CN115949743A - Self-learning method, device and equipment for hybrid vehicle clutch and storage medium - Google Patents
Self-learning method, device and equipment for hybrid vehicle clutch and storage medium Download PDFInfo
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Abstract
The invention discloses a self-learning method, a device, equipment and a storage medium of a hybrid vehicle clutch, wherein the method comprises the following steps: calculating the slope of a clutch pressure curve according to the oil pressure point of the clutch; acquiring target pressure points of the clutch within each pre-estimated set interval time in a superposition approximation mode according to the slope of the pressure curve of the clutch; comparing the target pressure point of the clutch within the estimated set interval time with the actually measured pressure point, and judging whether the target pressure point of the clutch is an inflection point on the slope of a clutch pressure curve; and when the target pressure point of the clutch is determined to be an inflection point on the slope of the clutch pressure curve, taking the target pressure point of the clutch as a clutch pressure self-learning value. The method and the device can improve the self-learning accuracy while improving the response of the clutch, not only ensure the driving performance of the vehicle, but also realize the self-learning function of the clutch.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a self-learning method, a self-learning device, self-learning equipment and a self-learning storage medium for a hybrid vehicle clutch.
Background
In the pressure building process of the clutch, when the oil pressure is lower than the oil pressure of a pressure point for starting to transmit torque, the volume of a piston cavity is continuously increased due to the fact that the oil pressure pushes a clutch piston, the pressure rise of the clutch is slow, and the time of the clutch combination process is too long. When the oil pressure is above the pressure point at which torque transmission begins, the piston chamber volume no longer changes as the clutch plates are engaged, and oil pressure can build up and rise rapidly, compressing the clutch plates. Therefore, before the clutch is engaged, the clutch is usually allowed to pre-build oil pressure to a point slightly below the pressure at which torque transfer begins, and clutch engagement can be achieved by rapidly increasing clutch oil pressure as the clutch is required to engage.
Conventionally, after a clutch is used for a long time, a pressure point oil pressure at which torque transmission of the clutch is started becomes smaller as a spring force of a clutch piston is attenuated. If the software is always controlled according to the pressure point oil pressure for fixing the start of torque transmission, when the pressure for starting torque transmission of the actual clutch is less than the pressure for starting torque transmission given by the software, the torque transmission of the clutch can be caused to cause drivability problems. In addition, the pressure point self-learning function of the conventional actual vehicle clutch for starting to transmit torque generally comprises the steps of disconnecting the connection between the output shaft of the clutch and the wheel end by using a synchronizer, setting different clutch oil pressures, and calculating the pressure point oil pressure for actually starting to transmit torque according to the inertia and the rotating speed change rate of the output shaft of the clutch. However, since the hybrid transmission is not provided with a synchronizer, the output end of the clutch is always connected with the wheels, and the pressure point self-learning of the starting torque transmission of the real vehicle can not be carried out by adopting the mode.
Therefore, how to self-learn the torque transmission pressure point of the actual vehicle clutch on the premise that the output shaft of the clutch cannot be decoupled from the wheels is a technical problem which needs to be solved urgently at present.
Disclosure of Invention
The invention mainly aims to provide a self-learning method, a device, equipment and a storage medium of a hybrid vehicle clutch, which can improve the responsiveness of the clutch and the accuracy of self-learning, ensure the driving performance of the vehicle and realize the function of self-learning of the clutch.
In a first aspect, the present application provides a self-learning method of a hybrid vehicle clutch, the method comprising the steps of:
calculating the slope of a clutch pressure curve according to an oil pressure point of the clutch, wherein the oil pressure point is a pressure value read by an oil pressure sensor;
acquiring target pressure points of the clutch within each pre-estimated set interval time in a superposition approximation mode according to the slope of the clutch pressure curve;
comparing the target pressure point of the clutch within the estimated set interval time with the actually measured pressure point, and judging whether the target pressure point of the clutch is an inflection point on the slope of a clutch pressure curve;
and when the target pressure point of the clutch is determined to be an inflection point on the slope of the clutch pressure curve, taking the target pressure point of the clutch as a clutch pressure self-learning value.
