CN116557156A

CN116557156A - Variable valve timing self-learning control method, device, system, vehicle, electronic device and storage medium

Info

Publication number: CN116557156A
Application number: CN202310618393.1A
Authority: CN
Inventors: 兰江明
Original assignee: United Automotive Electronic Systems Co Ltd
Current assignee: United Automotive Electronic Systems Co Ltd
Priority date: 2023-05-29
Filing date: 2023-05-29
Publication date: 2023-08-08

Abstract

The invention relates to the technical field of vehicles and discloses a variable valve timing self-learning control method, a device, a system, a vehicle, electronic equipment and a storage medium, wherein the method comprises the steps of acquiring first current data and historical data of a vehicle engine when the vehicle engine is in a target working condition, determining a parameter change rate and a parameter change direction and a current effective duration if each current parameter value meets a corresponding preset first parameter range, and controlling the starting execution of the variable valve timing self-learning if the current effective duration is larger than the corresponding preset effective duration; by predicting the current effective duration in advance, the VVT self-learning can be started on the premise of meeting the time requirement, the phenomenon that the VVT self-learning exits due to insufficient time for completing the VVT self-learning is avoided, the VVT angle is prevented from being repeatedly pulled, and adverse effects on torque stability, combustion, drivability and the like of an engine are avoided.

Description

Variable valve timing self-learning control method, device, system, vehicle, electronic device and storage medium

Technical Field

The invention relates to the technical field of vehicles, in particular to a variable valve timing self-learning control method, a device, a system, a vehicle, electronic equipment and a storage medium.

Background

The existing engine EMS (Engine Management System ) control of the hybrid electric vehicle also comprises VCU (Vehicle control unit, vehicle controller) motor related control and BMS (Battery Management System ) battery management, under the condition of different SOCs (State of Charge, battery Charge State), the working condition of the VCU requesting the engine to run has larger difference compared with that of a pure internal combustion engine vehicle, and has certain influence on part of engine functions, such as the engine VVT (Variable Valve Timing, variable valve timing technology) fine self-learning function. It is well known that the most important fuel injection and ignition timing calculation of an engine depends on the position relation management of a cam shaft and a crank shaft, and the function is the basis and the core of normal operation of the engine. According to engineering experience, deviations exist between the actual signal position and the theoretical position of the camshaft all the time, and the deviations mainly originate from position deviations during camshaft installation, manufacturing deviations of signal wheels of the camshaft, sensor deviations and the like, and as the service life increases, deviations caused by aging and sliding of pulleys, abrasion of timing chains, plastic deformation and the like are generated. The camshaft and crankshaft position relation management ensures accurate control of timing by two mechanisms of primary self-learning and refined self-learning.

The initial self-learning mechanism can take the learned position as 0 point of the camshaft position, and the introduction of the refined self-learning mechanism can distinguish manufacturing/installation deviation from deviation occurring in the using process. When the triggering condition of the initial self-learning is EOL (End Of Line) and the ECU (Engine control unit, engine controller) is operated for the first time in the whole vehicle offline process, when the reference position self-learning value data in the EEPROM (Electrically Erasable Programmable Read-Only Memory) changes, the external diagnostic equipment puts forward the requirement, and the like. The camshaft reference position self-learning is triggered only once in the life cycle of the ECU, and the initial self-learning is a precondition for VVT control and is used for eliminating larger deviation between the actual position and the theoretical position; the refined self-learning can be operated after the primary self-learning is finished, and the effect of the refined self-learning is to enable the self-learned position to be continuously approximated to the actual position, so that the learning value is more and more accurate. And related cam shaft faults can be timely reported when the cam shaft position deviation is always large.

Under the condition of low SOC (SOC is lower than 15%), the project enters a power following power generation mode, and the speed of the vehicle is basically following the accelerator during acceleration, but not the stable speed of the vehicle under the normal SOC to generate power. Because the rotation speed of the motor dragging engine rises faster in the power following mode, the smooth completion of the VVT refined self-learning cannot be ensured, and the VVT refined self-learning can be frequently entered and exited. The rotating speed is stable under the normal SOC, and the fine self-learning can be completed at about 5S under the normal steady state. For the special hybrid engine, high compression ratio (15-16) +Miller cycle+boost control is usually provided, when the self-learning is refined, the VVT moves to the reference position, the intake valve is closed later than the active position, the engine air inflow is larger, the engine knocking and pre-ignition tendency are increased, and meanwhile, the engine VVT repeatedly and greatly changes, so that certain challenges are brought to the engine torque precision control and drivability.

The VVT fine self-learning of an engine-only vehicle is typically done during an engine start idle phase, or a launch light load phase. In a certain extended range vehicle, in a WLTC (World Light Vehicle Test Cycle, world light vehicle test cycle working condition) cycle, a DHE (Dedicated Hybrid Engine, hybrid special engine) engine is mostly in an economic oil consumption region operation region, a rotating speed load region exceeds an idling speed and starting region of a conventional pure fuel vehicle, and fine self-learning is easy to repeatedly enter and exit, so that other whole vehicle functions are influenced.

If the refined self-learning condition is accidentally exited while the VVT is maintained at the reference position, the VVT will return to the original target angle, and after the self-learning condition is satisfied, the VVT will be pulled to the reference position, thereby easily causing a phenomenon of repeatedly entering and exiting the self-learning condition and repeatedly pulling the VVT angle, and further adversely affecting torque stability, combustion, drivability, and the like of the engine. It can be seen that the torque stability, combustion, drivability, etc. of the engine will be adversely affected in the related art because the variable valve timing self-learning is not reasonably controlled.

Disclosure of Invention

The invention provides a continuous variable valve timing self-learning control method, a device, a system, a vehicle, electronic equipment and a storage medium, which are used for solving the technical problem that torque stability, combustion, drivability and the like of an engine are adversely affected because the variable valve timing self-learning is not reasonably controlled in the related art.

The embodiment of the invention provides a variable valve timing self-learning control method, which comprises the following steps: if the current working condition of the vehicle engine is a target working condition, acquiring first current data and historical data of at least one first parameter of the vehicle engine, wherein the target working condition comprises working conditions except for a fuel cut-off sliding working condition; if the current parameter values of all the first parameters meet the preset first parameter range of each first parameter, determining the parameter change rate and the parameter change direction of each first parameter according to the first current data and the historical data of each first parameter, wherein the current parameter values are obtained based on the first current data; determining the current effective duration of each first parameter based on the parameter change rate, the parameter change direction, the current parameter value and the preset first parameter range of each first parameter; and if the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, controlling the start-up execution of the variable valve timing self-learning.

In an embodiment of the present invention, determining a current effective duration of a first parameter based on a parameter change rate, a parameter change direction, a current parameter value and a preset first parameter range of the first parameter includes any one of the following: if the parameter change direction is increased, determining an effective interval exiting duration according to the current parameter value, the parameter change rate and a preset parameter maximum value, determining the effective interval exiting duration as the current effective duration of the first parameter, wherein the preset parameter maximum value is obtained based on the preset first parameter range; if the parameter change direction is reduced, determining the time length for entering the effective interval according to the current parameter value, the parameter change rate and a preset parameter minimum value, determining the time length for entering the effective interval as the current effective time length of the first parameter, and obtaining the preset parameter minimum value based on the preset first parameter range.

In an embodiment of the present invention, after the control starts to perform the variable valve timing self-learning, the method further includes: monitoring real-time signal data of each first parameter of the vehicle engine in the process of self-learning of the variable valve timing; determining the data difference value of each first parameter according to the current signal data of each first parameter and the last signal data, wherein the current signal data is real-time signal data of the current monitoring moment, and the last signal data is real-time signal data of the last monitoring moment; if the data difference value of the first parameter is larger than the preset difference value of the first parameter, comparing the current signal data with a preset first parameter range of the first parameter, and determining the execution state of the self-learning of the variable valve timing based on the current comparison result; if the data difference values of all the first parameters are smaller than the preset difference value of the first parameters, the control continues to execute the variable valve timing self-learning.

In an embodiment of the present invention, determining the execution state of the variable valve timing self-learning based on the current comparison result includes: if the current comparison result is that the current signal data falls into a preset first parameter range of the first parameter, controlling to continuously execute variable valve timing self-learning; and if the current comparison result is that the current signal data exceeds a preset first parameter range of the first parameter, controlling to stop executing the variable valve timing self-learning.

In an embodiment of the present invention, before the control starts to perform the variable valve timing self-learning, the method further includes: acquiring second current data of at least one second parameter of the vehicle; and if the second current data of all the second parameters meet the preset second parameter range of each second parameter and the current effective duration of all the first parameters is longer than the preset effective duration of the first parameters, prompting to start to execute the variable valve timing self-learning.

In an embodiment of the present invention, a target temperature of the vehicle is obtained, the target temperature includes at least one of a water temperature, a cylinder head temperature, and an engine oil temperature, and the second parameter includes a target temperature; comparing the target temperature with a preset temperature range, if the target temperature falls into the preset target temperature range, and the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, prompting to start to execute variable valve timing self-learning, wherein the preset second parameter range comprises a preset target temperature range, and the preset target temperature range is determined according to the target temperature.

In an embodiment of the present invention, a target duration of the vehicle is obtained, where the target duration includes at least one of a stop duration and a start duration, and the second parameter includes a target duration; comparing the target duration with a preset target duration threshold, if the target duration falls within the preset target duration range and the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, prompting to start to execute variable valve timing self-learning, wherein the preset second parameter range comprises the preset target duration threshold, and the preset target duration threshold is determined according to the target duration.

In an embodiment of the present invention, the method further includes: if the second current data of at least one second parameter exceeds the preset second parameter range of each second parameter, prompting to pause starting to execute the variable valve timing self-learning; and suspending acquiring the first current data and the historical data of at least one first parameter of the vehicle engine until the second current data of all the second parameters acquired again meet the preset second parameter range of each second parameter.

