A supercharged engine adaptive control method, electronic equipment, and vehicle

Abstract

The present invention relates to the technical field of vehicle engine control, and more specifically to a supercharged engine adaptive control method, electronic equipment, and vehicle. The present invention proposes a supercharged engine adaptive control method that performs corresponding target intake pressure self-learning control and target boost pressure self-learning control according to different engine operating modes. The method also updates the target intake pressure self-learning correction coefficient and the target boost pressure self-learning correction coefficient based on a correction coefficient self-learning algorithm, correcting the engine's target intake pressure and target boost pressure in real time. This method improves the control accuracy of the engine's intake volume and effectively enhances the responsiveness of the engine's intake control.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle engine control, and in particular to a supercharged engine adaptive control method, electronic equipment, and a vehicle. Background Art

[0002] The existing technology for supercharged engine intake control usually adopts PID closed-loop control. However, due to differences in supercharged engine performance, engine operation differences under different atmospheric pressures, differences in engine operating modes, and control offsets caused by wear, fatigue and aging of the supercharged engine system, the engine intake control responsiveness effect will be inconsistent, which in turn greatly restricts the control effect of the supercharged engine control system. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an adaptive control method for a supercharged engine, which can correct the target intake pressure and target boost pressure of the engine in real time according to different working modes of the supercharged engine, thereby improving the control accuracy of the intake volume of the supercharged engine.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A supercharged engine adaptive control method mainly includes:

[0006] S1, based on whether the engine boost closed-loop control is activated and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure r Thr Pr eAct The size of determines the current working mode of the engine;

[0007] S2, according to the current working mode of the engine, respectively execute the corresponding target intake pressure self-learning control and target boost pressure self-learning control to obtain the target intake pressure self-learning value p in the current working mode DesrdFinal Raw And the target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw ;

[0008] S3, according to the target boost pressure self-learning value p BoostDesrd NewRaw The maximum allowable value of boost pressure p BoostMax , Minimum allowable value of boost pressure p BoostMin The comparison result of the target boost pressure self-learning value p BoostDesrd NewRaw Correction is performed to obtain the final target intake pressure value p Boost Final ;

[0009] S4, based on the comparison result of the actual intake volume of the engine with the maximum allowable value of the intake volume and the minimum allowable value of the intake volume, the target intake pressure self-learning value p is set. DesrdFinal Raw Correction is performed to obtain the final target intake pressure value pDesrdFinal ;

[0010] S5, based on the correction coefficient self-learning algorithm, the target intake pressure self-learning correction coefficient k corresponding to the target intake pressure self-learning control and the target boost pressure self-learning control is respectively Adpt1 And the target boost pressure self-learning correction coefficient k Adpt1 , and conduct self-learning updates.

[0011] Furthermore, the current engine operating mode specifically includes:

[0012] Working mode 1: Engine boost closed loop control is not activated, or the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure is r Thr Pr eAct Exceeds threshold 1 f1(n) and does not exceed threshold 2 f2(n);

[0013] Working mode 2: The engine boost closed loop control is activated, and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure is r Thr Pr eAct does not exceed a threshold value f1(n), wherein the threshold value f1(n) is calibrated according to the engine speed n;

[0014] Working mode 3: The engine boost closed loop control is activated, and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure is r Thr Pr eAct The second threshold value f2(n) is exceeded, and the second threshold value f2(n) is also calibrated according to the engine speed n.

[0015] Furthermore, the target intake pressure self-learning control includes: if the current engine is in working mode 1 or in working mode 2 and does not change for a preset time T1, then

[0016] The target intake pressure self-learning value p in the current working mode DesrdFinal Raw Satisfy the following formula:

[0017] p DesrdFinal Raw =(C1+k Lrn )×p DesrdN +(1-C1-k Lrn )×p Desrd

[0018] Where p DesrdN is the preset target intake pressure initial value, p Desrd is the actual intake pressure value measured, C1 is the preset weighting coefficient, k Lrn It is the target intake pressure self-learning correction coefficient. Its initial value is 0 and it is continuously updated through the correction coefficient self-learning algorithm and saved after the vehicle is powered off.

