Pressurizing self-learning method and system under full life cycle and electronic equipment

Abstract

The invention provides a supercharging self-learning method and system in a full life cycle and electronic equipment, and belongs to the technical field of engine control, and the method comprises the steps that the supercharging pressure difference and the target and actual supercharging pressure difference change rate are obtained and dynamically updated; determining a closed-loop control gain coefficient based on the target boost pressure ratio and dynamically updating the closed-loop control gain coefficient; according to the supercharging pressure difference, the updated target and actual supercharging pressure difference change rate and the closed-loop control gain coefficient, the P-item turbine actuator target opening degree, the D-item turbine actuator target opening degree and the I-item turbine actuator target opening degree are determined and dynamically updated; and according to the updated P-item turbine actuator target opening degree, the updated D-item turbine actuator target opening degree and the updated I-item turbine actuator target opening degree, the target turbine actuator target opening degree is obtained, and supercharging pressure closed-loop control is conducted. According to the method, various parameters of boost pressure closed-loop control are updated in real time, influences of different parameters are optimized, and boost control precision in different life cycles is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engine control, and in particular to a turbocharging self-learning method, system and electronic device under a full life cycle. BACKGROUND

[0002] The actual turbocharging pressure of an engine is an important parameter for turbocharging closed-loop control. In turbocharging closed-loop control, the target turbocharging pressure and the actual turbocharging pressure are used for closed-loop control, and the turbocharging actuator is controlled to move to achieve actual turbocharging pressure following the target turbocharging pressure, so as to ensure the achievement of intake air pressure, realize the intake of the engine, and thus ensure the power performance, economy and emission performance of the vehicle. At present, the influence of different parameters on turbocharging control parameters exists in engine control, and the turbocharging control accuracy under different life cycles needs to be improved. SUMMARY

[0003] The present application aims to solve at least one of the technical problems existing in the prior art, and proposes a turbocharging self-learning method, system and electronic device under a full life cycle.

[0004] In a first aspect, an embodiment of the present application provides a turbocharging self-learning method under a full life cycle, comprising:

[0005] obtaining a turbocharging pressure difference and a target and actual turbocharging pressure difference change rate and dynamically updating them;

[0006] obtaining a target turbocharging pressure ratio, determining a closed-loop control gain coefficient based on the target turbocharging pressure ratio and dynamically updating it;

[0007] determining a P-term turbocharger actuator target opening degree, a D-term turbocharger actuator target opening degree and an I-term turbocharger actuator target opening degree based on the turbocharging pressure difference, the updated target and actual turbocharging pressure difference change rate and the closed-loop control gain coefficient and dynamically updating them;

[0008] obtaining a target turbocharger actuator target opening degree based on the updated P-term turbocharger actuator target opening degree, D-term turbocharger actuator target opening degree and I-term turbocharger actuator target opening degree to perform turbocharging pressure closed-loop control.

[0009] In some embodiments, the obtaining of the turbocharging pressure difference and the target and actual turbocharging pressure difference change rate and the dynamic updating thereof comprises:

[0010] obtaining a target turbocharging pressure and an actual turbocharging pressure, and obtaining a turbocharging pressure difference based on the difference between the target turbocharging pressure and the actual turbocharging pressure;

[0011] obtaining a target turbocharging pressure change rate based on the target turbocharging pressure, the target turbocharging pressure at the last sampling time, the sampling period and the filtering time;

[0012] determining a filter time self-learning coefficient, and dynamically updating the target boost pressure change rate based on the filter time self-learning coefficient;

[0013] obtaining a first actual boost pressure change rate and a second actual boost pressure change rate;

[0014] determining a target and actual boost pressure difference change rate based on the target boost pressure change rate, the first actual boost pressure change rate, and the second actual boost pressure change rate.

[0015] In some embodiments, the determining a filter time self-learning coefficient, and dynamically updating the target boost pressure change rate based on the filter time self-learning coefficient, comprises:

[0016] determining a filter time self-learning coefficient based on a preset condition;

[0017] pre-setting a filter failure time;

[0018] obtaining an updated filter time according to a product of the filter time self-learning coefficient plus one and the filter failure time;

[0019] obtaining an updated target boost pressure change rate according to the target boost pressure, a target boost pressure at a previous sampling time, a sampling period, and the updated filter time.

[0020] In some embodiments, the obtaining a first actual boost pressure change rate and a second actual boost pressure change rate, comprises:

[0021] obtaining a throttle inlet actual pressure change rate according to a throttle inlet actual pressure, a throttle inlet actual pressure at a previous sampling time, a sampling period, and a filter time;

[0022] obtaining a throttle outlet actual pressure change rate according to a throttle outlet actual pressure, a throttle outlet actual pressure at a previous sampling time, a sampling period, and a filter time;

[0023] obtaining a first actual boost pressure change rate according to the throttle inlet actual pressure change rate, the throttle outlet actual pressure change rate, and a transition coefficient;

[0024] taking the throttle inlet actual pressure change rate as a second actual boost pressure change rate.

[0025] In some embodiments, the obtaining a target boost pressure ratio, and determining a closed-loop control gain coefficient based on the target boost pressure ratio and dynamically updating, comprises:

[0026] obtaining a target boost pressure ratio;

[0027] When the EGR rate is zero, calibrating a first gain coefficient request;

[0028] When the target EGR rate is equal to the actual EGR rate, calibrating a second gain coefficient request;

[0029] obtaining a closed-loop control gain coefficient based on the target supercharging pressure ratio, the engine speed, the actual compressor inlet pressure, the first gain coefficient request, and the second gain coefficient request;

[0030] A self-learning correction coefficient of a closed-loop control gain coefficient is determined, and the closed-loop control gain coefficient is dynamically updated based on the self-learning correction coefficient.

[0031] In some embodiments, obtaining the target boost pressure ratio includes:

[0032] The original compressor outlet target pressure is obtained according to the target boost pressure, the pressure drop coefficient of the intercooler, the intake air mass flow rate entering the cylinder, the compressor outlet temperature and the actual compressor outlet pressure;

[0033] determining a corrected pressure based on the original compressor outlet target pressure, the compressor inlet actual pressure, and the compressor inlet gas temperature;

[0034] Obtaining a compressor outlet corrected target pressure according to the corrected pressure and the original compressor outlet target pressure;

[0035] The target boost pressure ratio is obtained according to the ratio of the corrected target pressure at the compressor outlet to the actual pressure at the compressor inlet.

[0036] In some embodiments, determining the turbine actuator target opening (P), the turbine actuator target opening (D), and the turbine actuator target opening (I) according to the boost pressure difference, the updated rate of change of the target and actual boost pressure difference, and the closed-loop control gain coefficient, and dynamically updating the same includes:

[0037] According to the boost pressure difference and the engine speed, a P correction coefficient, a D correction coefficient and an I basic correction coefficient are obtained respectively;

[0038] Obtaining a dynamically updated P-term turbine actuator target opening and a dynamically updated D-term turbine actuator target opening based on the P-term correction coefficient, the D-term correction coefficient, the updated closed-loop control gain coefficient, and the rate of change of the target and actual boost pressure difference;

[0039] A dynamically updated I-term turbine actuator target opening is obtained based on the I-term basic correction coefficient.

[0040] In some embodiments, the dynamically updated P-term turbine actuator target opening and the dynamically updated D-term turbine actuator target opening based on the P-term correction coefficient, the D-term correction coefficient, the updated closed-loop control gain coefficient, and the target and actual boost pressure difference rate of change are obtained by:

[0041] The dynamically updated P-term turbine actuator target opening is obtained according to the updated closed-loop control gain coefficient, the P-term correction coefficient, the boost pressure difference, and the P-term parameter self-learning coefficient.

[0042] The dynamically updated D-term turbine actuator target opening is obtained according to the updated closed-loop control gain coefficient and the target and actual boost pressure difference rate of change, the D-term correction coefficient, and the D-term parameter self-learning coefficient.

[0043] In some embodiments, the dynamically updated I-term turbine actuator target opening based on the I-term basic correction coefficient comprises:

[0044] The corrected I-term turbine actuator target opening is obtained based on the I-term basic correction coefficient, the supercharger maximum opening learning coefficient, the I-term turbine actuator target opening learning value, and the open-loop transition coefficient learning coefficient.

[0045] The I-term turbine actuator target opening is obtained according to the product of the corrected I-term turbine actuator target opening and the closed-loop control gain coefficient.

[0046] In some embodiments, the corrected I-term turbine actuator target opening based on the I-term basic correction coefficient, the supercharger maximum opening learning coefficient, the I-term turbine actuator target opening learning value, and the open-loop transition coefficient learning coefficient comprises:

[0047] A first I-term compensation correction coefficient and a second I-term compensation correction coefficient are obtained.

[0048] When the boost closed loop is enabled, an I-term incremental initial value is determined based on the I-term basic correction coefficient, the first I-term compensation correction coefficient, the second I-term compensation correction coefficient, and the boost pressure difference.

[0049] An I-term turbine actuator target opening increment is determined according to the I-term incremental initial value and the supercharger maximum opening learning coefficient.

[0050] The I-term turbine actuator target opening increment is corrected based on the I-term turbine actuator target opening learning value to obtain a corrected I-term turbine actuator target opening.

[0051] When the boost closed loop is not enabled, the corrected I-term turbine actuator target opening is self-updated based on the open-loop transition coefficient learning coefficient.

