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

Abstract

The present application relates to the technical field of vehicles and discloses a variable valve timing self-learning control method, device, system, vehicle, electronic device and storage medium. The method comprises the following steps: obtaining 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 preset first parameter range corresponding to the current parameter value; and controlling to start executing variable valve timing self-learning if the current effective duration is greater than a preset effective duration corresponding to the current effective duration. The current effective duration is predicted in advance, so that the VVT self-learning is started only when the time requirement is met, the VVT self-learning is prevented from being exited due to insufficient time for completing the VVT self-learning, repeated pulling of the VVT angle is avoided, and adverse effects on the torque stability, combustion and drivability of the engine are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a variable valve timing self-learning control method, device, system, vehicle, electronic device and storage medium. BACKGROUND

[0002] The hybrid vehicle has engine EMS (Engine Management System) control, VCU (Vehicle control unit) motor related control and BMS (Battery Management System) battery management. Under different SOC (State of Charge) conditions, the working condition of the engine requested by the VCU is quite different from that of the pure internal combustion engine vehicle, which has a certain influence on some engine functions, such as the fine self-learning function of the engine VVT (Variable Valve Timing). As we all know, the most important fuel injection and ignition timing calculation of the engine depends on the management of the camshaft and crankshaft position relationship, which is the basis and core of the normal operation of the engine. According to engineering experience, there is always a deviation between the actual signal position of the camshaft and the theoretical position. These deviations mainly come from the position deviation of the camshaft installation, the manufacturing deviation of the camshaft signal wheel, the sensor deviation, etc. With the increase of service life, the deviation caused by the aging slip of the pulley, the wear and plastic deformation of the timing chain, etc. The management of the camshaft and crankshaft position relationship is guaranteed by the initial self-learning and fine self-learning mechanisms to ensure accurate control of the timing.

[0003] The initial self-learning mechanism can learn the position as the 0 point of the camshaft position, and the introduction of the fine self-learning mechanism can distinguish between manufacturing / installation deviation and deviation occurring during use. The trigger conditions of the initial self-learning are the first time the ECU (Engine control unit) runs when the vehicle is off-line in the EOL (End Of Line) process, the reference position self-learning value data in the EEPROM (Electrically Erasable Programmable Read-Only Memory) changes, and the external diagnostic device requests, etc. The camshaft reference position self-learning will only be triggered once in the life cycle of the ECU. The initial self-learning is a prerequisite for VVT control, which is used to eliminate the large deviation between the actual position and the theoretical position; the fine self-learning can only be run after the initial self-learning is completed, and its role is to constantly approach the actual position after self-learning, so that the learning value becomes more and more accurate. It can also timely report related camshaft faults when the camshaft position deviation abnormally increases.

[0004] A certain extended range vehicle, under low SOC conditions (SOC below 15%), the project will enter the power follow generation mode, during acceleration, basically speed follow throttle, instead of stable speed generation under normal SOC. Since the motor drags the engine speed to rise quickly in power follow mode, it cannot guarantee that the VVT fine self-learning is successfully completed, and it will frequently enter and exit the VVT fine self-learning. Under normal SOC, the speed is stable, and the fine self-learning can be completed in about 5S under normal state. For hybrid engines, high compression ratio (15-16) + Miller cycle + supercharging control are usually equipped, when fine self-learning, VVT moves to the reference position, the intake valve closes later than the active position, and the engine intake is larger, which increases the tendency of engine knock and pre-ignition. At the same time, due to the repeated large changes of engine VVT, it brings certain challenges to engine torque accuracy control and drivability.

[0005] The VVT fine self-learning of a pure engine vehicle is usually completed during engine starting idle speed stage or starting small load stage. However, for a certain extended range vehicle, the DHE (Dedicated Hybrid Engine) engine is mostly operated in the economic fuel consumption region in the WLTC (World Light Vehicle Test Cycle) cycle, and the speed and load range exceeds the idle speed and starting range of conventional pure fuel vehicles, so the fine self-learning is easily repeated to enter and exit, which affects other vehicle functions.

[0006] If the fine self-learning condition is accidentally exited halfway through the VVT being kept at the reference position, the VVT will return to the original target angle, and then be pulled to the reference position when the self-learning condition is met. Therefore, the phenomenon of repeatedly entering and exiting the self-learning condition and repeatedly pulling the VVT angle is likely to occur, which adversely affects the torque stability, combustion, drivability, etc. of the engine. It can be seen that, in the related art, since the variable valve timing self-learning is not reasonably controlled, the torque stability, combustion, drivability, etc. of the engine will be adversely affected. SUMMARY

[0007] The present application provides a continuous variable valve timing self-learning control method, device, system, vehicle, electronic equipment and storage medium, to solve the technical problem that in the related art, since the variable valve timing self-learning is not reasonably controlled, the torque stability, combustion, drivability, etc. of the engine will be adversely affected.

[0008] The embodiment of the present application provides a variable valve timing self-learning control method, the method comprises the following steps: if the current working condition of a vehicle engine is a target working condition, obtaining first current data and historical data of at least one first parameter of the vehicle engine, and the target working condition comprises working conditions except for the fuel cut working condition; if the current parameter value of all the first parameters meets 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, and the current parameter value is 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; if the current effective duration of all the first parameters is greater than the preset effective duration of the first parameter, controlling to start executing the variable valve timing self-learning.

[0009] In an embodiment of the present application, the current effective duration of a first parameter is determined based on the parameter change rate, the parameter change direction, the current parameter value and the preset first parameter range of the first parameter, and the current effective duration of the first parameter is determined according to any one of the following: if the parameter change direction is increasing, the exit effective interval duration is determined according to the current parameter value, the parameter change rate and the preset parameter maximum value, the preset parameter maximum value is obtained based on the preset first parameter range, and the exit effective interval duration is determined as the current effective duration of the first parameter; if the parameter change direction is decreasing, the entry effective interval duration is determined according to the current parameter value, the parameter change rate and the preset parameter minimum value, the preset parameter minimum value is obtained based on the preset first parameter range, and the entry effective interval duration is determined as the current effective duration of the first parameter.

[0010] In an embodiment of the present application, after the execution of the variable valve timing self-learning is controlled to start, the method further comprises the following steps: monitoring the real-time signal data of each first parameter of the vehicle engine in the process of the variable valve timing self-learning; determining the data difference value of each first parameter according to the current signal data and the last signal data of each first parameter, the current signal data is the real-time signal data at the current monitoring time, and the last signal data is the real-time signal data at the last monitoring time; if the data difference value of a first parameter is greater than the preset difference value of the first parameter, comparing the current signal data with the 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 value of all the first parameters is less than the preset difference value of the first parameter, controlling to continue executing the variable valve timing self-learning.

[0011] In an embodiment of the present application, the execution state of the variable valve timing self-learning is determined based on the current comparison result, including: if the current comparison result is that the current signal data falls within the preset first parameter range of the first parameter, the variable valve timing self-learning is controlled to continue to be executed; and if the current comparison result is that the current signal data exceeds the preset first parameter range of the first parameter, the variable valve timing self-learning is controlled to be stopped.

[0012] In an embodiment of the present application, before the variable valve timing self-learning is controlled to be started to be executed, the method further includes: obtaining second current data of at least one second parameter of the vehicle; and if the second current data of all the second parameters meets the preset second parameter range of each second parameter, and the current effective time length of all the first parameters is greater than the preset effective time length of the first parameter, it is prompted to start to execute the variable valve timing self-learning.

[0013] In an embodiment of the present application, the target temperature of the vehicle is obtained, the target temperature including at least one of water temperature, cylinder head temperature and oil temperature, and the second parameter includes the target temperature; the target temperature is compared with a preset temperature range, if the target temperature falls within the preset target temperature range, and the current effective time length of all the first parameters is greater than the preset effective time length of the first parameter, it is prompted to start to execute the variable valve timing self-learning, the preset second parameter range includes the preset target temperature range, and the preset target temperature range is determined according to the target temperature.

[0014] In an embodiment of the present application, the target time length of the vehicle is obtained, the target time length including at least one of shutdown time length and startup time length, and the second parameter includes the target time length; the target time length is compared with a preset target time length threshold value, if the target time length falls within the preset target time length range, and the current effective time length of all the first parameters is greater than the preset effective time length of the first parameter, it is prompted to start to execute the variable valve timing self-learning, the preset second parameter range includes the preset target time length threshold value, and the preset target time length threshold value is determined according to the target time length.

[0015] In an embodiment of the present application, 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, it is prompted to pause to start to execute the variable valve timing self-learning; and the obtaining of the first current data and the historical data of at least one first parameter of the engine of the vehicle is paused until the second current data of all the second parameters obtained again meets the preset second parameter range of each second parameter.

[0016] In an embodiment of the present application, before the first current data and the historical data of the at least one first parameter of the vehicle engine are acquired, the method further comprises: acquiring a current vehicle speed and high-precision map data of a future driving route, the high-precision map data of the future driving route comprising road condition data of a future driving road; predicting a future control strategy of the vehicle according to the current vehicle speed and the high-precision map data of the future driving route; predicting, based on the future control strategy, first future data of the at least one first parameter of the vehicle engine and second future data of a second parameter of the vehicle at a plurality of future time points; and if both a first comparison result and a second comparison result of a future time point satisfy, acquiring the first current data and the historical data of the at least one first parameter of the vehicle engine when the future time point 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.

[0017] In an embodiment of the present application, after the first current data of the at least one first parameter of the vehicle engine is acquired, before the variable valve timing self-learning is controlled to be started to be executed, the method further comprises: matching a current target position according to the first current data; determining a current relative operating angle based on the current target position and a reference position; and if the current relative operating angle is less than a preset first angle threshold, prompting the variable valve timing self-learning to be executed.