[0019] Furthermore, the target boost pressure self-learning control includes: if the current engine is in working mode 3 and does not change for a preset time T2, then

[0020] The target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw Satisfy the following formula:

[0021] p BoostDesrd NewRaw =[1-C2-k Adpt1 ]×p Boost +(C2+k Adpt1 )×[1+f(r Thr Pr eAct )×p Boost W ]

[0022] Where p Boost W is the preset target boost pressure initial value, p Boost is the actual boost pressure value measured, C2 is the preset weighting coefficient, k Adpt1 is the target boost pressure self-learning correction coefficient, its initial value is 0, and it is continuously updated through the correction coefficient self-learning algorithm and saved after the vehicle is powered off; f(r Thr Pr eAct ) is the ratio of the throttle outlet pressure to the throttle inlet pressure r Thr Pr eAct Calibration influencing parameters.

[0023] Furthermore, the target boost pressure self-learning value p BoostDesrd NewRaw Make corrections, including:

[0024] 1) Case 1: Target boost pressure self-learning value p in current working mode BoostDesrd NewRaw Greater than the maximum allowable boost pressure value p BoostMax , record the number of times this situation occurs in this working mode CNT1;

[0025] Then, the target boost pressure value p in the current working mode is Boost Final =p BoostMax ;

[0026] 2) The second case: the target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw Less than the minimum allowable boost pressure value p BoostMin , record the number of times this situation occurs in this working mode CNT2;

[0027] Then, the target boost pressure value p in the current working mode is Boost Final =p BoostMin ;

[0028] 3) The third case: the target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw Not greater than the maximum allowable boost pressure value p BoostMax, and not less than the minimum allowable boost pressure value p BoostMin , record the number of times this situation occurs in this working mode CNT3;

[0029] Then, the target boost pressure value p in the current working mode is Boost Final =p BoostDesrd NewRaw .

[0030] Furthermore, the target intake pressure self-learning value p DesrdFinal Raw Make corrections, including:

[0031] In the first case:

[0032] 11) The actual intake volume of the current engine is not less than the maximum intake volume allowed by the engine. The number of times this situation occurs in this working mode is recorded CNT11. The target intake pressure value in the current working mode is

[0033] p DesrdFinal =C3×p DesrdFinal Raw +(1-C3)×p Desrd

[0034] Where C3 is the preset weighting coefficient three;

[0035] 12) The actual intake volume of the current engine is not greater than the minimum intake volume allowed by the engine. The number of times this situation occurs in this working mode is recorded CNT12. The target intake pressure value p in the current working mode is DesrdFinal =p DesrdFinal Raw ;

[0036] 13) In other cases, the target intake pressure value p in the current working mode DesrdFinal =p DesrdFinal Raw .

[0037] In the second case:

[0038] 21) The actual intake volume of the current engine is not less than the maximum intake volume allowed by the engine. The number of times this situation occurs in this working mode is recorded CNT21. The target intake pressure value p in the current working mode is DesrdFinal =p DesrdFinal Raw ;

[0039] 22) The actual air intake of the current engine is not greater than the minimum air intake allowed by the engine. Record the number of times this situation occurs in this working mode CNT22.

[0040] The target intake pressure value p in the current working mode is DesrdFinal =C4×p DesrdFinal Raw +(1-C4)×p Desrd

[0041] Where C4 is the preset weighting coefficient four;

[0042] 23) In other cases, the target intake pressure value p in the current working mode DesrdFinal =p DesrdFinal Raw .

[0043] In the third case:

[0044] 31) The actual air intake of the current engine is not less than the maximum air intake allowed by the engine. Record the number of times this situation occurs in this working mode CNT31.

[0045] The target intake pressure value p in the current working mode is DesrdFinal =C5×p DesrdFinal Raw +(1-C5)×p Desrd

[0046] Where C5 is the preset weighting coefficient five;

[0047] 32) The current actual engine intake volume is not greater than the minimum intake volume allowed by the engine. Record the number of times this situation occurs in this working mode CNT32.

[0048] The target intake pressure value p in the current working mode is DesrdFinal =C6×p DesrdFinal Raw +(1-C6)×p Desrd

[0049] Where C6 is the preset weighting coefficient six;

[0050] 33) The current actual engine intake volume is less than the maximum intake volume allowed by the engine and greater than the minimum intake volume of the engine.

