Fuel injection parameter adjustment method and device, equipment and storage medium

Abstract

Embodiments of the present application disclose a fuel injection parameter adjustment method, device, equipment and storage medium. The fuel injection parameter adjustment method comprises: determining that a vehicle enters a light-off working condition; recording a set of initial fuel injection parameters of an engine; performing fuel injection parameter self-learning, obtaining a plurality of sets of test fuel injection parameters based on the set of initial fuel injection parameters; sequentially using the plurality of sets of test fuel injection parameters, running the engine, and calculating a plurality of excess air coefficients of the vehicle; wherein each set of test fuel injection parameters corresponds to an excess air coefficient; the running time of each set of test fuel injection parameters is a first self-learning time; based on comparing the plurality of excess air coefficients, determining an optimal self-learning result from the plurality of sets of test fuel injection parameters; and replacing the initial fuel injection parameters with the self-learning result.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of engine control technology, and particularly to a method, apparatus, device, and storage medium for adjusting fuel injection parameters. Background Technology

[0002] Vehicle emissions performance is typically assessed by the content of harmful substances in vehicle exhaust. Engine combustion efficiency directly affects vehicle emissions performance. In addition, variations in vehicle hardware and operating environments can also influence emissions performance.

[0003] In related technologies, optimal parameters are calibrated for engines to meet real-vehicle emission tests. However, the calibrated optimal parameters cannot adapt to various different conditions during vehicle operation, thus lacking robustness and resulting in poor emission performance. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method, apparatus, device, and storage medium for adjusting fuel injection parameters, which can optimize vehicle emission performance and improve robustness.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] This application provides a method for adjusting fuel injection parameters, including: determining that a vehicle has entered an ignition condition; recording a set of initial fuel injection parameters of the engine; performing fuel injection parameter self-learning to obtain multiple sets of test fuel injection parameters based on the initial fuel injection parameters; sequentially running the engine using the multiple sets of test fuel injection parameters and calculating multiple excess air coefficients of the vehicle; wherein each set of test fuel injection parameters corresponds to one excess air coefficient; the running time of each set of test fuel injection parameters is a first self-learning time; determining the optimal self-learning result among the multiple sets of test fuel injection parameters based on a comparison of the multiple excess air coefficients; and replacing the initial fuel injection parameters with the self-learning result.

[0007] In the above scheme, a set of initial injection parameters represents n initial injections of the engine within one working cycle; each set of test injection parameters represents n test injections of the engine within one working cycle; n is greater than or equal to 1; the initial injection parameters include: initial injection ratio; the test injection parameters include: test injection ratio; obtaining multiple sets of test injection parameters based on a set of initial injection parameters includes: adjusting the initial injection ratio of the i-th initial injection to the test injection ratio of one test injection, and retaining the other initial injection parameters to obtain at least one set of test injection parameters; i is greater than or equal to 1 and less than or equal to n; continuing to adjust the initial injection ratio of the (i+1)-th initial injection until the vehicle completes ignition, or until the adjustment of the initial injection ratio of the n-th initial injection is completed.

[0008] In the above scheme, adjusting the initial injection ratio of the i-th initial injection to the test injection ratio of a test injection includes: determining a learning start ratio and a learning end ratio based on the initial injection ratio of the i-th initial injection; increasing the learning start ratio by a unit ratio stepwise within the range of the learning start ratio to the learning end ratio to obtain at least one test injection ratio of a test injection; wherein the difference between the learning end ratio and the learning start ratio is greater than or equal to the unit ratio stepwise increase.

[0009] In the above scheme, determining the learning start ratio and learning end ratio based on the initial injection ratio of the i-th initial injection includes: subtracting a preset first value from the initial injection ratio to obtain the learning start ratio; and adding a preset second value to the initial injection ratio to obtain the learning end ratio.

[0010] In the above scheme, both the first value and the second value are 0.2; the unit proportion of the step increase is 0.1.

[0011] In the above scheme, the learning start ratio is greater than 0; the learning end ratio is less than 1.

[0012] In the above scheme, the first self-learning time is 10 seconds.