[0052] In a second aspect, an embodiment of the present invention provides a full life cycle boosting self-learning system, including:

[0053] A change rate update module is used to obtain the boost pressure difference and the change rate of the target and actual boost pressure difference and dynamically update them;

[0054] a coefficient updating module, configured to obtain a target boost pressure ratio, determine a closed-loop control gain coefficient based on the target boost pressure ratio, and dynamically update the closed-loop control gain coefficient;

[0055] an opening updating module for determining and dynamically updating a target opening of the turbine actuator P, a target opening of the turbine actuator D, and a target opening of the turbine actuator I according to the boost pressure difference, the updated rate of change of the target and actual boost pressure difference, and a closed-loop control gain coefficient;

[0056] The PID control parameter updating module is used to obtain the target turbine actuator target opening according to the updated P-term turbine actuator target opening, D-term turbine actuator target opening, and I-term turbine actuator target opening to perform boost pressure closed-loop control.

[0057] In some embodiments, the change rate updating module is specifically configured to: obtain a target boost pressure and an actual boost pressure, and obtain a boost pressure difference according to a difference between the target boost pressure and the actual boost pressure;

[0058] Obtaining a target boost pressure change rate according to the target boost pressure, the target boost pressure at the previous sampling moment, a sampling period, and a filtering time;

[0059] determining a filtering time self-learning coefficient, and dynamically updating the target boost pressure change rate based on the filtering time self-learning coefficient;

[0060] Obtaining a first actual boost pressure change rate and a second actual boost pressure change rate;

[0061] A target-actual supercharging pressure difference change rate is determined based on the target supercharging pressure change rate, the first actual supercharging pressure change rate, and the second actual supercharging pressure change rate.

[0062] In some embodiments, the change rate update module is specifically used to: determine a filter time self-learning coefficient based on a preset condition;

[0063] Pre-set filtering fault time;

[0064] Obtaining an updated filtering time according to the product of the filtering time self-learning coefficient plus one and the filtering fault time;

[0065] According to the target supercharging pressure, the target supercharging pressure at the last sampling time, a sampling period, and an updated filtering time, an updated target supercharging pressure change rate is obtained.

[0066] In some embodiments, the change rate updating module is specifically configured to: obtain a throttle inlet actual pressure change rate according to the throttle inlet actual pressure, the throttle inlet actual pressure at the last sampling time, a sampling period, and a filtering time;

[0067] obtain a throttle outlet actual pressure change rate according to the throttle outlet actual pressure, the throttle outlet actual pressure at the last sampling time, a sampling period, and a filtering time;

[0068] obtain a first actual supercharging pressure change rate according to the throttle inlet actual pressure change rate, the throttle outlet actual pressure change rate, and a transition coefficient;

[0069] take the throttle inlet actual pressure change rate as a second actual supercharging pressure change rate.

[0070] In a third aspect, an embodiment of the present application provides an electronic device, comprising:

[0071] one or more processors;

[0072] a memory configured to store one or more programs;

[0073] when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any of the above.

[0074] The present invention provides a full life cycle boost self-learning method, comprising: obtaining a boost pressure difference and a target and actual boost pressure difference change rate and dynamically updating them; obtaining a target boost pressure ratio, determining a closed-loop control gain coefficient based on the target boost pressure ratio and dynamically updating them; determining a P-item turbine actuator target opening, a D-item turbine actuator target opening and an I-item turbine actuator target opening according to the boost pressure difference, the updated target and actual boost pressure difference change rate and the closed-loop control gain coefficient and dynamically updating them; obtaining a target turbine actuator target opening according to the updated P-item turbine actuator target opening, the D-item turbine actuator target opening and the I-item turbine actuator target opening to perform closed-loop control of the boost pressure. In response to engine intake boost and torque increase requests, the boost system is controlled to maximize exhaust energy for boost. This closed-loop approach optimizes the acquisition of the boost pressure differential change rate for boost closed-loop control. A closed-loop control gain coefficient is also proposed, which uniformly affects all PID parameters, reducing PID calibration and improving boost control robustness. The various boost pressure closed-loop control parameters are updated in real time, optimizing the impact of different parameters on the boost control parameters, thereby improving boost control accuracy over different life cycles. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of a process flow of a full life cycle boosting self-learning method provided by an embodiment of the present invention;

[0076] Figure 2 Schematic diagram of the structure of an engine boost pressure control system according to an embodiment of the present invention;

[0077] Figure 3 This is a flow chart of a boosting self-learning method for the entire life cycle involved in an embodiment of the present invention;

[0078] Figure 4 This is a flowchart of an optional specific implementation method of step S3 in an embodiment of the present invention;

[0079] Figure 5 A structural block diagram of a full life cycle boosting self-learning system provided by an embodiment of the present invention;

[0080] Figure 6 This is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0081] For the purpose of making the technical solutions of the present application better understood by those skilled in the art, the exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Also, for the sake of clarity and conciseness, the description below omits the description of well-known functions and structures.

[0082] In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0083] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0084] The terms used herein are only used to describe specific embodiments, and are not intended to limit the present application. As used herein, the singular forms "a" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the terms "comprise" and / or "consist of", when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. The terms "connected" or "coupled" and / or similar terms are not limited to a physical or mechanical connection, but can include an electrical connection, whether direct or indirect.

[0085] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present application, and will not be interpreted in an overly formal or overly strict sense unless expressly so defined herein.

[0086] In the technical solutions of the present application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information comply with relevant laws and regulations, and do not violate public order and good customs. The use of user data in the technical solutions complies with relevant national laws and regulations (for example, "Information Security Technology Personal Information Security Specification" and the like). For example, appropriate measures are taken for personal information access control; restrictions are given to the display of personal information; the use purpose of personal information does not exceed the direct or reasonably related range; the identity of the personal information is eliminated when it is used, so as to avoid precise positioning to a specific individual.

[0087] The present application provides a full-life-cycle pressure boosting self-learning method. Figure 1A flowchart of a full-life-cycle supercharging self-learning method is provided for an embodiment of the present application.

[0088] As an embodiment of the present application, as shown in Figure 1 The full-life-cycle supercharging self-learning method comprises the following steps.

[0089] Step S1: Obtain the supercharging pressure difference and the target and actual supercharging pressure difference change rate and perform dynamic updating.

[0090] Step S2: Obtain the target supercharging pressure ratio, determine the closed-loop control gain coefficient based on the target supercharging pressure ratio and perform dynamic updating.

[0091] Step S3: Determine the P-term turbine actuator target opening, the D-term turbine actuator target opening and the I-term turbine actuator target opening based on the supercharging pressure difference, the updated target and actual supercharging pressure difference change rate and the closed-loop control gain coefficient and perform dynamic updating.

[0092] Step S4: Obtain the target turbine actuator target opening based on the updated P-term turbine actuator target opening, the D-term turbine actuator target opening and the I-term turbine actuator target opening to perform supercharging pressure closed-loop control.

[0093] It should be noted that the execution subject in the present embodiment can be an electronic device, which can be a computer device with data processing function, or other devices that can realize the same or similar functions, and the present embodiment does not limit the execution subject, which is taken as a computer device for illustration in the present embodiment.

[0094] Exemplarily, the present embodiment proposes a full-life-cycle supercharging self-learning method, which is described in detail in combination with the system structure as shown in Figure 2 Figure 2 ​As shown, the system structure includes: air filter 1; connecting the air filter mixed valve 2, for adjusting the pressure of EGR valve 10 outlet, improve the pressure difference between the two ends of EGR valve 10, two gas flow passages extend from mixed valve 2; connected to one of the gas flow passages of mixed valve 2 compressor 3; connecting the throttle valve 4 of the compressor 3; connecting the engine 5 of the throttle valve 4, for compressing fresh air to increase pressure; connecting the turbine 6 of the engine 5, for controlling the opening degree of the exhaust gas bypass valve; connecting the catalyst 7 of the turbine 6; connecting the particulate trap 8 of the catalyst 7; EGR cooler 9 is installed on the other gas flow passage of mixed valve 2, for receiving the exhaust gas output by particulate trap 8 and cooling, improve the exhaust gas flow; one end is connected with EGR cooler 9, the other end is connected with mixed valve 2 EGR valve 10, for controlling the exhaust gas flow into the cylinder; temperature sensor 11 is installed between EGR valve 10 and EGR cooler 9, for detecting the temperature of exhaust gas entering EGR valve 10; connecting the differential pressure sensor 12 of EGR valve 10, for detecting the pressure of EGR valve 10 inlet and outlet. The specific system architecture is shown in the following figure: 1, air filter; 2, mixed valve; 3, compressor; 4, throttle valve; 5, engine; 6, turbine; 7, catalyst; 8, particulate trap; 9, EGR cooler; 10, EGR valve; 11, temperature sensor; 12, differential pressure sensor.

[0095] Reference Figure 3 , combined Figure 3 The flow chart shown in the embodiment of the whole life cycle of the specific steps of the supercharging self-learning method is described.

[0096] In some embodiments, the supercharging pressure difference and the target and actual supercharging pressure difference change rate are obtained and dynamically updated, including: obtaining the target supercharging pressure and the actual supercharging pressure, and obtaining the supercharging pressure difference according to the difference between the target supercharging pressure and the actual supercharging pressure; obtaining the target supercharging pressure change rate according to the target supercharging pressure, the target supercharging pressure at the last sampling time, the sampling period and the filtering time; determining the filtering time self-learning coefficient, and dynamically updating the target supercharging pressure change rate based on the filtering time self-learning coefficient; obtaining the first actual supercharging pressure change rate and the second actual supercharging pressure change rate; determining the target and actual supercharging pressure difference change rate based on the target supercharging pressure change rate, the first actual supercharging pressure change rate and the second actual supercharging pressure change rate.