[0051] Then, the target intake pressure value p in the current working mode is DesrdFinal =p DesrdFinal Raw .

[0052] Furthermore, the correction coefficient self-learning algorithm includes:

[0053] When any one of the first, second and third situations occurs, the number of times CNT is accumulated, and its initial value is 0.

[0054] 1) If the number CNT is greater than the preset value and CNT3 is greater than the preset value, then k Adpt1 =k Adpat1 (z)+0.01;

[0055] 11) If CNT31 is not less than the preset value, then k Lrn =k Lrn(z) -0.015; 12) If CNT32 is not less than the preset value, then k Lrn =k Lrn (z) + 0.01; 13) In other cases, k Lrn =k Lrn (z);

[0056] And immediately clear CNT, CNT1~3 and CNT11~32 to zero, and restart counting when the corresponding conditions are met next time;

[0057] 2) If the number CNT is greater than the preset value, CNT1 is greater than the preset value, then k Adpt1 =k Adpat1 (z)-0.008;

[0058] 21) If CNT11 is not less than the preset value, then k Lrn =k Lrn (z) -0.02; 22) If CNT12 is not less than the preset value, then k Lrn =k Lrn (z) + 0.005; 23) In other cases, k Lrn =k Lrn (z);

[0059] And immediately clear CNT, CNT1~3 and CNT11~32 to zero, and restart counting when the corresponding conditions are met next time;

[0060] 3) If the number CNT is greater than the preset value and CNT2 is greater than the preset value, then k Adpt1 =k Adpat1 (z)-0.018;

[0061] 31) If CNT21 is not less than the preset value, then k Lrn =k Lrn (z) -0.01; 32) If CNT22 is not less than the preset value, then k Lrn =k Lrn (z) + 0.012; 33) In other cases, k Lrn =k Lrn (z);

[0062] And immediately clear CNT, CNT1~3 and CNT11~32 to zero, and restart counting when the corresponding conditions are met next time;

[0063] 4) In other cases: k Adpt1 =k Adpat1 (z), k Lrn =k Lrn (z);

[0064] Where n>m, kAdpat1 (z) is the target boost pressure self-learning correction coefficient obtained from the last self-learning update, and its default value is 0. Lrn (z) is the target intake pressure self-learning correction coefficient obtained from the last self-learning update, and its default value is 0.

[0065] Compared with the prior art, the present invention has the following main advantages:

[0066] The present invention proposes an adaptive control method for a supercharged engine. According to different working modes of the supercharged engine, corresponding target intake pressure self-learning control and target boost pressure self-learning control are respectively executed, and the target intake pressure self-learning correction coefficient and the target boost pressure self-learning correction coefficient are self-learned and updated based on the correction coefficient self-learning algorithm, and the target intake pressure and target boost pressure of the engine are corrected in real time, thereby improving the control accuracy of the intake amount of the supercharged engine and effectively improving the responsiveness of the engine intake control. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is an overall flow chart of the adaptive control method for a supercharged engine according to an embodiment of the present invention;

[0068] Figure 2 Schematic diagram of three working modes of the engine in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0070] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, or two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0071] In the present invention, unless otherwise expressly specified or limited, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "plurality" means at least two, for example, two, three, etc., unless otherwise expressly specified or limited.

[0072] Embodiment 1: This embodiment provides a method for adaptive control of a supercharged engine. Figure 1 As shown, it mainly includes:

[0073] S1, based on whether the engine boost closed-loop control is activated and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure r Thr Pr eAct The size of determines the current working mode of the engine;

[0074] S2, according to the current working mode of the engine, respectively execute the corresponding target intake pressure self-learning control and target boost pressure self-learning control to obtain the target intake pressure self-learning value p in the current working mode DesrdFinal Raw And the target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw ;

[0075] S3, according to the target boost pressure self-learning value p BoostDesrd NewRaw The maximum allowable value of boost pressure p BoostMax , Minimum allowable value of boost pressure p BoostMin The comparison result of the target boost pressure self-learning value p BoostDesrd NewRaw Correction is performed to obtain the final target intake pressure value p Boost Final ;