[0013] In the above scheme, determining that the vehicle has entered the ignition condition includes: recording the vehicle's real-time key parameters; the key parameters include: vehicle speed and catalyst status; if the key parameters meet preset conditions, then the vehicle is determined to have entered the ignition condition.

[0014] In the above scheme, the step of determining the optimal self-learning result among multiple sets of test fuel injection parameters based on comparing multiple excess air coefficients includes: comparing multiple excess air coefficients with target values; and determining the set of test fuel injection parameters corresponding to the excess air coefficient that is closest to the target value as the optimal self-learning result.

[0015] This application embodiment also provides a fuel injection parameter adjustment device, including: a determining module configured to determine that a vehicle enters an ignition condition; a recording module configured to record a set of initial fuel injection parameters of the engine; a self-learning module configured to perform fuel injection parameter self-learning, obtaining multiple sets of test fuel injection parameters based on the set of initial fuel injection parameters; and sequentially running the engine using the multiple sets of test fuel injection parameters and calculating multiple excess air coefficients of the vehicle; and determining the optimal self-learning result among the multiple sets of test fuel injection parameters based on a comparison of the multiple excess air coefficients; and replacing the initial fuel injection parameters with the self-learning result; wherein each set of test fuel injection parameters corresponds to one excess air coefficient; and the running time of each set of test fuel injection parameters is a first self-learning time.

[0016] This application also provides a computer device, including a memory and a processor. The memory stores a computer program that can run on the processor, and the processor executes the program to implement the steps in the above-described method.

[0017] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the above-described method.

[0018] Therefore, in this embodiment, multiple sets of test injection parameters can be obtained based on a set of initial injection parameters. The optimal parameter is then determined from these sets, and the injection parameters under ignition conditions are updated. This completes the self-learning of injection parameters under ignition conditions. Consequently, injection parameters can be adjusted in real-time according to the specific operating conditions of the vehicle during ignition, thereby optimizing the air-fuel ratio, reducing fuel consumption, and making the vehicle more adaptable to different situations, thus improving robustness. Attached Figure Description

[0019] Figure 1 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 1 ;

[0020] Figure 2 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 2 ;

[0021] Figure 3 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 3 ;

[0022] Figure 4 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 4 ;

[0023] Figure 5 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 5 ;

[0024] Figure 6 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 6 ;

[0025] Figure 7 This is a schematic diagram illustrating the comparison of excess air coefficients in a fuel injection parameter adjustment method provided in an embodiment of this application.

[0026] Figure 8 This is a schematic diagram of the composition structure of a fuel injection parameter adjustment device provided in an embodiment of this application;

[0027] Figure 9 This is a schematic diagram of the hardware entity of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application are further described in detail below with reference to the accompanying drawings and embodiments. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] In the following description, references to "some embodiments" refer to a subset of all possible embodiments. It is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first / second / third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first / second / third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used herein is for descriptive purposes only and is not intended to be limiting of this application.

[0031] This application provides a method for adjusting fuel injection parameters, which can be executed by a processor of a computer device. The computer device can be located in a vehicle or in vehicle testing equipment.

[0032] Figure 1 This is a schematic diagram illustrating the implementation process of a fuel injection parameter adjustment method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes steps S101 to S106.

[0033] S101. Confirm that the vehicle has entered the ignition condition.

[0034] It's important to note that the three-way catalytic converter is the most crucial external exhaust gas purification device installed in a car's exhaust system. It converts harmful gases such as CO (carbon monoxide), HC (hydrocarbons), and NOx (nitrogen oxides) in exhaust gases into harmless gases through oxidation-reduction reactions. Since the catalytic conversion efficiency of the three-way catalytic converter is temperature-dependent—generally, the lower the temperature, the lower the efficiency—the three-way catalytic converter needs to undergo an ignition activation process after the car is started. This process ensures the catalytic converter is in a suitable temperature environment to improve its catalytic conversion efficiency; this ignition activation process is called the ignition condition.

[0035] It should also be noted that the ignition condition is crucial to a vehicle's emissions performance. The ignition condition affects the ignition characteristics and conversion efficiency of the three-way catalytic converter. Furthermore, during the ignition condition, because the catalytic conversion efficiency of the three-way catalytic converter gradually reaches its maximum value, the vehicle's emissions performance is poor, especially the emission of HC (hydrocarbons).

