Engine target intake density learning optimization method

Abstract

The present application relates to a kind of engine target intake density learning optimization method, comprising: determining initial target intake density;Read the difference between current engine request air path torque and engine actual air path torque, and determine the target intake density increment under the Nth sampling period according to it;Set different operating conditions, and update the target intake density increment corresponding to the storage of different operating conditions according to learning condition, and the sum of the updated stored target intake density increment and initial target intake density is the optimized target intake density.The present application determines target intake based on request air path torque, and when the difference between request torque and actual torque occurs, the intake density is actively learned and optimized, thereby improving target intake, improving torque control responsiveness, and thereby improving the performance of engine power, economy, emission and NVH.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of engine control, more particularly, to an engine target intake density learning optimization method. BACKGROUND

[0002] Engine torque control affects the performance of engine power, economy, emission and NVH, so engine torque is an important parameter of engine control. If the engine torque control deviates, it will seriously affect the power of the engine. In the prior art, the intake amount is determined based on the torque in the bench calibration, and the accuracy of the target intake amount in the entire life cycle of the engine is not considered. SUMMARY

[0003] The technical problem to be solved by the present application is to provide an engine target intake density learning optimization method, which can optimize intake control, thereby improving the target intake amount and improving the torque control responsiveness.

[0004] The technical solution adopted by the present application to solve the technical problem is: constructing an engine target intake density learning optimization method, comprising:

[0005] determining an initial target intake density;

[0006] reading the difference between the current engine request air path torque and the actual engine air path torque, and determining the target intake density increment in the Nth sampling period according to the difference;

[0007] setting different working conditions, and updating the stored target intake density increment corresponding to different working conditions according to the learning situation, and the sum of the updated stored target intake density increment and the initial target intake density is the optimized target intake density.

[0008] According to the above scheme, the target intake density increment in the Nth sampling period is determined according to the following formula:

[0009] Delta rho (N) = k TrqToRho ×M AirTrqErr + Delta rho (N-1)

[0010] Wherein, N = 1, 2, 3…; Delta rho (N) is the target intake density increment in the Nth sampling period, Delta rho (N-1) is the target intake density increment in the (N-1) th sampling period, M AirTrqErr is the difference between the current engine request air path torque and the actual engine air path torque; k TrqToRho is the target intake density gain coefficient.

[0011] According to the above scheme, the target intake density gain coefficient is determined according to the following formula:

[0012]

[0013] wherein dM AirTrqErr is the rate of change of the torque difference M AirTrqErr , k Lrn is a target intake air density gain coefficient self-learning correction coefficient, λ is a filter time of the target intake air density gain coefficient k TrqToRho , f(M AirTrqErr , dM AirTrqErr ) is a correction coefficient at different torque differences M AirTrqErr and torque difference change rates dM AirTrqErr .

[0014] According to the above scheme, if the target intake air density increment Δrho(N) in the Nth sampling period reaches the maximum and minimum values, it is maintained unchanged, i.e. Δrho(N) = Δrho(N-1);

[0015] The maximum and minimum values both depend on the target intake air density rho Re qRaw (z) and the engine speed n, i.e. the maximum value is equal to f(n, rho Re qRaw (z))×(1+r Adap ), and the minimum value is equal to the negative value of f(n, rho Re qRaw (z))×(1+r Adap ), and the calibration of f(n, rho Re qRaw (z)) is based on the fact that the torque difference M AirTrqErr fluctuation range is within the preset range, and the absolute value of the torque difference M AirTrqErr is smaller than that of the torque difference M AirTrqErr before adjustment; r Adap is a maximum and minimum value self-learning correction coefficient.

[0016] According to the above scheme, when any one of conditions (1) to (3) is met

[0017] (1) the time after Δrho(N) reaches the maximum or minimum value exceeds the preset time;

[0018] (2) the real-time read torque difference M AirTrqErr is within the preset range;

[0019] (3) the stable condition is met at the same time, and the time exceeds the preset time;

[0020] the target intake air density increment Δrho is immediately stopped from being updated, wherein Δrho(N) refers to the target intake air density increment Δrho in the Nth sampling period.