[0076] S4, based on the comparison result of the actual intake volume of the engine with the maximum allowable value of the intake volume and the minimum allowable value of the intake volume, the target intake pressure self-learning value p is set. DesrdFinal Raw Correction is performed to obtain the final target intake pressure value p DesrdFinal ;

[0077] S5, based on the correction coefficient self-learning algorithm, the target intake pressure self-learning correction coefficient k corresponding to the target intake pressure self-learning control and the target boost pressure self-learning control is respectively Adpt1 And the target boost pressure self-learning correction coefficient k Adpt1 , and conduct self-learning updates.

[0078] Example 2: This embodiment provides a method for adaptively controlling a supercharged engine. The engine has three operating modes:

[0079] Working mode 1: Engine boost closed-loop control is not activated;

[0080] Working mode 2: The engine boost closed loop control is activated, and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure is r Thr Pr eAct does not exceed f1(n), which is calibrated according to the engine speed n:

[0081]

[0082] Working mode 3: The engine boost closed loop control is activated, and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure is r Thr Pr eAct Exceeding f2(n), f2(n) is also calibrated according to the engine speed n to obtain:

[0083]

[0084] Wherein, f2(n) is not less than f1(n);

[0085] In other cases, that is, the engine boost closed-loop control is activated but does not meet the second working mode and the third working mode, it means maintaining the previous working mode, and its default state is the first working mode.

[0086] Furthermore, according to the working mode of the engine, the target intake pressure self-learning value p in the current working mode is solved. DesrdFinal Raw , as follows:

[0087] 1) If the system is currently in working mode 1 or working mode 2, and the working mode remains unchanged for a preset time T1, the target intake pressure self-learning control is executed;

[0088] The target intake pressure self-learning value p in the current working mode DesrdFinal Raw Satisfy the following formula:

[0089] p DesrdFinal Raw =(C1+k Lrn )×p DesrdN +(1-C1-k Lrn )×p Desrd

[0090] Where p DesrdN is the preset target intake pressure initial value, p Desrd is the actual intake pressure value measured, C1 is the preset weighting coefficient, k Lrn The target intake pressure self-learning correction coefficient has an initial value of 0 and is continuously updated through the correction coefficient self-learning algorithm and saved after the vehicle is powered off.

[0091] 2) If the current mode is in operating mode 3 and the operating mode remains unchanged for a preset time T2, the target boost pressure needs to be improved to adjust the throttle inlet pressure and thus better control the intake pressure. Therefore, the target boost pressure self-learning control is executed;

[0092] The target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw Satisfy the following formula:

[0093] p BoostDesrd NewRaw=[1-C2-k Adpt1 ]×p Boost +(C2+k Adpt1 )×[1+f(r Thr Pr eAct )×p Boost W ]

[0094] Where p Boost W is the preset target boost pressure initial value, p Boost is the actual boost pressure value measured, C2 is the preset weighting coefficient, k Adpt1 is the target boost pressure self-learning correction coefficient, its initial value is 0, and it is continuously updated through the correction coefficient self-learning algorithm and saved after the vehicle is powered off; f(r Thr Pr eAct ) is the ratio of the throttle outlet pressure to the throttle inlet pressure r Thr Pr eAct Calibration influencing parameters.

[0095] Specifically, in the pressure ratio r Thr Pr eAct The larger the value is, the smaller the impact of the pressure change on the engine intake volume. In order to improve the accuracy of the system's control of the transient intake volume response, the target boost pressure needs to be adjusted to a greater degree to meet the target intake pressure requirements in the subsequent transient working mode. Based on the calibration during the subsequent actual vehicle verification test to achieve the transient responsiveness of the engine intake volume, the intake density deviation exceeds the preset value (±15mgl in this example) for a continuous time of T3 (0.5s in this example) under the same working mode, and the number of times does not exceed CNT times (3 in this example). Based on this, the calibration parameters obtained in this example are as follows:

[0096]

[0097] If the difference between the target intake density and the actual intake density rho Err > 0, then f(r Thr Pr eAct )=|f(r Thr Pr eAct )|;

[0098] If the difference between the target intake density and the actual intake density rho Err ≤0, then f(r Thr Pr eAct )=-|f(r Thr Pr eAct )|.