With reference to the foregoing first aspect, as an optional implementation manner, when a difference between a target pressure point and an actually measured pressure point of the clutch exceeds a preset range, the target pressure point of the clutch is used as a discontinuity point on a slope of the clutch pressure curve.
With reference to the first aspect, as an optional implementation manner, when the target pressure point of the clutch is not a discontinuity point on the slope of the clutch pressure curve, it is determined whether a difference between a next target pressure point and an actually measured pressure point exceeds a preset range.
With reference to the first aspect, as an alternative implementation manner, the calculating a slope of a clutch pressure curve according to an oil pressure point of the clutch includes: when the clutch is self-learned, recording the clutch pressure at the current moment as a first oil pressure point, and determining a second oil pressure point of the clutch after a preset time length;
and calculating the slope of the clutch pressure curve through the first oil pressure point, the second oil pressure point and the preset time length of the clutch.
With reference to the first aspect, as an optional implementation manner, according to a formula: and Pn = k T + P2, a target pressure point of the clutch in a set time interval is estimated, wherein Pn is estimated clutch pressure at the nth time, k is a slope, T is a set time interval, P2 is a second oil pressure point of the clutch, and the set time interval is 50ms.
With reference to the first aspect, as an alternative implementation manner, the clutch pressure self-learning value is stored.
With reference to the first aspect, as an optional implementation manner, it is determined whether a self-learning condition of the clutch is satisfied according to the oil temperature and the rotation speed of the clutch;
when the oil temperature of the clutch is within a set oil temperature range and the rotating speed of the clutch is within a set rotating speed range, determining that the clutch meets a self-learning condition;
and when the clutch oil temperature is not in a set oil temperature range or the clutch rotating speed is in a set rotating speed range, determining that the clutch does not meet self-learning conditions.
In a second aspect, the present application provides a self-learning apparatus for a hybrid vehicle clutch, the apparatus comprising:
the calculation module is used for calculating the slope of a clutch pressure curve according to an oil pressure point of the clutch, wherein the oil pressure point is a pressure value read by an oil pressure sensor;
the estimation module is used for acquiring target pressure points of the clutch within each estimated set interval time in a superposition approximation mode according to the slope of the clutch pressure curve;
the judging module is used for comparing the target pressure point and the actually measured pressure point of the clutch within the estimated set interval time and judging whether the target pressure point of the clutch is an inflection point on the slope of a clutch pressure curve or not;
a determination module for taking a target pressure point of the clutch as a clutch pressure self-learning value when it is determined that the target pressure point of the clutch is an inflection point on a slope of the clutch pressure curve.
In a third aspect, the present application further provides an electronic device, including: a processor; a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method of any of the first aspects.
In a fourth aspect, the present application also provides a computer readable storage medium storing computer program instructions which, when executed by a computer, cause the computer to perform the method of any of the first aspects.
The application provides a self-learning method, a device, equipment and a storage medium of a hybrid vehicle clutch, wherein the method comprises the following steps: calculating the slope of a clutch pressure curve according to an oil pressure point of the clutch, wherein the oil pressure point is a pressure value read by an oil pressure sensor; acquiring target pressure points of the clutch within each pre-estimated set interval time in a superposition approximation mode according to the slope of the clutch pressure curve; comparing the target pressure point of the clutch within the estimated set interval time with the actually measured pressure point, and judging whether the target pressure point of the clutch is an inflection point on the slope of a clutch pressure curve; and when the target pressure point of the clutch is determined to be an inflection point on the slope of the clutch pressure curve, taking the target pressure point of the clutch as a clutch pressure self-learning value. The method and the device can improve the responsiveness of the clutch and improve the accuracy of self-learning, not only ensure the driving performance of the vehicle, but also realize the function of self-learning of the clutch.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart of a self-learning method of a hybrid vehicle clutch provided in an embodiment of the present application;
FIG. 2 is a schematic illustration of a self-learning apparatus for a hybrid vehicle clutch as provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of a self-learning algorithm for a clutch provided in an embodiment of the present application;
FIG. 4 is a schematic diagram of an electronic device provided in an embodiment of the present application;
fig. 5 is a schematic diagram of a computer-readable program medium provided in an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
The embodiment of the application provides a self-learning method, a self-learning device, equipment and a storage medium of a hybrid vehicle clutch, which can improve the responsiveness of the clutch and the accuracy of self-learning, ensure the drivability of a vehicle and realize the function of self-learning of the clutch.