[0021] If the above scheme, while meeting any one of (1) ~ (3) at the same time meet the following conditions (4): engine request air path torque M AirTrq Req The difference M AirTrqAct The difference M AirTrqErr The fluctuation range does not exceed the preset value;

[0022] Record the working condition information and store the current Δrho update in the same working condition, and can be saved after the vehicle power off, the storage target intake density increment Δrho Stored The storage method is as follows:

[0023] Δrho Stored = k1 x Δrho Stored (z) + (1-k1) x Δrho

[0024] Where, Δrho Stored (z) is the last time the same working condition learning stored target intake density increment, k1 is the weighting factor;

[0025] The updated Δrho Stored Plus the initial target intake density rho ReqRaw Get the optimized target intake density rho Re qNew .

[0026] According to the above scheme, record the number of times CNT that meet any one of (1) ~ (3) at the same time meet condition (4) under the same working condition, and the number of times CNT1, CNT2 and CNT3 that meet conditions (1), (2) and (3) respectively, then CNT1+CNT2+CNT3=CNT;

[0027] If the same working condition appears CNT greater than 15, and CNT1 greater than 10, and CNT2 less than 2, then the maximum and minimum self-learning correction coefficient r Adap = r Adap (z) + 0.1; wherein r Adap (z) is the last time the maximum and minimum self-learning correction coefficient is updated, and after the update is completed, CNT, CNT1, CNT2 and CNT3 are all zero;

[0028] If the same working condition appears CNT greater than 15, and CNT3 greater than 10, and CNT2 less than 2, then the target intake density gain coefficient k Lrn = k Lrn (z) + 0.02; wherein k Lrn (z) is the last time the target intake density gain coefficient is updated, and after the update is completed, CNT, CNT1, CNT2 and CNT3 are all zero;

[0029] If, under the same operating conditions, CNT is greater than 15, CNT1 is not greater than 2, and CNT3 is greater than 2, then the target intake density gain coefficient k Lrn =k Lrn (z)-0.01; Maximum and minimum value self-learning correction coefficient r Adap =r Adap (z)-0.1; After the update is complete, CNT, CNT1, CNT2 and CNT3 will all be cleared to zero;

[0030] In other cases, the target intake density gain coefficient k Lrn And the self-learning correction coefficient r for maximum and minimum values Adap Remain unchanged.

[0031] According to the above scheme, if condition (4) is not met while any of conditions (1) to (3) are met, then the operating condition information is recorded and the target intake density increment stored under the corresponding same operating condition is updated. The stored target intake density increment Δrho Stored =0.

[0032] The present invention also provides an engine target intake air density learning and optimization device, comprising:

[0033] The initial target intake air density acquisition module is used to determine the initial target intake air density;

[0034] The target intake density increment determination module is used to read the difference between the current engine requested air path torque and the engine's actual air path torque, and determine the target intake density increment in the Nth sampling period accordingly.

[0035] The learning optimization module is used to set different operating conditions and update the stored target intake density increment corresponding to different operating conditions according to the learning progress. The sum of the updated stored target intake density increment and the initial target intake density is the optimized target intake density.

[0036] The present invention also provides an engine target intake density learning and optimization device, wherein the target intake density increment determination module determines the target intake density increment in the Nth sampling period according to the following formula:

[0037] Δrho(N)=k TrqToRho ×M AirTrqErr +Δrho(N-1)

[0038] Where N = 1, 2, 3…; Δrho(N) is the target intake density increment in the Nth sampling period, Δrho(N-1) is the target intake density increment in the (N-1)th sampling period, and M… AirTrqErr The difference between the current requested intake torque and the actual intake torque of the engine; k TrqToRho The target intake density gain coefficient.

[0039] The application further provides an automobile comprising the engine target intake density learning optimization device.

[0040] The application further provides an electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory stores a computer program, and when the program is executed by the processor, the processor executes the steps of the engine target intake density learning optimization method.