[0099] Furthermore, when any of the following three situations occurs, the number CNT is accumulated, and the number CNT is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0100] 1) Case 1: Target boost pressure self-learning value p in current working mode BoostDesrd NewRaw Greater than the maximum allowable boost pressure value pBoostMax , records the number of times this situation occurs in this working mode CNT1. CNT1 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0101] Then, the target boost pressure value p in the current working mode is Boost Final =p BoostMax ;

[0102] 11) The current actual engine intake volume is not less than the maximum intake volume allowed by the engine. The number of times this situation occurs in this working mode CNT11 is recorded. CNT11 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0103] Then, the target intake pressure value p in the current working mode is DesrdFinal =C3×p DesrdFinal Raw +(1-C3)×p Desrd

[0104] Wherein, C3 is the preset weighting coefficient three.

[0105] 12) The current actual engine intake volume is not greater than the minimum intake volume allowed by the engine. The number of times this situation occurs in this working mode CNT12 is recorded. CNT12 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0106] Then, the target intake pressure value p in the current working mode is DesrdFinal =p DesrdFinal Raw

[0107] 13) In other cases, the target intake pressure value p in the current working mode DesrdFinal =p DesrdFinal Raw .

[0108] 2) The second case: the target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw Less than the minimum allowable boost pressure value p BoostMin , records the number of times this situation occurs in this working mode CNT2. CNT2 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0109] Then, the target boost pressure value p in the current working mode is Boost Final =p BoostMin ;

[0110] 21) The current actual engine intake volume is not less than the maximum intake volume allowed by the engine. The number of times this situation occurs in this working mode CNT21 is recorded. CNT21 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0111] Then, the target intake pressure value p in the current working mode is DesrdFinal =p DesrdFinal Raw

[0112] 22) The current actual engine intake volume is not greater than the minimum intake volume allowed by the engine. The number of times this situation occurs in this working mode CNT22 is recorded. CNT22 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0113] Then, the target intake pressure value p in the current working mode is DesrdFinal =C4×p DesrdFinal Raw +(1-C4)×p Desrd

[0114] Wherein, C4 is the preset weighting coefficient four.

[0115] 23) In other cases, the target intake pressure value p in the current working mode DesrdFinal =p DesrdFinal Raw .

[0116] 3) The third case: the target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw Not greater than the maximum allowable boost pressure value p BoostMax , and not less than the minimum allowable boost pressure value p BoostMin , records the number of times this situation occurs in this working mode CNT3. CNT3 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0117] Then, the target boost pressure value p in the current working mode is Boost Final =p BoostDesrd NewRaw ;

[0118] 31) The current actual engine intake volume is not less than the maximum intake volume allowed by the engine. The number of times this situation occurs in this working mode CNT31 is recorded. CNT31 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0119] Then, the target intake pressure value p in the current working mode is DesrdFinal =C5×p DesrdFinal Raw +(1-C5)×p Desrd

[0120] Wherein, C5 is the preset weighting coefficient five.

[0121] 32) The current actual engine intake volume is not greater than the minimum intake volume allowed by the engine. The number of times this situation occurs in this working mode is recorded CNT32. CNT32 is updated at most once in each driving cycle and can be saved after the vehicle is powered off. Its default value is 0.

[0122] Then, the target intake pressure value p in the current working mode is DesrdFinal =C6×p DesrdFinal Raw +(1-C6)×p Desrd

[0123] Wherein, C6 is the preset weighting coefficient six.

[0124] 33) The actual intake volume of the current engine is less than the maximum intake volume allowed by the engine and greater than the minimum intake volume of the engine. In this case, the target intake pressure value p in the current working mode is DesrdFinal =p DesrdFinal Raw

[0125] Furthermore, the correction coefficient self-learning algorithm is as follows:

[0126] 1) If the number CNT is greater than the preset value, which is 50 in this example, and CNT3 is greater than the preset value, which is 40 in this example, then k Adpt1 =k Adpat1 (z) + 0.01, 11) If CNT31 is not less than the preset value, in this example it is 35, then k Lrn =k Lrn (z) -0.015; 12) If CNT32 is not less than the preset value, in this example it is 35, then k Lrn =k Lrn (z) + 0.01; 13) In other cases, k Lrn =k Lrn (z)

[0127] CNT, CNT1-3, and CNT11-32 are all cleared immediately, and the counting starts again when the corresponding conditions are met next time.