In order to achieve the technical effects, the general idea of the application is as follows:
a method of self-learning a hybrid vehicle clutch, the method comprising the steps of:
s101: and calculating the slope of a clutch pressure curve according to the oil pressure point of the clutch, wherein the oil pressure point is the pressure value read by the oil pressure sensor.
S102: and acquiring target pressure points of the clutch within each estimated set interval time in a superposition approximation mode according to the slope of the pressure curve of the clutch.
S103: and comparing the target pressure point of the clutch with the actually measured pressure point within the estimated set interval time, and judging whether the target pressure point of the clutch is an inflection point on the slope of the pressure curve of the clutch.
S104: and when the target pressure point of the clutch is determined to be an inflection point on the slope of the clutch pressure curve, taking the target pressure point of the clutch as a clutch pressure self-learning value.
Embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flow chart of a self-learning method for a hybrid vehicle clutch according to the present invention, and as shown in fig. 1, the method includes the steps of:
and S101, calculating the slope of a clutch pressure curve according to an oil pressure point of the clutch, wherein the oil pressure point is a pressure value read by an oil pressure sensor.
Specifically, in order to ensure the accuracy of inflection point self-learning of the hybrid vehicle clutch, it is required that the oil temperature of the clutch is in a proper range, namely 20 ℃ to 60 ℃, and when the clutch rotates, a radial centripetal force is generated on oil, so that the oil pressure at a position far away from the axle center of the clutch is higher than the oil pressure at a position near the axle center, and the point of starting to transmit torque of the clutch is influenced. Therefore, the clutch rotation speed is required to be within a certain range, namely 1500rpm to 2500rpm.
In one embodiment, whether the self-learning condition of the clutch is met is judged according to the temperature and the rotating speed of the clutch, namely when the oil temperature of the clutch is 20-60 ℃ and the rotating speed is 1500-2500 rpm, the clutch is determined to meet the self-learning condition. It should be noted that the inflection point of the clutch may be understood as a clutch half-engagement point or a pressure point at which torque starts to be transmitted.
Optionally, when the oil temperature of the clutch is not between 20 ℃ and 60 ℃ or the rotating speed is not between 1500rpm and 2500rpm, determining that the clutch does not meet the self-learning condition.
Optionally, the self-learning triggering condition is that the target clutch pressure of the hybrid transmission control unit HTCU is greater than the clutch half-engagement point, and it is ensured that the pressure build-up at this time may exceed the oil pressure of the clutch half-engagement point.
Optionally, the actual clutch pressure of the HTCU is less than the previously stored self learned clutch half-engagement point minus a threshold pressure. If it has not been previously detected that self-learning of the clutch half-engagement point has been performed, the actual clutch pressure needs to be less than the default clutch half-engagement point calibrated in the software minus a threshold pressure.
Optionally, the actual clutch pressure of the HTCU is greater than the minimum clutch self-learned starting pressure. The purpose is to ensure that the clutch oil has begun to push the clutch piston cavity.
The clutch half-engagement point is a point at which the clutch starts to transmit torque.
Optionally, in order to ensure stability of the clutch self-learning condition each time, after the self-learning condition is identified to be satisfied, the clutch pressure is established in an open-loop control manner. The target speed of the CAPM at this time is set to the CAMP speed required to maintain the target clutch pressure.
Note that, the HTCU: hybrid Transmission Control Unit, BLDC: brushless Direct Current (bldc), CAPM: clutch actor Pump Motor, kisspoint: clutch half-engagement point (pressure point at which torque starts to be transmitted), VCU: vehicle Control Unit (Vehicle Control Unit).