[0041] The application further provides a computer readable storage medium, which stores executable instructions, and the instructions make the processor implement the engine target intake density learning optimization method when executed by the processor.

[0042] The engine target intake density learning optimization method has the following beneficial effects:

[0043] The application determines the target intake amount based on the requested air path torque, and actively learns and optimizes the intake density when the requested torque and the actual torque differ, thereby improving the target intake amount, improving the torque control responsiveness, and improving the performance of engine power, economy, emission and NVH. BRIEF DESCRIPTION OF DRAWINGS

[0044] The application will be further described below with reference to the drawings and examples, and the drawings show:

[0045] Figure 1 is a flowchart of the engine target intake density learning optimization method of the application;

[0046] Figure 2 is a logic block diagram of the engine target intake density learning optimization method of the application. DETAILED DESCRIPTION

[0047] In order to have a clearer understanding of the technical features, objectives and effects of the application, the specific embodiments of the application will be described in detail with reference to the drawings.

[0048] As shown in Figure 1 , the engine target intake density learning optimization method of the application comprises:

[0049] S1, determining an initial target intake density.

[0050] According to CN202010761489.X "Gasoline engine fire path torque efficiency determination method and actual ignition efficiency determination method", the relationship between the target air path average indicated in-cylinder pressure p AirIMEP Re q and the requested air path torque M AirTrq Req can be found.

[0051] Patent CN202210332492.9 "Target intake density control method, device, equipment and readable storage medium" discloses a target air path average indicated cylinder pressure p AirIMEP Req The initial target intake density rho ReqRaw .

[0052] S2, read the difference between the current engine request air path torque and the actual engine air path torque, and determine the target intake density increment under the Nth sampling period. The initial target intake density rho ReqRaw is optimized to obtain the optimized target intake density rho Re qNew , so as to improve the torque control accuracy.

[0053] The present application learns and corrects under steady state condition. The steady state condition is:

[0054] 1. The engine speed n fluctuation range is within the preset range, and the example takes ±15rpm;

[0055] 2. The engine request air path torque M AirTrq Req fluctuation range is within the preset range, and the example takes ±5Nm;

[0056] 3. The engine does not appear oil request;

[0057] 4. The engine cooling water temperature is within the preset range, and the example takes 80℃-95℃;

[0058] 5. The difference between the engine basic ignition angle efficiency and the actual ignition angle efficiency does not exceed the preset value, and the example takes 0.7; The actual ignition angle efficiency is too small, and the engine combustion stability is poor, so the learning accuracy is low.

[0059] 6. The difference M AirTrqErr between the engine request air path torque M AirTrq Req and the actual engine air path torque M AirTrqAct fluctuation range is within the preset range, and the example takes ±5Nm;

[0060] 7. The engine actual EGR rate fluctuation range is within the preset range, and the example takes ±0.01;

[0061] 8. The engine actual equivalence ratio fluctuation range is within the preset range, and the example takes ±0.03;

[0062] 9. The difference between the target intake density and the actual intake density fluctuation range is within the preset range, and the example takes ±15mgpl; The target intake density refers to the final optimized target intake density rho Re qNew (z) of the last sampling period.

[0063] If all the above conditions are not met, exit the learning; read the current engine request air path torque M after the time when the above conditions are met simultaneously exceeds the preset time t1 (0.5s in this example) AirTrqReq The difference between the actual engine air path torque and the target engine air path torque M AirTrqErr , then

[0064] Δrho(N) = k TrqToRho × M AirTrqErr + Δrho(N-1)

[0065] Where N = 1, 2, 3…; Where Δrho(N) is the target intake density increment in the Nth sampling period, Δrho(N-1) is the target intake density increment in the (N-1)th sampling period, and the sampling period Δt is 10ms in this example. In particular, Δrho(0) occurs at the time when the time when the above conditions are met simultaneously exceeds the preset time t1, and Δrho(0) is equal to 0. k TrqToRho is the target intake density gain coefficient.