In an embodiment of the present invention, before obtaining the first current data and the historical data of the at least one first parameter of the vehicle engine, the method further includes: acquiring current speed of a vehicle and high-precision map data of a future driving route, wherein the high-precision map data of the future driving route comprises road condition data of a future driving road; predicting a future control strategy of the vehicle according to the current vehicle speed and the future driving route high-precision map data; predicting first future data of a first parameter of the vehicle engine at a plurality of future times and second future data of a second parameter of the vehicle based on the future control strategy; and if the first comparison result and the second comparison result of a future time are both met, controlling to acquire first current data and historical data of at least one first parameter of the vehicle engine when the future time is reached, wherein the first comparison result is a comparison result of the first future data and a preset first parameter range of the first parameter, and the second comparison result is a comparison result of the second future data and a preset second parameter range of the second parameter.

In an embodiment of the present invention, after obtaining the first current data of at least one first parameter of the vehicle engine, before controlling to start to perform the variable valve timing self-learning, the method further includes: obtaining a current target position according to the first current data matching; determining a current relative operating angle based on the current target position and a reference position; and if the current relative running angle is smaller than a preset first angle threshold value, prompting to start to execute the variable valve timing self-learning.

In an embodiment of the present invention, after the control starts to perform the variable valve timing self-learning, the method further includes: in the process of executing the variable valve timing self-learning, a real-time target position is obtained according to the real-time current data matching of the first parameter; determining a real-time relative operating angle based on the real-time target position and the reference position; if the real-time relative running angle is smaller than a preset second angle threshold, controlling to continuously execute variable valve timing self-learning, wherein the preset second angle threshold is larger than the preset first angle threshold; and if the real-time relative running angle is larger than the preset second angle threshold, controlling to stop executing the variable valve timing self-learning.

In an embodiment of the present invention, after the control starts to perform the variable valve timing self-learning, the method further includes: obtaining a current target position according to the first current data matching; determining a current relative operating angle based on the current target position and a reference position; and obtaining a current adjustment gradient by matching a preset relative operation angle-adjustment gradient table with the current relative operation angle, and controlling cam phase adjustment of the vehicle engine based on the current adjustment gradient.

In an embodiment of the present invention, before obtaining the first current data and the historical data of the at least one first parameter of the vehicle engine if the current working condition of the vehicle engine is the target working condition, the method further includes: acquiring the current working condition of the vehicle engine; and if the current working condition is the fuel cut-off sliding working condition, controlling the start-up execution of the variable valve timing self-learning.

In an embodiment of the present invention, if there is at least one current parameter value of the first parameter that exceeds a preset first parameter range of the first parameter, the method further includes: determining a predicted meeting time according to first current data, historical data and a preset first parameter range of a target parameter class, wherein the target parameter class is a first parameter of which the current parameter value exceeds the preset first parameter range; determining ideal satisfying time according to the predicted satisfying time of each target parameter category, wherein the ideal satisfying time is the latest predicted satisfying time; and when the ideal satisfying time is reached, controlling to acquire the first current data and the historical data of each new first parameter.

The embodiment of the invention also provides a variable valve timing self-learning control device, which comprises: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring first current data and historical data of at least one first parameter of a vehicle engine if the current working condition of the vehicle engine is a target working condition, and the target working condition comprises working conditions except for a fuel cut sliding working condition; the change determining module is used for determining the parameter change rate and the parameter change direction of each first parameter according to the first current data and the historical data of each first parameter if the current parameter values of all the first parameters meet the preset first parameter range of each first parameter, and the current parameter values are obtained based on the first current data; the duration prediction module is used for determining the current effective duration of each first parameter based on the parameter change rate, the parameter change direction, the current parameter value and the preset first parameter range of each first parameter; and the self-learning control module is used for controlling the start of the execution of the variable valve timing self-learning if the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters.

The embodiment of the invention also provides a variable valve timing self-learning control system, which comprises the variable valve timing self-learning control device and an executing mechanism; the execution mechanism is used for executing the variable valve timing self-learning based on the control of the self-learning control module after starting the execution of the variable valve timing self-learning, and driving the cam to adjust the phase.

The embodiment of the invention also provides a vehicle comprising the variable valve timing self-learning control device according to any one of the embodiments.

The embodiment of the invention also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method according to any one of the embodiments.

The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the method of any one of the embodiments when being executed by a processor.

In the scheme realized by the variable valve timing self-learning control method, the device, the system, the vehicle, the electronic equipment and the storage medium, the first current data and the historical data of one or more first parameters of the vehicle engine can be obtained when the vehicle engine is in a working condition other than the fuel cut sliding working condition, if the obtained current parameter value of each first parameter meets the corresponding preset first parameter range, the parameter change rate and the parameter change direction of each first parameter are obtained according to the first current data and the historical data of each first parameter, the current effective duration of each first parameter can be obtained by combining the current parameter value and the preset first parameter range of each first parameter, and if the current effective duration of all first parameters is greater than the corresponding preset effective duration, the starting of the variable valve timing self-learning is controlled; by means of the method, the parameters of the first parameters which are strongly related to the change of the working conditions of the engine are predicted in advance, the fact that the VVT self-learning (variable valve timing self-learning, hereinafter referred to as VVT self-learning) is started only when the predicted future parameter values of one or more first parameters of the vehicle engine are within the corresponding preset first parameter ranges can be ensured, the learning duration of the VVT self-learning can be ensured, the fact that certain parameters of the vehicle engine exceed the corresponding preset first parameter ranges after the VVT self-learning is started and the fact that the VVT self-learning is not completed is avoided, namely the phenomenon that the VVT self-learning exits due to insufficient learning time are avoided, the repeated pulling of the VVT angle is avoided, and adverse effects on torque stability, combustion, drivability and the like of the engine are avoided.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a self-learning position relative relationship of a VVT according to an embodiment of the invention;

FIG. 2 is a schematic diagram of a VVT refinement self-learning process according to an embodiment of the invention;

FIG. 3 is a schematic flow chart of a variable valve timing self-learning control method according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a rotational speed hysteresis operation according to an embodiment of the present invention;

fig. 5 is a schematic diagram of a hybrid engine VVT refined self-learning strategy according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a hybrid engine VVT refined self-learning starting condition according to an embodiment of the present invention;

fig. 7 is a schematic structural view of a variable valve timing self-learning control apparatus according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of an electronic device according to an embodiment of the invention;

fig. 9 is a schematic diagram of another structure of an electronic device according to an embodiment of the invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Along with the environmental protection guidelines of carbon reaching peak and carbon neutralization, higher requirements are put forward on the oil consumption and the emission of the engine in the automobile industry. In recent years, new energy automobiles mainly using hybrid power have been greatly increased in sales. The wide range of the operators in China, the wide cross-domain of things, the wide cross-domain of the north and the south, and the charging and endurance mileage anxiety caused by the shortage of the infrastructure of the charging piles and the charging stations for the cross-province vehicle demands, determine a quite long time, and the mixed driving route will be the main stream. Particularly, PHEV (PLUG-IN HYBRID vehicle) with the pure electric range of about 100-200Km can become an ideal vehicle, can meet the commute of business hours IN the market, can also travel IN a long distance across provinces, and thoroughly solves the charging and the range anxiety of pure EV (ELECTRIC VEHICLE).

The most important fuel injection and ignition time sequence calculation of the engine depends on the position relation management of the cam shaft and the crank shaft. However, since the actual signal position and the theoretical position of the camshaft always have deviations, the deviations mainly originate from the position deviations during the installation of the camshaft, the manufacturing deviations of the signal wheel of the camshaft, the sensor deviations and the like, and as the service life increases, the deviations and the like caused by the aging and sliding of the belt pulley, the abrasion of the timing chain, the plastic deformation and the like are caused. The camshaft and crankshaft position relation management ensures accurate control of timing by two mechanisms of primary self-learning and refined self-learning.

Referring to fig. 1, fig. 1 is a schematic diagram of a position relative relationship of VVT position self-learning according to an embodiment of the invention, which is based on BOSCH (BOSCH) camshaft four-tooth signal disc electric signals. As shown in fig. 1, the upper curve is the theoretical position, and the lower curve is the actual position.

The process of VVT refinement self-learning is described in terms of the change of the VVT angle over time for a certain vehicle operation period. Wherein the VVT angle, i.e., the valve timing, pushes the camshaft operating angle, may be achieved by a camshaft phase adjuster. Referring to fig. 2, fig. 2 is a schematic diagram of a VVT refined self-learning process according to an embodiment of the invention. As shown in the frame selection area (rectangular frame area) of fig. 2, the wave line located below is the target angle that the VVT should walk according to the normal rotation speed and load table, the horizontal line above is the VVT reference position, six lines between the horizontal line and the wave line are the system that satisfies the VVT refined self-learning condition, the EMS pulls the VVT angle from the target position represented by the wave line to the reference position represented by the horizontal line, and if the self-learning condition does not exit, the VVT will remain in the reference position for a calibrated self-learning time, and then return to the VVT target angle of the wave line. The number of times each driving cycle remains self-learning time at the reference position can be calibrated, usually 3 to 5 times in order to obtain a more stable learning value.

If the refined self-learning condition accidentally exits while the VVT is kept at the reference position, the VVT will return to the light blue target angle, and after the self-learning condition is satisfied, the VVT will be pulled to the reference position, so that the phenomenon of repeatedly entering and exiting the self-learning condition and repeatedly pulling the VVT angle easily occurs, and further adverse effects are caused on torque stability, combustion, drivability and the like of the engine. It can be seen that, during fine self-learning, the traveling engine VVT may move toward the reference position, thereby possibly causing a series of problems in engine torque response, combustion stability, drivability, and the like.