[0036] S102. Record a set of initial fuel injection parameters for the engine.

[0037] In this embodiment of the application, after determining that the vehicle has entered the ignition condition, a set of current fuel injection parameters of the engine can be recorded as a set of initial fuel injection parameters, wherein the fuel injection parameters may include the injection ratio and the injection proportion.

[0038] Here, a set of initial injection parameters represents the n initial injections of the engine within one working cycle. That is, before adjusting the injection parameters, under ignition conditions, the engine performs n injections during one revolution, each injection following a specific injection parameter. Recording these n injection parameters yields a set of initial injection parameters. For example, if the engine performs 3 injections within one working cycle under ignition conditions, the injection ratios of these 3 injections can be recorded as a set of initial injection parameters.

[0039] S103. Perform self-learning of fuel injection parameters, and obtain multiple sets of test fuel injection parameters based on a set of initial fuel injection parameters.

[0040] In this embodiment, after obtaining a set of initial injection parameters, self-learning (i.e., adaptive learning) of the injection parameters can be performed. During the self-learning process, adjustments of varying degrees can be made multiple times based on this initial set of injection parameters, thereby obtaining multiple sets of test injection parameters. The adjustment of the injection parameters can follow a certain pattern to ensure that various feasible adjustment schemes can be explored, thus adapting to various situations and enhancing robustness.

[0041] S104. The engine is run sequentially using multiple sets of test injection parameters, and multiple excess air coefficients of the vehicle are calculated; each set of test injection parameters corresponds to one excess air coefficient; the running time of each set of test injection parameters is the first self-learning time.

[0042] It should be noted that the excess air coefficient refers to the ratio of the actual amount of air supplied for fuel combustion to the theoretical amount of air. The excess air coefficient is an important parameter reflecting the fuel-air mixture ratio. It can be calculated using a gas analyzer. The excess air coefficient is represented by the symbol "λ" and is therefore also known as the Lambda value. If more fuel is added to the mixture, making it richer, the Lambda value is less than 1; if more air is added to the mixture, making it leaner, the Lambda value is greater than 1.

[0043] In this embodiment, multiple sets of test injection parameters can be used sequentially to run the engine and calculate the corresponding multiple excess air coefficients. The running time for each set of test injection parameters is the first self-learning time. That is, using the first set of test injection parameters, the engine is run for the first self-learning time, and the corresponding first excess air coefficient is calculated; then, using the second set of test injection parameters, the engine is run for the first self-learning time, and the corresponding second excess air coefficient is calculated; and so on.

[0044] In some embodiments of this application, the first self-learning time can be 10 seconds. This avoids both inaccurate calculations caused by too short an engine running time and low self-learning efficiency caused by too long an engine running time, thus balancing the accuracy of the excess air coefficient and the efficiency of self-learning.

[0045] S105. Based on the comparison of multiple excess air coefficients, the optimal self-learning result is determined among multiple sets of test injection parameters.

[0046] In this embodiment of the application, after obtaining multiple excess air coefficients, the multiple excess air coefficients can be compared to determine the optimal excess air coefficient, and the set of test fuel injection parameters corresponding to the optimal excess air coefficient can be used as the self-learning result; that is, the optimal one is determined from multiple sets of test fuel injection parameters and used as the self-learning result.

[0047] S106. Replace the initial injection parameters with the self-learning results.

[0048] In this embodiment, after determining the optimal self-learning result from multiple sets of tested fuel injection parameters, the initial fuel injection parameters can be replaced with the self-learning result. That is, the fuel injection parameters under ignition conditions are updated, thus completing one self-learning of the fuel injection parameters. Accordingly, under ignition conditions, the vehicle can operate according to the new fuel injection parameters, thereby optimizing the air-fuel ratio and reducing fuel consumption.

[0049] It is understood that, in this embodiment of the application, multiple sets of test injection parameters can be obtained based on a set of initial injection parameters, and the optimal one can be determined from among the multiple sets of test injection parameters. The injection parameters under the ignition condition are then updated, thus completing the self-learning of the injection parameters under the ignition condition. In this way, the injection parameters can be adjusted in real time according to the specific operating conditions of the vehicle under the ignition condition, thereby optimizing the air-fuel ratio, reducing fuel consumption, and making the vehicle more adaptable to different conditions (including high temperature, high altitude, and low temperature conditions), thus improving robustness.