[0018] In an embodiment of the present application, after the variable valve timing self-learning is controlled to be started to be executed, the method further comprises: in a process of executing the variable valve timing self-learning, matching a real-time target position according to real-time current data 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 operating angle is less than a preset second angle threshold, controlling the variable valve timing self-learning to be continuously executed, the preset second angle threshold being greater than the preset first angle threshold; and if the real-time relative operating angle is greater than the preset second angle threshold, controlling the variable valve timing self-learning to be stopped.

[0019] In an embodiment of the present application, after the variable valve timing self-learning is controlled to be started to be executed, the method further comprises: matching a current target position according to the first current data; determining a current relative operating angle based on the current target position and a reference position; matching a current adjustment gradient to a preset relative operating angle-adjustment gradient table according to the current relative operating angle, and controlling a cam phase adjustment of the vehicle engine based on the current adjustment gradient.

[0020] In an embodiment of the present application, if the current working condition of the vehicle engine is a target working condition, before the first current data and the historical data of at least one first parameter of the vehicle engine are obtained, the method further comprises: obtaining the current working condition of the vehicle engine; and if the current working condition is a fuel-cut coasting working condition, controlling to start performing variable valve timing self-learning.

[0021] In an embodiment of the present application, if there is at least one first parameter whose current parameter value is out of a preset first parameter range of the first parameter, the method further comprises: determining a predicted meeting time according to the first current data, the historical data and the preset first parameter range of a target parameter category, wherein the target parameter category is the first parameter whose current parameter value is out of the preset first parameter range; determining an ideal meeting time according to the predicted meeting time of each target parameter category, wherein the ideal meeting time is the latest predicted meeting time; and when the ideal meeting time is reached, controlling to obtain new first current data and historical data of each first parameter.

[0022] The embodiment of the present application further provides a variable valve timing self-learning control device, which comprises: an obtaining module, used for obtaining first current data and historical data of at least one first parameter of a vehicle engine if a current working condition of the vehicle engine is a target working condition, wherein the target working condition comprises a working condition other than a fuel-cut coasting working condition; a change determining module, used for determining 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 if current parameter values of all first parameters meet preset first parameter ranges of the first parameters, wherein the current parameter values are obtained based on the first current data; a time length predicting module, used for determining a current effective time length 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 a self-learning control module, used for controlling to start performing variable valve timing self-learning if the current effective time lengths of all first parameters are greater than preset effective time lengths of the first parameters.

[0023] The embodiment of the present application further provides a variable valve timing self-learning control system, which comprises the variable valve timing self-learning control device according to any one of the above embodiments and an execution mechanism; the execution mechanism is used for performing the variable valve timing self-learning and driving cam phase adjustment based on the control of the self-learning control module to start performing the variable valve timing self-learning.

[0024] The embodiment of the present application further provides a vehicle, which comprises the variable valve timing self-learning control device according to any one of the above embodiments.

[0025] The embodiment of the present application further provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any one of the above embodiments when executing the computer program.

[0026] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method according to any one of the above embodiments.

[0027] In the scheme implemented by the variable valve timing self-learning control method, device, system, vehicle, electronic device and storage medium, the first current data and the historical data of one or more first parameters of the vehicle engine are acquired when the vehicle engine is in a working condition other than the fuel-cut coasting working condition, if the current parameter value of each acquired first parameter meets the preset first parameter range corresponding to the first parameter, 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 in combination with the current parameter value of each first parameter and the preset first parameter range, if the current effective duration of all first parameters is greater than the preset effective duration corresponding to the first parameter, the variable valve timing self-learning is controlled to be executed. In the above manner, the parameters of the first parameters that are strongly related to the engine working condition are predicted in advance, it is ensured that the parameter value of one or more first parameters of the vehicle engine in the future can be kept in the preset first parameter range corresponding to the first parameter for a sufficient duration, and then the VVT self-learning (variable valve timing self-learning, hereinafter referred to as VVT self-learning) is started, the learning duration of the VVT self-learning is ensured, and the VVT self-learning is prevented from being exited due to insufficient learning time, the VVT angle is prevented from being repeatedly pulled, and the adverse effects on the torque stability, combustion, drivability and the like of the engine are avoided. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0029] Figure 1 A schematic diagram of the relative position relationship of the VVT position self-learning provided by an embodiment of the present application;

[0030] Figure 2 A schematic diagram of a VVT fine self-learning process provided for an embodiment of the present application;

[0031] Figure 3 A flowchart of a variable valve timing self-learning control method provided for an embodiment of the present application;

[0032] Figure 4 A schematic diagram of a rotation speed hysteresis operation provided for an embodiment of the present application;

[0033] Figure 5 A schematic diagram of a hybrid engine VVT fine self-learning strategy provided for an embodiment of the present application;

[0034] Figure 6 A schematic diagram of a hybrid engine VVT fine self-learning start condition provided for an embodiment of the present application;

[0035] Figure 7 A structural schematic diagram of a variable valve timing self-learning control device provided for an embodiment of the present application;

[0036] Figure 8 A structural schematic diagram of an electronic device in an embodiment of the present application;

[0037] Figure 9 Another structural schematic diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0039] With the environmental protection policy of carbon peak and carbon neutral, higher requirements are put forward for the fuel consumption and emissions of engines in the automobile industry. In recent years, new energy vehicles dominated by hybrid power have experienced a substantial increase in sales. China is vast in territory, and the east-west and north-south span is wide. For cross-provincial vehicle needs, charging and range anxiety caused by the lack of charging pile and charging station infrastructure determines that the hybrid route will be the mainstream for a long time. Especially the PHEV (PLUG-IN HYBRID ELECTRIC VEHICLE) with a pure electric cruising range of about 100-200Km will be a relatively ideal means of transportation, which can meet the needs of commuting in the city and long-distance travel across provinces, and completely solve the charging and range anxiety of pure EV (ELECTRIC VEHICLE).

[0040] The most important injection, ignition timing calculation of engine depends on the camshaft and crankshaft position relationship management. But there is always a deviation between the actual signal position and the theoretical position of the camshaft, which is mainly caused by the position deviation of the camshaft installation, the manufacturing deviation of the camshaft signal wheel, the sensor deviation, and the deviation caused by the aging slip of the pulley, the wear and plastic deformation of the timing chain, etc. with the increase of service life. The camshaft and crankshaft position relationship management is ensured by the initial self-learning and fine self-learning mechanisms to ensure the accurate control of the timing.

[0041] The initial self-learning mechanism can take the learned position as the 0 point of the camshaft position, and the introduction of the fine self-learning mechanism can distinguish the manufacturing / installation deviation from the deviation occurring during use. The trigger conditions of the initial self-learning are the first running of the ECU (Engine control unit, engine controller) when the EOL (End Of Line) vehicle off-line process is run, the change of the reference position self-learning value data in the EEPROM (Electrically Erasable Programmable Read-Only Memory), the demand of the external diagnostic device, etc. The camshaft reference position self-learning will only be triggered once in the life cycle of the ECU, and the initial self-learning is the prerequisite of VVT control, which is used to eliminate the large deviation between the actual position and the theoretical position; the fine self-learning can be run after the initial self-learning is completed, and its function is to make the learned position approach the actual position constantly, so that the learning value becomes more and more accurate. The related camshaft fault can also be reported in time when the camshaft position deviation abnormally increases.

[0042] Please refer to Figure 1 , Figure 1 A schematic diagram of the relative relationship of the VVT position self-learning provided by an embodiment of the present application is based on the BOSCH (Bosch) camshaft four-tooth signal disc electrical signal. As shown in Figure 1 , the upper curve is the theoretical position, and the lower curve is the actual position.

[0043] The VVT fine self-learning process is illustrated by the relationship between the VVT angle and time after a certain vehicle runs for a period of time. The VVT angle, i.e. the variable valve timing driving camshaft operating angle, can be realized by the camshaft phase adjuster. Please refer to Figure 2 , Figure 2 A schematic diagram of the VVT fine self-learning process provided by an embodiment of the present application. As shown in Figure 2The lower wavy line shown in the frame area (rectangular frame area) is the target angle of VVT according to the normal speed and load table, the upper horizontal line is the VVT reference position, the six lines between the horizontal line and the wavy line are the process of the system meeting the VVT fine self-learning condition, the EMS pulls the VVT angle from the target position represented by the wavy line to the reference position represented by the horizontal line, as long as the self-learning condition does not exit, the VVT will be kept at the reference position for a certain self-learning time, and then return to the VVT target angle of the wavy line. The number of times of keeping the self-learning time at the reference position in each driving cycle can be calibrated, and in order to obtain a more stable learning value, it is usually 3-5 times.

[0044] If the fine self-learning condition exits unexpectedly in the middle of keeping the VVT at the reference position, the VVT will return to the light blue target angle, and then be pulled to the reference position again when the self-learning condition is met, thus, the phenomenon of repeatedly entering and exiting the self-learning condition and repeatedly pulling the VVT angle is easy to occur, and further, it has an adverse effect on the torque stability, combustion, drivability and the like of the engine. It can be seen that when the fine self-learning is performed, the engine VVT in motion will move to the reference position, which may cause a series of problems in engine torque response, combustion stability, drivability and the like.

[0045] To solve the above problems, the embodiment of the present application provides a variable valve timing self-learning control method, by acquiring the first current data and historical data of one or more first parameters of the vehicle engine in the working condition of the vehicle engine except the oil cut coasting working condition, if the current parameter value of each first parameter meets the corresponding preset first parameter range, then according to the first current data and historical data of each first parameter, the parameter change rate and parameter change direction of each first parameter are obtained, and 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, if the current effective duration of all first parameters is greater than the corresponding preset effective duration, then the variable valve timing self-learning is controlled to be executed. Through the above-mentioned manner, the parameters of the first parameters which are strongly related to the engine working condition are predicted in advance, which can ensure that the parameter values of one or more first parameters of the vehicle engine in the future can be kept in the corresponding preset first parameter range for a sufficient duration, and then the VVT self-learning (variable valve timing self-learning, hereinafter referred to as VVT self-learning) is started, which can ensure the learning duration of VVT self-learning, avoid the situation that the VVT self-learning is exited due to that some parameters of the vehicle engine exceed the corresponding preset first parameter range after the VVT self-learning is started and before the VVT self-learning is completed, avoid the repeated pulling of the VVT angle, and avoid the adverse effect on the torque stability, combustion, drivability and the like of the engine. The present application will be described in detail through specific embodiments.