In order to solve the above problems, an embodiment of the present invention provides a variable valve timing self-learning control method, which obtains first current data and historical data of one or more first parameters of a vehicle engine in a working condition other than a fuel cut-off sliding working condition of the vehicle engine, and if the obtained current parameter value of each first parameter meets a preset first parameter range corresponding to the current data and historical data of each first parameter, obtains a parameter change rate and a parameter change direction of each first parameter according to the first current data and the historical data of each first parameter, and combines the current parameter value and the preset first parameter range of each first parameter to obtain a current effective duration of each first parameter, and if the current effective duration of all first parameters is greater than the corresponding preset effective duration, then controls to start and execute variable valve timing self-learning. By means of the method, the parameters of the first parameters which are strongly related to the change of the working conditions of the engine are predicted in advance, the fact that the VVT self-learning (variable valve timing self-learning, hereinafter referred to as VVT self-learning) is started only when the predicted future parameter values of one or more first parameters of the vehicle engine are within the corresponding preset first parameter ranges can be ensured, the learning duration of the VVT self-learning can be ensured, the phenomenon that certain parameters of the vehicle engine exceed the corresponding preset first parameter ranges after the VVT self-learning is started and the VVT self-learning is not completed is avoided, the VVT self-learning is stopped is avoided, the repeated pulling of the VVT angle is avoided, and adverse effects on torque stability, combustion, drivability and the like of the engine are avoided. The present invention will be described in detail with reference to specific examples.

Referring to fig. 3, fig. 3 is a schematic flow chart of a variable valve timing self-learning control method according to an embodiment of the invention, including the following steps:

step S310, if the current working condition of the vehicle engine is the target working condition, obtaining the first current data and the historical data of at least one first parameter of the vehicle engine.

Wherein the target operating condition is an operating condition other than the fuel cut-off sliding operating condition. The first parameter may be at least one of parameters such as rotation speed and load that are strongly related to engine operating conditions. That is, the first parameter may be one (rotation speed or load), and the first parameter may be a plurality of (rotation speed, load, etc.). For each first parameter, a first current data and a historical data may be collected, and the number of the historical data may be one or more. Correspondingly, a corresponding preset first parameter range is set for each first parameter.

The first current data and the historical data both comprise parameter values and acquisition moments of the parameters. For example, the first current data is a current parameter value and a current parameter acquisition time acquired at a time when the variable valve timing self-learning is required to be started, and the historical data is a historical parameter value and a corresponding historical parameter acquisition time of one or more historical times acquired in a previous certain historical period. Each current parameter value and each historical parameter value are configured with corresponding parameter names according to the same rule so as to facilitate subsequent calculation. For some series-parallel or extended range vehicles, the current working condition of the vehicle engine of the vehicle can be defaulted to be the target working condition all the time when the engine fuel cut-off sliding working condition is very little or even not, and the confirmation and judgment of the current working condition are not needed.

Of course, if it is not known in advance whether the vehicle has a fuel cut-off sliding condition, in an exemplary embodiment, before the first current data and the historical data of the at least one first parameter of the vehicle engine are obtained if the current condition of the vehicle engine is the target condition, the method further includes: acquiring the current working condition of a vehicle engine; and if the current working condition is the fuel cut-off sliding working condition, controlling the start execution of the variable valve timing self-learning. That is, if the current operating condition of the current vehicle engine is already a fuel cut-off coasting operating condition, the start VVT self-learning may be directly controlled without having to make other determinations and predictions. The method is characterized in that the method is used for preferentially carrying out the refined self-learning to the fuel cut-off sliding working condition, so that the influence of the movement of the VVT to the reference position on the normal running power of the whole vehicle can be avoided, and the method is also a scheme to be preferentially considered for the hybrid vehicle.

In the present embodiment, the vehicle engine may be an engine of a hybrid vehicle.

The first current data may be data of a first parameter, such as a rotation speed, a load, etc., acquired at a preset time a, at which it is necessary to determine whether to start VVT self-learning, as defined by a person skilled in the art. The history data may be data of the first parameter acquired before the preset time a. The time difference between the time of the history collection of the history data and the preset time a, for example, the history data within the first 1 minute, etc., can be defined by those skilled in the art.

The data of the first parameter is often characterized by linear (may be a straight line or a curve, etc.) change along with time, so that the prediction of the current effective duration can be conveniently performed later.

Step S320, if the current parameter values of all the first parameters meet the preset first parameter ranges of the first parameters, determining the parameter change rate and the parameter change direction of each first parameter according to the first current data and the historical data of each first parameter.

Wherein the current parameter value is derived based on the first current data. That is, the first current data includes a current parameter value and a current acquisition time, and the corresponding historical data includes a historical parameter value and a historical acquisition time.

Whether the current parameter value meets the preset first parameter range or not can be determined according to whether the current parameter value falls into a numerical interval formed by the first preset parameter range or not, if the current parameter value is larger than a preset parameter minimum value of the first preset parameter range and smaller than the preset parameter minimum value, the current parameter value is considered to meet the preset first parameter range, and if not, the current parameter value is not met.

For example, each first parameter is provided with a preset first parameter range, and the preset first parameter range is [ min, max ], where min is a preset parameter minimum value, and max is a preset parameter maximum value. Whether the current parameter value of the first parameter meets the preset first parameter range of the first parameter or not is judged, namely whether the current parameter value is larger than a preset parameter minimum value or not and smaller than a preset parameter maximum value or not is judged. If each current parameter value meets the corresponding preset first parameter range, the current parameter values of all the first parameters can be determined to meet the preset first parameter ranges of the first parameters.

In this embodiment, if the current parameter values of all the first parameters satisfy the preset first parameter ranges corresponding to the first parameters, before determining the parameter change rate and the parameter change direction of each first parameter according to the first current data and the historical data of each first parameter, the method further includes: judging whether the current parameter value of each first parameter meets the preset first parameter range of the first parameter, if all the current parameter values of the first parameters meet the preset first parameter range of each first parameter, executing step S320, if at least one current parameter value of the first parameter does not meet the preset first parameter range of the first parameter, exiting the judgment, and judging when the current parameter value of each first parameter meets the preset first parameter range, or continuously acquiring the first current data and the historical data of the new first parameter at proper time, or acquiring the first current data and the historical data of the new first parameter at intervals of preset time.

In this embodiment, if there is at least one current parameter value of the first parameter that exceeds (does not satisfy) the preset first parameter range of the first parameter, the method further includes: determining a predicted meeting time according to first current data, historical data and a preset first parameter range of a target parameter class, wherein the target parameter class is a first parameter of which the current parameter value does not meet the preset first parameter range; determining ideal meeting time according to the predicted meeting time of each target parameter class, wherein the ideal meeting time is the latest predicted meeting time; when the ideal satisfying time is reached, control re-executes step S310 to acquire new first current data and history data for each first parameter.

In this embodiment, an exemplary manner of determining the predicted satisfaction time may be:

determining a parameter change rate and a parameter change direction according to the current acquisition time t1 and the current parameter value M1 of the first current data, and the historical acquisition time t2 and the historical parameter value M2 of the historical data;

determining a preset parameter maximum value or a preset parameter minimum value as a current extremum according to the parameter change direction, and determining a predicted satisfaction time according to the current extremum, the parameter change rate and the current reference value;

and determining the predicted meeting time according to the predicted meeting time and the current collecting time.

In the present embodiment, the parameter change direction is increased if M1 is greater than M2, otherwise, the parameter change direction is decreased if M1 is less than M2. If M1 is equal to M2, the determination of the predicted satisfaction moment is carried out again after the preset time interval.

In this embodiment, a method for determining the rate of change of the parameter may be:

kx= (M2-M1)/(t 2-t 1) formula (1),

wherein Kx is the parameter change rate, M2 is the historical parameter value, M1 is the current parameter value, t2 is the historical acquisition time, and t1 is the current acquisition time.

The current extremum is determined in the following manner: if the parameter change direction is increasing, the current extremum E0 is the preset parameter maximum E1, and if the parameter change direction is decreasing, the current extremum E0 is the preset parameter minimum E2.

The method for determining the predicted satisfaction time length comprises the following steps:

Δtx=abs [ (E0-M1)/Kx ] formula (2),

wherein Deltatx is the predicted satisfaction time, kx is the parameter change rate, M1 is the current parameter value, and E0 is the current extremum.

The determination mode of the predicted meeting time is as follows:

tx= Δtx+t1 formula (3),

tx is the predicted meeting time, deltatx is the predicted meeting time length, and t1 is the current collecting time.

Note that, in the formula (2) and the formula (3), the first current data may be the data collected in the step S310, or may be the data collected at the updated current time.

By the method, when the current parameter value of one first parameter does not meet the preset first parameter range, new data are not blindly acquired again for judgment, so that the waste of calculation power resources is caused, but prediction is performed based on the known data, so that the parameter value of the first parameter at the future moment X can meet the preset first parameter range, and the data are restarted after the moment X is reached. When the current parameter values of the first parameters do not meet the preset first parameter range, the predicted meeting time determined by the first parameters which reach the preset first parameter range at the latest can be used as the ideal meeting time N, and the data re-acquisition is started when the ideal meeting time N is reached.

It should be appreciated that the historical data may be one or more sets of data, in which case the direction of change of the parameter may be determined from the most recent sets of data, and correspondingly, the rate of change of the parameter may be determined from the most recent sets of data. Or if there are two parameter changing directions, the parameter changing direction determined by the data sets in the latest time is taken as the final parameter changing direction. For the parameter rate of change, the rates of change for two adjacent sets of data may be averaged, or the rates of change may be determined based on any two sets of data, taking the average, median or mode of the multiple rates of change. Of course, the rate of change of the parameter may also be determined in other ways known to those skilled in the art.