[0066] Where dM AirTrqErr is the rate of change of the torque difference M AirTrqErr . The larger k TrqToRho , the faster the target intake density increases; the smaller k TrqToRho , the slower the target intake density increases; the selection criteria is to improve the effect of improving torque accuracy as fast as possible, but the faster it is, the more torque control fluctuations it will cause, so the calibration basis is to ensure that the torque difference M AirTrqErr fluctuation range is within the preset range (±5Nm in this example), while the absolute value of the torque difference M AirTrqErr is smaller than the absolute value of the torque difference M AirTrqErr before adjustment. k Lrn is the target intake density gain coefficient self-learning correction coefficient, whose default value is 0 and can be saved after the vehicle is powered off.

[0067] λ is the filter time of the target intake density gain coefficient k TrqToRho , in ms. The calibration data in this example is shown in Table 1.

[0068] Table 1

[0069]

[0070] f(M AirTrqErr , dM AirTrqErr ) is the correction coefficient at different torque differences M AirTrqErr and torque difference rates dM AirTrqErr , as shown in Table 2.

[0071] Table 2

[0072]

[0073] In particular, once the above conditions are not met simultaneously, or the time when the above conditions are met simultaneously does not exceed t1, Δrho(N) is reset to 0.

[0074] In particular, once Δrho(N) reaches its allowed maximum and minimum values, it is maintained unchanged, i.e. Δrho(N) = Δrho(N-1). The allowed maximum and minimum values of Δrho(N) depend on the target intake air density rho Re qRaw (z) and engine speed n, i.e. the maximum value is equal to f(n, rho Re qRaw (z)) x (1 + r Adap ), and the minimum value is equal to the negative of f(n, rho Re qRaw (z)) x (1 + r Adap ). In the present example, they are calibrated as follows, based on the difference M AirTrqErr between the torque after the adjustment and the torque before the adjustment, and the range of fluctuations of the torque after the adjustment being within a preset range (in the present example, ±5 Nm). AirTrqErr The absolute value of the difference M AirTrqErr between the torque after the adjustment and the torque before the adjustment is smaller than the absolute value of the difference M Adap between the torque before the adjustment and the torque before the adjustment. r Re qRaw is a maximum and minimum self-learning correction coefficient, which has a default value of 0 and can be saved after the vehicle is powered off.

[0075] The value of f(n, rho Re qRaw (z)) is shown in Table 3.

[0076] Table 3

[0077]

[0078] S3, different working conditions are set, and the stored target intake air density increment corresponding to different working conditions is updated according to the learning condition. The sum of the updated stored target intake air density increment and the initial target intake air density is the optimized target intake air density.

[0079] Once one of the following conditions is met:

[0080] 1) The time when Δrho(N) reaches its maximum or minimum value exceeds t2, where t2 is equal to 0.2 s;

[0081] 2) The real-time read torque difference M AirTrqErr is within a preset range, in the present example, ±5 Nm;

[0082] 3) The stable conditions are met simultaneously for a time exceeding a preset time t3. Where t3 is greater than t1, in the present example, t3-t1 = 1.5 s;

[0083] Then stop updating Δrho immediately, where Δrho(N) means Δrho in the Nth sampling period.

[0084] A. If the following condition is met at the same time as one of the above conditions is met: the engine requested air path torque M AirTrqReq and the engine actual air path torque M AirTrqAct are different, and the difference M AirTrqErr fluctuates within a preset range, in this example ±5 Nm.

[0085] Record the working condition information from t1 to t3 when the above conditions are met at the same time, including the average engine speed, average equivalence ratio, average EGR rate, average basic ignition efficiency, average engine requested air path torque M Store the current Δrho update in the same working condition, and do not update and store other working conditions, and the stored target intake air density increment Δrho Stored The storage method is as follows:

[0086] Δrho Stored = k1 x Δrho Stored (z) + (1-k1) x Δrho

[0087] Where Δrho Stored (z) is the last learned and stored target intake air density increment in the same working condition, and the default value is 0 if it has never been learned and updated, and the weighting coefficient k1 is 0.2.