[0097] Specifically, when the supercharging control closed loop is activated, several parameters need to be determined before the supercharging closed loop control is performed:

[0098] 1. Supercharging pressure difference The method for obtaining the supercharging pressure difference includes: obtaining the supercharging pressure difference by the difference between the target supercharging pressure And the actual supercharging pressure ​ ; Among them, the target boost pressure and actual boost pressure The method for obtaining the actual boost pressure can refer to the disclosed technology. For example, the method for obtaining the actual boost pressure of the engine can refer to the disclosed technology: obtain the intake pressure at the throttle outlet and the intake pressure at the throttle inlet; determine whether the throttle is in normal control mode, anti-overshoot control mode or full-open control mode, and for different modes, select the corresponding transition coefficient to combine the intake pressure at the throttle outlet and the intake pressure at the throttle inlet to calculate the actual boost pressure of the engine. Based on the different working modes of the throttle, taking into account the boost pressure control deviation, intake pressure control deviation and intake pressure response, the calculation method of the actual boost pressure is optimized, and the learning parameters of the actual boost pressure are continuously updated in different life cycles, thereby improving the control accuracy of the intake pressure.

[0099] 2. Change rate of target and actual boost pressure difference :

[0100] 2.1) Target boost pressure change rate How to obtain:

[0101]

[0102] in, is the target boost pressure change rate The value at the last sampling moment, the default value is 0; The target boost pressure at the previous sampling moment. Its default value is the target boost pressure in the first sampling cycle when the boost control closed loop is activated. is the sampling period, which can be 0.01s in this embodiment; is the filtering time.

[0103] In some embodiments, a filtering time self-learning coefficient is determined, and the target boost pressure change rate is dynamically updated based on the filtering time self-learning coefficient, including: determining the filtering time self-learning coefficient based on preset conditions; presetting a filtering fault time; obtaining an updated filtering time by multiplying the filtering fault time by the filtering time self-learning coefficient plus one; and obtaining an updated target boost pressure change rate based on the target boost pressure, the target boost pressure at the previous sampling moment, the sampling period, and the updated filtering time.

[0104] Specifically, the filtering time ;in, It is the self-learning coefficient of the filter time. Its default value is 0. It can be continuously updated through self-learning and saved after the vehicle is powered off. is the preset filtering fault time, which can be 50ms in this embodiment for example.

[0105] The determination method of the filter time self-learning coefficient is introduced as follows:

[0106] The first case, if the following conditions appear, 1) the supercharging control closed loop is in the active state; 2) the difference between the target supercharging pressure and the actual supercharging pressure in the entire cycle of the supercharging control closed loop after the supercharging control closed loop activation time exceeds the preset time t0 does not exceed the preset value one, which can be ±2 kPa in this embodiment; 3) the change rate of the difference between the target supercharging pressure and the actual supercharging pressure in the entire cycle of the supercharging control closed loop after the supercharging control closed loop activation time exceeds the preset time t0 does not exceed the preset value two, which can be ±10 kPa / 10 ms in this embodiment; 4) the change rate of the target supercharging pressure exceeds the preset value three, which can be ±20 kPa / 10 ms in this embodiment. If the above conditions are met at the same time, the cumulative number CNT1 is increased by 1, and the cumulative number CNT1 is updated only once when the supercharging closed loop activation condition is met each time. Once the supercharging closed loop condition is not met, the cumulative number is suspended. Once the cumulative number CNT2 is updated, the cumulative number CNT1 is cleared. The default value of CNT1 is 0, and is saved after the vehicle is powered off.

[0107] If the cumulative number CNT1 is greater than the preset number, which can be 50, the filter time self-learning coefficient is updated, wherein is the last learning update value, and the default value is 0. At the same time, CNT1 is cleared.

[0108] The second case, if the following conditions appear, 1) the supercharging control closed loop is in the active state; 2) the difference between the target supercharging pressure and the actual supercharging pressure in the entire cycle of the supercharging control closed loop after the supercharging control closed loop activation time exceeds the preset time t0 exceeds the preset value four, which can be ±4 kPa in this embodiment; 3) the change rate of the difference between the target supercharging pressure and the actual supercharging pressure in the entire cycle of the supercharging control closed loop after the supercharging control closed loop activation time exceeds the preset time t0 (which can be 0.2 s in this embodiment) does not exceed the preset value five, which can be ±20 kPa / 10 ms in this embodiment; 4) the change rate of the target supercharging pressure exceeds the preset value six, which can be ±10 kPa / 10 ms in this embodiment. If the above conditions are met at the same time, the cumulative number CNT2 is increased by 1, and the cumulative number CNT2 is updated only once when the supercharging closed loop activation condition is met each time. Once the supercharging closed loop condition is not met, the cumulative number is suspended. Once the cumulative number CNT1 is updated, the cumulative number CNT2 is cleared. The default value of CNT2 is 0, and is saved after the vehicle is powered off.

[0109] ​If the accumulated number CNT2 is greater than a preset number, which can be 50 in this embodiment, the filter time self-learning coefficient , wherein, is the last learning updated learning value, and the default value is 0. At the same time, CNT2 is cleared.

[0110] The third case, if the following conditions occur, 1) the total mileage of the vehicle (the total mileage is paused and not cleared after the vehicle is powered off) exceeds the preset value, which can be 50000km in this embodiment, and the supercharging does not enter the closed loop enablement, the filter time self-learning coefficient is cleared, and the total mileage of the vehicle is cleared. 2) The interval of the total mileage of the engine running with supercharging entering the closed loop enablement does not exceed the preset value, which can be 0.3km in this embodiment, the filter time self-learning coefficient is cleared, and the total mileage of the vehicle is cleared.

[0111] The fourth case, .

[0112] Finally, the obtained filter time is processed into a multiple of 10ms, the adjusted value is not more than 4ms, and is limited within 10ms and 100ms.

[0113] In some embodiments, obtaining the first actual supercharging pressure change rate and the second actual supercharging pressure change rate comprises: obtaining an actual throttle inlet pressure change rate according to an actual throttle inlet pressure, an actual throttle inlet pressure at a last sampling time, a sampling period and a filter time; obtaining an actual throttle outlet pressure change rate according to an actual throttle outlet pressure, an actual throttle outlet pressure at a last sampling time, a sampling period and a filter time; obtaining the first actual supercharging pressure change rate according to the actual throttle inlet pressure change rate, the actual throttle outlet pressure change rate and a transition coefficient; and taking the actual throttle inlet pressure change rate as the second actual supercharging pressure change rate.

[0114] In some embodiments, determining the target and actual supercharging pressure difference change rate based on the target supercharging pressure change rate, the first actual supercharging pressure change rate and the second actual supercharging pressure change rate comprises: comparing the second actual supercharging pressure change rate with the first actual supercharging pressure change rate; when the second actual supercharging pressure change rate is greater than or less than the first actual supercharging pressure change rate, determining the target and actual supercharging pressure difference change rate according to a comparison result of the target supercharging pressure change rate and the second actual supercharging pressure change rate; and when the second actual supercharging pressure change rate is equal to the first actual supercharging pressure change rate, obtaining the target and actual supercharging pressure difference change rate according to a difference between the target supercharging pressure change rate and the first actual supercharging pressure change rate.

[0115] Specifically, 2.2) throttle inlet actual pressure change rate :

[0116]

[0117] wherein, is the throttle inlet actual pressure change rate the value of the last sampling time, and the default value is 0; is the throttle inlet actual pressure of the last sampling time, and the default value is the throttle inlet actual pressure in the first sampling period when the boost control closed loop is activated.

[0118] 2.3) throttle outlet actual pressure change rate :

[0119]

[0120] wherein, is the throttle outlet actual pressure change rate the value of the last sampling time, and the default value is 0; is the throttle outlet actual pressure of the last sampling time, and the default value is the throttle outlet actual pressure in the first sampling period when the boost control closed loop is activated.

[0121] 2.4) actual boost pressure change rate one (first actual boost pressure change rate) and actual boost pressure change rate two (second actual boost pressure change rate):

[0122]

[0123] wherein, is a transition coefficient, wherein The method for obtaining the actual boost pressure of the engine can refer to the method disclosed in the prior art, for example, obtaining the intake pressure at the throttle outlet and the intake pressure at the throttle inlet; determining whether the throttle is in a normal control mode, an anti-overcontrol mode or a full-open control mode, and selecting a corresponding transition coefficient to calculate the engine actual boost pressure in combination with the intake pressure at the throttle outlet and the intake pressure at the throttle inlet for different modes. Based on different working modes of the throttle, while considering the boost pressure control deviation, the intake pressure control deviation and the intake pressure response, the calculation method of the actual boost pressure is optimized, and the learning parameters of the actual boost pressure are continuously updated in different life cycles, thereby improving the control accuracy of the intake pressure.

[0124] actual boost pressure change rate two (second actual boost pressure change rate): .

[0125] 2.5) target-to-actual boost pressure difference rate of change :

[0126] A) actual boost pressure rate of change two greater than actual boost pressure rate of change one , target-to-actual boost pressure difference rate of change is:

[0127] A.1) target boost pressure rate of change less than actual boost pressure rate of change two , target-to-actual boost pressure difference rate of change equals target boost pressure rate of change minus actual boost pressure rate of change two .

