Oil injection parameter adjustment method and apparatus, device, and storage medium

Abstract

Embodiments of the present application disclose an oil injection parameter adjustment method and apparatus, a device, and a storage medium. The oil injection parameter adjustment method comprises: determining that a vehicle has entered a series-parallel hybrid working condition; recording a set of initial oil injection parameters of an engine; performing oil injection parameter self-learning and, on the basis of the set of initial oil injection parameters, obtaining multiple sets of test oil injection parameters; sequentially using the multiple sets of test oil injection parameters to operate an engine, and calculating multiple test oil consumptions of the vehicle, each set of test oil injection parameters corresponding to one test oil consumption, and the operation time of each set of test oil injection parameters being a first self-learning time; on the basis of a comparison of the multiple test oil consumptions, determining an optimal self-learning result among the multiple sets of test oil injection parameters; and replacing the initial oil injection parameter with the self-learning result.

Description

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

[0001] The present application relates to, but is not limited to, the field of engine control technology, and in particular to a method, device, equipment, and storage medium for adjusting fuel injection parameters. Background Art

[0002] A hybrid vehicle (HEV) is a vehicle whose propulsion system consists of two or more separate propulsion systems that operate simultaneously. The vehicle's driving power is provided by the individual propulsion systems individually or collectively, depending on the vehicle's actual driving conditions. HEVs typically refer to hybrid electric vehicles (HEVs), which use a traditional internal combustion engine (diesel or gasoline) and an electric motor as their power sources.

[0003] In related technologies, when hybrid vehicles are in high-power output conditions such as climbing a hill, as the intake air temperature rises, the engine output torque becomes unstable, and the SOC (State of Charge) cannot reach the equilibrium point. As a result, the engine speed is too high and enters the enrichment state, resulting in increased vehicle fuel consumption and deterioration of NVH (Noise Vibration Harshness) quality, reducing the user experience.

[0004] Summary of the Invention

[0005] In view of this, embodiments of the present application provide a method, device, equipment, and storage medium for adjusting fuel injection parameters, which can reduce fuel consumption and improve robustness.

[0006] The technical solution of the embodiment of the present application is implemented as follows:

[0007] An embodiment of the present application provides a method for adjusting injection parameters, including: determining that a vehicle enters a series-parallel hybrid operating state; recording a set of initial injection parameters of the engine; performing self-learning of the injection parameters, and obtaining multiple sets of test injection parameters based on a set of the initial injection parameters; sequentially using the multiple sets of the test injection parameters to operate the engine, and calculating multiple test fuel consumptions of the vehicle; wherein each set of the test injection parameters corresponds to one test fuel consumption; the operating time of each set of the test injection parameters is a first self-learning time; based on a comparison of the multiple test fuel consumptions, determining the optimal self-learning result among the multiple sets of the test injection parameters; and replacing the initial injection parameters with the self-learning result.

[0008] In the above scheme, one group of the initial injection parameters represents n initial injections of the engine in one working cycle; each group of the test injection parameters represents n+1 test injections of the engine in one working cycle; n is greater than or equal to 1; based on one group of the initial injection parameters, multiple groups of test injection parameters are obtained, including: dividing the initial injection parameters of the i-th initial injection into the test injection parameters of two test injections, and retaining the other initial injection parameters to obtain at least one group of test injection parameters; i is greater than or equal to 1 and less than or equal to n; continuing to divide the initial injection parameters of the i+1-th initial injection until the second self-learning time is reached, or until the division of the initial injection parameters of the n-th initial injection is completed.

[0009] In the above solution, the first self-learning time is 10 seconds; the second self-learning time is 2 minutes.

[0010] In the above scheme, the initial injection parameters include: an initial injection angle; the test injection parameters include: a test injection angle; dividing the initial injection parameters of the i-th initial injection into the test injection parameters of two test injections includes: using the initial injection angle of the i-th initial injection as the test injection angle of one test injection; and determining the test injection angle of another test injection between the initial injection angles of two adjacent initial injections.

[0011] In the above scheme, determining the test injection angle of another test injection between the initial injection angles of two adjacent initial injections includes: taking the median of the initial injection angles of two adjacent initial injections as the test injection angle of another test injection.

[0012] In the above scheme, the initial injection parameters also include: an initial injection ratio; the test injection parameters also include: a test injection ratio; the test injection parameters of dividing the initial injection parameters of the i-th initial injection into two test injections also include: dividing the initial injection ratio of the i-th initial injection into two test injection ratios; wherein the sum of the test injection ratios of the two test injections is equal to the initial injection ratio of the i-th initial injection.

[0013] In the above solution, the test injection ratios of the two test injections are equal.