[0050] In some embodiments of this application, the initial injection parameters include: an initial injection ratio, and the test injection parameters include: a test injection ratio. This can be achieved through... Figure 2 The shown S201 to S202 are implemented to achieve this. Figure 1 S103, shown below, will be explained in conjunction with each step.

[0051] S201. Adjust the initial injection ratio of the i-th initial injection to the test injection ratio of a test injection, and retain the other initial injection parameters to obtain at least one set of test injection parameters.

[0052] In this embodiment, a set of initial injection parameters represents n initial injections of the engine within one working cycle, and each set of test injection parameters represents n test injections of the engine within one working cycle; where n is greater than or equal to 1. Simultaneously, i is greater than or equal to 1 and less than or equal to n, meaning that the i-th initial injection can be any one of the n initial injections.

[0053] In other words, an initial fuel injection of the engine can be adjusted to a test fuel injection to obtain test fuel injection parameters. Specifically, before fuel injection parameter self-learning, the engine performs n initial fuel injections in one working cycle, and these n initial fuel injections correspond to n initial fuel injection parameters. Then, the initial fuel injection parameter of the i-th initial fuel injection in the n initial fuel injections can be adjusted to the test fuel injection parameter of a test fuel injection. Then, the test fuel injection parameter of the obtained test fuel injection, together with the other initial fuel injection parameters from the original n initial fuel injections, gives a set of test fuel injection parameters, that is, the n test fuel injection parameters corresponding to the n test fuel injections.

[0054] For example, before the fuel injection parameter self-learning, the engine performed three initial fuel injections in one working cycle, i.e., n=3. Each of the three initial fuel injections has three initial fuel injection parameters. The initial fuel injection parameters of the first initial fuel injection can be adjusted to the test fuel injection parameters of a test fuel injection; then, by combining the test fuel injection parameters of the test fuel injection with the original initial fuel injection parameters of the second and third initial fuel injections, a set of test fuel injection parameters is obtained.

[0055] In this embodiment, the i-th initial fuel injection can be adjusted once to obtain a set of test fuel injection parameters. Correspondingly, the i-th initial fuel injection can also be adjusted multiple times to obtain multiple sets of test fuel injection parameters.

[0056] It should be noted that the n initial injections or n test injections in this application are ordered according to the injection time sequence. That is, within one working cycle, the engine performs the 1st initial injection, the 2nd initial injection, and so on up to the nth initial injection in chronological order; correspondingly, within one working cycle, the engine performs the 1st test injection, the 2nd test injection, and so on up to the nth test injection in chronological order. Further details will not be elaborated below.

[0057] S202. Continue to adjust the initial injection ratio of the (i+1)th initial injection until the vehicle is started, or until the initial injection ratio of the nth initial injection is adjusted.

[0058] In this embodiment, after adjusting the initial injection parameters for the i-th initial injection, the initial injection parameters for the (i+1)-th initial injection can be adjusted. That is, the initial injection parameters for the (i+1)-th initial injection can be adjusted to the test injection parameters for a single test injection. Then, the obtained test injection parameters for a single test injection, along with the other initial injection parameters from the original n initial injections, are combined to obtain a set of test injection parameters. This process is repeated until the vehicle ignites, or until the initial injection parameters for the n-th initial injection are adjusted.

[0059] In this embodiment, when the vehicle re-enters the ignition state, it can operate under the ignition state according to the obtained self-learning results, thereby optimizing the air-fuel ratio and reducing the vehicle's fuel consumption. Furthermore, when the vehicle re-enters the ignition state, it can continue the self-learning of injection parameters according to the previous progress. For example, if the previous self-learning adjusted the initial injection parameters for the original second initial injection, the current self-learning can directly begin adjusting the initial injection parameters for the original third initial injection, and so on.

[0060] In this embodiment of the application, the vehicle's ignition condition can last for about 2 minutes, and the running time of each set of test fuel injection parameters (i.e., the first self-learning time) can be 10 seconds. Therefore, under the ignition condition, the vehicle can run multiple sets of test fuel injection parameters in sequence and calculate the corresponding multiple excess air coefficients.