[0046] Referring to Figure 3 as shown, Figure 3 A flowchart of a variable valve timing self-learning control method provided by an embodiment of the present application includes the following steps:

[0047] In step S310, if the current operating condition of the vehicle engine is a target operating condition, first current data and historical data of at least one first parameter of the vehicle engine are obtained.

[0048] The target operating condition is an operating condition other than the fuel-cut coasting operating condition. The first parameter can be at least one of parameters such as speed and load that are strongly correlated with the operating condition of the engine. That is, the first parameter can be one (speed or load), and the first parameter can also be multiple (speed, load, etc.). For each first parameter, a first current data and historical data can be collected, and the number of historical data can be one or more. Correspondingly, for each first parameter, a corresponding preset first parameter range is set.

[0049] The first current data and the historical data each include a parameter value and a parameter collection time. For example, the first current data is a current parameter value collected at a time when it is needed to determine whether to start variable valve timing self-learning and a current parameter collection time, and the historical data is a historical parameter value collected at one or more historical times in a certain historical period and a corresponding historical parameter collection time. Each current parameter value and historical parameter value is configured with a corresponding parameter name according to the same rule, so as to facilitate subsequent calculation. For some vehicles with series-parallel or extended-range configurations, the engine fuel-cut coasting operating condition is extremely rare or even nonexistent, and thus the current operating condition of the vehicle engine of the above vehicle can be defaulted to be always in the target operating condition, and confirmation and determination of the current operating condition are not needed.

[0050] Of course, if it is not known in advance whether the vehicle has a fuel-cut coasting operating condition, in an exemplary embodiment, before the first current data and the historical data of at least one first parameter of the vehicle engine are obtained, if the current operating condition of the vehicle engine is a target operating condition, the method further includes: obtaining the current operating condition of the vehicle engine; and if the current operating condition is a fuel-cut coasting operating condition, controlling to start performing variable valve timing self-learning. That is, if the current operating condition of the current vehicle engine is already a fuel-cut coasting operating condition, variable valve timing self-learning can be directly controlled to start, and no other determination and prediction is needed. Prioritizing fine self-learning to be performed in the fuel-cut coasting operating condition can avoid the movement of the variable valve timing to the reference position from affecting the normal driving power of the vehicle, which is also a solution that is preferred for hybrid vehicles.

[0051] In the present embodiment, the vehicle engine can be an engine of a hybrid vehicle.

[0052] The first current data can be data of the first parameter collected at the preset time A defined by those skilled in the art, which is required to determine whether to start VVT self-learning, such as speed, load, etc. The historical data can be data of the first parameter collected before the preset time A. The historical collection time of the historical data can be limited by those skilled in the art to the time difference between the preset time A, such as the historical data within the previous 1 minute, etc.

[0053] The data of the first parameter often has the characteristic of linear (which can be straight line, curve, etc.) change over time, so it can be convenient for subsequent prediction of the current effective duration.

[0054] Step S320, if all the current parameter values of the first parameter meet the preset first parameter range of each first parameter, the parameter change rate and the parameter change direction of each first parameter are determined according to the first current data and the historical data of each first parameter.

[0055] Among them, the current parameter value is obtained based on the first current data. That is, the first current data includes the current parameter value and the current collection time, and the corresponding historical data includes the historical parameter value and the historical collection time.

[0056] Whether the current parameter value meets the preset first parameter range can be determined according to whether the current parameter value falls within the numerical interval formed by the first preset parameter range. If the current parameter value is greater than the preset parameter minimum value of the first preset parameter range and less than the preset parameter minimum value, it is considered that the current parameter value meets the preset first parameter range, otherwise, it does not meet.

[0057] For example, each first parameter is provided with a preset first parameter range, which is

min, max

[0058] In the embodiment, if the current parameter values of all the first parameters satisfy the preset first parameter ranges corresponding to the first parameters respectively, 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 comprises: judging whether the current parameter value of each first parameter satisfies the preset first parameter range of the first parameter, if the current parameter values of all the first parameters satisfy the preset first parameter ranges corresponding to the first parameters respectively, performing step S320, if the current parameter value of at least one first parameter does not satisfy the preset first parameter range of the first parameter, exiting the judgment, and waiting for a suitable opportunity, such as continuously collecting new first current data and historical data of the first parameter to judge, or collecting new first current data and historical data of the first parameter after a preset time interval to judge.

[0059] In the embodiment, if the current parameter value of at least one first parameter exceeds (does not satisfy) the preset first parameter range of the first parameter, the method further comprises: determining a predicted satisfaction time according to the first current data, the historical data and the preset first parameter range of the target parameter category, wherein the target parameter category is the first parameter whose current parameter value does not satisfy the preset first parameter range; determining an ideal satisfaction time according to the predicted satisfaction time of each target parameter category, wherein the ideal satisfaction time is the latest predicted satisfaction time; when the ideal satisfaction time is reached, controlling to re-perform step S310 to obtain new first current data and historical data of each first parameter.

[0060] In the embodiment, an exemplary determination manner of the predicted satisfaction time can be:

[0061] determining the parameter change rate and the parameter change direction according to the current collection time t1 and the current parameter value M1 of the first current data, and the historical collection time t2 and the historical parameter value M2 of the historical data;

[0062] determining the preset parameter maximum value or the preset parameter minimum value as the current extreme value according to the parameter change direction, and determining the predicted satisfaction time length according to the current extreme value, the parameter change rate and the current reference value;

[0063] determining the predicted satisfaction time according to the predicted satisfaction time length and the current collection time.

[0064] In the embodiment, if M1 is greater than M2, the parameter change direction is increasing, otherwise, if M1 is less than M2, the parameter change direction is decreasing. If M1 is equal to M2, the determination of the predicted satisfaction time is re-performed after a preset time interval.

[0065] In the embodiment, a determination manner of the parameter change rate can be:

[0066] Kx = (M2 - M1) / (t2 - t1) Formula (1),

[0067] Wherein, Kx is a parameter change rate, M2 is a historical parameter value, M1 is a current parameter value, t2 is a historical collection time, and t1 is a current collection time.

[0068] The current extreme value E0 is a preset parameter maximum value E1 if the parameter change direction is increasing, and the current extreme value E0 is a preset parameter minimum value E2 if the parameter change direction is decreasing.

[0069] A determination manner of the predicted meeting time length is:

[0070] △tx = abs[(E0 - M1) / Kx] Formula (2),

[0071] Wherein, △tx is a predicted meeting time length, Kx is a parameter change rate, M1 is a current parameter value, and E0 is a current extreme value.

[0072] A determination manner of the predicted meeting time is:

[0073] Tx = △tx + t1 Formula (3),

[0074] Wherein, Tx is a predicted meeting time, △tx is a predicted meeting time length, and t1 is a current collection time.

[0075] It should be noted that in Formula (2) and Formula (3), the first current data can be the data collected in step S310, or the data collected at the updated current time.

[0076] Through the above manner, when the current parameter value of the first parameter does not meet the preset first parameter range, instead of blindly re-collecting new data for judgment, leading to waste of computing resource, the future time X of the first parameter value meeting the preset first parameter range is predicted based on the known data, and the data reacquisition is started after the time X is reached. When the current parameter values of multiple first parameters do not meet the preset first parameter range, the predicted meeting time of the first parameter determined by the latest first parameter meeting the preset first parameter range can be used as an ideal meeting time N, and the data reacquisition is started after the ideal meeting time N is reached.

[0077] It should be appreciated that the historical data can be one or more sets of data, in which case the parameter change direction can be determined by the most recently collected set of data, and the parameter change rate can also be determined by the most recently collected set of data. Alternatively, if there are two parameter change directions, the parameter change direction determined by the most recently collected set of data is taken as the final parameter change direction. For the parameter change rate, the change rates of adjacent sets of data can be averaged, or the change rate can be determined based on any two sets of data, and the average, median or mode of the multiple change rates can be taken. Of course, the parameter change rate can also be determined in other ways known to those skilled in the art.

[0078] In step S330, the current effective duration of each first parameter is determined based on the parameter change rate, the parameter change direction, the current parameter value and the preset first parameter range of each first parameter.

[0079] In an embodiment, determining the current effective duration of a first parameter based on the parameter change rate, the parameter change direction, the current parameter value and the preset first parameter range of the first parameter includes any one of the following:

[0080] If the parameter change direction is increasing, the exit effective interval duration is determined based on the current parameter value, the parameter change rate and the preset parameter maximum value, the exit effective interval duration is determined as the current effective duration of the first parameter, and the preset parameter maximum value is obtained based on the preset first parameter range.

[0081] If the parameter change direction is decreasing, the entry effective interval duration is determined based on the current parameter value, the parameter change rate and the preset parameter minimum value, the entry effective interval duration is determined as the current effective duration of the first parameter, and the preset parameter minimum value is obtained based on the preset first parameter range.