[0088] Add the updated Δrho Stored to the initial target intake air density rho ReqRaw to obtain the optimized target intake air density rho Re qNew .

[0089] Record the number of times CNT that the same working condition meets condition A, and the number of times CNT1, CNT2 and CNT3 that the above conditions 1), 2) and 3) are met respectively, then CNT1+CNT2+CNT3=CNT, and CNT, CNT1, CNT2 and CNT3 can be saved after the vehicle is powered off.

[0090] If CNT is greater than 15, CNT1 is greater than 10, and CNT2 is less than 2 in the same working condition, then the maximum and minimum self-learning correction coefficient r Adap = r Adap (z) + 0.1; where r Adap (z) is the last learned and updated maximum and minimum self-learning correction coefficient, and the default value is 0. After updating, CNT, CNT1, CNT2 and CNT3 are all cleared to zero.

[0091] If CNT is greater than 15, and CNT3 is greater than 10, and CNT2 is less than 2 under the same working condition, the target intake air density gain coefficient k Lrn = k Lrn (z) + 0.02; wherein k Lrn (z) is the last learning updated target intake air density gain coefficient, and its default value is 0. After the update, CNT, CNT1, CNT2 and CNT3 are all cleared.

[0092] If CNT is greater than 15, and CNT1 is not greater than 2, and CNT3 is greater than 2 under the same working condition, the target intake air density gain coefficient k Lrn = k Lrn (z) - 0.01; the maximum and minimum self-learning correction coefficient r Adap = r Adap (z) - 0.1; after the update, CNT, CNT1, CNT2 and CNT3 are all cleared.

[0093] The above three conditions are updated at most one of them in each driving cycle, and only once.

[0094] In other cases, the target intake air density gain coefficient k Lrn and the maximum and minimum self-learning correction coefficient r Adap remain unchanged.

[0095] B. If the following conditions are not met during the above conditions meet one of them: the engine requested air path torque M AirTrqReq and the difference M AirTrqAct between the engine actual air path torque M AirTrqErr fluctuation range does not exceed the preset value, which is ±5 Nm in this example.

[0096] Similarly, record the working condition information (average engine speed, average equivalence ratio, average EGR rate, average basic ignition efficiency, average engine requested air path torque ) from t1 to t3 when the above conditions are met at the same time, update the target intake air density increment stored in the corresponding same working condition (other working conditions are not updated), and can be saved after the vehicle is powered off. The stored target intake air density increment Δrho Stored is as follows: Δrho Stored = 0. And immediately use the updated Δrho Stored to add the initial target intake air density rho ReqRaw to obtain the optimized target intake air density rho Re qNew

[0097] C. In other cases, continue to update Δrho. At this time, the optimized target intake air density rho Re qNew remains unchanged.

[0098] In particular, if the steady-state learning condition is not met, the optimized target intake air density rho Re qNew Remains unchanged, that is, rho Re qNew = rho Re qRaw + delta rho Stored (z).

[0099] Embodiment two

[0100] The application also provides an engine target intake air density learning optimization device, comprising:

[0101] An initial target intake air density acquisition module is configured to determine an initial target intake air density;

[0102] A target intake air density increment determination module is configured to read the difference between the current engine requested air path torque and the actual engine air path torque, and determine the target intake air density increment in the Nth sampling period according to the difference;

[0103] A learning optimization module is configured to set different working conditions, and update the stored target intake air density increments corresponding to the different working conditions according to the learning condition, and the sum of the updated stored target intake air density increments and the initial target intake air density is the optimized target intake air density.

[0104] Embodiment three

[0105] The application also provides an automobile comprising the engine target intake air density learning optimization device.

[0106] Embodiment four

[0107] The application also provides an electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory stores a computer program, and when the program is executed by the processor, the processor executes the steps of the engine target intake air density learning optimization method.

[0108] Embodiment five

[0109] The application also provides a computer readable storage medium, which stores executable instructions, and the instructions make the processor implement the engine target intake air density learning optimization method when executed by the processor.