When the self-learning of the clutch is started, the clutch pressure at the current moment is recorded and used as a first oil pressure point, timing is started from the first oil pressure point, when the clutch pressure is greater than the first oil pressure point and the timing duration is greater than a first preset duration, a second oil pressure point is determined, and the slope of a clutch pressure curve is calculated according to the first oil pressure point, the second oil pressure point and the first preset duration. For convenience of understanding and illustration, at the beginning of the self-learning of the clutch, the clutch pressure at the current moment is recorded and named as P _ FirstPoint; and starting to time from the current moment, wherein the name of the timer is T _ SincefFirstPoint, and when the clutch pressure is greater than P _ FirstPoint and the T _ SincefFirstPoint is greater than T1, a second oil pressure point is selected and named as P _ NewPoint. Where T1 is a calibratable variable, the slope k of the clutch pressure curve is calculated according to the formula k = (P _ NewPoint-P _ FirstPoint)/T _ sinceffirstpoint.
Note that, the first oil pressure point: when self-learning begins, reading a pressure value from an oil pressure sensor; second oil pressure point: when the clutch pressure is greater than P _ FirstPoint and T _ sinceffirstpoint is greater than T1, the pressure value read from the oil pressure sensor is taken as the second oil pressure point.
And S102, acquiring target pressure points of the clutch within each estimated set interval time in a superposition approximation mode according to the slope of the pressure curve of the clutch.
Specifically, after calculating the slope of a clutch pressure curve according to the oil pressure points, obtaining target pressure points of the clutch in each estimated set time interval on the curve in a superposition approximation mode, and facilitating understanding and illustrating, after determining the slope of the clutch pressure curve, taking a second oil pressure point as a base point, waiting for 50ms, estimating the clutch pressure according to a formula Pn = k T + P2, wherein Pn is the estimated clutch pressure at the nth time, k is the slope, T is the set time interval, P2 is the second oil pressure point of the clutch, when the estimated target pressure and the measured pressure difference value within 50ms are not in the preset range, continuing to calculate the target pressure within the next 50ms in an accumulation approximation mode, and judging whether the difference value between the estimated pressure and the next 50ms is in the preset range, if not, estimating the target pressure point in the next interval time in the superposition approximation mode, and if the difference value between the estimated target pressure and the measured pressure is in the preset range, taking the target pressure point as a sudden change point on the clutch pressure curve. For example, after determining the clutch pressure curve and waiting 50ms later, a predicted clutch pressure P1 is calculated. The calculation method is P1= k × T + P2, P1 is the first estimated clutch pressure, and if calculated a second time, is P2, i.e., P2= k × T + P2.
And S103, comparing the target pressure point and the actually measured pressure point of the clutch within the estimated set interval time, and judging whether the target pressure point of the clutch is an inflection point on the slope of the pressure curve of the clutch.
Specifically, the target pressure of the clutch is estimated once every 50ms, the estimated target pressure is compared with the actual measured pressure, when the difference value between the actual measured pressure and the target pressure is smaller than or equal to a set threshold value, the next target pressure is estimated on the slope of the clutch pressure curve continuously in a superposition approximation mode after 50ms, the estimated target pressure is compared with the actual measured pressure, when the difference value between the estimated target pressure and the actual measured pressure is larger than the set threshold value, the estimated target pressure point is used as a sudden change point on the slope of the clutch pressure curve, namely the estimated target pressure point is within a preset range and is used as a sudden change point on the curve. For example, the actual Clutch pressure P _ Clutch and the Estimated target pressure P _ Estimate at the moment are compared, if the P _ Clutch is less than or equal to the P _ Estimate _1+ delta P, the next target pressure is Estimated after 50ms, the next target pressure is compared with the measured pressure, and when the difference value between the predicted point P _ Estimated _ n and the actual oil pressure P _ Clutch is greater than delta P, the predicted point P _ Estimated _ n-x closest to the actual kisspesstoint is found, and x is determined.
It will be appreciated that the kisspeint oil pressure of the clutch is also measured beforehand, depending on the previously determined temperature and speed. Given a CAPM speed, pressure build-up begins. At this time, the oil pressure of each Estimated point can be calculated by software, for example, P _ Estimated _1 to P _ Estimated _100, 100 is a point n larger than Δ P. It is determined which predicted oil pressure is closest to the predicted kisssopoint oil pressure. E.g., P _ Estimated _90 is closest. Then x can be determined to be 10 at this point. Where ap is typically set to 10kPa.