[0082] Taking the first parameter as the rotational speed as an example, the first current data is the current rotational speed and the current collection time, the historical data is the rotational speed at the last time and the last time, the parameter change rate is the rotational speed change rate, the exit effective interval duration is the time of exiting the effective rotational speed interval, and the entry effective interval duration is the time of entering the effective rotational speed interval. The rotational speed change rate is calculated according to the relative time of the current rotational speed and the rotational speed signal at the last time. Assuming that the current rotational speed is NN1, the time is tt1, the rotational speed at the last time tt0 is NN0, and the rotational speed change rate is:

[0083] KN = (NN1-NN0) / (tt1-tt0) Formula (4),

[0084] where KN is the rotational speed change rate, NN1 is the current rotational speed, tt1 is the current time, NN0 is the rotational speed at the last time, and tt0 is the last time.

[0085] The fine self-learning rotation speed interval in the early stage of calibration (i.e., the preset first parameter range) is [N1, N2], therefore, when the rotation speed increases, the time of exiting the effective rotation speed interval is:

[0086] △tn2=abs[(N2-N) / KN] Formula (5),

[0087] wherein, △tn2 is the time of exiting the effective rotation speed interval, N2 is the preset parameter maximum value, N is the real-time rotation speed (which can be the current rotation speed or the rotation speed updated based on the current time), and KN is the rotation speed change rate.

[0088] When the load decreases, the time of entering the effective rotation speed interval is:

[0089] △tn1=abs[(N1-N) / KN] Formula (6),

[0090] wherein, △tn1 is the time of entering the effective rotation speed interval, N1 is the preset parameter minimum value, N is the real-time rotation speed (which can be the current rotation speed or the rotation speed updated based on the current time), and KN is the rotation speed change rate.

[0091] Taking the first parameter as the load for example, the first current data is the current load and the current collection 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 exit effective interval length is the time of exiting the effective load interval, and the entry effective interval length is the time of entering the effective load interval.

[0092] The prediction calculation method for the load is to calculate the load change rate according to the relative time of the current load and the load signal at the last time. Assuming that the current load is RR1, the time is tt1, the load at the last time tt0 is RR0, and the load change rate is:

[0093] KR=(RR1-RR0) / (tt1-tt0) Formula (7),

[0094] wherein, KR is the load change rate, RR1 is the current load, tt1 is the current time, RR0 is the load at the last time, and tt0 is the last time.

[0095] The fine self-learning load interval in the early stage of calibration is [R1, R2], therefore, when the load increases, the time of exiting the effective load interval is:

[0096] △tr2=abs[(R2-R) / KR] Formula (8),

[0097] Wherein, the time of exiting the effective load range is Δtr2, R2 is a preset parameter maximum value, R is a real-time load (which can be a current load or a load updated based on the current time), and KR is a load change rate.

[0098] When the load decreases, the time of entering the effective load range is:

[0099] Δtr1 = abs[(R1-R) / KR] Formula (9),

[0100] Wherein, the time of entering the effective load range is Δtr1, R1 is a preset parameter minimum value, R is a real-time load (which can be a current load or a load updated based on the current time), and KR is a load change rate.

[0101] In the above manner, the current effective duration of the first parameter can be more accurately predicted, thereby providing more accurate and reasonable guidance for whether to start the self-learning in the future.

[0102] Step S340, if the current effective duration of all the first parameters is greater than the preset effective duration of the first parameter, the control starts to execute the variable valve timing self-learning.

[0103] Different first parameters can be set with the same or different preset effective durations, and different first parameters can also be set with different preset first parameter ranges. When the current effective duration of each first parameter is greater than the preset effective duration corresponding thereto, it can be considered that the current effective duration of all the first parameters is greater than the preset effective duration of the first parameter.

[0104] If the current effective duration of each first parameter is greater than the preset effective duration corresponding thereto, it indicates that the time condition for self-learning is currently met. Then, the self-learning can be started.

[0105] In the embodiment, after the control starts to execute 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 the variable valve timing self-learning; determining a data difference value of each first parameter according to current signal data and previous signal data of each first parameter, the current signal data being real-time signal data at a current monitoring time, and the previous signal data being real-time signal data at a previous monitoring time; if a data difference value of a first parameter is greater than a preset difference value of the first parameter, comparing the current signal data with a preset first parameter range of the first parameter, and determining an execution state of the variable valve timing self-learning based on a comparison result; and if data difference values of all the first parameters are less than preset difference values of the first parameters, controlling to continue to execute the variable valve timing self-learning.

[0106] The real-time signal data is new signal data obtained by updating the first current data, and the real-time signal data still includes a parameter value and a collection time. By differentially calculating the parameter value of the real-time signal data at the collection time, a data difference value is obtained, and based on the size relationship between the data difference value and a preset difference value, it is determined whether to reevaluate the condition of VVT self-learning (i.e., the execution state of variable valve timing self-learning needs to be determined based on the current comparison result). It should be noted that the previous signal data can be data for which the condition of VVT self-learning is evaluated last time, or can be signal data collected last time in a real time dimension.

[0107] In the embodiment, determining the execution state of variable valve timing self-learning based on the current comparison result includes: if the current comparison result is that the current signal data falls within a preset first parameter range of a first parameter, controlling to continue executing variable valve timing self-learning; and if the current comparison result is that the current signal data exceeds the preset first parameter range of the first parameter, controlling to stop executing variable valve timing self-learning. Whether the current signal data falls within the preset first parameter range of the first parameter is determined according to whether the parameter value of the current signal data is greater than a preset parameter minimum value and less than a preset parameter maximum value.

[0108] In the process of monitoring the data of the first parameter after starting to execute VVT self-learning, hysteresis operation is performed. When the parameter value of the first parameter does not change much (the data difference value is less than a preset difference value), the determination of whether the current parameter value meets the preset first parameter range is not reperformed. Only when the data difference value is greater than the preset difference value, the determination of whether the current parameter value meets the preset first parameter range is reperformed. At this time, the current parameter value is updated to the real-time parameter value of the real-time signal data.

[0109] Taking the first parameter as load as an example, hysteresis operation is performed on the load of the engine. The operation method is that the load signal at a certain moment is INPUT1, and the difference between INPUT1 and the load signal INPUT0 at the last moment is calculated. If the absolute value of the difference is greater than or equal to a preset difference value 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 the refined self-learning remains INPUT0.

[0110] Taking the first parameter as speed as an example, hysteresis operation is performed on the speed of the engine. The operation method is that the speed signal at a certain moment is INPUT2, and the difference between INPUT2 and the speed signal INPUT3 at the last moment is calculated. If the absolute value of the difference is greater than or equal to a preset difference value DN (which can be calibrated, such as 300 rpm), the speed condition output to the refined self-learning is updated to INPUT2; if the absolute value of the difference is less than DN, the speed condition output to the refined self-learning remains INPUT3.

[0111] The method provided in the above embodiments acquires the first current data and historical data of the vehicle engine when the engine is under target operating conditions. If each current parameter value meets its corresponding preset first parameter range, the parameter change rate, parameter change direction, and current effective duration are determined. If the current effective duration is greater than its corresponding preset effective duration, the variable valve timing self-learning is initiated. By predicting the current effective duration in advance, VVT self-learning is only initiated when the time requirement is met, avoiding VVT self-learning exiting due to insufficient time to complete, avoiding repeated adjustments to the VVT ​​angle, and avoiding adverse effects on engine torque stability, combustion, and drivability. Please refer to [link to relevant documentation]. Figure 4 , Figure 4 A schematic diagram of a speed hysteresis calculation provided in an embodiment of the present invention is shown below. Figure 4 As shown, given the load signal INPUT1 at a certain moment and the load signal INPUT0 at the previous moment, the difference between the two is determined. Figure 4 Is the "ABS()" value greater than the preset difference? Figure 4 If the first parameter (Delta) is greater than the first parameter (INPUT), then INPUT1 is output; otherwise, INPUT0 is output. It should be noted that the hysteresis operation for other first parameters differs from the previous method. Figure 4 The similarity shown is not limited here.

[0112] By performing delayed calculations on the data of the first parameter during the VVT ​​self-learning process, and determining whether a re-comparison with the preset first parameter range is necessary based on the result of the delayed calculations, the VVT ​​self-learning state is determined again based on the comparison result. This avoids repeated comparisons and the resulting waste of computing resources.

[0113] Continuing with the examples involved in the aforementioned formulas (5), (6), (8), and (9), when the calculated Δtn1 or Δtn2 is greater than the effective time B1 (preset effective duration) for refined self-learning, VVT refined self-learning is performed; otherwise, refined self-learning is not performed. When the calculated Δtr1 or Δtr2 is greater than the effective time B2 for refined self-learning, VVT refined self-learning is performed; otherwise, refined self-learning is not performed.

[0114] In an embodiment, before the control starts the execution of the variable valve timing self-learning, the method further comprises: obtaining second current data of at least one second parameter of the vehicle; if the second current data of all the second parameters meets the preset second parameter range of each second parameter, and the current effective time length of all the first parameters is greater than the preset effective time length of the first parameter, it is determined that the current state of the engine of the vehicle meets the self-learning condition, so as to prompt the execution of the variable valve timing self-learning.

[0115] The second parameter is a physical factor related enabling condition, such as water temperature, cylinder head temperature, oil temperature, downtime, and time after starting. For water temperature, cylinder head temperature, and oil temperature, the preset second parameter range can be composed of two extreme value intervals. For downtime and time after starting, which can only increase and cannot decrease, the preset second parameter range can be composed of a preset threshold value.

[0116] In the embodiment, taking the target temperature as an example, the target temperature includes at least one of water temperature, cylinder head temperature, and oil temperature, the method can be: obtaining the target temperature of the vehicle; comparing the target temperature with the preset temperature range, if the target temperature falls within the preset target temperature range, and the current effective time length of all the first parameters is greater than the preset effective time length of the first parameter, it is determined that the current state of the engine of the vehicle meets the self-learning condition, so as to prompt the execution of the variable valve timing self-learning. The preset second parameter range includes the preset target temperature range, and the preset target temperature range is determined according to the target temperature, that is, different target temperature parameters have the same or different target temperature ranges. For example, the oil temperature has a corresponding oil temperature range, and the water temperature has a corresponding water temperature preset range. The preset target time length threshold value, the preset effective time length, the preset first parameter range, and the preset second parameter range are similar, and the corresponding values can be determined by the person skilled in the art according to the needs.