Step S330, determining the current effective duration of each first parameter based on the parameter change rate, the parameter change direction, the current parameter value and the preset first parameter range of each first parameter.

In one embodiment, determining the current effective duration of a first parameter based on a parameter change rate, a parameter change direction, a current parameter value, and a preset first parameter range of the first parameter includes any one of:

if the parameter change direction is increased, determining the exiting effective interval duration according to the current parameter value, the parameter change rate and the preset parameter maximum value, determining the exiting effective interval duration as the current effective duration of a first parameter, wherein the preset parameter maximum value is obtained based on a preset first parameter range;

If the parameter change direction is reduced, determining the time length for entering the effective interval according to the current parameter value, the parameter change rate and the preset parameter minimum value, determining the time length for entering the effective interval as the current effective time length of a first parameter, and obtaining the preset parameter minimum value based on the preset first parameter range.

Taking a first parameter as a rotating speed as an example, the first current data is the current rotating speed and the current collecting time, the historical data is the rotating speed at the last time and the last time, the parameter change rate is the rotating speed change rate, the time for exiting the effective interval is the time for exiting the effective rotating speed interval, and the time for entering the effective interval is the time for entering the effective rotating speed interval. And calculating the rotation speed change rate according to the relative time between the current rotation speed and the rotation speed signal at the last moment. Assuming that the current rotation speed NN1 is at time tt1 and the rotation speed at the last time tt0 is NN0, the rotation speed change rate is:

KN= (NN 1-NN 0)/(tt 1-tt 0) formula (4),

where KN is the rate of change of rotation speed, NN1 is the current rotation speed, tt1 is the current time, NN0 is the rotation speed at the previous time, and tt0 is the previous time.

The refined self-learning rotation speed interval (i.e. the preset first parameter range) of the early calibration is [ N1, N2], so when the rotation speed is increased, the time for exiting the effective rotation speed interval is as follows:

Δt2=abs [ (N2-N)/KN ] formula (5),

wherein Δtn2 is the time of exiting the effective rotation speed interval, N2 is the preset parameter maximum value, N is the real-time rotation speed (may be the current rotation speed or the rotation speed updated based on the current time), and KN is the rotation speed change rate.

When the load decreases, the time to enter the effective rotation speed interval is:

Δtn1=abs [ (N1-N)/KN ] formula (6),

wherein Δtn1 is the time of entering the effective rotation speed interval, N1 is the minimum value of the preset parameter, N is the real-time rotation speed (the current rotation speed or the rotation speed updated based on the current time), and KN is the rotation speed change rate.

Taking a first parameter as a load as an example, the first current data is the current load and the current acquisition time, the historical data is the load at the last time and the last time, the parameter change rate is the load change rate, the effective interval exiting duration is the effective load interval exiting time, and the effective interval entering duration is the effective load interval entering time.

The load prediction calculation method is to calculate the load change rate according to the relative time between the current load and the load signal at the previous moment. Assuming that the current load RR1 is tt1 and the load at the previous time tt0 is RR0, the load change rate is:

KR= (RR 1-RR 0)/(tt 1-tt 0) equation (7),

where KR is the load change rate, RR1 is the current load, tt1 is the current time, RR0 is the load at the previous time, and tt0 is the previous time.

The refined self-learning load interval of the early calibration is [ R1, R2], so when the load increases, the time of exiting the effective load interval is:

Δtr2=abs [ (R2-R)/KR ] formula (8),

wherein Δtr2 is the time of exiting the payload section, R2 is the preset parameter maximum, R is the real-time load (which may be the current load or the load updated based on the current time), and KR is the load change rate.

When the load decreases, the time to enter the payload section is:

Δtr1=abs [ (R1-R)/KR ] formula (9),

wherein Δtr1 is the time of entering the payload section, R1 is a preset parameter minimum, R is the real-time load (may be the current load or the load updated based on the current time), and KR is the load change rate.

By the method, the current effective duration of the first parameter can be predicted more accurately, and more accurate and reasonable guidance is provided for whether the follow-up self-learning is started or not.

Step S340, if the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, controlling to start and execute the variable valve timing self-learning.

The different first parameters may be set to the same or different preset valid durations, and the corresponding different first parameters may also be set to different preset first parameter ranges. When the current effective duration determined by each first parameter is greater than the corresponding preset effective duration, the current effective durations of all the first parameters can be considered to be greater than the preset effective duration of the first parameters.

And if the current effective duration of each first parameter is larger than the corresponding preset effective duration, the current time condition for self-learning is met. The self-learning may be turned on.

In the present embodiment, after the control starts to perform the variable valve timing self-learning, the method further includes: monitoring real-time signal data of each first parameter of a vehicle engine in the process of self-learning of the variable valve timing; determining the data difference value of each first parameter according to the current signal data of each first parameter and the last signal data, wherein the current signal data is real-time signal data of the current monitoring moment, and the last signal data is real-time signal data of the last monitoring moment; if the data difference value of the first parameter is larger than the preset difference value of the first parameter, comparing the current signal data with a preset first parameter range of the first parameter, and determining the execution state of the variable valve timing self-learning based on the current comparison result; if the data difference values of all the first parameters are smaller than the preset difference value of the first parameters, the control continues to execute the variable valve timing self-learning.

The real-time signal data, that is, new signal data obtained by updating the first current data, still includes the parameter value and the acquisition time. And (3) calculating the difference value of the parameter value of the real-time signal data believed to be acquired, obtaining a data difference value, and determining whether to reevaluate the VVT self-learning condition (namely, determining the execution state of the variable valve timing self-learning based on the current comparison result) based on the magnitude relation between the data difference value and the preset difference value. It should be noted that, the previous signal data may be data evaluated under the condition that VVT self-learning is performed last time, or may be signal data acquired last time in the real time dimension.

In the present embodiment, determining the execution state of the variable valve timing self-learning based on the current comparison result includes: if the current comparison result is that the current signal data falls into a preset first parameter range of a first parameter, controlling to continuously execute variable valve timing self-learning; and if the current comparison result is that the current signal data exceeds a preset first parameter range of a first parameter, controlling to stop executing the variable valve timing self-learning. Whether the current signal data falls into the preset first parameter range of the first parameter is based on whether the parameter value of the current signal data is larger than the preset parameter minimum value and smaller than the preset parameter maximum value.

After the execution of the VVT self-learning is started, hysteresis operation is performed in the process of monitoring the data of the first parameter, when the parameter value of the first parameter is not changed greatly (the data difference value is smaller than the preset difference value), the determination of whether the current parameter value meets the preset first parameter range is not performed again, and only when the data difference value is larger than the preset difference value, the determination of whether the current parameter value meets the preset first parameter range is performed again, and at the moment, the current parameter value is updated to be the real-time parameter value of the real-time signal data.

Taking the first parameter as a load as an example, carrying out hysteresis operation on the load of the engine, wherein the operation method is that a load signal at a certain moment is INPUT1, and the load signal at the last moment is INPUT0, if the absolute value of the difference is greater than or equal to a preset difference DR (which can be calibrated, such as 20%), the load condition output to the refined self-learning is updated to INPUT1; if the absolute value of the difference is less than DR, the load condition output to refined self-learning remains at INPUT0.

Taking the first parameter as the rotating speed as an example, carrying out hysteresis operation on the rotating speed of the engine, wherein the operation method is that the rotating speed signal at a certain moment is INPUT2 and the rotating speed signal at the last moment is INPUT3, if the absolute value of the difference is greater than or equal to a preset difference DN (which can be calibrated, such as 300 rpm), the rotating speed condition output to the refined self-learning is updated to INPUT2; if the absolute value of the difference is less than DN, the rotational speed condition output to refined self-learning remains at INPUT3.

According to the method provided by the embodiment, the first current data and the historical data of the vehicle engine are obtained when the vehicle engine is in the target working condition, if each current parameter value meets the corresponding preset first parameter range, the parameter change rate, the parameter change direction and the current effective duration are determined, and if the current effective duration is greater than the corresponding preset effective duration, the starting execution of the variable valve timing self-learning is controlled; by predicting the current effective duration in advance, the VVT self-learning can be started on the premise of meeting the time requirement, the phenomenon that the VVT self-learning exits due to insufficient time for completing the VVT self-learning is avoided, the VVT angle is prevented from being repeatedly pulled, and adverse effects on torque stability, combustion, drivability and the like of an engine are avoided. Referring to fig. 4, fig. 4 is a schematic diagram of a rotational speed hysteresis operation according to an embodiment of the present invention, as shown in fig. 4, for a load signal INPUT at a certain moment is INPUT1 and a load signal INPUT0 at a previous moment, whether a difference value (ABS () ") between the INPUT signal and the load signal is greater than a preset difference value (Delta in fig. 4) is determined, if so, INPUT1 is output, otherwise INPUT0 is output. It should be appreciated that the manner of hysteresis operation for the other first parameters is similar to that illustrated in fig. 4 and is not limited thereto.

In the VVT self-learning process, hysteresis operation is carried out on the data of the first parameter, whether comparison with a preset first parameter range is needed again is determined according to the result of the hysteresis operation, and if comparison is needed, the state of the VVT self-learning is determined again based on the comparison result. Therefore, the waste of calculation power resources caused by repeated comparison can be avoided.

Continuing to take the examples related to the formula (5), the formula (6), the formula (8) and the formula (9) as examples, when the Δt1 or Δt2 is calculated to be greater than the refined self-learning effective time B1 (the preset effective duration), performing VVT refined self-learning; otherwise, the fine self-learning is not performed; when the calculated Deltatr 1 or Deltatr 2 is larger than the effective time B2 of the refined self-learning, performing VVT refined self-learning; otherwise, no fine self-learning is performed.