[0128] A.2) target boost pressure rate of change greater than actual boost pressure rate of change two , target-to-actual boost pressure difference rate of change equals target boost pressure rate of change minus actual boost pressure rate of change one .

[0129] A.3) otherwise, target-to-actual boost pressure difference rate of change equals 0.

[0130] Note that the priority of A.1) to A.3) is decreasing.

[0131] B) actual boost pressure rate of change two less than actual boost pressure rate of change one , target-to-actual boost pressure difference rate of change is:

[0132] B.1) target boost pressure rate of change less than actual boost pressure rate of change two , target-to-actual boost pressure difference rate of change equals target boost pressure rate of change minus actual boost pressure rate of change one .

[0133] B.2) target boost pressure rate of change greater than actual boost pressure rate of change two , target-to-actual boost pressure difference rate of change equals target boost pressure rate of change minus actual boost pressure rate of change two .

[0134] B.3) otherwise, the target-to-actual boost pressure difference rate of change equals 0.

[0135] C otherwise, the target-to-actual boost pressure difference rate of change equals the target boost pressure rate of change minus the actual boost pressure rate of change .

[0136] In some embodiments, the obtaining the target boost pressure ratio, determining the closed-loop control gain coefficient based on the target boost pressure ratio and dynamically updating the closed-loop control gain coefficient comprises: obtaining the target boost pressure ratio; calibrating a first gain coefficient request when the EGR rate is zero; calibrating a second gain coefficient request when the target EGR rate is equal to the actual EGR rate; obtaining the closed-loop control gain coefficient based on the target boost pressure ratio, the engine speed, the compressor inlet actual pressure, the first gain coefficient request and the second gain coefficient request; determining a self-learning correction coefficient of the closed-loop control gain coefficient, and dynamically updating the closed-loop control gain coefficient based on the self-learning correction coefficient.

[0137] In some embodiments, the obtaining the target boost pressure ratio comprises: obtaining an original compressor outlet target pressure according to the target boost pressure, a pressure drop coefficient of the intercooler, an intake mass flow into the cylinder, a compressor outlet temperature and a compressor outlet actual pressure; determining a corrected pressure based on the original compressor outlet target pressure, the compressor inlet actual pressure and a gas temperature at the compressor inlet; obtaining a corrected compressor outlet target pressure according to the corrected pressure and the original compressor outlet target pressure; and obtaining the target boost pressure ratio according to a ratio of the corrected compressor outlet target pressure to the compressor inlet actual pressure.

[0138] Specifically, when the boost control closed loop is activated, several parameters need to be determined before boost closed loop control is performed: 3, the target boost pressure ratio , which is an important parameter of closed loop control, and the method for obtaining the same comprises:

[0139] 1) the corrected compressor outlet target pressure :

[0140]

[0141] wherein, is the original compressor outlet target pressure:

[0142]

[0143] wherein, is a pressure drop coefficient of the intercooler, and in an exemplary embodiment, can be 0.000357; for the mass flow rate of intake air into the cylinder; for the compressor outlet temperature; for the actual pressure at the compressor outlet.

[0144] for the correction pressure determined based on the compressor inlet gas temperature:

[0145]

[0146] wherein, for the actual pressure at the compressor inlet;

[0147]

[0148] for the gas temperature at the inlet of the compressor; for the atmospheric pressure; for the air filter pressure drop correction coefficient, which may be 0.000122 in this embodiment.

[0149] Specifically, the temperature at the outlet of the mixing valve is taken, and the method for obtaining the temperature is as follows:

[0150]

[0151] wherein, for the conversion from Celsius to Kelvin, which may be 273°C in this embodiment; for the mass flow rate of fresh air flowing through the mixing valve body; for the mass flow rate of exhaust gas flowing through the EGR valve body; for the temperature at the outlet of the air filter; for the weighting coefficient determined based on the mass flow rate of fresh air flowing through the mixing valve body The method for determining the weighting coefficient is mainly based on bench simulation calibration, that is, the sensor is installed on the bench to obtain the calibration.

[0152] for the correction coefficient of The calibration data is shown in Table 1, and the purpose is to correct the influence of the compressor inlet temperature on the supercharging control accuracy, and to ensure that the supercharging closed-loop control method and the calibration parameters will not be affected by the different compressor inlet temperatures.

[0153] Table 1

[0154]

[0155] It should be noted that the target supercharging pressure ratio may be obtained by dividing the compressor outlet correction target pressure by the actual pressure at the compressor inlet​ To calculate.

[0156] Specifically, when the boost control closed loop is activated, several parameters need to be determined before performing the boost closed loop control: 4. Closed loop control gain coefficient , mainly based on the target boost pressure ratio and engine speed (As the main input of closed-loop control to achieve the boost pressure at different engine speeds, and subsequently combine the boost pressure difference and its rate of change for closed-loop control), while considering the correction of EGR rate and compressor inlet pressure to closed-loop control, the closed-loop control gain coefficient is obtained as follows:

[0157]

[0158] It is the self-learning correction coefficient of the closed-loop control gain coefficient. Its default value is 0, and it can be continuously updated through self-learning and saved after the vehicle is powered off. When the EGR rate is 0, that is, When equal to 0 ( is the target EGR rate, is the actual EGR rate), calibrate the gain coefficient request (First gain coefficient request) The influence ratio of different target boost pressure ratios on closed-loop control parameters at different engine speeds is filled in as (First gain coefficient request). For example, in this embodiment, the first gain coefficient request calibration reference is shown in Table 2 below:

[0159] Table 2

[0160]

[0161] At target EGR rate and actual EGR rate Ratio Equal to 0.99~1.01 (allowing a certain tolerance in the engineering development process, which is considered as the target EGR rate Equal to the actual EGR rate ), calibrate the gain coefficient request two (Second gain coefficient request) Different target boost pressure ratios at different engine speeds The impact on the closed-loop control parameters, fill in its impact ratio as (Second gain coefficient request). For example, in this embodiment, the second gain coefficient request calibration reference is shown in Table 3 below:

[0162] Table 3

[0163]

[0164] Specifically, the self-learning correction coefficient of the closed-loop control gain coefficient The determination method is as follows: first, certain conditions need to be met for learning: a) supercharging closed-loop enabled; b) throttle fully open; c) stable intake pressure (gas pressure after the throttle); d) engine speed fluctuation within a preset range, which can be ±15 rpm in the embodiment; e) target supercharging pressure fluctuation within a preset range, which can be ±2 kPa in the embodiment; f) target and actual supercharging pressure difference fluctuation within a preset range, which can be ±2 kPa in the embodiment; g) target and actual supercharging pressure difference rate of change fluctuation within a preset range, which can be ±10 kPa / 10 ms in the embodiment; h) target supercharging pressure ratio fluctuation within a preset range, which can be ±0.01 in the embodiment; i) actual compressor inlet pressure fluctuation within a preset range, which can be ±2 kPa in the embodiment. When the above conditions are met at the same time, read the engine speed average value , target supercharging pressure ratio average value , actual compressor inlet pressure average value , closed-loop control gain coefficient average value , target and actual supercharging pressure difference average value , target and actual supercharging pressure difference rate of change average value . The recorded average value information is stored. The target and actual supercharging pressure difference and the target and actual supercharging pressure difference rate of change under the same working conditions (i.e., engine speed , target supercharging pressure ratio , actual compressor inlet pressure ) are recorded.

[0165] Exemplarily, the last recorded target and actual supercharging pressure difference is recorded as , the target and actual supercharging pressure difference rate of change is recorded as , the self-learning correction coefficient of the closed-loop control gain coefficient is recorded as ​, the default value of which is 0. When the same working condition is encountered next time, the target and actual supercharging pressure difference is recorded as , the target and actual supercharging pressure difference change rate is recorded as , the self-learning correction coefficient of the closed-loop control gain coefficient is updated as follows:

[0166]

[0167] Among them, the reference disclosure document exhaust turbocharged engine electronic throttle valve control system and method put forward three kinds of throttle valve control mode, including: throttle valve normal control mode, throttle valve full open control mode and throttle valve anti-hunting control mode. Here, the weighting coefficient k1 in the self-learning correction coefficient of the closed-loop control gain coefficient 1) when the throttle valve normal control mode, take 0.5; 2) when the throttle valve full open control mode, take 0.1; 3) when the throttle valve anti-hunting control mode, take 0.3; the weighting coefficient k2 is 0.8, to ensure the control effect of the supercharging pressure difference, so that the supercharging pressure difference change rate can also have certain improvement promoting effect.

[0168] It should be noted that the self-learning correction coefficient updated is stored in the same working condition. Other working conditions are not updated.

[0169] In some embodiments, according to the supercharging pressure difference, the updated target and actual supercharging pressure difference change rate, and the closed-loop control gain coefficient, the P term turbine actuator target opening, the D term turbine actuator target opening, and the I term turbine actuator target opening are determined and dynamically updated, including: obtaining P term correction coefficient, D term correction coefficient and I term basic correction coefficient according to the supercharging pressure difference and engine speed respectively; obtaining dynamically updated P term turbine actuator target opening and dynamically updated D term turbine actuator target opening based on the P term correction coefficient, the D term correction coefficient, the updated closed-loop control gain coefficient and the target and actual supercharging pressure difference change rate; obtaining dynamically updated I term turbine actuator target opening based on the I term basic correction coefficient.