[0110] Those skilled in the art will appreciate that embodiments of the application can be readily used as software, hardware, or a combination of software and hardware. In one

[0111] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart blocks and / or diagrams can represent code, programs, or subprograms that can be executed on computing devices. In some embodiments, alternative software implementations can be utilized. Figure 1 The flowchart blocks and / or diagrams can represent code, programs, or subprograms that can be executed on computing devices. In some embodiments, alternative software implementations can be utilized.

[0112] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart blocks and / or diagrams can represent code, programs, or subprograms that can be executed on computing devices. In some embodiments, alternative software implementations can be utilized. Figure 1 The flowchart blocks and / or diagrams can represent code, programs, or subprograms that can be executed on computing devices. In some embodiments, alternative software implementations can be utilized.

[0113] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart blocks and / or diagrams can represent code, programs, or subprograms that can be executed on computing devices. In some embodiments, alternative software implementations can be utilized. Figure 1 The flowchart blocks and / or diagrams can represent code, programs, or subprograms that can be executed on computing devices. In some embodiments, alternative software implementations can be utilized.

[0114] The embodiments of the application described above are intended to be merely exemplary and those skilled in the art will recognize that many changes can be made to the embodiments described while still falling within the scope of the application.

Claims

1. A method for learning and optimizing the target intake air density of an engine, characterized in that, include: Determine the initial target intake air density; Read the difference between the current requested airflow torque of the engine and the actual airflow torque of the engine, and determine the target intake density increment in the Nth sampling period accordingly. Set different operating conditions and update the target intake density increment corresponding to different operating conditions according to the learning situation. The sum of the updated target intake density increment and the initial target intake density is the optimized target intake density. When any of the conditions (1) to (3) are satisfied (1) The time after reaching its maximum or minimum value exceeds the preset time; (2) The difference in torque read in real time Within the preset range; (3) The stable conditions are met simultaneously for a period of time exceeding the preset time; Then immediately stop updating the target intake density increment. ,in It refers to the target intake density increment in the Nth sampling period. ; If any of the conditions (1) to (3) are met at the same time as the following condition (4): the engine requests airflow torque Actual airflow torque of the engine difference The fluctuation range does not exceed the preset value; Record operating condition information and the current The update is stored within the corresponding operating conditions and can be saved after the vehicle is powered off; it stores the target intake density increment. The storage method is as follows: in, k1 represents the target intake density increment stored in the previous learning under the same operating conditions, and k1 is the weighting coefficient. Updated Add the initial target intake density Optimized target intake density ; If condition (4) is not met while any of conditions (1) to (3) are met, then the operating condition information is recorded and the target intake density increment stored under the corresponding same operating condition is updated. .

2. The engine target intake air density learning optimization method according to claim 1, characterized in that, The target intake density increment for the Nth sampling period is determined using the following formula: Where N = 1, 2, 3…; This represents the target intake density increment during the Nth sampling period. This represents the target intake density increment during the (N-1)th sampling period. This is the difference between the current requested airflow torque of the engine and the actual airflow torque of the engine. The target intake density gain coefficient.

3. The engine target intake density learning optimization method according to claim 2, characterized in that, The target intake air density gain coefficient is determined using the following formula: in, The difference in torque rate of change, The self-learning correction coefficient for the target intake density gain coefficient. Target intake density gain coefficient Filtering time, To the difference of different torques Rate of change of torque The correction factor.

4. The engine target intake air density learning optimization method according to claim 3, characterized in that, If the target intake density increment in the Nth sampling period If its maximum and minimum allowable values ​​are reached, then it remains unchanged. ; Its maximum and minimum allowable values ​​both depend on the target intake air density. The value is determined by the engine speed n, i.e., the maximum value is equal to The minimum value is equal to The negative value, The calibration is based on the difference in torque after adjusting the air volume. While the fluctuation range is within the preset range, the difference in torque The absolute value of the difference between the torque before and after adjustment The absolute value is small; The maximum and minimum values ​​are self-learning correction coefficients.