In one embodiment, before the control starts to perform the variable valve timing self-learning, the method further includes: acquiring second current data of at least one second parameter of the vehicle; if the second current data of all the second parameters meet the preset second parameter range of each second parameter, and the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, determining that the current state of the vehicle engine meets the self-learning condition, and prompting to start to execute the variable valve timing self-learning.

Wherein the second parameter is a physical factor related enabling condition, such as water temperature, cylinder head temperature, engine oil temperature, downtime, time after start-up, etc. The preset second parameter range may be formed by two preset extremum intervals for parameters such as water temperature, cylinder head temperature and engine oil temperature, and may be formed by a preset threshold value (threshold value) for data which can only be increased or not be decreased such as downtime and time after starting.

In this embodiment, taking the example that the second parameter includes the target temperature, the target temperature includes at least one of the water temperature, the cylinder head temperature, and the engine oil temperature, the method may be: acquiring a target temperature of a vehicle; comparing the target temperature with a preset temperature range, if the target temperature falls into the preset target temperature range, and the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, determining that the current state of the vehicle engine meets the self-learning condition to prompt the start of the execution of the variable valve timing self-learning, wherein the preset second parameter range comprises the preset target temperature range, and the preset target temperature range is determined according to the target temperature, namely, the parameters of different target temperatures have the same or different target temperature ranges. If the engine oil temperature has a corresponding engine oil temperature range, the water temperature has a corresponding water temperature preset range, and the like. The preset target duration threshold value, the preset effective duration, the preset first parameter range and the preset second parameter range are similar to the preset target duration threshold value, and the corresponding numerical values can be obtained by a person skilled in the art according to the needs facing different parameters.

In this embodiment, a target time length of the vehicle is obtained, the target time length includes at least one of a stop time length and a start time length, and the second parameter includes the target time length; comparing the target duration with a preset target duration threshold, if the target duration falls within a preset target duration range, and the current effective duration of all the first parameters is larger than the preset effective duration of the first parameters, determining that the current state of the vehicle engine meets the self-learning condition to prompt the start of executing the variable valve timing self-learning, wherein the preset second parameter range comprises the preset target duration threshold, and the preset target duration threshold is determined according to the target duration. Wherein the preset target duration threshold for the different second parameters may be the same or different, which is decided based on preset settings by a person skilled in the art.

In this embodiment, the method further includes: if the second current data of at least one second parameter exceeds the preset second parameter range of each second parameter, prompting to pause starting to execute the variable valve timing self-learning; and suspending acquiring the first current data and the historical data of at least one first parameter of the vehicle engine until the second current data of all the second parameters acquired again meet the preset second parameter range of each second parameter. Since the data of the second parameters changes relatively less quickly, once one parameter of the second parameters does not satisfy the preset second parameter range, it is necessary to wait for a certain time for the data of the second parameters to satisfy the preset second parameter range, and thus, a decision of whether the condition for restarting the VVT self-learning is satisfied can be made for a while in a pause to further reduce the waste of computational resources. And the second parameter data can be acquired again according to the time interval set by a person skilled in the art, and monitoring and judging are carried out until each piece of second current data meets the corresponding preset second parameter range, and then the first parameter data is acquired again.

In an embodiment, before obtaining the first current data and the historical data of the at least one first parameter of the vehicle engine, the method further comprises:

the method comprises the steps of acquiring current speed of a vehicle and high-precision map data of a future driving route, wherein the high-precision map data of the future driving route comprises road condition data of a future driving road. The road condition data comprises, but is not limited to, congestion state, route length, traffic light state, speed limit state, gradient (up-down slope) information, current vehicle position and the like of a future planned driving road, and can be obtained based on the high-precision map data;

the future control strategy of the vehicle is predicted according to the current vehicle speed and the high-precision map data of the future driving route, and the reasonable operation of the vehicle, such as one or more control modes of deceleration, idling, downhill, uphill, uniform speed driving and the like, can be predicted based on the current vehicle speed, the current position and the road condition data, so that the future control strategy of the vehicle at a certain time in the future is formed. The formulation of the future control strategy may be formulated in other manners known to those skilled in the art, and in this embodiment, it is necessary to know the prediction data of the vehicle at a certain time in the future based on the future control strategy;

Predicting first future data of a first parameter of the vehicle engine at a plurality of future times based on the future control strategy, and second future data of a second parameter of the vehicle, the first future data and the second future data being either set by a person skilled in the art based on different vehicle driving conditions (the future vehicle driving conditions being determined by the future control strategy), predicted by a pre-trained prediction model, or determined by other means known to a person skilled in the art, without limitation;

if the first comparison result and the second comparison result of a future moment are both met, when the future moment is reached, the first current data and the historical data of at least one first parameter of the vehicle engine are obtained, wherein the first comparison result is the comparison result of the first future data and the preset first parameter range of the first parameter, and the second comparison result is the comparison result of the second future data and the preset second parameter range of the second parameter, namely, if at a future time point Ti, the parameters of the first parameter and the second parameter meet the requirements, and when the time point Ti is reached, whether the VVT self-learning needs to be started or not is judged, so that the calculation power waste caused by repeated judgment of the VVT self-learning is avoided, and the resources of the controller are saved.

By introducing the high-precision map, the working condition of the VVT refined self-learning can be predicted more accurately according to the real-time road condition and the adjustment of the vehicle driving intention, and the working condition of the VVT refined self-learning can be organized more scientifically and reasonably.

In one embodiment, after obtaining the first current data of the at least one first parameter of the vehicle engine, the method further includes, prior to controlling the start of the execution of the variable valve timing self-learning: obtaining a current target position according to the first current data matching; determining a current relative operating angle based on the current target position and the reference position; if the current relative running angle is smaller than a preset first angle threshold value, determining that the current state of the vehicle engine meets the self-learning condition so as to prompt the start-up execution of the variable valve timing self-learning. Otherwise, if the current relative running angle is greater than the preset first angle threshold, the VVT self-learning is not started.

With continued reference to fig. 2, in general, the target angle that the VVT should travel, that is, the lower wave curve in fig. 2, may be obtained by looking up a table according to, for example, the rotation speed and the load, and the target angle belongs to the driven variable (which varies according to the parameter value of the first parameter such as the rotation speed and the load). The engine has a VVT actual angle feedback to the control system at each fixed speed load, so that the prediction of the VVT movement angle is correlated to the speed and load predictions. If the relative reference position (relative operation angle, i.e., the VVT operation angle in fig. 2) of the VVT operation angle is smaller than or equal to the preset first angle threshold D, entering a refined self-learning condition, and starting VVT self-learning; once greater than D, the refined self-learning strategy is rapidly exited.

If at least two of the second class parameters, the first class parameters and the current relative operation angles are judged, each dimension is required to meet the judging conditions, namely if second current data of all the second parameters meet the preset second parameter ranges of all the second parameters, the current effective duration of all the first parameters is larger than the preset effective duration of the first parameters, and meanwhile, the start of the VVT self-learning is triggered only when the current relative operation angles are smaller than the preset first angle threshold, otherwise, the VVT self-learning is not started if any one of the conditions is not met.

In one embodiment, after the control starts to perform the variable valve timing self-learning, the method further includes: in the process of executing the variable valve timing self-learning, the current target position is changed at the moment due to possible changes of rotation speed, load and the like, so that the real-time target position can be obtained according to the real-time current data matching of the first parameter; determining a real-time relative operating angle based on the real-time target position and the reference position; if the real-time relative running angle is smaller than a preset second angle threshold, controlling to continuously execute the variable valve timing self-learning, wherein the preset second angle threshold is larger than the preset first angle threshold; and if the real-time relative running angle is larger than the preset second angle threshold value, controlling to stop executing the variable valve timing self-learning. In this embodiment, the preset second angle threshold may be added with a certain additional correction amount for the preset first angle threshold. The determination mode of the preset second angle threshold value is as follows:

D2 =d1+dh formula (10),

wherein D2 is a preset second angle threshold, D1 is a preset first angle threshold, DH is an additional correction amount.

When the predicted time of the rotating speed and the load is more than the preset effective duration, adding an additional correction DH (calibratable) to the range threshold D of the VVT operating angle, so that the refined self-learning cannot be stopped from being performed by the horse because the VVT moving range is slightly higher than D, the VVT moving angle allowance is reserved, and conditions are created as much as possible to enable the refined self-learning to be completed efficiently and reliably.

In one embodiment, after the control starts to perform the variable valve timing self-learning, the method further includes: obtaining a current target position according to the first current data matching; determining a current relative operating angle based on the current target position and the reference position; and obtaining a current adjustment gradient by matching a preset relative operation angle-adjustment gradient table with the current relative operation angle, and controlling cam phase adjustment of the vehicle engine based on the current adjustment gradient. The preset relative running angle-adjusting gradient table can be preset by a person skilled in the art, and the influence on the normal running of the vehicle in the adjusting process can be reduced by guiding the adjustment of the cams under different current relative running angles according to a certain preset adjusting gradient (adjusting rate) rule. The adjustment gradient can be uniform or variable, and a plurality of different adjustment gradients can be determined according to different rotating speeds and loads (data of a first reference class), such as quick adjustment, slow adjustment and the like.

In another embodiment, the cam phase may be adjusted by defining a preset adjustment time and controlling the cam phase to complete adjustment within the preset adjustment time.