[0061] In this embodiment, once the initial injection parameters for the nth initial injection are adjusted, the self-learning process can end, and the currently obtained optimal set of test injection parameters can be used as the self-learning result. The vehicle can then operate under ignition conditions according to the currently obtained self-learning result. Furthermore, when the vehicle re-enters the ignition condition, the initial injection parameters for the first initial injection can be adjusted again.

[0062] Understandably, adjusting the engine's initial fuel injection to a test fuel injection is necessary to obtain at least one set of test fuel injection parameters. This allows for real-time adjustment of the fuel injection parameters based on the specific operating conditions of the vehicle during ignition, thereby optimizing the air-fuel ratio, reducing fuel consumption, and making the vehicle more adaptable to different situations, thus improving robustness.

[0063] In some embodiments of this application, it can be achieved through Figure 3 The shown S301 to S302 implement this. Figure 2 The step S201 shown will be explained in conjunction with each step.

[0064] S301. Based on the initial injection ratio of the i-th initial injection, determine the learning start ratio and the learning end ratio.

[0065] S302. Within the range of the learning start ratio to the learning end ratio, the unit ratio is increased step by step to obtain at least one test injection ratio for a single test injection.

[0066] In this embodiment, firstly, the learning start ratio and learning end ratio can be determined based on the initial injection ratio of the i-th initial injection. Then, within the range of the learning start ratio to the learning end ratio, the ratio can be increased step-by-step by a unit amount to obtain at least one test injection ratio for a single test injection. The difference between the learning end ratio and the learning start ratio needs to be greater than or equal to the unit amount of the step-by-step increase, thereby ensuring that at least one step adjustment can be performed to obtain at least one test injection ratio.

[0067] In some embodiments of this application, it can be achieved through Figure 4 The shown S401 to S402 implement this. Figure 3 The steps shown in S301 will be explained in conjunction with each step.

[0068] S401. Subtract the preset first value from the initial injection ratio to obtain the learning start ratio.

[0069] S402. Add the initial injection ratio to the preset second value to obtain the learning end ratio.

[0070] In some embodiments of this application, the first value and the second value are both 0.2; the unit increment is 0.1.

[0071] For example, before the fuel injection parameters were self-learned, the engine performed three initial fuel injections within one working cycle, i.e., n=3. The initial injection ratios for the three initial fuel injections were P1, P2, and P3, respectively.

[0072] Therefore, P1±0.2 can be used as the self-learning range. That is, P1 minus 0.2 is used as the learning start ratio, and P1 plus 0.2 is used as the learning end ratio. Then, within the range of P1±0.2, the value is increased by 0.1 in increments to obtain the test injection ratios for multiple test fuel injections.

[0073] It should be noted that during the self-learning process, based on the initial injection ratio of the first initial injection, the initial injection ratios for the second and third initial injections remain unchanged. Furthermore, during the self-learning process, the injection testing steps must be defined within the engine's intake stroke.

[0074] In some embodiments of this application, the learning start ratio is greater than 0; the learning end ratio is less than 1. It should be noted that the injection ratio refers to the ratio of the amount of fuel injected each time to the total amount of fuel injected in one working cycle of the engine; therefore, the injection ratio needs to be greater than 0 and less than 1. Similarly, the test injection ratio obtained through self-learning needs to be greater than 0 and less than 1.

[0075] Understandably, during the process of adjusting the i-th initial injection to a test injection, the initial injection ratio of the i-th initial injection is used to determine the learning start ratio and the learning end ratio. Then, within the range of the learning start ratio to the learning end ratio, the ratio is increased step by step to obtain at least one test injection ratio for a test injection. In this way, self-learning of the injection ratio is achieved. That is, under ignition conditions, the injection parameters can be adjusted in real time according to the specific operating conditions of the vehicle, thereby optimizing the air-fuel ratio, reducing vehicle fuel consumption, and making the vehicle more adaptable to different situations, thus improving robustness.

[0076] In some embodiments of this application, it can be achieved through Figure 5 The shown S501 to S502 implement this. Figure 1 S101, shown below, will be explained in conjunction with each step.