[0117] In the embodiment, the target time length of the vehicle is obtained, the target time length includes at least one of downtime and starting time, and the second parameter includes the target time length; the target time length is compared with the preset target time length threshold value, if the target time length falls within the preset target time length range, and the current effective time length of all the first parameters is greater than the preset effective time length of the first parameter, it is determined that the current state of the engine of the vehicle meets the self-learning condition, so as to prompt the execution of the variable valve timing self-learning. The preset second parameter range includes the preset target time length threshold value, and the preset target time length threshold value is determined according to the target time length. The preset target time length threshold value of different second parameters can be the same or different, which is determined based on the preset setting of the person skilled in the art.

[0118] In the embodiment, the method further comprises: if the second current data of the at least one second parameter is out of the preset second parameter range of each second parameter, prompting to suspend starting to execute the variable valve timing self-learning; and suspending to acquire the first current data and the historical data of the at least one first parameter of the vehicle engine until the second current data of all the second parameters re-acquired meets the preset second parameter range of each second parameter. Since the data of the second parameters changes relatively slowly, once the parameter of one second parameter does not meet the preset second parameter range, a certain time is needed to wait for the data of the second parameter to meet the preset second parameter range, so the determination of whether the condition of restarting the VVT self-learning is met can be suspended for a period of time to further reduce the waste of computing resources. The re-acquisition of the data of the second parameters can also be performed according to the time interval set by the person skilled in the art, and the monitoring and judgment are performed until each second current data meets the preset second parameter range corresponding thereto, and then the data of the first parameters is re-acquired.

[0119] In an embodiment, before acquiring the first current data and the historical data of the at least one first parameter of the vehicle engine, the method further comprises:

[0120] Acquiring the current speed of the vehicle and the high-precision map data of the future driving route, the high-precision map data of the future driving route comprising road condition data of the future driving road. The road condition data comprises but is not limited to congestion state, route length, traffic light state, speed limit state, slope (uphill and downhill) information, current vehicle position, etc. of the future planned driving road, and the above road condition data can be obtained based on the high-precision map data;

[0121] Predicting the future control strategy of the vehicle according to the current speed and the high-precision map data of the future driving route, and based on the current speed, the current position and the road condition data, the reasonable operation of the vehicle can be predicted, such as one or more control modes of deceleration, idling, downhill, uphill, constant speed driving, etc. to form the future control strategy of the vehicle in a certain future time. The above future control strategy can also be formulated in other ways known to the person skilled in the art, and in the embodiment, the predicted data of the vehicle in a certain future time needs to be known based on the future control strategy;

[0122] Based on the future control strategy, predicting the first future data of the first parameter of the vehicle engine and the second future data of the second parameter of the vehicle at multiple future time points. The first future data and the second future data can be set by the person skilled in the art based on different vehicle driving states (determined by the future control strategy), can be predicted by a pre-trained prediction model, and can also be determined in other ways known to the person skilled in the art, which are not limited herein;

[0123] If the first comparison result and the second comparison result at a future time point both satisfy, the first current data and the historical data of at least one first parameter of the vehicle engine are obtained when a future time point 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, that is, if the parameters of the first parameter and the parameters of the second parameter both satisfy the requirements at a future time point Ti, whether the VVT self-learning needs to be started is determined when the time point Ti is reached, so as to avoid the waste of computing power caused by repeated determination of the VVT self-learning, and save the resources of the controller.

[0124] By introducing the high-precision map, whether the real-time road conditions and the vehicle driving intention are adjusted or not, the working conditions that can be completed by the VVT fine self-learning are more accurately predicted, and the working conditions of the VVT fine self-learning can be more scientifically and reasonably organized.

[0125] In an embodiment, after obtaining the first current data of at least one first parameter of the vehicle engine, the method further comprises: matching the first current data to obtain a current target position; determining a current relative operating angle based on the current target position and a reference position; if the current relative operating angle is less than a preset first angle threshold, it is determined that the current state of the vehicle engine satisfies the self-learning condition, so as to prompt to start the execution of the variable valve timing self-learning. Otherwise, if the current relative operating angle is greater than the preset first angle threshold, the VVT self-learning will not be started.

[0126] Continuing to refer to Figure 2 , generally, the target angle of the VVT can be obtained by looking up the table according to the speed, load, etc., that is Figure 2 the lower wave curve in FIG. 1, the target angle is a driven variable (changes according to the parameter values of the first parameters such as speed, load, etc.). The engine has an actual VVT angle feedback to the control system at each fixed speed and load, therefore, the prediction of the VVT operating angle is associated with the prediction of the speed and the load. If the VVT operating angle is less than or equal to a preset first angle threshold D relative to the reference position (relative operating angle, that is Figure 2 the VVT operating angle in FIG. 1), the fine self-learning condition is entered, and the VVT self-learning is started; once it is greater than D, the fine self-learning strategy is quickly exited.

[0127] If the judgment is made on at least two of the second category parameters, the first category parameters and the current relative operating angle, each dimension needs to meet the judgment condition, that is, if the second current data of all second parameters meet the preset second parameter range of each second parameter, and the current effective time length of all first parameters is greater than the preset effective time length of the first parameter, and the current relative operating angle is less than the preset first angle threshold, the VVT self-learning is triggered, otherwise, if any one condition is not met, the VVT self-learning is not started.

[0128] In an embodiment, after the control starts to execute the variable valve timing self-learning, the method further includes: during the execution of the variable valve timing self-learning, the current target position changes due to the changes of the speed, load and the like, and the real-time target position can be matched according to the real-time current data of the first parameter; the real-time relative operating angle is determined based on the real-time target position and the reference position; if the real-time relative operating angle is less than a preset second angle threshold, the control continues to execute the variable valve timing self-learning, and the preset second angle threshold is greater than the preset first angle threshold; if the real-time relative operating angle is greater than the preset second angle threshold, the control stops executing the variable valve timing self-learning. In this embodiment, the preset second angle threshold can be the preset first angle threshold plus a certain additional correction amount. One determination method of the preset second angle threshold is:

[0129] D2 = D1 + DH Formula (10),

[0130] wherein D2 is the preset second angle threshold, D1 is the preset first angle threshold, and DH is the additional correction amount.

[0131] When the time of the speed and load prediction meets greater than the preset effective time length, an additional correction amount DH (which can be calibrated) is added to the range threshold D of the VVT operating angle, so that the fine self-learning does not immediately exit the fine self-learning because the VVT movement range is slightly higher than D, leaving sufficient VVT movement angle margin and trying to create conditions for efficient and reliable completion of the fine self-learning.

[0132] In an embodiment, after the control initiates the execution of the variable valve timing self-learning, the method further comprises: 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; obtaining a current adjustment gradient by matching the current relative operating angle with a preset relative operating angle-adjustment gradient table, and controlling the cam phase adjustment of the vehicle engine based on the current adjustment gradient. The preset relative operating angle-adjustment gradient table can be preset by a person skilled in the art, and the adjustment of the cam under different current relative operating angles can be guided according to a certain preset adjustment gradient (adjustment rate) rule, so as to reduce the influence on the normal operation of the vehicle during the adjustment. The adjustment gradient can be uniform or variable, and a plurality of different adjustment gradients can be determined according to different speeds and loads (first reference category data), such as fast, slow, and slower.

[0133] In another embodiment, the adjustment of the cam phase can also be achieved by specifying a preset adjustment time and controlling the control cam phase to complete the adjustment within the preset adjustment time.

[0134] When considering fine self-learning, the engine VVT in motion will move towards the reference position, which may cause a series of problems in engine torque response, combustion stability, drivability, etc. The embodiment of the present application proposes a hybrid engine VVT fine self-learning strategy as shown in Figure 4 . The fine self-learning is preferentially performed in the fuel-cut coasting condition, so that the movement of the VVT towards the reference position can be avoided to affect the normal driving power of the vehicle, which can be a preferred solution for hybrid vehicles. Please refer to Figure 5 , Figure 5 A schematic diagram of the hybrid engine VVT fine self-learning strategy provided by the embodiment of the present application is shown in Figure 5 . When the vehicle engine is in the fuel-cut coasting condition, the VVT self-learning is directly controlled and can be completed (i.e., the fine self-learning in Figure 5 is completed), and when the vehicle engine is in the target condition (other than the fuel-cut coasting condition), step S310 needs to be started to predict the fine self-learning function, and whether the self-learning condition is met, 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 meets the corresponding preset second parameter range, whether the VVT operating angle is greater than the preset first angle threshold, etc. If it is met, the fine learning is completed, and if it is not met, the fine self-learning is not performed, i.e., the VVT self-learning is not started, and waits for the self-learning condition to be met.

[0135] For some series-parallel or range-extender vehicles, the engine fuel cut condition is rare or even non-existent. For such cases, the predictable fine self-learning function provided in the embodiment is used to perform the fine self-learning strategy when the self-learning condition is met and it is predicted that the fine self-learning can be reliably completed; if the self-learning condition is not met or it is predicted that the fine self-learning cannot be reliably completed, the fine self-learning is not performed. In this way, repeated entry and exit of the fine self-learning can be avoided, and the VVT reference position is pulled back and forth, but the fine self-learning cannot be reliably completed. Through the prediction strategy, the VVT fine self-learning process is predictable and controllable.