[0014] In the above scheme, determining whether the vehicle enters the series-parallel hybrid operating state includes: recording the real-time key parameters of the vehicle; the key parameters include: vehicle speed, fuel consumption and injection parameters; if the key parameters meet preset conditions, it is determined that the vehicle enters the series-parallel hybrid operating state.

[0015] In the above scheme, before performing the injection parameter self-learning, the injection parameter adjustment method also includes: recording the real-time environmental parameters of the vehicle; the environmental parameters include: temperature, atmospheric pressure and intake manifold temperature; the injection parameter self-learning includes: if the environmental parameters indicate that the vehicle is in a high temperature environment, a low temperature environment or a high altitude environment, then performing the injection parameter self-learning.

[0016] An embodiment of the present application also provides an injection parameter adjustment device, comprising: a determination module, configured to determine whether the vehicle enters a series-parallel hybrid operating state; a recording module, configured to record a set of initial injection parameters of the engine; a self-learning module, configured to perform injection parameter self-learning, and obtain multiple sets of test injection parameters based on a set of the initial injection parameters; and, sequentially using the multiple sets of the test injection parameters, running the engine and calculating multiple test fuel consumptions of the vehicle; and, based on a comparison of the multiple test fuel consumptions, determining the optimal self-learning result among the multiple sets of the test injection parameters; and replacing the initial injection parameters with the self-learning results; wherein, each set of the test injection parameters corresponds to one test fuel consumption; and the running time of each set of the test injection parameters is the first self-learning time.

[0017] An embodiment of the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the steps in the above method when executing the program.

[0018] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above method when the computer program is executed by a processor.

[0019] Thus, in the embodiment of the present application, multiple sets of test injection parameters can be derived based on a set of initial injection parameters. The one with the lowest fuel consumption can then be determined from the multiple test injection parameter sets, and the injection parameters for the series-parallel hybrid operating condition can be updated. Thus, self-learning of the injection parameters for the series-parallel hybrid operating condition is completed. In this way, the injection parameters can be adjusted in real time according to the specific operating conditions of the vehicle under the series-parallel hybrid operating condition, thereby reducing the vehicle's fuel consumption, making the vehicle more adaptable to different conditions, and improving robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] FIG1 is a schematic diagram of a first implementation flow of a method for adjusting fuel injection parameters according to an embodiment of the present application;

[0021] FIG2 is a schematic diagram of a series-parallel hybrid operating condition in an embodiment of the present application;

[0022] FIG3 is a second schematic diagram of a flow chart of a method for adjusting fuel injection parameters according to an embodiment of the present application;

[0023] FIG4 is a third schematic diagram of a flow chart of a method for adjusting fuel injection parameters according to an embodiment of the present application;

[0024] FIG5 is a fourth schematic diagram of a flow chart of a method for adjusting fuel injection parameters according to an embodiment of the present application;

[0025] FIG6 is a fifth flow chart of an implementation method of a fuel injection parameter adjustment method provided in an embodiment of the present application;

[0026] FIG7 is a sixth schematic diagram of a flow chart of an implementation method of a fuel injection parameter adjustment method provided in an embodiment of the present application;

[0027] FIG8 is a seventh schematic diagram of a flow chart of a method for adjusting fuel injection parameters according to an embodiment of the present application;

[0028] FIG9 is a schematic diagram of the structure of a fuel injection parameter adjustment device provided in an embodiment of the present application;

[0029] FIG10 is a schematic diagram of a hardware entity of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions of this application are further elaborated in detail below with reference to the accompanying drawings and embodiments. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0031] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset 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 the objects. It is understood that the specific order or sequence of "first / second / third" may be interchanged where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing this application only and are not intended to limit this application.

[0033] The embodiment of the present application provides a method for adjusting fuel injection parameters, which can be executed by a processor of a computer device, wherein the computer device can be set in a vehicle or in a vehicle testing device.

[0034] FIG1 is a schematic diagram of an implementation flow of a method for adjusting fuel injection parameters provided in an embodiment of the present application. As shown in FIG1 , the method includes steps S101 to S106 .

[0035] S101. Determine whether the vehicle enters a series-parallel hybrid operating mode.

[0036] In the embodiment of the present application, referring to FIG2 , the series-parallel hybrid operating condition means that the engine and the drive motor can independently drive the vehicle forward (i.e., parallel hybrid), or the engine can drive the generator to generate electricity and provide power to the drive motor, and then the drive motor assists in driving the vehicle forward (i.e., series hybrid).

[0037] Continuing with Figure 2, in a parallel hybrid, the engine and drive motor each independently provide power to the drivetrain, providing a superior vehicle performance. However, this results in higher power consumption, hindering SOC equilibrium. In a series hybrid, the engine drives the generator, forming a series power train consisting of engine, generator, and drive motor. This results in lower fuel consumption at low and medium speeds compared to conventional fuel vehicles, but higher fuel consumption at high speeds.