[0077] S501 records key parameters of the vehicle in real time; key parameters include: vehicle speed and catalyst status.

[0078] S502. If the key parameters meet the preset conditions, then the vehicle is determined to enter the ignition condition.

[0079] In this embodiment, key vehicle parameters, such as vehicle speed and catalytic converter status, can be recorded in real time. Furthermore, the vehicle's entry into ignition mode is determined based on these key parameters; that is, if the key parameters meet preset conditions, the vehicle is confirmed to be in ignition mode.

[0080] In some embodiments of this application, it can be achieved through Figure 6 The shown S601 to S602 implement this. Figure 1 S105, shown below, will be explained in conjunction with each step.

[0081] S601. Compare multiple excess air coefficients with target values.

[0082] S602. The set of test injection parameters corresponding to the excess air coefficient that is closest to the target value is determined as the optimal self-learning result.

[0083] In this embodiment of the application, reference is made to Figure 7A target value can be pre-set for the excess air coefficient, which is a theoretically optimal value. For example, the target value can be set to 1.4. Then, after calculating multiple excess air coefficients (i.e., the actual values ​​of the excess air coefficients) through self-learning of the injection parameters, the actual values ​​of the excess air coefficients can be compared with the target values ​​to determine the excess air coefficient that is closest to the target value. In other words, the optimal set of test injection parameters is determined as the self-learning result.

[0084] Understandably, by comparing multiple excess air coefficients with a target value, the excess air coefficient closest to the target value is determined, thus identifying the optimal self-learning result. In this way, the vehicle can operate using the optimal self-learning result during ignition, thereby optimizing the air-fuel ratio and reducing fuel consumption.

[0085] Figure 8 This is a schematic diagram of the composition of a fuel injection parameter adjustment device provided in an embodiment of this application, as shown below. Figure 8 As shown, the fuel injection parameter adjustment device 800 includes: a determination module 810, a recording module 820, and a self-learning module 830. The determination module 810 is configured to determine when the vehicle enters the ignition condition. The recording module 820 is configured to record a set of initial fuel injection parameters of the engine. The self-learning module 830 is configured to perform fuel injection parameter self-learning, obtaining multiple sets of test fuel injection parameters based on the initial set of fuel injection parameters; sequentially running the engine using the multiple sets of test fuel injection parameters and calculating multiple excess air coefficients of the vehicle; determining the optimal self-learning result among the multiple sets of test fuel injection parameters based on a comparison of the multiple excess air coefficients; and replacing the initial fuel injection parameters with the self-learning result. Each set of test fuel injection parameters corresponds to one excess air coefficient; the running time of each set of test fuel injection parameters is the first self-learning time.

[0086] In some embodiments of this application, a set of initial injection parameters represents n initial injections of the engine within one working cycle; each set of test injection parameters represents n test injections of the engine within one working cycle; n is greater than or equal to 1. The initial injection parameters include: initial injection ratio; the test injection parameters include: test injection ratio.

[0087] The self-learning module 830 is also configured to adjust the initial injection ratio of the i-th initial injection to the test injection ratio of a test injection, and retain other initial injection parameters to obtain at least one set of test injection parameters; continue to adjust the initial injection ratio of the (i+1)-th initial injection until the vehicle completes ignition, or until the adjustment of the initial injection ratio of the n-th initial injection is completed; where i is greater than or equal to 1 and less than or equal to n.

[0088] In some embodiments of this application, the first self-learning time is 10 seconds.

[0089] In some embodiments of this application, the self-learning module 830 is further configured to determine a learning start ratio and a learning end ratio based on the initial injection ratio of the i-th initial injection; within the range of the learning start ratio to the learning end ratio, the learning module increases by a unit ratio step by step to obtain at least one test injection ratio for a test injection; wherein the difference between the learning end ratio and the learning start ratio is greater than or equal to the unit ratio step by step.

[0090] In some embodiments of this application, the self-learning module 830 is further configured to subtract a preset first value from the initial injection ratio to obtain a learning start ratio; and to add a preset second value to the initial injection ratio to obtain a learning end ratio.