[0170] Specifically, when the supercharging control closed loop is activated, the above parameters are determined before the supercharging closed loop control is performed. Next, the P term, D term and I term values in the closed loop PID control algorithm are determined, the values of which represent the supercharging actuator (turbine) target opening. Based on the turbine actuator target opening obtained by PID: P term turbine actuator target opening , D term turbine actuator target opening , I term turbine actuator target opening .​

[0171] In some embodiments, the target turbo actuator target opening is obtained according to the updated P term turbo actuator target opening, the D term turbo actuator target opening and the I term turbo actuator target opening, so as to perform supercharging pressure closed loop control.

[0172] Specifically, the final output turbo actuator target opening is :

[0173]

[0174]

[0175] wherein, the supercharging actuator opening maximum value is the turbo actuator target opening minimum value is the un-limited supercharging actuator target opening is. The final turbo actuator target opening is taken as the target opening of the actuator, so that the supercharging pressure control is closed loop.

[0176] It can be understood that the method described in the embodiment is the key point of supercharging self-learning in the whole life cycle. The beneficial effects of the method described in the embodiment include: the supercharging control adopts a closed loop control method, the supercharging closed loop control is described in detail, the supercharging pressure difference change rate is optimized, the supercharging closed loop control coefficient is proposed, which has the same influence on the PID parameters, reduces the PID calibration, and updates the control parameters in real time, the supercharging pressure closed loop control parameters are described in detail, and the influence of different parameters on the supercharging control parameters is optimized, so as to improve the supercharging control precision in different life cycles.

[0177] The turbocharging self-learning method in the whole life cycle provided by the embodiment comprises: obtaining a turbocharging pressure difference and a target and actual turbocharging pressure difference change rate and performing dynamic updating; obtaining a target turbocharging pressure ratio, determining a closed-loop control gain coefficient based on the target turbocharging pressure ratio and performing dynamic updating; determining a P-term turbocharger actuator target opening degree, a D-term turbocharger actuator target opening degree and an I-term turbocharger actuator target opening degree based on the turbocharging pressure difference, the updated target and actual turbocharging pressure difference change rate and the closed-loop control gain coefficient and performing dynamic updating; and obtaining a target turbocharger actuator target opening degree based on the updated P-term turbocharger actuator target opening degree, the D-term turbocharger actuator target opening degree and the I-term turbocharger actuator target opening degree to perform turbocharging pressure closed-loop control. In the embodiment, in order to respond to engine intake turbocharging and engine torque increase requests, the turbocharging system control is performed to realize more exhaust energy to realize turbocharging, the turbocharging control adopts a closed-loop control method, the turbocharging pressure difference change rate is optimized for turbocharging closed-loop control, and a closed-loop control gain coefficient is proposed, which has the same influence on PID parameters, reduces the calibration of the PID, improves the robustness of the turbocharging control, and updates the parameters of the turbocharging pressure closed-loop control in real time, optimizes the influence of different parameters on the turbocharging control parameters, and thus improves the turbocharging control precision in different life cycles.

[0178] As another embodiment of the application, as shown in Figure 4 The turbocharging self-learning method in the whole life cycle provided by the embodiment comprises: obtaining a turbocharging pressure difference and a target and actual turbocharging pressure difference change rate and performing dynamic updating; obtaining a target turbocharging pressure ratio, determining a closed-loop control gain coefficient based on the target turbocharging pressure ratio and performing dynamic updating; determining a P-term turbocharger actuator target opening degree, a D-term turbocharger actuator target opening degree and an I-term turbocharger actuator target opening degree based on the turbocharging pressure difference, the updated target and actual turbocharging pressure difference change rate and the closed-loop control gain coefficient and performing dynamic updating; and obtaining a target turbocharger actuator target opening degree based on the updated P-term turbocharger actuator target opening degree, the D-term turbocharger actuator target opening degree and the I-term turbocharger actuator target opening degree to perform turbocharging pressure closed-loop control. In the embodiment, in order to respond to engine intake turbocharging and engine torque increase requests, the turbocharging system control is performed to realize more exhaust energy to realize turbocharging, the turbocharging control adopts a closed-loop control method, the turbocharging pressure difference change rate is optimized for turbocharging closed-loop control, and a closed-loop control gain coefficient is proposed, which has the same influence on PID parameters, reduces the calibration of the PID, improves the robustness of the turbocharging control, and updates the parameters of the turbocharging pressure closed-loop control in real time, optimizes the influence of different parameters on the turbocharging control parameters, and thus improves the turbocharging control precision in different life cycles.

[0179] Step S31: obtaining a P-term correction coefficient, a D-term correction coefficient and an I-term basic correction coefficient based on the turbocharging pressure difference and the engine speed respectively;

[0180] Step S32: obtaining a dynamically updated P-term turbocharger actuator target opening degree and a dynamically updated D-term turbocharger actuator target opening degree based on the P-term correction coefficient, the D-term correction coefficient, the updated closed-loop control gain coefficient and the target and actual turbocharging pressure difference change rate;

[0181] Step S33: obtaining a dynamically updated I-term turbocharger actuator target opening degree based on the I-term basic correction coefficient.

[0182] In some embodiments, the dynamically updated P term turbine actuator target opening and the dynamically updated D term turbine actuator target opening are obtained based on the P term correction coefficient, the D term correction coefficient, the updated closed-loop control gain coefficient, and the target and actual boost pressure difference rate of change, including: obtaining the dynamically updated P term turbine actuator target opening according to the updated closed-loop control gain coefficient, the P term correction coefficient, the boost pressure difference, and the P term parameter self-learning coefficient; and obtaining the dynamically updated D term turbine actuator target opening according to the updated closed-loop control gain coefficient and the target and actual boost pressure difference rate of change, the D term correction coefficient, and the D term parameter self-learning coefficient.

[0183] In some embodiments, the dynamically updated I term turbine actuator target opening is obtained based on the I term basic correction coefficient, including: obtaining a corrected I term turbine actuator target opening based on the I term basic correction coefficient, a supercharger maximum opening learning coefficient, an I term turbine actuator target opening learning value, and an open-loop transition coefficient learning coefficient; and obtaining the I term turbine actuator target opening according to the product of the corrected I term turbine actuator target opening and a closed-loop control gain coefficient.

[0184] Specifically, 5, determine the P term, D term, and I term values in the closed-loop PID control algorithm, which represent the boost actuator (turbine) target opening, based on the turbine actuator target opening obtained by the PID:

[0185] 5.1) P term turbine actuator target opening :

[0186] When the boost closed loop is not enabled, ;

[0187] When the boost closed loop is enabled, ; wherein, is a P term parameter self-learning coefficient, which has a default value of 0 and can be continuously self-learned and updated, and is saved after the vehicle is powered off; is a P term correction coefficient; is a boost pressure difference; is a closed-loop control gain coefficient. In this embodiment, the value of can be as shown in Table 4:

[0188] Table 4

[0189]

[0190] Specifically, the P term parameter self-learning coefficient is obtained as follows: first, certain conditions need to be met: a) the boost closed loop is enabled; b) the throttle is fully open; c) the intake pressure (the gas pressure after the throttle) is stable; d) the engine speed fluctuation in the preset range, the embodiment can take ±15rpm; e) target supercharging pressure fluctuation in the preset range, the embodiment can take ±2kPa; f) target and actual supercharging pressure difference fluctuation out of the preset range, the embodiment can take ±3kPa; g) target and actual supercharging pressure difference change rate fluctuation in the preset range, the embodiment can take ±10kPa / 10ms; h) target supercharging pressure ratio fluctuation in the preset range, the embodiment can take ±0.01; i) actual compressor inlet pressure fluctuation in the preset range, the embodiment can take ±2kPa. When the above conditions are met at the same time, read the engine speed within t1 (the embodiment can take 2s) time average value , target supercharging pressure ratio average value , actual compressor inlet pressure average value , closed-loop control gain coefficient average value , target and actual supercharging pressure difference average value . The recorded average value information is stored. The target and actual supercharging pressure difference under the same working condition (i.e. engine speed , target supercharging pressure ratio , actual compressor inlet pressure ) is recorded.

[0191] Exemplarily, the last stored target and actual supercharging pressure difference under the same working condition is read recorded as , and the closed-loop control gain coefficient is recorded as . When the same working condition is encountered next time, the read target and actual supercharging pressure difference is recorded as , and the closed-loop control gain coefficient is recorded as ; then the P item parameter self-learning coefficient is updated as:

[0192]

[0193] The disclosed technology exhaust turbocharged engine electronic throttle valve control system and method propose three control modes for the throttle valve, specifically including: throttle valve normal control mode, throttle valve full opening control mode and throttle valve anti-overcontrol control mode. Here, the P item parameter self-learning coefficient The weighting coefficient k1,1 in the above formula is: 1) when the throttle normal control mode is adopted, 0.6 is taken; 2) when the throttle full opening control mode is adopted, 0.1 is taken; and 3) when the throttle anti-hunting control mode is adopted, 0.5 is taken.

[0194] It should be noted that the P-term parameter self-learning correction coefficient after being updated is stored in the same working condition. Other working conditions are not updated.