Considering the fine self-learning, the traveling engine VVT may move to the reference position, so that a series of problems in terms of engine torque response, combustion stability, drivability and the like may be brought about. The method is characterized in that the method is used for preferentially carrying out refined self-learning to the fuel cut-off sliding working condition, so that the influence of the movement of the VVT to the reference position on the normal running power of the whole vehicle can be avoided, and the method can be a scheme to be preferentially considered by the hybrid vehicle. Referring to fig. 5, fig. 5 is a schematic diagram of a hybrid engine VVT refinement self-learning strategy provided in an embodiment of the present invention, as shown in fig. 5, when the vehicle engine is in a fuel cut-off sliding condition, starting is directly controlled and the VVT self-learning can be completed (i.e., the refinement self-learning in fig. 5 is completed), when the vehicle engine is in a target condition (other conditions except the fuel cut-off sliding condition), step S310 needs to be executed to predict the refinement self-learning function, and determine whether the self-learning condition is satisfied, such as whether the current effective duration of the first parameter is greater than the corresponding preset effective duration, whether the second current data of the second parameter satisfies the corresponding preset second parameter range, whether the VVT movement angle is greater than the preset first angle threshold, etc., if the VVT movement angle is satisfied, the refinement self-learning is completed, and if the VVT self-learning is not satisfied, i.e., the self-learning is not started, and the self-learning condition is still satisfied.

For some series-parallel or extended range vehicles, engine fuel cut-off conditions are minimal to no. For this case, the predictable fine self-learning function provided by the embodiment is used to execute a fine self-learning strategy when the self-learning condition is satisfied and the prediction can reliably complete the self-learning; and when the self-learning condition is not satisfied or the prediction cannot reliably complete the refined self-learning, the refined self-learning is not performed. Thus, the situation that the VVT is pulled back and forth to the reference position but the self-learning cannot be reliably finished after repeated in and out of the fine self-learning can be avoided. The VVT fine self-learning process can be predicted and controlled by the prediction strategy.

The judging conditions of whether the VVT fine self-learning (VVT self-learning) of the fuel cut-off sliding working condition is started or not include rotating speed at [ NC1, NC2], load at [ RC1, RC2], fuel cut-off flag bit C_flg, water temperature at [ TC1, TC2] and the like, and the variables mentioned above can be calibrated to adapt to different conditions of different vehicles. And when the oil-cut sliding working condition occurs, the VVT is rapidly moved to the reference position to finish the fine self-learning. Because the reverse-trailing engine rotates in the sliding process, the relative position relation between the camshaft and the crankshaft is changed, the VVT fine self-learning requirement can be met, and the drivability, NVH (Noise, vibration, harshness, noise, vibration and harshness), combustion stability and the like cannot be influenced.

When the hybrid vehicle does not have the fuel cut-off sliding working condition, such as the range extension, the engine is used as a power source, and the working condition of the engine is rarely or basically not dragged. In this case, the predictable fine self-learning function provided by the present embodiment may also be used. The refinement self-learning condition provided by the present embodiment is exemplarily described below from a specific embodiment, and the prediction function details the implementation procedure of the predictable refinement self-learning strategy.

First, the refined self-learning conditions are: the basic requirement that the VVT refined self-learning needs to meet is that the deviation value of the position of the cam shaft and the crank shaft is learned stably, so that the self-learning value of the ECU is continuously close to the actual deviation, and more accurate electric control oil injection, ignition control and the like are realized.

Referring to fig. 6, fig. 6 is a schematic diagram of a hybrid engine VVT refined self-learning starting condition according to an embodiment of the present invention. As shown in fig. 6, includes:

and (5) rotating speed conditions. The fine self-learning is performed in a preferred rotation speed interval, and when the rotation speed is in [ N1, N2] (calibratable), a fine self-learning strategy (i.e. starting VVT self-learning) can be executed.

Load conditions. Considering that the position movement of the VVT may have influence on exhaust energy, boost control and the like, and unsafe influence on an engine running under a large load, the fine self-learning of the VVT is generally limited to a medium and small load, and when the load is positioned in [ R1, R2] (calibratable), a fine self-learning strategy can be executed.

The rotation speed condition and the load condition are the first parameters.

VVT motion angle condition (current relative running angle). Since the VVT is moved to the reference position, in order to prevent influence on driving comfort, engine operation stability, and the like in the refined self-learning process, when the VVT operation angle is smaller than D (calibratable), a refined self-learning strategy may be executed.

Water temperature conditions. For a special mixed engine, atkinson and Miller circulation is commonly used, if the VVT is located at a reference position or moves to the reference position greatly at low temperature, the situation that combustion is poor and even fire is easy to occur due to more residual waste gas in a cylinder is easy to occur, and therefore, the embodiment of the invention can execute a fine self-learning strategy when the water temperature is located at [ T1, T2] (calibration is possible). It should be noted that the water temperature may be replaced by the cylinder head temperature, or the engine cylinder head temperature may be additionally introduced while the water temperature condition is maintained.

Engine oil temperature conditions. The engine oil temperature condition is similar to the water temperature condition, the refined self-learning is defined to the engine oil temperature range in which the engine operation is relatively stable, and the influence of the execution of the refined self-learning strategy on the engine stability, NVH and the like is avoided, so that the refined self-learning strategy can be executed when the engine oil temperature is positioned at [ T3, T4] (calibratable).

Down time conditions. For a hybrid engine, the VCU is used for energy total coordination control, so that the engine can be in a frequent start-stop state under specific SOC and specific road conditions. When the vehicle has completed the fine self-learning function in the previous driving cycle and the stop time is very short, the aging related to the timing chain of the engine can be considered to be not changed greatly in a short time, and the self-learning value of the engine in the previous cycle can meet the requirement of control precision. Therefore, it may be set that the VVT refinement self-learning strategy can be executed only when the downtime is not less than TT 1.

Post-start time condition. The engine has a period of rotation speed dragging process after starting, the rotation speed running is not particularly stable in the process, and the process needs to be avoided in the fine self-learning process to obtain a relatively stable self-learning value. Therefore, it can be set that when the post-start time is not less than TT2, a fine self-learning strategy can be executed.

The water temperature, the engine oil temperature, the downtime and the time after starting are the second parameters.

Among the above 7 conditions, the latter four conditions belong to enabling conditions related to physical factors, and in the case of engine operation condition determination, frequent entry and exit of the refined self-learning strategy are not basically caused. Therefore, the prediction function design is carried out for the 1 st, 2 nd and 3 rd conditions (the rotating speed condition, the load condition and the VVT movement angle) which are strongly related with the change of the working condition of the engine. For low SOC situations, if the driver demand is strong, such as sudden stepping on the throttle, and the energy regulating capability of the battery is limited, the rotational speed load of the engine may change sharply in response to the torque demand, possibly resulting in frequent entry into a refined self-learning condition, but the duration is insufficient to complete the refined self-learning situation. By the predictable function provided by the embodiment, calculation is performed by combining with a real-time working condition, and the refined self-learning function is not executed for the case that the duration (the current effective duration) meeting the refined self-learning condition is lower than the preset effective duration B; and according to the prediction function, the condition that the duration time of the refined self-learning condition is more than or equal to the preset effective duration time B is met, and then the refined self-learning function is executed. Through the function, the VVT is refined, self-learning is controllably and effectively executed.

According to the method provided by the embodiment, the first current data and the historical data of one or more first parameters of the vehicle engine are obtained when the vehicle engine is in a working condition other than the fuel cut-off sliding working condition, if the obtained current parameter value of each first parameter meets the corresponding preset first parameter range, the parameter change rate and the parameter change direction of each first parameter are obtained according to the first current data and the historical data of each first parameter, the current effective duration of each first parameter can be obtained by combining the current parameter value and the preset first parameter range of each first parameter, and if the current effective duration of all the first parameters is greater than the corresponding preset effective duration, the starting execution of the variable valve timing self-learning is controlled. By means of the method, the parameters of the first parameters which are strongly related to the change of the working conditions of the engine are predicted in advance, the fact that the VVT self-learning (variable valve timing self-learning, hereinafter referred to as VVT self-learning) is started only when the predicted future parameter values of one or more first parameters of the vehicle engine are within the corresponding preset first parameter ranges can be ensured, the learning duration of the VVT self-learning can be ensured, the phenomenon that certain parameters of the vehicle engine exceed the corresponding preset first parameter ranges after the VVT self-learning is started and the VVT self-learning is not completed is avoided, the VVT self-learning is stopped is avoided, the repeated pulling of the VVT angle is avoided, and adverse effects on torque stability, combustion, drivability and the like of the engine are avoided.

The VVT refined self-learning strategy (variable valve timing self-learning control method) of the hybrid engine provided by the embodiment of the invention is used for preferentially judging whether the vehicle has the fuel cut-off sliding working condition, if so, the VVT refined self-learning is carried out under the fuel cut-off sliding working condition, so that the influence on the drivability, combustion stability and the like of the running vehicle is avoided; for hybrid vehicles without fuel cut-off coasting conditions, such as partially extended range vehicles, a predictable VVT fine self-learning strategy is applied. Therefore, the fine self-learning can be controlled and orderly carried out, and a series of negative problems caused by repeated pulling of the VVT to the reference position due to the fact that the duration time for meeting the self-learning condition is short and the fine self-learning cannot be reliably completed are avoided.

By using the predictable VVT refined self-learning strategy provided by the embodiment of the invention, whether the VVT refined self-learning strategy is suitable to be executed can be determined according to the prediction condition. Self-learning is carried out under the working condition that self-learning can be completed; when the self-learning condition is not provided, the execution of the self-learning strategy is avoided, so that the VVT is prevented from being repeatedly pulled to the reference position in one driving cycle, and the problems of load fluctuation, torque fluctuation, NVH and the like are avoided.

By using the predictable VVT refined self-learning strategy provided by the embodiment of the invention, the refined self-learning strategy can be closed at low temperature, so that the running stability of the engine at low temperature is prevented from being influenced by the movement of the VVT to the reference position, and bad driving feeling caused by fire, vehicle shrugging and the like is avoided.