And step S104, when the target pressure point of the clutch is determined to be an inflection point on the slope of the clutch pressure curve, taking the target pressure point of the clutch as a clutch pressure self-learning value.
It will be appreciated that at the nth time predicted Clutch pressure P _ Estimated _ n is calculated, and that if P _ Clutch > P _ Estimated _ n + Δ P, the nth-x times predicted Clutch pressure P _ Estimated _ n-x is taken as an inflection point on the slope of the Clutch pressure curve and as a Clutch pressure self-learned value.
In one embodiment, the clutch pressure self-learning value is stored and executed after the target pressure point of the clutch is used as the clutch pressure self-learning value.
It can be understood that during the pressure build-up process of the clutch, when the actual clutch pressure is less than kisdisplacement, the oil pressure rises more slowly and the slope is smaller. When the actual pressure is higher than kisspoint, the oil pressure rising slope increases significantly. Based on the characteristic, in the whole vehicle series-parallel connection switching process, if a fixed rotating speed is given to a clutch oil pump motor (CAPM), the inflection point of a clutch pressure curve, namely the inflection point of the slope, is identified through software, and then the real kisssopoint pressure can be determined.
Referring to fig. 2, fig. 2 is a schematic diagram illustrating a self-learning apparatus for a hybrid vehicle clutch according to the present invention, as shown in fig. 2, the apparatus includes:
the calculation module 201: the method is used for calculating the slope of a clutch pressure curve according to an oil pressure point of the clutch, wherein the oil pressure point is a pressure value read by an oil pressure sensor.
The estimation module 202: the method is used for acquiring the target pressure point of the clutch within each estimated set interval time in a superposition approximation mode according to the slope of the clutch pressure curve.
The judging module 203: the method is used for comparing the target pressure point and the measured pressure point of the clutch within the pre-estimated set interval time and judging whether the target pressure point of the clutch is an inflection point on the slope of the pressure curve of the clutch.
The determination module 204: and the controller is used for taking the target pressure point of the clutch as a clutch pressure self-learning value when the target pressure point of the clutch is determined to be an inflection point on the slope of the clutch pressure curve.
Further, in a possible implementation manner, the determining module 203 is further configured to, when a difference between the target pressure point and the measured pressure point of the clutch is within a preset range, use the target pressure point of the clutch as a discontinuity point on a slope of the clutch pressure curve.
Further, in a possible embodiment, the determining module 203 is further configured to determine whether a difference between a next target pressure point and a measured pressure point is within a preset range when the target pressure point of the clutch is not a discontinuity on the slope of the clutch pressure curve.
Further, in a possible embodiment, the calculation module 201 is further configured to, when the clutch is self-learning, record a clutch pressure at the current time as a first oil pressure point, and determine a second oil pressure point of the clutch after a preset time period;
and calculating the slope of the clutch pressure curve through the first oil pressure point, the second oil pressure point and the preset time length of the clutch.
Further, in a possible implementation, the estimation module 202 is further configured to, according to the formula: and Pn = k T + P2, a target pressure point of the clutch in a set time interval is estimated, wherein Pn is estimated clutch pressure at the nth time, k is a slope, T is a set time interval, P2 is a second oil pressure point of the clutch, and the set time interval is 50ms.
Further, in a possible implementation, the control device further comprises a storage module, wherein the storage module is used for storing the clutch pressure self-learning value.
Further, in a possible implementation manner, the determining module 204 is further configured to determine whether a self-learning condition of the clutch is satisfied according to the oil temperature and the rotating speed of the clutch;
when the oil temperature of the clutch is within a set oil temperature range and the rotating speed of the clutch is within a set rotating speed range, determining that the clutch meets self-learning conditions;
and when the clutch oil temperature is not in a set oil temperature range or the clutch rotating speed is in a set rotating speed range, determining that the clutch does not meet a self-learning condition.