[0195] 5.2) D-term turbo actuator target opening :

[0196] When the boost closed loop is not enabled, ;

[0197] When the boost closed loop is enabled, wherein, is a D-term parameter self-learning coefficient, the default value of which is 0, and which can be continuously self-learned and updated, and saved after the vehicle is powered off. is a D-term correction coefficient, the value of which in the embodiment can be as shown in Table 5:

[0198] Table 5

[0199]

[0200] Specifically, the D-term parameter self-learning coefficient is obtained as follows: firstly, certain conditions need to be met: a) the boost closed loop is enabled; b) the throttle is fully opened; c) the intake pressure (the pressure of the gas after the throttle) is stable; d) the engine speed fluctuates within a preset range, which in the embodiment can be ±15 rpm; e) the target boost pressure fluctuates within a preset range, which in the embodiment can be ±2 kPa; f) the target and actual boost pressure difference fluctuates within a preset range, which in the embodiment can be ±2 kPa; g) the target and actual boost pressure difference change rate fluctuates within a preset range, which in the embodiment can be ±20 kPa / 10 ms; h) the target boost pressure ratio fluctuates within a preset range, which in the embodiment can be ±0.01; i) the compressor inlet actual pressure fluctuates within a preset range, which in the embodiment can be ±2 kPa. When the above conditions are met at the same time, the engine speed average value , the target boost pressure ratio average value , the compressor inlet actual pressure average value , and the closed loop control gain coefficient​ average value , target and actual boost pressure difference change rate average value The recorded average value information is stored. The same working conditions (i.e. engine speed , target boost pressure ratio , Actual pressure at compressor inlet ) under the target and actual boost pressure difference change rate Write it down.

[0201] For example, the difference between the target and actual boost pressure stored last time under the same working condition is read. Recorded as , the closed-loop control gain coefficient is recorded as The next time you encounter the same working condition, the difference between the target and the actual boost pressure is Recorded as , the closed-loop control gain coefficient is recorded as , then the self-learning coefficient of parameter D is Updated to:

[0202]

[0203] in, Update the learning value for the last learning. The throttle valve has three control modes, specifically including: normal throttle valve control mode, full throttle valve control mode and throttle valve overshoot prevention control mode. Here, the D parameter self-learning coefficient The weighting coefficient k1 in 1) is 0.6 in normal throttle control mode; 2) is 0.1 in full throttle control mode; 3) is 0.5 in throttle overshoot prevention control mode.

[0204] In particular, 1) if the target and actual boost pressure difference change rate occurs within the continuous time t1 The rate of change of the difference between the target and actual boost pressure average value The difference exceeds The cumulative time exceeds 0.5*t1, and the number CNT2 is recorded. CNT2 is accumulated at most once in each update and will be cleared after the learning value is updated. If CNT2 exceeds the preset value, the preset value in this embodiment can be 5, then 2) If the target and actual boost pressure difference change rate occurs within the continuous time t1 The rate of change of the difference between the target and actual boost pressure average value The difference does not exceed If the cumulative time exceeds 0.7*t1, then . 3) Other cases .

[0205] It should be noted that the self-learning correction coefficient after the update is stored in the same working condition. Other working conditions are not updated.

[0206] In some embodiments, based on the I term basic correction coefficient, the supercharger maximum opening learning coefficient, the I term turbine actuator target opening learning value, and the open loop transition coefficient learning coefficient, the corrected I term turbine actuator target opening is obtained, including: obtaining a first I term compensation correction coefficient and a second I term compensation correction coefficient; when the supercharging closed loop is enabled, determining an I term increment initial value based on the I term basic correction coefficient, the first I term compensation correction coefficient, the second I term compensation correction coefficient, and the supercharging pressure difference; determining an increment of the I term turbine actuator target opening according to the I term increment initial value and the supercharger maximum opening learning coefficient; correcting the increment of the I term turbine actuator target opening based on the I term turbine actuator target opening learning value to obtain the corrected I term turbine actuator target opening; when the supercharging closed loop is not enabled, performing self-learning update on the corrected I term turbine actuator target opening based on the open loop transition coefficient learning coefficient.

[0207] 5.3) I term turbine actuator target opening :

[0208] 5.3.1) When the supercharging closed loop is enabled, first determine the increment of the I term turbine actuator target opening :

[0209] First calculate the I term increment initial value :

[0210]

[0211] wherein, is the I term basic correction coefficient, is the I term compensation correction coefficient one (the first I term compensation correction coefficient), is the I term compensation correction coefficient two (the second I term compensation correction coefficient).

[0212] In some embodiments, if the I term increment initial value is greater than zero, it is determined whether the first condition or the second condition is met; wherein the first condition is that the un-restricted supercharging actuator target opening is greater than or equal to the maximum value of the supercharging actuator opening, and the second condition is that the supercharger actuator actual opening reaches the maximum value; when the first condition or the second condition is met, the increment of the I term turbine actuator target opening is determined to be zero.

[0213] 1) If > 0, and one of the following two conditions is met, A) the turbocharger actuator target opening is greater than the turbocharger actuator actual opening ≥ (the first condition), or B) the turbocharger actuator actual opening also reaches its maximum value (the second condition), then . Wherein, is the turbocharger actuator target opening maximum value, and is ultimately limited to within 100%; is the turbocharger actuator target opening minimum value, which in the exemplary embodiment can be taken as 0%; is the turbocharger maximum opening learning coefficient, which has a default value of 0 and can be continuously self-learned and updated, and is saved after the vehicle is powered off; is the turbocharger actuator target opening without limitation.

[0214] is the turbocharger actuator target opening maximum value default value, which depends on the engine speed is calibrated as shown in Table 6 below:

[0215] Table 6

[0216]

[0217] In some embodiments, if the I term increment initial value is less than zero, it is determined whether the third condition or the fourth condition is met; wherein the third condition is that the turbocharger actuator target opening without limitation is less than or equal to the turbocharger actuator target opening minimum value, and the fourth condition is that the turbocharger actuator actual opening reaches the minimum value; when the third condition or the fourth condition is met, the increment of the I term turbocharger actuator target opening is determined to be zero.

[0218] 2) If < 0, and one of the following two conditions is met, A) the turbocharger actuator target opening is greater than the turbocharger actuator actual opening ≤ (the third condition), or B) the turbocharger actuator actual opening also reaches its minimum value (the fourth condition), then . Wherein, is the turbocharger actuator target opening minimum value, which in the exemplary embodiment can be taken as 0% to avoid integral saturation.

[0219] In some embodiments, if the I term increment initial value is equal to zero, the I term increment initial value is taken as the increment of the I term turbocharger actuator target opening.

[0220] 3) Otherwise, .

[0221] In some embodiments, the increment of the I term turbine actuator target opening is corrected to obtain a corrected I term turbine actuator target opening; and the I term turbine actuator target opening is obtained according to the product of the corrected I term turbine actuator target opening and a closed-loop control gain coefficient.

[0222] Specifically, the corrected I term turbine actuator target opening :

[0223]

[0224] wherein, is the I term turbine actuator target opening of the last sampling period after correction, and the default value is 0. and are respectively the minimum value and the maximum value of the I term, and in the exemplary embodiment, can be respectively-30% and 30%. is the I term turbine actuator target opening calculated in the last sampling period, and the default value is 0. is the I term turbine actuator target opening learning value, and the method for obtaining the same can refer to the disclosed technology waste gas turbine supercharging closed-loop adaptive system and control method. If the I term learning value is not updated, then is 0; if it is updated and is not updated at present, then the learning value updated before is adopted; and if it is updated at present, then the new learning value is adopted.

[0225] Specifically, the I term turbine actuator target opening .

[0226] 5.3.2) When the supercharging closed loop is not enabled:

[0227]

[0228] wherein, is an open-loop transition coefficient, and the purpose is to avoid the fluctuation of the supercharging control pressure after the open loop exits, , is an open-loop transition coefficient learning coefficient, and the default value is 0, and can be continuously self-learned and updated, and is saved after the vehicle is powered off. In the exemplary embodiment, can be 5%.

[0229] 5.4) The final output turbine actuator target opening :

[0230]

[0231]

[0232] wherein, The maximum value of the opening of the supercharger actuator; The minimum value of the target opening of the turbo actuator; The target opening of the supercharger actuator without limitation. The final target opening of the turbo actuator is used as the target opening of the actuator, so that the supercharging pressure control is closed-loop.

[0233] In particular, the maximum opening learning coefficient of the supercharger is obtained as follows:

[0234] The first case: certain conditions need to be met first: a) supercharging closed-loop enabled; b) throttle fully open;

[0235] c) stable intake pressure (gas pressure after the throttle); d) engine speed fluctuation within a preset range, which can be ±15 rpm in this embodiment; e) target supercharging pressure fluctuation within a preset range, which can be ±2 kPa in this embodiment; f) target and actual supercharging pressure difference fluctuation within a preset range, which can be ±2 kPa in this embodiment; g) target and actual supercharging pressure difference exceeding a preset range, which can be 10 kPa in this embodiment; h) target and actual supercharging pressure difference change rate fluctuation within a preset range, which can be ±10 kPa / 10 ms in this embodiment; i) the target opening of the supercharger actuator reaches its current maximum allowable opening .

[0236] If the above conditions are met at the same time, and the following consecutive conditions are met: 1) the above conditions are continuously met at the same time for more than a preset time t1 (which can be 3 s in this embodiment), and 2) the above conditions are continuously met at the same time for more than a preset number of times CNT2 10 times; wherein the updating method of CNT2 is to update it only once when all the above a) ~ i) conditions are met at each time, and once any one of the conditions a) ~ h) is not met, CNT2 is cleared. The default value of CNT2 is 0, and it is saved after the vehicle is powered off. Then .