[0136] The determination condition of whether to start the VVT fine self-learning (VVT self-learning) in the fuel cut coasting condition includes that the speed is located in [NC1, NC2], the load is located in [RC1, RC2], the fuel cut flag C_flg, the water temperature is located in [TC1, TC2], etc. The variables mentioned above can be calibrated to adapt to different situations of different vehicles. When the fuel cut coasting condition occurs, the VVT is quickly moved to the reference position to complete the fine self-learning. Since the engine is rotating in the coasting process, the relative position relationship between the camshaft and the crankshaft is still changing, which can meet the requirements of the VVT fine self-learning and will not affect the drivability, NVH (Noise, Vibration, Harshness), combustion stability, etc.

[0137] When the hybrid vehicle does not have the fuel cut coasting condition, such as the range extender, the engine is used as a power source, and there is little or no engine reverse drag condition. In this case, the predictable fine self-learning function provided in the embodiment can also be used. The fine self-learning condition provided in the embodiment and the prediction function are described in detail below from a specific embodiment to introduce the implementation process of the predictable fine self-learning strategy.

[0138] First, the fine self-learning condition is that the basic requirement for the VVT fine self-learning is that the camshaft and crankshaft position deviation value needs to be learned stably, so that the self-learning value of the ECU is constantly close to the actual deviation, to realize more accurate electronic control fuel injection, ignition control, etc.

[0139] The specific implementation scheme is shown in Figure 6 , please refer to Figure 6 , Figure 6 is a schematic diagram of the hybrid engine VVT fine self-learning start condition provided in the embodiment. As shown in Figure 6 , it includes:

[0140] Speed condition. The fine self-learning is performed in an optimal speed range, and when the speed is located in [N1, N2] (which can be calibrated), the fine self-learning strategy (i.e. starting the VVT self-learning) can be performed.

[0141] Load condition. Considering that VVT position movement can have an impact on exhaust gas energy and supercharging control, and can have an unsafe impact on the engine operating at a large load, the VVT refinement self-learning is usually limited to small and medium loads. When the load is located in [R1, R2] (calibratable), the refinement self-learning strategy can be executed.

[0142] The above-mentioned speed condition and load condition are the first parameters mentioned above.

[0143] VVT movement angle condition (current relative operating angle). Since the VVT can move to the reference position, in order to prevent the refinement self-learning process from affecting the driving comfort and engine operating stability, when the VVT operating angle is less than D (calibratable), the refinement self-learning strategy can be executed.

[0144] Water temperature condition. For hybrid engines, Atkinson and Miller cycles are commonly used. If the VVT is located at the reference position or moves to the reference position by a large margin at low temperature, there is more residual exhaust gas in the cylinder, which can easily lead to poor combustion or even misfire. Therefore, the embodiment of the present application designs that when the water temperature is located in [T1, T2] (calibratable), the refinement self-learning strategy can be executed. It should be noted that the water temperature here can be replaced by the cylinder head temperature, or the water temperature condition can be retained while additionally introducing the engine cylinder head temperature.

[0145] Oil temperature condition. Similar to the water temperature condition, the refinement self-learning is limited to the oil temperature range where the engine operates relatively stably, so as to avoid the impact of executing the refinement self-learning strategy on the engine stability and NVH. Therefore, it can be set that when the oil temperature is located in [T3, T4] (calibratable), the refinement self-learning strategy can be executed.

[0146] Stop time condition. For hybrid engines, since the VCU is performing energy total coordination control, the engine can be in a frequent start-stop state under certain SOC and certain road conditions. When the vehicle has completed the refinement self-learning function in the last driving cycle, and the stop time is very short, we can consider that the aging of the engine timing chain related will not change greatly in a short time, and the self-learning value of the engine in the last cycle can meet the control accuracy requirement. Therefore, it can be set that when the stop time ≥ TT1, the VVT refinement self-learning strategy can be executed.

[0147] Time after starting condition. After starting, the engine has a process of rotating at a certain speed. During this process, the speed operation is not very stable, and the refinement self-learning needs to avoid this process to obtain a relatively stable self-learning value. Therefore, it can be set that when the time after starting ≥ TT2, the refinement self-learning strategy can be executed.

[0148] The water temperature, oil temperature, stop time, and start time are the second parameters.

[0149] Among the above 7 conditions, the last four conditions belong to the enabling conditions related to physical factors, and in the case of engine operating condition determination, the fine self-learning strategy will not be frequently entered and exited. Therefore, the prediction function is designed for the first, second, and third conditions (rotation speed condition and load condition, VVT movement angle) that are strongly related to engine operating conditions. For the low SOC condition, if the driver's demand is strong, such as rapid pressing and releasing of the accelerator, the battery's energy regulation capability is limited, and in order to respond to the torque demand, the engine's rotation speed and load will change rapidly, which may lead to frequent entry into the fine self-learning condition, but the duration is not enough to complete the fine self-learning. Through the prediction function provided in this embodiment, the real-time operating conditions are calculated, and for the case where the duration (current effective duration) of meeting the fine self-learning condition is less than the preset effective duration B, the fine self-learning function is not executed; according to the prediction function, for the case where the duration of meeting the fine self-learning condition is greater than or equal to the preset effective duration B, the fine self-learning function is executed. Through this function, the VVT fine self-learning is controllably and effectively executed.

[0150] The method provided in the above embodiment, by acquiring the first current data and historical data of one or more first parameters of the vehicle engine in the operating condition of the vehicle engine except the oil-off coasting condition, if the current parameter value of each first parameter meets the preset first parameter range corresponding thereto, then according to the first current data and historical data of each first parameter, the parameter change rate and parameter change direction of each first parameter are obtained, and 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, if the current effective duration of all first parameters is greater than the preset effective duration corresponding thereto, then the variable valve timing self-learning is controlled to be executed. Through the above method, the parameters of the first parameters that are strongly related to the engine operating condition are predicted in advance, which can ensure that the parameter values of one or more first parameters of the vehicle engine in the future can be maintained within the preset first parameter range for a sufficient duration, and then the VVT self-learning (variable valve timing self-learning, hereinafter referred to as VVT self-learning) is started, which can ensure the learning duration of the VVT self-learning, avoid the VVT self-learning from being exited due to the fact that some parameters of the vehicle engine are out of the corresponding preset first parameter range after the VVT self-learning is started and before the VVT self-learning is completed, avoid repeated pulling of the VVT angle, and avoid the adverse effects on the torque stability, combustion, drivability, and the like of the engine.

[0151] The VVT fine self-learning strategy (variable valve timing self-learning control method) provided by the embodiment of the present application can preferentially determine whether the vehicle is in the fuel cut coasting condition, and if so, the VVT fine self-learning is performed in the fuel cut coasting condition, so as to avoid the influence on the driving performance and combustion stability of the vehicle in motion. For the hybrid vehicle without the fuel cut coasting condition, such as the partial range extender vehicle, the predictable VVT fine self-learning strategy is applied. In this way, the fine self-learning can be controllable and orderly, and a series of negative problems caused by repeatedly pulling the VVT to the reference position can be avoided due to the short duration of meeting the self-learning condition and the unreliable completion of the fine self-learning.

[0152] The predictable VVT fine self-learning strategy provided by the embodiment of the present application can determine whether it is suitable to perform the VVT fine self-learning strategy according to the prediction. In the condition that the self-learning can be completed, the self-learning is performed; when the self-learning condition is not met, the self-learning strategy is avoided, so as to prevent the VVT from being repeatedly pulled to the reference position in one driving cycle, and to avoid the problems of load fluctuation, torque fluctuation, NVH, etc.

[0153] The predictable VVT fine self-learning strategy provided by the embodiment of the present application can be closed at low temperature, so as to avoid the influence of the movement of the VVT to the reference position on the operation stability of the engine at low temperature, and to avoid the bad driving feeling caused by misfire and vehicle vibration, etc.

[0154] The predictable VVT fine self-learning strategy provided by the embodiment of the present application can avoid the repeated execution of the fine self-learning strategy in the adjacent driving cycle with too short downtime. The problem of repeated triggering of the VVT fine self-learning due to the frequent start and stop of the engine caused by the changeable working condition of the hybrid vehicle is avoided.

[0155] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0156] In an 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. As shown in the figure, the variable valve timing self-learning control device includes an acquisition module 701, a change determination module 702, a time length prediction module 703, and a self-learning control module 704. The functions of each module are described in detail as follows: Figure 7

[0157] ​The acquisition module 701 is configured to acquire first current data and historical data of at least one first parameter of the vehicle engine if the current working condition of the vehicle engine is a target working condition, and the target working condition includes a working condition other than the fuel-cut coasting working condition; the change determination module 702 is configured to determine 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 if the current parameter value of each first parameter meets the preset first parameter range of the first parameter, and the current parameter value is obtained based on the first current data; the duration prediction module 703 is configured to determine 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 704 is configured to control the start of the variable valve timing self-learning if the current effective duration of each first parameter is greater than the preset effective duration of the first parameter.

[0158] In an embodiment, the change determination module includes any one of the following:

[0159] The first change determination submodule is configured to determine an exit effective interval duration according to the current parameter value, the parameter change rate and a preset parameter maximum value if the parameter change direction is increasing, and determine the current effective duration of a first parameter as the exit effective interval duration, and the preset parameter maximum value is obtained based on the preset first parameter range.

[0160] The second change determination submodule is configured to determine an entry effective interval duration according to the current parameter value, the parameter change rate and a preset parameter minimum value if the parameter change direction is decreasing, and determine the current effective duration of a first parameter as the entry effective interval duration, and the preset parameter minimum value is obtained based on the preset first parameter range.

[0161] In an embodiment, the device further includes a difference prediction module configured to monitor real-time signal data of each first parameter of the vehicle engine in the process of the variable valve timing self-learning after the self-learning control module controls the start of the variable valve timing self-learning; determine a data difference of each first parameter according to the current signal data and the last signal data of each first parameter, the current signal data being real-time signal data at a current monitoring time, and the last signal data being real-time signal data at a last monitoring time; compare the current signal data with the preset first parameter range of a first parameter if there is a data difference of a first parameter greater than a preset difference value of the first parameter, determine an execution state of the variable valve timing self-learning based on a comparison result, and control the continuous execution of the variable valve timing self-learning if the data difference of each first parameter is less than the preset difference value of the first parameter.