[0091] In some embodiments of this application, the first value and the second value are both 0.2; the unit increment is 0.1.

[0092] In some embodiments of this application, the learning start ratio is greater than 0; the learning end ratio is less than 1.

[0093] In some embodiments of this application, the recording module 820 is further configured to record key parameters of the vehicle in real time; the key parameters include vehicle speed and catalyst status. The determining module 810 is further configured to determine that the vehicle has entered the ignition state if the key parameters meet preset conditions.

[0094] In some embodiments of this application, the determining module 810 is further configured to compare multiple excess air coefficients with target values; and to determine the set of test injection parameters corresponding to the excess air coefficient that is closest to the target value as the optimal self-learning result.

[0095] The descriptions of the apparatus embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. In some embodiments, the functions or modules included in the apparatus provided in this application can be used to perform the methods described in the method embodiments above. For technical details not disclosed in the apparatus embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0096] It should be noted that, in the embodiments of this application, if the above-mentioned fuel injection parameter adjustment method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware, software, or firmware, or any combination of hardware, software, and firmware.

[0097] This application provides a computer device including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements some or all of the steps in the above-described method.

[0098] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements some or all of the steps in the above-described method. The computer-readable storage medium can be transient or non-transient.

[0099] This application provides a computer program including computer-readable code, wherein when the computer-readable code is executed in a computer device, a processor in the computer device performs some or all of the steps in the above-described method.

[0100] This application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, it implements some or all of the steps in the above-described method. This computer program product can be implemented specifically through hardware, software, or a combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium; in other embodiments, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc.

[0101] It should be noted that the descriptions of the various embodiments above tend to emphasize the differences between them, while their similarities or commonalities can be referred to interchangeably. The descriptions of the above embodiments of the device, storage medium, computer program, and computer program product are similar to the descriptions of the above method embodiments and have similar beneficial effects. For technical details not disclosed in the embodiments of the device, storage medium, computer program, and computer program product of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0102] Figure 9 This application provides a hardware entity diagram of a computer device as an embodiment of the present application, such as... Figure 9 As shown, the hardware entity of the computer device 1100 includes a processor 1101 and a memory 1102, wherein the memory 1102 stores a computer program that can run on the processor 1101, and the processor 1101 executes the program to implement the steps in the method of any of the above embodiments.

[0103] The memory 1102 stores computer programs that can run on the processor. The memory 1102 is configured to store instructions and applications that can be executed by the processor 1101. It can also cache data to be processed or already processed (e.g., image data, audio data, voice communication data, and video communication data) in the processor 1101 and various modules in the computer device 1100. It can be implemented by flash memory or random access memory (RAM).

[0104] The processor 1101 executes the steps of the fuel injection parameter adjustment method described above when executing the program. The processor 1101 typically controls the overall operation of the computer device 1100.

[0105] This application provides a computer storage medium storing one or more programs that can be executed by one or more processors to implement the steps of the fuel injection parameter adjustment method as described in any of the above embodiments.

[0106] It should be noted that the descriptions of the storage medium and device embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0107] The aforementioned processor can be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor. It is understood that other electronic devices can also implement the functions of the aforementioned processor, and this application does not specifically limit the specific implementation.

[0108] The aforementioned computer storage media / memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc.; or it can be various terminals that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0109] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above steps / processes do not imply a sequential order of execution; the execution order of each step / process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above embodiments of this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0110] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0112] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Furthermore, in the various embodiments of this application, all functional units can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units. Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0114] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, magnetic disks, or optical disks.