[0237] The second case: certain conditions need to be met first: a) supercharging closed-loop enabled; b) throttle fully open; c) stable intake pressure (gas pressure after the throttle); d) engine speed fluctuation within a preset range, which can be ±15 rpm in this embodiment; e) target supercharging pressure fluctuation within a preset range, which can be ±2 kPa in this embodiment; f) target and actual supercharging pressure difference exceeding a preset range, which can be 10 kPa in this embodiment; g) target and actual supercharging pressure difference change rate The fluctuation is not within the preset range, and the embodiment can take ±20 kPa / 10 ms; or the target and actual supercharging pressure difference The fluctuation is not within the preset range, and the embodiment can take ±3 kPa; h) The target opening of the supercharger actuator reaches its current maximum allowable opening .

[0238] If the above conditions are met at the same time, and the following continuous conditions are met: 1) The time when the above conditions are continuously met at the same time exceeds the preset time t2 (the embodiment can take 1 s), 2) The cumulative number of times CNT3 when the above conditions are continuously met at the same time exceeds the preset number of times 10 times. The updating method of CNT3 is to update only once when CNT3 meets all the conditions of the above second case a)~h) at each time, and once any one of the conditions of the second case a)~h) does not meet, CNT3 is cleared. The default value of CNT3 is 0, and it is saved after the vehicle is powered off. Then .

[0239] In the third case: ; wherein, is the last updated supercharger maximum opening learning coefficient .

[0240] In particular, the open-loop transition coefficient learning coefficient is obtained as follows:

[0241] The fluctuation of the actual supercharging pressure at the outlet of the supercharger compressor is considered to be within a preset range; the judgment basis is:

[0242]

[0243] wherein, is the original value of the actual supercharging pressure at the outlet of the supercharger, is the original value of the actual supercharging pressure at the outlet of the supercharger in the Nth sampling period, is the first-order low-pass filtered actual supercharging pressure at the outlet of the supercharger, is the filtered actual supercharging pressure at the outlet of the supercharger in the Nth sampling period, is the filtered actual supercharging pressure at the outlet of the supercharger in the N-1th sampling period, N=1, 2, 3…, is equal to the original value of the actual supercharging pressure at the outlet of the supercharger in the 0th sampling period , in particular, the 0th sampling period occurs at the time when the vehicle is powered on; the sampling period interval The embodiment can be 10 ms. is the coefficient: (the embodiment can take the number of engine cylinders N as 4, The calibration speed is 1000 rpm, The purpose of such arrangement is to normalize the process, without special calibration under different cylinder numbers and speeds, only calibration of 4-cylinder engine and speed of 1000 rpm , so as to reduce calibration test work, The filter coefficient of the actual supercharger outlet pressure in this embodiment can be 0.25.

[0244] 1) In the condition that the supercharger closed loop condition is not met from the first sampling period to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein The condition is met from the first sampling period when the supercharger closed loop condition is not met to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein The condition is met from the first sampling period when the supercharger closed loop condition is not met to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein

[0245] The condition is met from the first sampling period when the supercharger closed loop condition is not met to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein The condition is met from the first sampling period when the supercharger closed loop condition is not met to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein The condition is met from the first sampling period when the supercharger closed loop condition is not met to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein

[0246] 2) In the condition that the supercharger closed loop condition is not met from the first sampling period to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein The condition is met from the first sampling period when the supercharger closed loop condition is not met to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein The condition is met from the first sampling period when the supercharger closed loop condition is not met to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein

[0247] The condition is met from the first sampling period when the supercharger closed loop condition is not met to the 10th sampling period after the time t ends, the supercharger compressor outlet actual supercharging pressure fluctuation is considered to be within the preset range, wherein .

[0248] 3) In other cases, .

[0249] In this embodiment, the supercharging control adopts a closed loop control method, and the supercharging closed loop control is described in detail, the supercharging pressure difference change rate is optimized, and the supercharging closed loop control coefficient is proposed, which has the same influence on the PID parameters, reduces the calibration of the PID, and updates the control parameters in real time, and the supercharging pressure closed loop control parameters are described in detail, and the influence of different parameters on the supercharging control parameters is optimized, so as to improve the supercharging control precision in different life cycles.

[0250] Referring to Figure 5 , Figure 5 The structure block diagram of an embodiment of the supercharging self-learning system in the whole life cycle of the application is shown in FIG. 1. Figure 5 As shown in FIG. 1, the supercharging self-learning system in the whole life cycle comprises:

[0251] The change rate updating module 10 is used for obtaining the supercharging pressure difference and the target and actual supercharging pressure difference change rate and dynamically updating;

[0252] The coefficient updating module 20 is configured to obtain a target supercharging pressure ratio, determine a closed-loop control gain coefficient based on the target supercharging pressure ratio, and dynamically update the closed-loop control gain coefficient.

[0253] The opening degree updating module 30 is configured to determine a P-term turbine actuator target opening degree, a D-term turbine actuator target opening degree, and an I-term turbine actuator target opening degree based on the supercharging pressure difference, the updated target and actual supercharging pressure difference rate, and the closed-loop control gain coefficient, and dynamically update the P-term turbine actuator target opening degree, the D-term turbine actuator target opening degree, and the I-term turbine actuator target opening degree.

[0254] The PID control parameter updating module 40 is configured to obtain a target turbine actuator target opening degree based on the updated P-term turbine actuator target opening degree, the D-term turbine actuator target opening degree, and the I-term turbine actuator target opening degree, so as to perform supercharging pressure closed-loop control.

[0255] The supercharging self-learning system in the whole life cycle provided in the embodiment adopts a closed-loop control method for supercharging control, and the supercharging closed-loop control is described in detail. The supercharging pressure difference rate is optimized, and the supercharging closed-loop control coefficient is proposed, which has the same influence on the PID parameters, reduces the PID calibration, and updates the control parameters in real time. The supercharging pressure closed-loop control parameters are described in detail, and the influence of different parameters on the supercharging control parameters is optimized, so as to improve the supercharging control precision in different life cycles.

[0256] In addition, the technical details not described in detail in the supercharging self-learning system in the whole life cycle embodiment are described in detail in the supercharging self-learning method provided in any embodiment of the application, which will not be described here.

[0257] Based on the same inventive concept, the embodiment of the application further provides an electronic device. Figure 6 As shown in FIG. 1, the electronic device provided in the embodiment of the application includes one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the supercharging self-learning method in the whole life cycle of any of the above embodiments. The one or more I / O interfaces 103 are connected between the processor and the memory, and are configured to realize information interaction between the processor and the memory. Figure 6

[0258] ​The processor 101 is a device with data processing capability, including but not limited to a central processing unit (CPU) and the like; the memory 102 is a device with data storage capability, including but not limited to a random access memory (RAM, more specifically SDRAM, DDR, etc.), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), and a flash memory (FLASH); the I / O interface (read-write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus) and the like.

[0259] In some embodiments, the processor 101, the memory 102, and the I / O interface 103 are connected to each other through the bus 104, and further connected to other components of the computing device.

[0260] In some embodiments, the one or more processors 101 include a field programmable gate array.

[0261] The embodiments of the present application also provide a computer readable medium. The computer readable medium stores a computer program, wherein the program is executed by a processor to implement the steps in the supercharged self-learning method in the full life cycle as described in any of the above embodiments. The computer readable storage medium can be a volatile or non-volatile computer readable storage medium.

[0262] The embodiments of the present application also provide a computer program product, including a computer readable code or a non-volatile computer readable storage medium carrying the computer readable code, when the computer readable code is run in a processor of an electronic device, the processor in the electronic device executes the supercharged self-learning method in the full life cycle.

[0263] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functions of the modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations. In the hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be performed by several physical components in cooperation. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer readable storage medium, which can include computer storage media (or non-transitory media) and communication media (or transitory media).

[0264] As those skilled in the art will appreciate, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable program instructions, data structures, program modules or other data. Computer storage media include, but are not limited to, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), static random access memory (SRAM), flash memory or other memory technology, portable compact disc read only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Further, as those skilled in the art will appreciate, communication media typically embodies computer readable program instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics changed or set in a manner so as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as wireless networks, cellular telephone networks, code division multiple access (CDMA) networks, and other terrestrial and satellite radio frequency communication networks. Thus the computer readable program instructions and / or other program modules can be embodied in a computer readable storage medium, which can be any device or article that is enab!ed to store and / or carry computer readable program instructions and / or data structures. The computer readable storage medium can also be distributed over networked computer systems so that the computer readable program instructions and / or other program modules are stored and executed in a distributed fashion.

[0265] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0266] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0267] The computer program product described herein can be embodied in a specific manner by hardware, software, or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), and the like.

[0268] The computer program product described herein can be embodied in a specific manner by hardware, software, or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), and the like.

[0269] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data storage cycles that change state. The instructions can be executed by one or more processors of a computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process such that the instructions which execute via the one or more processors of the computer or other programmable data processing devices create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0270] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0271] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to the present application. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions ("instructions"). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0272] Example embodiments have been disclosed and, although a particular terminology is employed, it will be understood in view of the disclosure that the terminology is used only in the generic and descriptive sense, and not for purposes of limitation. In some instances, it will be apparent to those skilled in the art that features, characteristics or aspects described with reference to a particular embodiment can be used alone or in combination with other embodiments, unless explicitly stated otherwise. Accordingly, it will be understood that various modifications can be made to the embodiments disclosed in this specification and still be within the scope of the present application as set forth in the appended claims.

