaranteed.
Example 2
Referring to fig. 4, a schematic flow chart of an implementation method for self-learning of an electronic parking position is shown, and the implementation method includes the following steps:
controlling and implementing position self-learning: the electronic parking controller receives a position self-learning control instruction sent by an upper computer, the position self-learning control instruction comprises a self-learning command and a function resetting instruction, if the controller receives the self-learning command, a preset position self-learning algorithm is executed, system fault self-checking is firstly carried out, if the self-checking succeeds in entering a self-learning state, and of course, the initial state and the system fault state can be fed back by the self-checking, and resetting is possibly needed.
If a function reset instruction is received, returning to an initial state under a system fault state, a self-learning success state and a self-learning failure state;
after the position self-learning succeeds, the controller receives a time recording instruction sent by the upper computer and a time mark numerical value, the time mark numerical value is stored in the data table after the controller receives the time recording instruction sent by the upper computer, and the position numerical value of the driving motor and/or the pawl corresponding to the time mark numerical value is also stored in the data table.
And after receiving an information reading instruction, the controller reads the time mark numerical value and the position numerical value recorded in the position self-learning process from the data table.
The above numerical values specifically include: the parking device comprises a parking actuator unlocking position, a parking actuator parking position, a pawl unlocking position, a pawl top tooth position, a pawl parking position and a time mark numerical value.
The implementation method accords with the current vehicle diagnosis trend, and can be repeatedly learned and verified for many times.
In a preferred embodiment, in order to ensure that the position self-learning can only be triggered in a controlled and safe situation, the following "handshake check" is provided: the upper computer sends a random code to the controller through CAN communication, and generates secret key data through an encryption algorithm, the controller generates returned secret key data through the same encryption algorithm as the upper computer and returns the returned secret key data to the upper computer through CAN communication, and the upper computer detects whether the two secret key data are consistent; if the service connection is consistent with the service connection failure, the service connection is successful, and if the service connection is inconsistent with the service connection failure, the service connection failure is fed back.
The implementation method is carried out in the delivery test stage or the after-sales maintenance stage of the electronic parking gear by adopting the CAN diagnostic protocol and the diagnostic judgment software, is safer and more stable for a vehicle system, ensures the accuracy of the self-learning position, and avoids causing functional failure.
The above description is only an embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, extension, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (9)
1. The self-learning method for the electronic parking gear is characterized by comprising the following steps of:
setting position reference values of a driving motor and/or a pawl at an unlocking end and a parking end respectively according to design parameters of a parking system;
when entering a self-learning state, performing self-learning of the positions of the driving motor and/or the pawl at an unlocking end and a parking end to obtain self-learning values of the positions of the driving motor and/or the pawl at the unlocking end and the parking end;
determining target positions of the driving motor and/or the pawl at an unlocking end and a parking end according to the position self-learning value and the position reference value;
the determining the target positions of the driving 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 the following steps:
assuming that a parking motor control unlocking target position value is PmdeTarget, a parking motor control parking target position value is PmdeTarget, a pawl control unlocking target position value is PdpeTarget, a pawl control top tooth target position value is Pptt _ Target, a pawl control parking target position value is Ppe _ Target, a parking motor control unlocking self-learning position value is PmdeWhudy, a parking motor control parking self-learning position value is PmdeWhudy, a pawl control unlocking self-learning position value is PdpeWhudy, a parking motor reference working angle is Pmdvalid, a reference angle between a pawl unlocking position and a top tooth position is PdpyValid, and an angle between an unlocking position and a pawl parking position is PpVvalid, 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。
2. the location self-learning method according to claim 1, wherein the location reference value specifically comprises any one or more of: the device comprises a parking motor unlocking reference position, a parking motor parking reference position, a parking motor reference working angle, a pawl unlocking position reference position, an angle between a pawl unlocking position and a top tooth position, and an angle between a pawl unlocking position and a pawl parking position.
3. The position self-learning method according to claim 2, wherein the performing of the position self-learning of the drive motor and/or the pawl at the unlocking end and the parking end specifically comprises:
judging the current position state according to the driving motor and/or the pawl reference position, if the current position state is in a parking state, firstly executing an unlocking process by the parking motor, outputting the parking motor at a duty ratio of more than 70% before reaching the parking motor unlocking reference position to ensure the output capacity of the parking motor, outputting the parking motor at a duty ratio of less than 40% after reaching the parking motor unlocking reference position to ensure that an executing mechanism of a parking system smoothly reaches a limit point, wherein the parking motor position after the unlocking process is the parking motor unlocking self-learning position, the pawl position is the pawl unlocking self-learning position at the moment, and the parking motor position self-learning is entered after the unlocking position self-learning is completed;
and if the parking motor is in the unlocking state, the parking motor firstly executes the parking process to perform self-learning of the parking position, and then executes the unlocking process to perform self-learning of the unlocking position.