5. The engine target intake air density learning optimization method according to claim 3, characterized in that, Record the number of times CNT satisfies any of the conditions (1) to (3) and simultaneously satisfies condition (4) under the same working conditions, and the number of times CNT1, CNT2 and CNT3 satisfy conditions (1), (2) and (3) respectively. Then CNT1 + CNT2 + CNT3 = CNT. If, under the same operating conditions, CNT is greater than 15, CNT1 is greater than 10, and CNT2 is less than 2, then the maximum and minimum values ​​are adjusted by self-learning correction coefficients. ;in The maximum and minimum values ​​from the last learning update are used as self-learning correction coefficients. After the update is complete, CNT, CNT1, CNT2, and CNT3 are all cleared to zero. If, under the same operating conditions, CNT is greater than 15, CNT3 is greater than 10, and CNT2 is less than 2, then the self-learning correction coefficient for the target intake density gain coefficient is... ;in The target intake density gain coefficient is the self-learning correction coefficient for the last learning update. After the update is completed, CNT, CNT1, CNT2 and CNT3 are all cleared to zero. If, under the same operating conditions, CNT is greater than 15, CNT1 is not greater than 2, and CNT3 is greater than 2, then the self-learning correction coefficient for the target intake density gain coefficient is... ; Maximum and minimum value self-learning correction coefficient ; After the update is complete, CNT, CNT1, CNT2, and CNT3 will all be reset to zero. In other cases, the target inlet density gain coefficient self-learning correction coefficient and maximum and minimum self-learning correction coefficients Remain unchanged.

6. An engine target intake air density learning and optimization device, characterized in that, include: The initial target intake air density acquisition module is used to determine the initial target intake air density; The target intake density increment determination module is used to read the difference between the current engine requested air path torque and the engine's actual air path torque, and determine the target intake density increment in the Nth sampling period accordingly. The learning optimization module is used to set different operating conditions and update the stored target intake density increment corresponding to different operating conditions according to the learning progress. The sum of the updated stored target intake density increment and the initial target intake density is the optimized target intake density. When any of the conditions (1) to (3) are satisfied (1) The time after reaching its maximum or minimum value exceeds the preset time; (2) The difference in torque read in real time Within the preset range; (3) The stable conditions are met simultaneously for a period of time exceeding the preset time; Then immediately stop updating the target intake density increment. ,in It refers to the target intake density increment in the Nth sampling period. ; If any of the conditions (1) to (3) are met at the same time as the following condition (4): the engine requests airflow torque Actual airflow torque of the engine difference The fluctuation range does not exceed the preset value; Record operating condition information and the current The update is stored within the corresponding operating conditions and can be saved after the vehicle is powered off; it stores the target intake density increment. The storage method is as follows: in, k1 represents the target intake density increment stored in the previous learning under the same operating conditions, and k1 is the weighting coefficient. Updated Add the initial target intake density Optimized target intake density ; If condition (4) is not met while any of conditions (1) to (3) are met, then the operating condition information is recorded and the target intake density increment stored under the corresponding same operating condition is updated. .

7. The engine target intake air density learning and optimization device according to claim 6, characterized in that, The target intake density increment determination module determines the target intake density increment for the Nth sampling period according to the following formula: Where N = 1, 2, 3…; This represents the target intake density increment during the Nth sampling period. This represents the target intake density increment during the (N-1)th sampling period. This is the difference between the current requested airflow torque of the engine and the actual airflow torque of the engine. The target intake density gain coefficient.

8. A car, characterized in that, Includes the engine target intake density learning optimization device as described in claim 6 or 7.

9. An electronic device, comprising: The system comprises a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; characterized in that the memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the engine target intake density learning optimization method according to any one of claims 1 to 5.

10. A computer-readable storage medium having executable instructions stored thereon, characterized in that, When executed by the processor, this instruction causes the processor to implement the engine target intake density learning optimization method as described in any one of claims 1 to 5.

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