Referring to fig. 3, fig. 3 is a schematic diagram of a self-learning algorithm of the clutch provided by the present invention, as shown in fig. 3:
t1 is the time interval between the first oil pressure point P _ FirstPoint and the second oil pressure point P _ NewPoint. And selecting proper T1 according to the actually measured clutch pressure curve to ensure that the calculated slope k of the clutch pressure curve is consistent with the reality.
T2 is the time interval for each Estimated clutch pressure P _ Estimated _ n. T2 should be small enough to ensure that the real kisspoint point is not missed. Where the T2 time is 50ms.
According to the measured pressure curve, when the difference value between the predicted point P _ Estimated _ n and the real oil pressure P _ Clutch is larger than delta P, then the predicted point P _ Estimated _ n-x closest to the real kisssopoint is found, and x is determined. The real kisssopoint will typically be detected when the gearbox is tested off-line.
It is understood that when the clutch begins self-learning, a first oil pressure point is recorded, timing is started from the first oil pressure point, a second oil pressure point is selected after the clutch pressure is greater than the first oil pressure point and exceeds a certain time, and the slope of the clutch pressure curve is calculated according to the first oil pressure point, the second oil pressure point and the interval duration between the first oil pressure point and the second oil pressure point. After the slope is determined, after the second oil pressure point is taken as a reference T2, namely after 50ms, the first clutch target pressure is estimated in a superposition approximation mode, whether the difference value between the estimated first target pressure and the actual measurement pressure is larger than delta P or not is judged, if not, after continuously waiting for 50ms, the second target pressure is calculated and is differed with the actual measurement pressure to judge whether the difference value is larger than delta P or not, and when the Nth target pressure is larger than delta P, the preset kisssoint point is subtracted from the Nth estimated target pressure and the obtained real kisssoint point is used.
It should be noted that, in practical engineering applications, the drivability of the whole vehicle usually has a high requirement on the responsiveness of the clutch. To ensure clutch responsiveness, a clutch oil pressure is pre-established before the clutch needs to be engaged. However, the lower the pre-established oil pressure is, the longer the response time when the clutch is engaged; the pre-established oil pressure is higher than the kisspeint oil pressure, and the clutch pre-established pressure process can transfer torque to cause the problem of the drivability of the whole vehicle.
By the method and the system, the HTCU can perform pre-build-up pressure control according to the real kisssopoint, so that the vehicle driving performance is guaranteed while the clutch responsiveness is improved.
An electronic device 400 according to this embodiment of the invention is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 4, electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to: the at least one processing unit 410, the at least one memory unit 420, and a bus 430 that couples various system components including the memory unit 420 and the processing unit 410.
Wherein the storage unit stores program code that can be executed by the processing unit 410 such that the processing unit 410 performs the steps according to various exemplary embodiments of the present invention as described in the above section "example methods" of the present specification.
The storage unit 420 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 421 and/or a cache memory unit 422, and may further include a read only memory unit (ROM) 423.
The storage unit 420 may also include a program/utility 424 having a set (at least one) of program modules 425, such program modules 425 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may comprise an implementation of a network environment.
The electronic device 400 may also communicate with one or more external devices (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 400 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 450. Also, the electronic device 400 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 460. As shown, the network adapter 460 communicates with the other modules of the electronic device 400 over the bus 430. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
According to an aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 5, a program product 500 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed, for example, synchronously or asynchronously in multiple modules.