[0115] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for adjusting fuel injection parameters, characterized in that, include: Confirm that the vehicle has entered the ignition state; Record a set of initial fuel injection parameters for the engine; The fuel injection parameters are self-learned, and multiple sets of test fuel injection parameters are obtained based on a set of initial fuel injection parameters. The engine is run sequentially using multiple sets of the aforementioned test injection parameters, and multiple excess air coefficients of the vehicle are calculated; wherein, each set of the aforementioned test injection parameters corresponds to one aforementioned excess air coefficient; the running time of each set of the aforementioned test injection parameters is the first self-learning time; Based on the comparison of multiple excess air coefficients, the optimal self-learning result is determined among multiple sets of test injection parameters; Replace the initial fuel injection parameters with the self-learning results; Wherein, a set of initial injection parameters represents n initial injections of the engine within one working cycle; each set of test injection parameters represents n test injections of the engine within one working cycle; n is greater than or equal to 1; the initial injection parameters include: initial injection ratio; the test injection parameters include: test injection ratio; The step of obtaining multiple sets of test injection parameters based on a set of initial injection parameters includes: adjusting the initial injection ratio of the i-th initial injection to the test injection ratio of a test injection, while retaining the other initial injection parameters, to obtain at least one set of test injection parameters; continuing to adjust the initial injection ratio of the (i+1)-th initial injection until the vehicle completes ignition, or until the adjustment of the initial injection ratio of the n-th initial injection is completed; wherein i is greater than or equal to 1 and less than or equal to n.

2. The method for adjusting injection parameters according to claim 1, characterized in that, The step of adjusting the initial injection ratio of the i-th initial injection to the test injection ratio of the first test injection includes: Based on the initial injection ratio of the i-th initial injection, the learning start ratio and the learning end ratio are determined; Within the range of the learning start ratio to the learning end ratio, the ratio is increased step by step to obtain at least one test injection ratio for one test injection; wherein the difference between the learning end ratio and the learning start ratio is greater than or equal to the step-increase unit ratio.

3. The method for adjusting injection parameters according to claim 2, characterized in that, The determination of the learning start ratio and learning end ratio based on the initial injection ratio of the i-th initial injection includes: Subtracting a preset first value from the initial spray ratio yields the learning start ratio; The initial spray ratio is added to a preset second value to obtain the learning end ratio.

4. The method for adjusting injection parameters according to claim 3, characterized in that, Both the first value and the second value are 0.2; The increment of the unit ratio is 0.

1.

5. The method for adjusting injection parameters according to claim 3, characterized in that, The learning start ratio is greater than 0; The learning completion rate is less than 1.

6. The method for adjusting injection parameters according to claim 1, characterized in that, The first self-learning time is 10 seconds.

7. The method for adjusting injection parameters according to claim 1, characterized in that, The determination that the vehicle has entered the ignition condition includes: Record the vehicle's key parameters in real time; these key parameters include: vehicle speed and catalyst status. If the key parameters meet the preset conditions, then the vehicle is determined to have entered the ignition state.

8. The method for adjusting injection parameters according to claim 1, characterized in that, The process of determining the optimal self-learning result from multiple sets of test injection parameters based on comparisons of multiple excess air coefficients includes: Compare the multiple excess air coefficients with the target value; The set of test injection parameters corresponding to the excess air coefficient that is closest to the target value is determined as the optimal self-learning result.

9. A fuel injection parameter adjustment device, characterized in that, include: The determination module is configured to determine when a vehicle enters the ignition state. The recording module is configured to record a set of initial fuel injection parameters of the engine; The self-learning module is configured to perform self-learning of fuel injection parameters, and obtain multiple sets of test fuel injection parameters based on a set of initial fuel injection parameters; Furthermore, the engine is run using multiple sets of the aforementioned test injection parameters in sequence, and multiple excess air coefficients of the vehicle are calculated. Furthermore, based on the comparison of multiple excess air coefficients, the optimal self-learning result is determined among multiple sets of test injection parameters; and the initial injection parameters are replaced with the self-learning result; wherein each set of test injection parameters corresponds to one excess air coefficient; and the running time of each set of test injection parameters is the first self-learning time. Wherein, a set of initial injection parameters represents n initial injections of the engine within one working cycle; each set of test injection parameters represents n test injections of the engine within one working cycle; n is greater than or equal to 1; the initial injection parameters include: initial injection ratio; the test injection parameters include: test injection ratio; The self-learning module is further configured to adjust the initial injection ratio of the i-th initial injection to the test injection ratio of the test injection, and retain the other initial injection parameters to obtain at least one set of test injection parameters; continue to adjust the initial injection ratio of the (i+1)-th initial injection until the vehicle completes ignition, or until the adjustment of the initial injection ratio of the n-th initial injection is completed; wherein i is greater than or equal to 1 and less than or equal to n.

10. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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