[0128] 2) If the number CNT is greater than the preset value, which is 50 in this example, and CNT1 is greater than the preset value, which is 40 in this example, then k Adpt1 =k Adpat1 (z) -0.008, 21) If CNT11 is not less than the preset value, in this example it is 35, then k Lrn =k Lrn (z) -0.02; 22) If CNT12 is not less than the preset value, in this example it is 35, then k Lrn =k Lrn(z) + 0.005; 23) In other cases, k Lrn =k Lrn (z)

[0129] CNT, CNT1-3, and CNT11-32 are all cleared immediately, and the counting starts again when the corresponding conditions are met next time.

[0130] 3) If the number CNT is greater than the preset value, which is 50 in this example, and CNT2 is greater than the preset value, which is 40 in this example, then k Adpt1 =k Adpat1 (z) -0.018, 31) If CNT21 is not less than the preset value, in this example it is 35, then k Lrn =k Lrn (z) -0.01; 32) If CNT22 is not less than the preset value, in this example it is 35, then k Lrn =k Lrn (z) + 0.012; 33) In other cases, k Lrn =k Lrn (z)

[0131] CNT, CNT1-3, and CNT11-32 are all cleared immediately, and the counting starts again when the corresponding conditions are met next time.

[0132] 4) In other cases, k Adpt1 =k Adpat1 (z), k Lrn =k Lrn (z)

[0133] k Adpat1 (z) is the target boost pressure self-learning correction coefficient obtained from the last self-learning update, and its default value is 0. Lrn (z) is the target intake pressure self-learning correction coefficient obtained from the last self-learning update, and its default value is 0.

[0134] The priority of the judgment execution of the CNT values ​​of the above four times is gradually reduced, and only one item can be executed at a time.

[0135] Embodiment 3: Based on the same inventive concept, this embodiment also provides a vehicle electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the above-described boost engine adaptive control method when executing the program.

[0136] Embodiment 4: Based on the same inventive concept, this embodiment further provides a manual-automatic vehicle, which is provided with the vehicle electronic equipment as described above.

[0137] Furthermore, all parts of this application that are not described in detail are the same as the existing technology or are implemented using the existing technology.

[0138] In summary:

[0139] The present invention proposes an adaptive control method for a supercharged engine. According to different working modes of the engine, corresponding target intake pressure self-learning control and target boost pressure self-learning control are respectively executed, and the target intake pressure self-learning correction coefficient and the target boost pressure self-learning correction coefficient are self-learned and updated based on the correction coefficient self-learning algorithm, and the target intake pressure and target boost pressure of the engine are corrected in real time, thereby improving the control accuracy of the engine intake amount and effectively improving the responsiveness of the engine intake control.

[0140] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0144] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A supercharged engine adaptive control method, characterized in that: include: The current engine operating mode is determined based on whether the engine boost closed-loop control is activated and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure; According to the current working mode of the engine, the corresponding target intake pressure self-learning control and target boost pressure self-learning control are respectively executed to obtain the target intake pressure self-learning value and the target boost pressure self-learning value under the current working mode; According to the comparison result of the target boost pressure self-learning value with the maximum allowable boost pressure value and the minimum allowable boost pressure value, the target boost pressure self-learning value is corrected to obtain the final target intake pressure value; According to the comparison result of the actual intake volume of the engine with the maximum allowable intake volume and the minimum allowable intake volume, the target intake pressure self-learning value is corrected to obtain the final target intake pressure value; Based on the correction coefficient self-learning algorithm, the target intake pressure self-learning correction coefficient and the target boost pressure self-learning correction coefficient corresponding to the target intake pressure self-learning control and the target boost pressure self-learning control are self-learned and updated respectively.