In summary, the present application provides a self-learning method, apparatus, device and storage medium for a hybrid vehicle clutch, the method includes the steps of: calculating the slope of a clutch pressure curve according to an oil pressure point of the clutch, wherein the oil pressure point is a pressure value read by an oil pressure sensor; acquiring target pressure points of the clutch within each pre-estimated set interval time in a superposition approximation mode according to the slope of the clutch pressure curve; comparing the target pressure point of the clutch within the estimated set interval time with the actually measured pressure point, and judging whether the target pressure point of the clutch is an inflection point on the slope of a clutch pressure curve; and when the target pressure point of the clutch is determined to be the inflection point on the slope of the clutch pressure curve, taking the target pressure point of the clutch as a clutch pressure self-learning value. The method and the device can improve the responsiveness of the clutch and improve the accuracy of self-learning, not only ensure the driving performance of the vehicle, but also realize the function of self-learning of the clutch.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (10)
1. A method of self-learning a hybrid vehicle clutch, comprising:
calculating the slope of a clutch pressure curve according to an oil pressure point of the clutch, wherein the oil pressure point is a pressure value read by an oil pressure sensor;
acquiring target pressure points of the clutch within each pre-estimated set interval time in a superposition approximation mode according to the slope of the clutch pressure curve;
comparing the target pressure point and the actually measured pressure point of the clutch within the estimated set interval time, and judging whether the target pressure point of the clutch is an inflection point on the slope of a clutch pressure curve;
and when the target pressure point of the clutch is determined to be an inflection point on the slope of the clutch pressure curve, taking the target pressure point of the clutch as a clutch pressure self-learning value.
2. The method of claim 1, wherein comparing the target pressure points of the clutch with the measured pressure points one by one within the pre-estimated set interval time to determine whether the target pressure point of the clutch is an inflection point on the slope of the clutch pressure curve comprises:
and when the difference value between the target pressure point and the measured pressure point of the clutch exceeds a preset range, taking the target pressure point of the clutch as a catastrophe point on the slope of the pressure curve of the clutch.
3. The method of claim 2, further comprising:
and when the target pressure point of the clutch is not a catastrophe point on the slope of the clutch pressure curve, judging whether the difference value between the next target pressure point and the actually measured pressure point exceeds a preset range.
4. The method of claim 1, wherein calculating a slope of a clutch pressure curve based on an oil pressure point of the clutch comprises:
when the clutch is self-learned, recording the clutch pressure at the current moment as a first oil pressure point, and determining a second oil pressure point of the clutch after a preset time length;
and calculating the slope of the clutch pressure curve through the first oil pressure point, the second oil pressure point and the preset time length of the clutch.
5. The method of claim 1, wherein estimating the target pressure point for the clutch at the set interval comprises:
according to the formula: and Pn = k T + P2, a target pressure point of the clutch in a set time interval is estimated, wherein Pn is estimated clutch pressure at the nth time, k is a slope, T is a set time interval, P2 is a second oil pressure point of the clutch, and the set time interval is 50ms.
6. The method of claim 1, wherein the step of using the target pressure point of the clutch as a clutch pressure self-learning value comprises:
and storing the self-learning value of the clutch pressure.
7. The method of claim 1, further comprising:
judging whether the self-learning condition of the clutch is met or not according to the oil temperature and the rotating speed of the clutch;
when the oil temperature of the clutch is within a set oil temperature range and the rotating speed of the clutch is within a set rotating speed range, determining that the clutch meets a self-learning condition;
and when the clutch oil temperature is not in a set oil temperature range or the clutch rotating speed is in a set rotating speed range, determining that the clutch does not meet self-learning conditions.
8. A self-learning device for a hybrid vehicle clutch, comprising:
the calculation module is used for calculating the slope of a clutch pressure curve according to an oil pressure point of the clutch, wherein the oil pressure point is a pressure value read by an oil pressure sensor;
the pre-estimation module is used for acquiring target pressure points of the clutch within each pre-estimated set interval time in a superposition approximation mode according to the slope of the clutch pressure curve;
the judging module is used for comparing the target pressure point and the actually measured pressure point of the clutch within the estimated set interval time and judging whether the target pressure point of the clutch is an inflection point on the slope of a clutch pressure curve or not;
a determination module for taking a target pressure point of the clutch as a clutch pressure self-learning value when it is determined that the target pressure point of the clutch is an inflection point on a slope of the clutch pressure curve.
9. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores computer program instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 7.
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| CN202211604426.9A CN115949743B (en) | 2022-12-13 | 2022-12-13 | Self-learning method, device, equipment and storage medium for hybrid vehicle clutch |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117864102A (en) * | 2024-02-02 | 2024-04-12 | 重庆赛力斯凤凰智创科技有限公司 | Vehicle clutch control method, device, computer equipment and storage medium |
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