Claims

1. A supercharging self-learning method for the entire life cycle, characterized in that: include: Obtaining the boost pressure difference and the rate of change of the target and actual boost pressure difference and dynamically updating them; Obtaining a target boost pressure ratio, determining a closed-loop control gain coefficient based on the target boost pressure ratio, and dynamically updating the closed-loop control gain coefficient; Determining and dynamically updating the turbine actuator target opening (P), the turbine actuator target opening (D), and the turbine actuator target opening (I) according to the boost pressure difference, the updated rate of change of the target and actual boost pressure difference, and the closed-loop control gain coefficient; The target turbine actuator target opening is obtained based on the updated P-term turbine actuator target opening, D-term turbine actuator target opening, and I-term turbine actuator target opening to perform boost pressure closed-loop control.

2. The method according to claim 1, characterized in that The obtaining of the boost pressure difference and the rate of change of the target and actual boost pressure difference and dynamically updating the same include: obtaining a target boost pressure and an actual boost pressure, and obtaining a boost pressure difference according to a difference between the target boost pressure and the actual boost pressure; Obtaining a target boost pressure change rate according to the target boost pressure, the target boost pressure at the previous sampling moment, a sampling period, and a filtering time; determining a filtering time self-learning coefficient, and dynamically updating the target boost pressure change rate based on the filtering time self-learning coefficient; Obtaining a first actual boost pressure change rate and a second actual boost pressure change rate; A target-actual supercharging pressure difference change rate is determined based on the target supercharging pressure change rate, the first actual supercharging pressure change rate, and the second actual supercharging pressure change rate.

3. The method according to claim 2, characterized in that The determining of the filtering time self-learning coefficient and the dynamically updating of the target boost pressure change rate based on the filtering time self-learning coefficient include: Determine the filter time self-learning coefficient based on preset conditions; Pre-set filtering fault time; Obtaining an updated filtering time according to the product of the filtering time self-learning coefficient plus one and the filtering fault time; An updated target boost pressure change rate is obtained according to the target boost pressure, the target boost pressure at the last sampling moment, the sampling period, and the updated filtering time.

4. The method according to claim 2, characterized in that The obtaining of the first actual boost pressure change rate and the second actual boost pressure change rate includes: The actual throttle inlet pressure change rate is obtained according to the actual throttle inlet pressure, the actual throttle inlet pressure at the previous sampling moment, the sampling period, and the filtering time; The actual throttle outlet pressure change rate is obtained according to the actual throttle outlet pressure, the actual throttle outlet pressure at the previous sampling moment, the sampling period, and the filtering time; Obtaining a first actual boost pressure change rate according to the throttle inlet actual pressure change rate, the throttle outlet actual pressure change rate, and a transition coefficient; The actual throttle inlet pressure change rate is used as the second actual boost pressure change rate.

5. The method according to claim 1, wherein The acquiring of the target boost pressure ratio, determining a closed-loop control gain coefficient based on the target boost pressure ratio and dynamically updating the closed-loop control gain coefficient includes: Obtaining a target boost pressure ratio; When the EGR rate is zero, calibrating a first gain coefficient request; When the target EGR rate is equal to the actual EGR rate, calibrating a second gain coefficient request; obtaining a closed-loop control gain coefficient based on the target supercharging pressure ratio, the engine speed, the actual compressor inlet pressure, the first gain coefficient request, and the second gain coefficient request; A self-learning correction coefficient of a closed-loop control gain coefficient is determined, and the closed-loop control gain coefficient is dynamically updated based on the self-learning correction coefficient.

6. The method according to claim 5, characterized in that The obtaining of the target boost pressure ratio includes: The original compressor outlet target pressure is obtained according to the target boost pressure, the pressure drop coefficient of the intercooler, the intake air mass flow rate entering the cylinder, the compressor outlet temperature and the actual compressor outlet pressure; determining a corrected pressure based on the original compressor outlet target pressure, the compressor inlet actual pressure, and the compressor inlet gas temperature; Obtaining a compressor outlet corrected target pressure according to the corrected pressure and the original compressor outlet target pressure; The target boost pressure ratio is obtained according to the ratio of the corrected target pressure at the compressor outlet to the actual pressure at the compressor inlet.

7. The method according to claim 1, characterized in that The step of determining the turbine actuator target opening (P), the turbine actuator target opening (D), and the turbine actuator target opening (I) according to the boost pressure difference, the updated target and actual boost pressure difference change rate, and the closed-loop control gain coefficient and dynamically updating the same includes: According to the boost pressure difference and the engine speed, a P correction coefficient, a D correction coefficient and an I basic correction coefficient are obtained respectively; Obtaining a dynamically updated P-term turbine actuator target opening and a dynamically updated D-term turbine actuator target opening based on the P-term correction coefficient, the D-term correction coefficient, the updated closed-loop control gain coefficient, and the rate of change of the target and actual boost pressure difference; A dynamically updated I-term turbine actuator target opening is obtained based on the I-term basic correction coefficient.

8. The method according to claim 7, characterized in that The dynamically updated P-term turbine actuator target opening and the dynamically updated D-term turbine actuator target opening are obtained based on the P-term correction coefficient, the D-term correction coefficient, the updated closed-loop control gain coefficient, and the target and actual boost pressure difference change rate, including: According to the updated closed-loop control gain coefficient, P-term correction coefficient, boost pressure difference and P-term parameter self-learning coefficient, a dynamically updated P-term turbine actuator target opening is obtained; The dynamically updated D-term turbine actuator target opening is obtained based on the updated closed-loop control gain coefficient, the target and actual boost pressure difference change rate, the D-term correction coefficient, and the D-term parameter self-learning coefficient.

9. The method according to claim 7, characterized in that The dynamically updated I-term turbine actuator target opening obtained based on the I-term basic correction coefficient includes: Based on the I basic correction coefficient, the supercharger maximum opening learning coefficient, the I turbine actuator target opening learning value, and the open-loop transition coefficient learning coefficient, a corrected I turbine actuator target opening is obtained; The I-term turbine actuator target opening is obtained according to the product of the corrected I-term turbine actuator target opening and the closed-loop control gain coefficient.

10. The method according to claim 9, characterized in that The method of obtaining a corrected turbine actuator target opening item I based on the basic correction coefficient item I, the supercharger maximum opening learning coefficient, the turbine actuator target opening learning value item I, and the open-loop transition coefficient learning coefficient includes: Obtaining the first I compensation correction coefficients and the second I compensation correction coefficients; When the boost closed loop is enabled, determining an initial value of an increment based on the I basic correction coefficient, the first I compensation correction coefficient, the second I compensation correction coefficient, and the boost pressure difference; Determining an increment of a turbine actuator target opening item I according to the initial value of the increment item I and a supercharger maximum opening learning coefficient; Correcting the increment of the I-item turbine actuator target opening based on the I-item turbine actuator target opening learning value to obtain a corrected I-item turbine actuator target opening; When the boost closed loop is not enabled, the corrected I-term turbine actuator target opening is self-learned and updated based on the open-loop transition coefficient learning coefficient.

11. A boost self-learning system in the whole life cycle, characterized by: include: A change rate update module is used to obtain the boost pressure difference and the change rate of the target and actual boost pressure difference and dynamically update them; a coefficient updating module, configured to obtain a target boost pressure ratio, determine a closed-loop control gain coefficient based on the target boost pressure ratio, and dynamically update the closed-loop control gain coefficient; an opening updating module for determining and dynamically updating a target opening of the turbine actuator P, a target opening of the turbine actuator D, and a target opening of the turbine actuator I according to the boost pressure difference, the updated rate of change of the target and actual boost pressure difference, and a closed-loop control gain coefficient; The PID control parameter updating module is used to obtain the target turbine actuator target opening according to the updated P-term turbine actuator target opening, D-term turbine actuator target opening, and I-term turbine actuator target opening to perform boost pressure closed-loop control.

12. The system according to claim 11, wherein: The change rate updating module is specifically configured to: obtain a target boost pressure and an actual boost pressure, and obtain a boost pressure difference according to a difference between the target boost pressure and the actual boost pressure; Obtaining a target boost pressure change rate according to the target boost pressure, the target boost pressure at the previous sampling moment, a sampling period, and a filtering time; determining a filtering time self-learning coefficient, and dynamically updating the target boost pressure change rate based on the filtering time self-learning coefficient; Obtaining a first actual boost pressure change rate and a second actual boost pressure change rate; A target-actual supercharging pressure difference change rate is determined based on the target supercharging pressure change rate, the first actual supercharging pressure change rate, and the second actual supercharging pressure change rate.

13. The system according to claim 12, wherein: The change rate update module is specifically used to: determine the filter time self-learning coefficient based on preset conditions; Pre-set filtering fault time; Obtaining an updated filtering time according to the product of the filtering time self-learning coefficient plus one and the filtering fault time; An updated target boost pressure change rate is obtained according to the target boost pressure, the target boost pressure at the last sampling moment, the sampling period, and the updated filtering time.

14. The system according to claim 12, wherein: The change rate updating module is specifically used to obtain the change rate of the actual throttle inlet pressure according to the actual throttle inlet pressure, the actual throttle inlet pressure at the previous sampling moment, the sampling period and the filtering time; The actual throttle outlet pressure change rate is obtained according to the actual throttle outlet pressure, the actual throttle outlet pressure at the previous sampling moment, the sampling period, and the filtering time; Obtaining a first actual boost pressure change rate according to the throttle inlet actual pressure change rate, the throttle outlet actual pressure change rate, and a transition coefficient; The actual throttle inlet pressure change rate is used as the second actual boost pressure change rate.

15. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 10.

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