4. The position self-learning method of claim 1, wherein after performing the position self-learning of the drive motor and/or the pawl at the unlocking end and the parking end, the position self-learning method further comprises:
and checking the positions of the driving motor and/or the pawl, judging whether the position self-learning values meet preset conditions or not, wherein when the preset conditions are met, the position self-learning is successful, and otherwise, the position self-learning is failed.
5. The position self-learning method according to claim 4, wherein the preset condition is as follows: the unlocking self-learning position value of the parking motor is larger than the unlocking reference position value of the parking motor; the parking self-learning position value of the parking motor is smaller than the parking reference position value of the parking motor; the pawl unlocking self-learning position value is greater than the pawl unlocking position reference position value; the angle difference value between the unlocking self-learning position of the pawl and the current position of the parking state pawl is located in a preset range of angles between the unlocking position of the pawl and the position of the top tooth, or the angle difference value between the unlocking self-learning position of the pawl and the current position of the parking state pawl is larger than the preset range of the unlocking position of the pawl and the parking angle of the pawl.
6. The location self-learning method according to claim 4, further comprising:
the parking controller judges the current system state after receiving the self-learning control instruction, and if a position sensor fault or a system fault influencing function realization exists, the controller enters the system fault state;
and if the controller returns to the initial state in the system fault state, the self-learning success state and the self-learning failure state after receiving the reset instruction, the controller does not respond to the reset instruction when self-learning is in progress.
7. An electronic parking gear position self-learning implementation method is characterized by comprising the following steps:
the method comprises the steps that a controller of the electronic parking receives a position self-learning control instruction sent by an upper computer, the position self-learning control instruction comprises a self-learning command and a function resetting instruction, if the controller receives the self-learning command, the position self-learning method according to any one of claims 1-6 is executed, and if the function resetting instruction is received, the position self-learning method returns to an initial state under a system fault state, a self-learning success state and a self-learning failure state;
after the position self-learning succeeds, the controller receives a time recording instruction and a time mark numerical value sent by an upper computer, the controller stores the time mark numerical value to a data table after receiving the time recording instruction, and the data table also stores the position numerical value of the driving motor and/or the pawl corresponding to the time mark numerical value;
and after receiving an information reading instruction, the controller reads the time mark numerical value and the position numerical value recorded in the position self-learning process from the data table.
8. The implementation method of claim 7, 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 following steps of service connection confirmation:
the upper computer sends a random code to the controller through CAN communication, first secret key data are generated through an encryption algorithm, second secret key data are generated and transmitted back by the controller through the same encryption algorithm as the upper computer and transmitted back to the upper computer through CAN communication, and the upper computer detects whether the first secret key data and the second secret key data are consistent; if the service connection is consistent with the service connection failure, the service connection is successful, and if the service connection is inconsistent with the service connection failure, the service connection failure is fed back.
9. The implementation method of claim 7, wherein the implementation method is performed using a CAN diagnostic protocol and diagnostic decision software, and/or wherein the implementation method is performed during a factory test phase or an after-sales maintenance phase of the electronic parking lot.
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| CN202011211039.XA CN112413118B (en) | 2020-11-03 | 2020-11-03 | Self-learning method and implementation method for electronic parking gear position |
| PCT/CN2021/098933 WO2022095445A1 (en) | 2020-11-03 | 2021-06-08 | Electronic parking gear position self-learning method and implementation method |
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| CN115075938A (en) * | 2022-06-27 | 2022-09-20 | 长城汽车股份有限公司 | Self-learning control method and device for turbocharger, vehicle and storage medium |
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| US6701797B2 (en) * | 2001-01-04 | 2004-03-09 | Ford Global Technologies, Llc | Parking assembly |
| JP4389985B2 (en) * | 2007-09-04 | 2009-12-24 | トヨタ自動車株式会社 | Actuating device for state switching element, range switching device for automatic transmission, and parking device |
| CN104455375B (en) * | 2014-11-27 | 2017-02-01 | 盛瑞传动股份有限公司 | Self-learning gear control method and device for transmission control unit |
| JP6668801B2 (en) * | 2016-02-12 | 2020-03-18 | 株式会社デンソー | Range switching control device |
| JP6705428B2 (en) * | 2017-06-16 | 2020-06-03 | 株式会社デンソー | Shift range control device |
| CN111271446A (en) * | 2018-12-04 | 2020-06-12 | 广州汽车集团股份有限公司 | Parking mechanism position calibration method and device and automobile |
| CN110056643B (en) * | 2019-03-07 | 2021-12-17 | 广汽零部件有限公司 | Gear self-learning method of knob gear shifter |
| CN110261128B (en) * | 2019-04-28 | 2022-03-11 | 广汽零部件有限公司 | Offline self-learning and gear detection method for automobile automatic gear knob gear shifter |
| CN110920589B (en) * | 2019-12-03 | 2021-01-26 | 株洲齿轮有限责任公司 | Electronic parking gear self-learning method, electronic parking method, controller and vehicle |
| CN111412280A (en) * | 2020-03-27 | 2020-07-14 | 广州小鹏汽车科技有限公司 | Electric automobile and P gear control method thereof |
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