[0162] In an embodiment, the difference prediction module further comprises a stop control module, configured to control to continue to perform the variable valve timing self-learning if the current comparison result is that the current signal data falls within a preset first parameter range of a first parameter; and control to stop performing the variable valve timing self-learning if the current comparison result is that the current signal data exceeds a preset first parameter range of a first parameter.

[0163] In an embodiment, the device further comprises a second current data determination module, configured to obtain second current data of at least one second parameter of the vehicle before the self-learning control module controls to start performing the variable valve timing self-learning; and determine that the current state of the engine of the vehicle meets the self-learning condition if the second current data of all the second parameters meets a preset second parameter range of each second parameter, and the current effective time length of all the first parameters is greater than a preset effective time length of the first parameter, to prompt to start performing the variable valve timing self-learning.

[0164] In an embodiment, the second current data determination module comprises a temperature determination module, configured to obtain a target temperature of the vehicle, the target temperature comprising at least one of a water temperature, a cylinder head temperature and an oil temperature, and the second parameter comprising the target temperature; compare the target temperature with a preset temperature range, and determine that the current state of the engine of the vehicle meets the self-learning condition if the target temperature falls within the preset target temperature range and the current effective time length of all the first parameters is greater than a preset effective time length of the first parameter, to prompt to start performing the variable valve timing self-learning, the preset second parameter range comprising the preset target temperature range, and the preset target temperature range being determined according to the target temperature.

[0165] In an embodiment, the second current data determination module comprises a time length determination module, configured to obtain a target time length of the vehicle, the target time length comprising at least one of a shutdown time length and a startup time length, and the second parameter comprising the target time length; compare the target time length with a preset target time length threshold, and determine that the current state of the engine of the vehicle meets the self-learning condition if the target time length falls within a preset target time length range and the current effective time length of all the first parameters is greater than a preset effective time length of the first parameter, to prompt to start performing the variable valve timing self-learning, the preset second parameter range comprising the preset target time length threshold, and the preset target time length threshold being determined according to the target time length.

[0166] In an embodiment, the device further comprises 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 pause obtaining the first current data and the historical data of at least one first parameter of the engine of the vehicle until the second current data of all the second parameters obtained again meets the preset second parameter range of each second parameter.

[0167] In an embodiment, the device further comprises a future time prediction module, configured to, before the obtaining module obtains the first current data and the historical data of the at least one first parameter of the vehicle engine, obtain a current vehicle speed and high-precision map data of a future driving route of the vehicle, the high-precision map data of the future driving route comprising road condition data of a future driving road; predict a future control strategy of the vehicle according to the current vehicle speed and the high-precision map data of the future driving route; predict, based on the future control strategy, first future data of the at least one first parameter of the vehicle engine and second future data of a second parameter of the vehicle at a plurality of future times; and if both the first comparison result and the second comparison result of a future time meet, control the obtaining module to obtain the first current data and the historical data of the 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.

[0168] In an embodiment, the device further comprises a front angle determination module, configured to, after the obtaining module obtains the first current data of the at least one first parameter of the vehicle engine, control the self-learning control module to start to execute variable valve timing self-learning, and match a current target position according to the first current data; determine a current relative running angle based on the current target position and a reference position; and if the current relative running angle is less than a preset first angle threshold, determine that a current state of the vehicle engine meets a self-learning condition, so as to prompt the variable valve timing self-learning to be started to be executed.

[0169] In an embodiment, the device further comprises a rear angle determination module, configured to, after the self-learning control module controls the variable valve timing self-learning to be started to be executed, in a process of executing the variable valve timing self-learning, match a real-time target position according to real-time current data of the first parameter; determine a real-time relative running angle based on the real-time target position and the reference position; if the real-time relative running angle is less than a preset second angle threshold, control the variable valve timing self-learning to be continuously executed, the preset second angle threshold being greater than the preset first angle threshold; and if the real-time relative running angle is greater than the preset second angle threshold, control the variable valve timing self-learning to be stopped to be executed.

[0170] In an embodiment, the device further comprises an adjustment module, configured to, after the self-learning control module controls the variable valve timing self-learning to be started to be executed, match a current target position according to the first current data; determine a current relative running angle based on the current target position and the reference position; match a current adjustment gradient to a preset relative running angle-adjustment gradient table through the current relative running angle, and control a cam phase adjustment of the vehicle engine based on the current adjustment gradient.

[0171] In an embodiment, the device further comprises a working condition determining module, configured to acquire the current working condition of the vehicle engine before the acquiring module acquires 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 control the starting of the execution of the variable valve timing self-learning if the current working condition is the fuel-cut coasting working condition.

[0172] In an embodiment, the device further comprises a data re-acquiring 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 category if there is at least one first parameter whose current parameter value exceeds the preset first parameter range of the first parameter, wherein the target parameter category is the first parameter whose current parameter value exceeds the preset first parameter range; determine an ideal meeting time according to the predicted meeting time of each target parameter category, wherein the ideal meeting time is the latest predicted meeting time; and control the acquisition of new first current data and historical data of each first parameter when the ideal meeting time is reached.

[0173] The embodiment of the present application provides a variable valve timing self-learning control device, which acquires the first current data and the historical data of one or more first parameters of a vehicle engine when the vehicle engine is in a working condition state other than the fuel-cut coasting working condition, and if the current parameter value of each acquired first parameter meets the preset first parameter range corresponding to the first parameter, obtains 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, obtains the current effective time length of each first parameter in combination with the current parameter value and the preset first parameter range of each first parameter, and controls the starting of the execution of the variable valve timing self-learning if the current effective time length of all first parameters is greater than the preset effective time length corresponding to the first parameter. Through the above method, the parameters of the first parameters which are strongly related to the working condition of the engine are predicted in advance, which can ensure that the parameter values of one or more first parameters of the vehicle engine in the future can be kept in the preset first parameter range corresponding to the first parameter for a sufficient time length before the VVT self-learning (variable valve timing self-learning, hereinafter referred to as VVT self-learning) is started, which can ensure the learning time length of the VVT self-learning, avoid the situation that the parameter of the vehicle engine exceeds the preset first parameter range corresponding to the parameter after the VVT self-learning is started and the VVT self-learning is exited before the VVT self-learning is completed, avoid repeatedly pulling the VVT angle, and avoid the adverse effects on the torque stability, combustion, drivability and the like of the engine.

[0174] The specific limitations of the variable valve timing self-learning control device can refer to the limitations of the variable valve timing self-learning control method described above, which will not be repeated here. Each module in the variable valve timing self-learning control device described above can be implemented by software, hardware, and their combination in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in hardware form, or can be stored in the memory in the electronic device in software form, so that the processor invokes the execution of the corresponding operations of each module.

[0175] In an embodiment, a variable valve timing self-learning control system is provided, which comprises Figure 7 The variable valve timing self-learning control device shown, and an execution mechanism; wherein the execution mechanism is configured to control the execution of the variable valve timing self-learning after the execution of the variable valve timing self-learning control module, and drive the cam phase adjustment.

[0176] In an embodiment, a vehicle is provided, which comprises a variable valve timing self-learning control device as Figure 7 shown.

[0177] In an embodiment, an electronic device is provided, which can be a server, and its internal structure diagram can be as Figure 8 shown. The electronic device comprises a processor, a memory, a network interface, and a database connected through a system bus. The processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device comprises a non-volatile and / or volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is configured to communicate with an external client through a network connection. The computer program is executed by the processor to implement the functions or steps of a variable valve timing self-learning control method server side.

[0178] In an embodiment, an electronic device is provided, which can be a client, and its internal structure diagram can be as Figure 9 shown. The electronic device comprises a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. The processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device comprises a non-volatile 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 the computer program in the non-volatile storage medium. The network interface of the electronic device is configured to communicate with an external server through a network connection. The computer program is executed by the processor to implement the functions or steps of a variable valve timing self-learning control method client side.

[0179] In one embodiment, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implements the following steps when executing the computer program:

[0180] If the current working condition of the vehicle engine is a target working condition, the first current data and the historical data of at least one first parameter of the vehicle engine are obtained, and the target working condition includes a working condition other than the fuel-cut coasting working condition;

[0181] If the current parameter value of all the first parameters meets the preset first parameter range of each first parameter, the parameter change rate and the parameter change direction of each first parameter are determined according to the first current data and the historical data of each first parameter, and the current parameter value is obtained based on the first current data;

[0182] The current effective duration of each first parameter is determined based on the parameter change rate, the parameter change direction, the current parameter value, and the preset first parameter range of each first parameter;

[0183] If the current effective duration of all the first parameters is greater than the preset effective duration of the first parameter, the control starts to perform the variable valve timing self-learning.

[0184] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0185] If the current working condition of the vehicle engine is a target working condition, the first current data and the historical data of at least one first parameter of the vehicle engine are obtained, and the target working condition includes a working condition other than the fuel-cut coasting working condition;

[0186] If the current parameter value of all the first parameters meets the preset first parameter range of each first parameter, the parameter change rate and the parameter change direction of each first parameter are determined according to the first current data and the historical data of each first parameter, and the current parameter value is obtained based on the first current data;

[0187] The current effective duration of each first parameter is determined based on the parameter change rate, the parameter change direction, the current parameter value, and the preset first parameter range of each first parameter;

[0188] If the current effective duration of all the first parameters is greater than the preset effective duration of the first parameter, the control starts to perform the variable valve timing self-learning.

[0189] It should be noted that the functions or steps that the computer readable storage medium or the electronic device can implement are described above, and the related descriptions of the server side and the client side in the foregoing method embodiments are correspondingly referred to. To avoid repetition, they will not be described one by one here.