By using the predictable VVT fine self-learning strategy provided by the embodiment of the invention, the repeated execution of the fine self-learning strategy can be avoided in the adjacent driving cycle with too short downtime. The problem of repeated triggering of VVT refined self-learning caused by frequent start and stop of an engine due to variable working conditions of the hybrid vehicle is avoided.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

In one embodiment, a variable valve timing self-learning control device is provided, which corresponds to the variable valve timing self-learning control method in the above embodiment one by one. As shown in fig. 7, the variable valve timing self-learning control apparatus includes an acquisition module 701, a change determination module 702, a duration prediction module 703, and a self-learning control module 704. The functional modules are described in detail as follows:

The obtaining module 701 is configured to obtain first current data and historical data of at least one first parameter of the vehicle engine if a current working condition of the vehicle engine is a target working condition, where the target working condition includes working conditions other than the fuel cut-off sliding working condition; the change determining module 702 is configured to determine a parameter change rate and a parameter change direction of each first parameter according to first current data and historical data of each first parameter if current parameter values of all the first parameters meet a preset first parameter range of each first parameter, where the current parameter values are obtained based on the first current data; a duration prediction module 703, configured to determine a current effective duration of each first parameter based on a parameter change rate, a parameter change direction, a current parameter value, and a preset first parameter range of each first parameter; the self-learning control module 704 is configured to control start-up to perform variable valve timing self-learning if the current effective durations of all the first parameters are greater than the preset effective duration of the first parameters.

In one embodiment, the change determination module includes any one of:

the first change determining submodule is used for determining the exiting effective interval duration according to the current parameter value, the parameter change rate and the preset parameter maximum value if the parameter change direction is increased, determining the exiting effective interval duration as the current effective duration of a first parameter, and obtaining the preset parameter maximum value based on the preset first parameter range;

And the second change determining submodule is used for determining the time length for entering the effective interval according to the current parameter value, the parameter change rate and the preset parameter minimum value if the parameter change direction is reduced, determining the time length for entering the effective interval as the current effective time length of a first parameter, and obtaining the preset parameter minimum value based on the preset first parameter range.

In one embodiment, the device further includes a difference prediction module, where the difference prediction module is configured to monitor real-time signal data of each first parameter of the vehicle engine during the variable valve timing self-learning process after the self-learning control module controls to start the variable valve timing self-learning; determining the data difference value of each first parameter according to the current signal data of each first parameter and the last signal data, wherein the current signal data is real-time signal data of the current monitoring moment, and the last signal data is real-time signal data of the last monitoring moment; if the data difference value of the first parameter is larger than the preset difference value of the first parameter, comparing the current signal data with a preset first parameter range of the first parameter, and determining the execution state of the variable valve timing self-learning based on the current comparison result; if the data difference values of all the first parameters are smaller than the preset difference value of the first parameters, the control continues to execute the variable valve timing self-learning.

In an embodiment, the difference prediction module further includes a stop control module, configured to control to continue to perform variable valve timing self-learning if the current comparison result indicates that the current signal data falls within a preset first parameter range of a first parameter; and if the current comparison result is that the current signal data exceeds a preset first parameter range of a first parameter, controlling to stop executing the variable valve timing self-learning.

In one embodiment, the apparatus further includes a second current data determination module for obtaining second current data of at least one second parameter of the vehicle before the self-learning control module controls the execution of the variable valve timing self-learning; if the second current data of all the second parameters meet the preset second parameter range of each second parameter, and the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, determining that the current state of the vehicle engine meets the self-learning condition, and prompting to start to execute the variable valve timing self-learning.

In an embodiment, the second current data determination module includes a temperature determination module configured to obtain a target temperature of the vehicle, the target temperature including at least one of a water temperature, a cylinder head temperature, and an oil temperature, the second parameter including the target temperature; comparing the target temperature with a preset temperature range, if the target temperature falls into the preset target temperature range, and the current effective duration of all the first parameters is larger than the preset effective duration of the first parameters, determining that the current state of the vehicle engine meets the self-learning condition to prompt the start of executing the variable valve timing self-learning, wherein the preset second parameter range comprises a preset target temperature range, and the preset target temperature range is determined according to the target temperature.

In an embodiment, the second current data determining module includes a duration determining module configured to obtain a target duration of the vehicle, where the target duration includes at least one of a stop duration and a start duration, and the second parameter includes the target duration; comparing the target duration with a preset target duration threshold, if the target duration falls within a preset target duration range, and the current effective duration of all the first parameters is larger than the preset effective duration of the first parameters, determining that the current state of the vehicle engine meets the self-learning condition to prompt the start of executing the variable valve timing self-learning, wherein the preset second parameter range comprises the preset target duration threshold, and the preset target duration threshold is determined according to the target duration.

In an embodiment, the engine further includes a pause control module configured to prompt to pause starting to perform the variable valve timing self-learning if the second current data of at least one second parameter exceeds a preset second parameter range of each second parameter; and suspending acquiring the first current data and the historical data of at least one first parameter of the vehicle engine until the second current data of all the second parameters acquired again meet the preset second parameter range of each second parameter.

In an embodiment, the device further includes a future time prediction module, configured to acquire current speed of the vehicle and future travel route high-precision map data, where the future travel route high-precision map data includes road condition data of a future travel road, before the acquisition module acquires first current data and historical data of at least one first parameter of an engine of the vehicle; predicting a future control strategy of the vehicle according to the current vehicle speed and the future driving route high-precision map data; predicting first future data of a first parameter of the vehicle engine and second future data of a second parameter of the vehicle at a plurality of future times based on a future control strategy; and if the first comparison result and the second comparison result of the future moment are both met, controlling to acquire the first current data and the historical data of at least one first parameter of the vehicle engine when the future moment is reached, wherein the first comparison result is a comparison result of the first future data and a preset first parameter range of the first parameter, and the second comparison result is a comparison result of the second future data and a preset second parameter range of the second parameter.

In an embodiment, the device further includes a front angle determining module, configured to obtain, after the obtaining module obtains first current data of at least one first parameter of the vehicle engine, a current target position according to the first current data before the self-learning control module controls to start to perform variable valve timing self-learning; determining a current relative operating angle based on the current target position and the reference position; if the current relative running angle is smaller than a preset first angle threshold value, determining that the current state of the vehicle engine meets the self-learning condition so as to prompt the start-up execution of the variable valve timing self-learning.

In an embodiment, the apparatus further includes a rear angle determining module, configured to obtain a real-time target position according to real-time current data matching of the first parameter in a process of executing the variable valve timing self-learning after the self-learning control module controls to start executing the variable valve timing self-learning; the method comprises the steps of carrying out a first treatment on the surface of the Determining a real-time relative operating angle based on the real-time target position and the reference position; if the real-time relative running angle is smaller than a preset second angle threshold, controlling to continuously execute the variable valve timing self-learning, wherein the preset second angle threshold is larger than the preset first angle threshold; and if the real-time relative running angle is larger than the preset second angle threshold value, controlling to stop executing the variable valve timing self-learning.

In an embodiment, the device further includes an adjustment module, configured to obtain a current target position according to the first current data matching after the self-learning control module controls to start the execution of the variable valve timing self-learning; determining a current relative operating angle based on the current target position and the reference position; and obtaining a current adjustment gradient by matching a preset relative operation angle-adjustment gradient table with the current relative operation angle, and controlling cam phase adjustment of the vehicle engine based on the current adjustment gradient.

In an embodiment, the device further includes a working condition determining module, configured to obtain a current working condition of the vehicle engine before the obtaining of the first current data and the historical data of the at least one first parameter of the vehicle engine if the current working condition of the vehicle engine is the target working condition; and if the current working condition is the fuel cut-off sliding working condition, controlling the start execution of the variable valve timing self-learning.

In an embodiment, the apparatus further includes a data reacquiring control module configured to determine a predicted meeting time according to the first current data, the historical data, and the preset first parameter range of the target parameter class if there is at least one current parameter value of the first parameter that exceeds the preset first parameter range of the first parameter, where the target parameter class is the first parameter whose current parameter value exceeds the preset first parameter range; determining ideal meeting time according to the predicted meeting time of each target parameter class, wherein the ideal meeting time is the latest predicted meeting time; when the ideal satisfying time is reached, the control obtains the first current data and the historical data of each new first parameter.

The embodiment of the invention provides a variable valve timing self-learning control device, which is characterized in that first current data and historical data of one or more first parameters of a vehicle engine are obtained when the vehicle engine is in a working condition except an oil-cut sliding working condition, if the obtained current parameter value of each first parameter meets a corresponding preset first parameter range, the parameter change rate and the parameter change direction of each first parameter are obtained according to the first current data and the historical data of each first parameter, the current effective duration of each first parameter can be obtained by combining the current parameter value of each first parameter and the preset first parameter range, and if the current effective duration of all the first parameters is larger than the corresponding preset effective duration, the starting execution of the variable valve timing self-learning is controlled. By means of the method, the parameters of the first parameters which are strongly related to the change of the working conditions of the engine are predicted in advance, the fact that the VVT self-learning (variable valve timing self-learning, hereinafter referred to as VVT self-learning) is started only when the predicted future parameter values of one or more first parameters of the vehicle engine are within the corresponding preset first parameter ranges can be ensured, the learning duration of the VVT self-learning can be ensured, the phenomenon that certain parameters of the vehicle engine exceed the corresponding preset first parameter ranges after the VVT self-learning is started and the VVT self-learning is not completed is avoided, the VVT self-learning is stopped is avoided, the repeated pulling of the VVT angle is avoided, and adverse effects on torque stability, combustion, drivability and the like of the engine are avoided.

The specific limitation of the variable valve timing self-learning control device may be referred to as limitation of the variable valve timing self-learning control method hereinabove, and will not be described in detail herein. The respective modules in the above-described variable valve timing self-learning control apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or independent of a processor in the electronic device, or may be stored in software in a memory in the electronic device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a variable valve timing self-learning control system is provided, which includes the variable valve timing self-learning control device shown in fig. 7, and an actuator; the execution mechanism is used for executing the variable valve timing self-learning based on the control of the self-learning control module, and then driving the cam phase adjustment.