2. The adaptive control method for a supercharged engine according to claim 1, characterized in that: The current engine operating mode includes: Working mode 1: the engine boost closed-loop control is not activated, or the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure exceeds threshold 1 and does not exceed threshold 2; Working mode 2: The engine boost closed-loop control is activated, and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure does not exceed threshold 1; Working mode three: the engine boost closed-loop control is activated, and the ratio of the actual throttle outlet pressure to the actual throttle inlet pressure exceeds threshold value two; the threshold value one and the threshold value two are both calibrated according to the engine speed n, and the threshold value two is greater than the threshold value one.

3. The adaptive control method for a supercharged engine according to claim 2, characterized in that: When the engine is in working mode 1 or working mode 2 and lasts for the preset time T1, the target intake pressure self-learning control is performed: The target intake pressure self-learning value p in the current working mode DesrdFinalRaw , by the preset target intake pressure initial value p DesrdN , measured actual intake pressure value p Desrd , weighted coefficient C1 and target intake pressure self-learning correction coefficient k Lrn Calculated.

4. The adaptive control method for a supercharged engine according to claim 2, characterized in that: When the engine is in working mode 3 and lasts for the preset time T2, the target boost pressure self-learning control is performed: The target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw , by the preset target boost pressure initial value p BoostW , measured actual boost pressure value p Boost , weighted coefficient C2, target boost pressure self-learning correction coefficient k Adpt1 and influencing parameters f(r Thr Pr eAct ) is calculated, the influencing parameter f(r Thr Pr eAct ) According to the ratio of throttle outlet pressure to throttle inlet pressure r Thr Pr eAct Calibration obtained.

5. The adaptive control method for a supercharged engine according to claim 4, characterized in that: The target boost pressure self-learning value p BoostDesrd NewRaw Make corrections, including: Case 1: Target boost pressure self-learning value p BoostDesrd NewRaw Greater than the maximum allowable boost pressure value p BoostMax , record the number of times this situation occurs CNT1; then, the target boost pressure value p in the current working mode Boost Final =p BoostMax ; The second case: target boost pressure self-learning value p BoostDesrd NewRaw Less than the minimum allowable boost pressure value p BoostMin , record the number of times this situation occurs CNT2; then, the target boost pressure value p Boost Final =p BoostMin ; The third case: the target boost pressure self-learning value p in the current working mode BoostDesrd NewRaw Not greater than the maximum allowable boost pressure value p BoostMax , and not less than the minimum allowable boost pressure value p BoostMin , record the number of times this situation occurs CNT3; then, the target boost pressure value p in the current working mode Boost Final =p BoostDesrd NewRaw .

6. The adaptive control method for a supercharged engine according to claim 3, characterized in that: The target intake pressure self-learning value p DesrdFinal Raw Make corrections, including: According to the comparison results of the actual engine intake volume with the maximum allowable value of intake volume and the minimum allowable value of intake volume, and based on the real-time current working mode target boost pressure self-learning value p BoostDesrd NewRaw And the measured actual intake pressure value p Desrd , calculate and determine the target intake pressure value p in the current working mode DesrdFinal .

7. The adaptive control method for a supercharged engine according to claim 5, characterized in that: The correction coefficient self-learning algorithm includes: When any of the first, second, and third situations occurs, the number of times CNT is accumulated; According to the comparison results of the times CNT, CNT1, CNT2, CNT3 and the actual intake volume of the engine and the intake volume threshold, combined with the target boost pressure self-learning correction coefficient k obtained in the last self-learning update Adpat1 (z) and the target intake pressure self-learning correction coefficient k obtained from the last self-learning update Lrn (z), the self-learning correction coefficient k for the target intake pressure Lrn And the target boost pressure self-learning correction coefficient k Adpt1 Perform self-learning updates.

8. A vehicle electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the supercharged engine adaptive control method according to any one of claims 1 to 7 is implemented.

9. A non-transitory readable storage medium having a program stored thereon, characterized in that: When the program is executed by a vehicle electronic device, the supercharged engine adaptive control method according to any one of claims 1 to 7 is implemented.

10. A manual-automatic vehicle, characterized in that: The vehicle electronic device comprising the vehicle electronic device according to claim 8.

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