[0190] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile 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. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0191] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified. In actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions.

[0192] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones. Such modifications or replacements do not change the essence of the corresponding technical solutions, and should be included in the protection scope of the present application.

Claims

1. A variable valve timing self-learning control method, characterized in that, The method includes: If the current operating condition of the vehicle engine is the target operating condition, the first current data and historical data of at least one first parameter of the vehicle engine are obtained. The target operating condition includes operating conditions other than the fuel cut-off coasting condition. The first parameter includes speed and / or load. The vehicle engine source includes hybrid vehicles or range-extended vehicles. If the current parameter values ​​of all first parameters satisfy the preset range of each first parameter, the parameter change rate and parameter change direction of each first parameter are determined based on the first current data and historical data of each first parameter, and the current parameter value is obtained based on the first current data; The current effective duration of each first parameter is determined based on the parameter change rate, parameter change direction, current parameter value, and preset first parameter range. If the current effective duration of all the first parameters is greater than the preset effective duration of the first parameters, control to start the variable valve timing self-learning; The current effective duration of the first parameter is determined based on the parameter change rate, parameter change direction, current parameter value, and preset first parameter range, including any one of the following: If the parameter changes in the direction of increase, the duration of exiting the effective interval is determined based on the current parameter value, the parameter change rate, and the preset maximum value of the parameter. The duration of exiting the effective interval is then determined as the current effective duration of the first parameter. The preset maximum value of the parameter is obtained based on the preset range of the first parameter. If the parameter changes in a decreasing direction, the duration of entering the effective interval is determined based on the current parameter value, the parameter change rate, and the preset parameter minimum value. The duration of entering the effective interval is then determined as the current effective duration of the first parameter. The preset parameter minimum value is obtained based on the preset first parameter range.

2. The variable valve timing self-learning control method as described in claim 1, characterized in that, After controlling the initiation of variable valve timing self-learning, the method further includes: During the self-learning process of the variable valve timing, real-time signal data of each first parameter of the vehicle engine are monitored; The data difference of each first parameter is determined based on the current signal data and the previous signal data of each first parameter, wherein the current signal data is the real-time signal data at the current monitoring time, and the previous signal data is the real-time signal data at the previous monitoring time. If there is a data difference of a first parameter that is greater than a preset difference of the first parameter, compare the current signal data with the preset range of the first parameter, and determine the execution state of the variable valve timing self-learning based on the current comparison result. If the data differences of all the first parameters are less than the preset difference of the first parameter, the control continues to execute the variable valve timing self-learning.

3. The variable valve timing self-learning control method as described in claim 2, characterized in that, Determining the execution state of the variable valve timing self-learning based on the current comparison results includes: If the current comparison result is that the current signal data falls within the preset range of the first parameter, control continues to execute the variable valve timing self-learning; If the current comparison result indicates that the current signal data exceeds the preset range of the first parameter, the variable valve timing self-learning process is stopped.

4. The variable valve timing self-learning control method as described in claim 1, characterized in that, Before controlling the initiation of variable valve timing self-learning, the method further includes: Obtain second current data for at least one second parameter of the vehicle; If the current data of all second parameters meets the preset range of each second parameter, and the current effective duration of all first parameters is greater than the preset effective duration of the first parameter, then prompt to start the variable valve timing self-learning.

5. The variable valve timing self-learning control method as described in claim 4, characterized in that, The target temperature of the vehicle is obtained, the target temperature including at least one of water temperature, cylinder head temperature and engine oil temperature, and the second parameter includes the target temperature; Compare the target temperature with the preset target temperature range. If the target temperature falls within 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, prompt to start the variable valve timing self-learning. The preset second parameter range includes the preset target temperature range, which is determined based on the target temperature.

6. The variable valve timing self-learning control method as described in claim 4, characterized in that, Obtain the target duration of the vehicle, wherein the target duration includes at least one of the shutdown duration and the start-up duration, and the second parameter includes the target duration; Compare 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, prompt to start the variable valve timing self-learning. The preset second parameter range includes the preset target duration threshold, and the preset target duration threshold is determined based on the target duration.

7. The variable valve timing self-learning control method as described in claim 4, characterized in that, The method further includes: If the second current data of at least one second parameter exceeds the preset range of each second parameter, prompt to pause the start of variable valve timing self-learning; In addition, the acquisition of first current data and historical data of at least one first parameter of the vehicle engine is suspended until the second current data of all second parameters acquired again meet the preset second parameter range of each second parameter.

8. The variable valve timing self-learning control method according to any one of claims 1-7, characterized in that, Before acquiring first current data and historical data of at least one first parameter of the vehicle engine, the method further includes: Acquire high-precision map data of the vehicle's current speed and future driving route, wherein the high-precision map data of the future driving route includes road condition data of the future driving route; Predict the future control strategy of the vehicle based on the current vehicle speed and the high-precision map data of the future driving route; Based on the future control strategy, the first future data of the first parameter of the vehicle engine and the second future data of the second parameter of the vehicle are predicted at multiple future moments. If both the first comparison result and the second comparison result are satisfied at a future time, when the future time is reached, the system acquires the first current data and historical data of at least one first parameter of the vehicle engine, wherein the first comparison result is the comparison result of the first future data with a preset first parameter range of the first parameter, and the second comparison result is the comparison result of the second future data with a preset second parameter range of the second parameter.

9. The variable valve timing self-learning control method as described in any one of claims 1-7, characterized in that, After obtaining the first current data of at least one first parameter of the vehicle engine, and before controlling the start of variable valve timing self-learning, the method further includes: The current target location is obtained by matching the first current data. Determine the current relative running angle based on the current target position and the reference position; If the current relative operating angle is less than a preset first angle threshold, a prompt will be made to initiate variable valve timing self-learning.

10. The variable valve timing self-learning control method according to any one of claims 1-7, characterized in that, After controlling the initiation of variable valve timing self-learning, the method further includes: During the process of performing variable valve timing self-learning, the real-time target position is obtained by matching the real-time current data of the first parameter; The real-time relative running angle is determined based on the real-time target position and the reference position; If the real-time relative operating angle is less than the preset second angle threshold, then control continues to execute the variable valve timing self-learning, where the preset second angle threshold is greater than the preset first angle threshold. If the real-time relative operating angle is greater than the preset second angle threshold, then control stops the execution of variable valve timing self-learning.

11. The variable valve timing self-learning control method according to any one of claims 1-7, characterized in that, After controlling the initiation of variable valve timing self-learning, the method further includes: The current target location is obtained by matching the first current data. Determine the current relative running angle based on the current target position and the reference position; The current adjustment gradient is obtained by matching the current relative operating angle with a preset relative operating angle-adjustment gradient table, and the cam phase adjustment of the vehicle engine is controlled based on the current adjustment gradient.

12. The variable valve timing self-learning control method according to any one of claims 1-7, characterized in that, If the current operating condition of the vehicle engine is the target operating condition, before acquiring the first current data and historical data of at least one first parameter of the vehicle engine, the method further includes: Obtain the current operating condition of the vehicle's engine; If the current operating condition is a coasting condition with fuel cut-off, the control will initiate the variable valve timing self-learning process.

13. The variable valve timing self-learning control method according to any one of claims 1-7, characterized in that, If at least one of the current parameter values ​​of the first parameter exceeds the preset range of the first parameter, the method further includes: The prediction satisfaction time is determined based on the first current data, historical data, and preset first parameter range of the target parameter category, wherein the target parameter category is the first parameter whose current parameter value exceeds the preset first parameter range; The ideal satisfaction time is determined based on the predicted satisfaction time for each of the target parameter categories, wherein the ideal satisfaction time is the latest predicted satisfaction time. When the ideal satisfaction time is reached, the control acquires the first current data and historical data for each first parameter.

14. A variable valve timing self-learning control device, characterized in that, The device includes: The acquisition module is used to acquire first current data and historical data of at least one first parameter of the vehicle engine if the current operating condition of the vehicle engine is the target operating condition. The target operating condition includes operating conditions other than the fuel cut-off coasting condition. The first parameter includes speed and / or load, and the vehicle engine source includes hybrid vehicles or range-extended vehicles. The change determination module is used to determine the parameter change rate and parameter change direction of each first parameter based on the first current data and historical data of each first parameter if the current parameter values ​​of all first parameters meet the preset first parameter range of each first parameter. The current parameter value is obtained based on the first current data. The duration prediction module is used to determine the current effective duration of each first parameter based on the parameter change rate, parameter change direction, current parameter value, and preset first parameter range of each first parameter. The self-learning control module is used to control the start of 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. When the duration prediction module determines the current effective duration of a first parameter based on the parameter change rate, parameter change direction, current parameter value, and preset first parameter range, it includes any one of the following: If the parameter changes in the direction of increase, the duration of exiting the effective interval is determined based on the current parameter value, the parameter change rate, and the preset maximum value of the parameter. The duration of exiting the effective interval is then determined as the current effective duration of the first parameter. The preset maximum value of the parameter is obtained based on the preset range of the first parameter. If the parameter changes in a decreasing direction, the duration of entering the effective interval is determined based on the current parameter value, the parameter change rate, and the preset parameter minimum value. The duration of entering the effective interval is then determined as the current effective duration of the first parameter. The preset parameter minimum value is obtained based on the preset first parameter range.

15. A variable valve timing self-learning control system, characterized in that, The system includes the variable valve timing self-learning control device as described in claim 14, and an actuator; The actuator is used to initiate the variable valve timing self-learning based on the self-learning control module, and then execute the variable valve timing self-learning and drive the cam phase adjustment.

16. A vehicle, characterized in that, The vehicle includes the variable valve timing self-learning control device as described in claim 14.

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 13.

18. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 13.

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