In one embodiment, a vehicle is provided that includes a variable valve timing self-learning control device as shown in fig. 7.

In one embodiment, an electronic device is provided, which may be a server, and an internal structure thereof may be as shown in fig. 8. The electronic device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes non-volatile and/or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external client through a network connection. The computer program, when executed by a processor, performs functions or steps on the service side of a variable valve timing self-learning control method.

In one embodiment, an electronic device is provided, which may be a client, and the internal structure of which may be as shown in fig. 9. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external server through a network connection. The computer program, when executed by a processor, implements functions or steps of a client side of a variable valve timing self-learning control method.

In one embodiment, an electronic device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:

if the current working condition of the vehicle engine is a target working condition, acquiring first current data and historical data of at least one first parameter of the vehicle engine, wherein the target working condition comprises working conditions except for a fuel cut-off sliding working condition;

If the current parameter values of all the first parameters meet the preset first parameter range of each first parameter, determining the parameter change rate and the parameter change direction of each first parameter according to the first current data and the historical data of each first parameter, wherein the current parameter values are obtained based on the first current data;

determining the current effective duration of each first parameter based on the parameter change rate, the parameter change direction, the current parameter value and the preset first parameter range of each first parameter;

and if the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, controlling the start-up execution of the variable valve timing self-learning.

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

It should be noted that, the functions or steps that can be implemented by the computer readable storage medium or the electronic device may correspond to the descriptions of the server side and the client side in the foregoing method embodiments, and are not described herein one by one for avoiding repetition.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.

The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.

Claims

1. A variable valve timing self-learning control method, characterized by comprising:

2. The variable valve timing self-learning control method according to claim 1, characterized in that determining a current effective duration of a first parameter based on a parameter change rate of the first parameter, a parameter change direction, a current parameter value, and a preset first parameter range, includes any one of:

if the parameter change direction is increased, determining an effective interval exiting duration according to the current parameter value, the parameter change rate and a preset parameter maximum value, determining the effective interval exiting duration as the current effective duration of the first parameter, wherein the preset parameter maximum value is obtained based on the preset first parameter range;

If the parameter change direction is reduced, determining the time length for entering the effective interval according to the current parameter value, the parameter change rate and a preset parameter minimum value, determining the time length for entering the effective interval as the current effective time length of the first parameter, and obtaining the preset parameter minimum value based on the preset first parameter range.

3. The variable valve timing self-learning control method according to claim 1, characterized in that after the control starts to execute the variable valve timing self-learning, the method further comprises:

monitoring real-time signal data of each first parameter of the vehicle engine in the process of self-learning of the variable valve timing;

determining the data difference value of each first parameter according to the current signal data of each first parameter and the last signal data, wherein the current signal data is real-time signal data of the current monitoring moment, and the last signal data is real-time signal data of the last monitoring moment;

if the data difference value of the first parameter is larger than the preset difference value of the first parameter, comparing the current signal data with a preset first parameter range of the first parameter, and determining the execution state of the self-learning of the variable valve timing based on the current comparison result;

If the data difference values of all the first parameters are smaller than the preset difference value of the first parameters, the control continues to execute the variable valve timing self-learning.

4. The variable valve timing self-learning control method according to claim 3, characterized in that determining an execution state of the variable valve timing self-learning based on a current comparison result includes:

if the current comparison result is that the current signal data falls into a preset first parameter range of the first parameter, controlling to continuously execute variable valve timing self-learning;

and if the current comparison result is that the current signal data exceeds a preset first parameter range of the first parameter, controlling to stop executing the variable valve timing self-learning.

5. The variable valve timing self-learning control method according to claim 1, characterized in that before the control starts to perform the variable valve timing self-learning, the method further comprises:

acquiring second current data of at least one second parameter of the vehicle;

and if the second current data of all the second parameters meet the preset second parameter range of each second parameter and the current effective duration of all the first parameters is longer than the preset effective duration of the first parameters, prompting to start to execute the variable valve timing self-learning.

6. The variable valve timing self-learning control method according to claim 5, characterized in that a target temperature of the vehicle is obtained, the target temperature including at least one of a water temperature, a cylinder head temperature, and an engine oil temperature, the second parameter including a target temperature;

comparing the target temperature with a preset temperature range, if the target temperature falls into the preset target temperature range, and the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, prompting to start to execute variable valve timing self-learning, wherein the preset second parameter range comprises a preset target temperature range, and the preset target temperature range is determined according to the target temperature.

7. The variable valve timing self-learning control method according to claim 5, characterized in that a target period of time of the vehicle is acquired, the target period of time including at least one of a stop period of time and a start period of time, the second parameter including a target period of time;

comparing the target duration with a preset target duration threshold, if the target duration falls within the preset target duration range and the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, prompting to start to execute variable valve timing self-learning, wherein the preset second parameter range comprises the preset target duration threshold, and the preset target duration threshold is determined according to the target duration.

8. The variable valve timing self-learning control method according to claim 5, characterized in that the method further comprises:

if the second current data of at least one second parameter exceeds the preset second parameter range of each second parameter, prompting to pause starting to execute the variable valve timing self-learning;

and suspending acquiring the first current data and the historical data of at least one first parameter of the vehicle engine until the second current data of all the second parameters acquired again meet the preset second parameter range of each second parameter.

9. The variable valve timing self-learning control method according to any one of claims 1 to 8, characterized in that before acquiring first current data and history data of at least one first parameter of the vehicle engine, the method further comprises:

acquiring current speed of a vehicle and high-precision map data of a future driving route, wherein the high-precision map data of the future driving route comprises road condition data of a future driving road;

predicting a future control strategy of the vehicle according to the current vehicle speed and the future driving route high-precision map data;

predicting first future data of a first parameter of the vehicle engine at a plurality of future times and second future data of a second parameter of the vehicle based on the future control strategy;

And if the first comparison result and the second comparison result of a future time are both met, controlling to acquire first current data and historical data of at least one first parameter of the vehicle engine when the future time is reached, wherein the first comparison result is a comparison result of the first future data and a preset first parameter range of the first parameter, and the second comparison result is a comparison result of the second future data and a preset second parameter range of the second parameter.

10. The variable valve timing self-learning control method according to any one of claims 1 to 8, characterized in that after acquiring first current data of at least one first parameter of the vehicle engine, the method further includes, before controlling start of execution of variable valve timing self-learning:

obtaining a current target position according to the first current data matching;

determining a current relative operating angle based on the current target position and a reference position;

and if the current relative running angle is smaller than a preset first angle threshold value, prompting to start to execute the variable valve timing self-learning.

11. The variable valve timing self-learning control method according to any one of claims 1 to 8, characterized in that after the control starts to execute the variable valve timing self-learning, the method further includes:

In the process of executing the variable valve timing self-learning, a real-time target position is obtained according to the real-time current data matching of the first parameter;

determining a real-time relative operating angle based on the real-time target position and the reference position;

if the real-time relative running angle is smaller than a preset second angle threshold, controlling to continuously execute variable valve timing self-learning, wherein the preset second angle threshold is larger than the preset first angle threshold;

and if the real-time relative running angle is larger than the preset second angle threshold, controlling to stop executing the variable valve timing self-learning.

12. The variable valve timing self-learning control method according to any one of claims 1 to 8, characterized in that after the control starts to execute the variable valve timing self-learning, the method further includes:

and obtaining a current adjustment gradient by matching a preset relative operation angle-adjustment gradient table with the current relative operation angle, and controlling cam phase adjustment of the vehicle engine based on the current adjustment gradient.

13. The variable valve timing self-learning control method according to any one of claims 1 to 8, characterized in that, before acquiring the first current data and the history data of at least one first parameter of the vehicle engine if the current condition of the vehicle engine is the target condition, the method further comprises:

acquiring the current working condition of the vehicle engine;

and if the current working condition is the fuel cut-off sliding working condition, controlling the start-up execution of the variable valve timing self-learning.

14. The variable valve timing self-learning control method according to any one of claims 1 to 8, characterized in that if there is at least one first parameter whose current parameter value exceeds a preset first parameter range of the first parameter, the method further includes:

determining a predicted meeting time according to first current data, historical data and a preset first parameter range of a target parameter class, wherein the target parameter class is a first parameter of which the current parameter value exceeds the preset first parameter range;

determining ideal satisfying time according to the predicted satisfying time of each target parameter category, wherein the ideal satisfying time is the latest predicted satisfying time;

and when the ideal satisfying time is reached, controlling to acquire the first current data and the historical data of each new first parameter.

15. A variable valve timing self-learning control apparatus, characterized by comprising:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring first current data and historical data of at least one first parameter of a vehicle engine if the current working condition of the vehicle engine is a target working condition, and the target working condition comprises working conditions except for a fuel cut sliding working condition;

the change determining module is used for determining the parameter change rate and the parameter change direction of each first parameter according to the first current data and the historical data of each first parameter if the current parameter values of all the first parameters meet the preset first parameter range of each first parameter, and the current parameter values are obtained based on the first current data;

the duration prediction module is used for determining the current effective duration of each first parameter based on the parameter change rate, the parameter change direction, the current parameter value and the preset first parameter range of each first parameter;

and the self-learning control module is used for controlling the start of the execution of the variable valve timing self-learning if the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters.

16. A variable valve timing self-learning control system, characterized in that the system comprises the variable valve timing self-learning control device according to claim 15, and an actuator;

The execution mechanism is used for executing the variable valve timing self-learning based on the control of the self-learning control module after starting the execution of the variable valve timing self-learning, and driving the cam to adjust the phase.

17. A vehicle characterized in that it includes the variable valve timing self-learning control device according to claim 15.

18. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 14 when the computer program is executed.

19. A computer readable storage medium storing a computer program, which when executed by a processor implements the method of any one of claims 1 to 14.