[0038] S102: Record a set of initial fuel injection parameters of the engine.

[0039] In an embodiment of the present application, after determining that the vehicle has entered a series-parallel hybrid operating state, a current set of fuel injection parameters of the engine may be recorded as a set of initial fuel injection parameters, wherein the fuel injection parameters may include an injection angle and an injection ratio.

[0040] Here, a set of initial injection parameters represents n initial injections within one engine operating cycle. That is, before adjusting the injection parameters, n injections are performed during one engine rotation, each injection performed according to a specific injection parameter. Thus, by recording the injection parameters for these n injections, a set of initial injection parameters is obtained. For example, in a series-parallel hybrid operation, the engine performs three injections within one operating cycle. The injection angles and injection ratios for these three injections can be recorded as a set of initial injection parameters.

[0041] S103 , performing fuel injection parameter self-learning, and obtaining multiple groups of test fuel injection parameters based on a group of initial fuel injection parameters.

[0042] In the embodiment of the present application, 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, multiple adjustments of varying degrees can be made based on the initial set of injection parameters, thereby obtaining multiple sets of test injection parameters. The injection parameters can be adjusted according to a specific pattern to ensure that various feasible adjustment options can be explored, thereby adapting to various situations and enhancing robustness.

[0043] S104. Using multiple sets of test injection parameters in sequence, running the engine, and calculating multiple test fuel consumptions of the vehicle; wherein each set of test injection parameters corresponds to one test fuel consumption; and the running time of each set of test injection parameters is the first self-learning time.

[0044] In the embodiment of the present application, multiple sets of test injection parameters can be sequentially used to operate the engine and calculate the corresponding multiple test fuel consumptions. The operating 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 operated for the first self-learning time, and the corresponding first test fuel consumption is calculated; then, using the second set of test injection parameters, the engine is operated for the first self-learning time, and the corresponding second test fuel consumption is calculated; and so on.

[0045] In some embodiments of the present application, the first self-learning time can be 10 seconds. This avoids both inaccurate fuel consumption calculations caused by too short an engine run time and inefficient self-learning caused by too long an engine run time, thus achieving a balance between fuel consumption accuracy and self-learning efficiency.

[0046] S105 . Based on comparison of multiple test fuel consumptions, determine an optimal self-learning result among multiple groups of test injection parameters.

[0047] In an embodiment of the present application, after obtaining multiple test fuel consumptions, the multiple test fuel consumptions can be compared to determine the lowest test fuel consumption, and a set of test injection parameters corresponding to the lowest test fuel consumption can be used as a self-learning result; that is, the optimal one is determined from multiple sets of test injection parameters as a self-learning result.

[0048] S106: Replace the initial injection parameters with the self-learning results.

[0049] In this embodiment of the present application, after determining the optimal self-learning result from multiple test injection parameter sets, the initial injection parameters can be replaced with the self-learning result. This means that the injection parameters for the series-parallel hybrid operation are updated, thus completing a single self-learning of the injection parameters. Accordingly, under the series-parallel hybrid operation, the vehicle can operate according to the new injection parameters, thereby reducing fuel consumption.

[0050] It is understandable that under the series-parallel hybrid working condition, since parallel hybrid and / or series hybrid can be selectively adopted, the situation faced by the engine in order to achieve the best fuel consumption is very complicated. In the embodiment of the present application, multiple groups of test injection parameters can be obtained based on a set of initial injection parameters, and the one with the lowest fuel consumption can be determined from the multiple groups of test injection parameters, and the injection parameters under the series-parallel hybrid working condition can be updated. In this way, the self-learning of the injection parameters under the series-parallel hybrid working condition is completed. In this way, the injection parameters can be adjusted in real time according to the specific conditions of the vehicle operation under the series-parallel hybrid working condition, thereby reducing the fuel consumption of the vehicle and making the vehicle more adaptable to different conditions (including high temperature, plateau and cold conditions), thereby improving robustness.

[0051] In some embodiments of the present application, S103 shown in FIG. 1 may be implemented through S201 to S202 shown in FIG. 3 , which will be described in conjunction with each step.

[0052] S201 : Split the initial injection parameters of the i-th initial injection into test injection parameters of two test injections, and retain the other initial injection parameters to obtain at least one set of test injection parameters.

[0053] In the embodiment of the present application, a set of initial injection parameters represents n initial injections within one engine operating cycle, and each set of test injection parameters represents n+1 test injections within one engine operating cycle. Here, n is greater than or equal to 1. Furthermore, i is greater than or equal to 1 and less than or equal to n. That is, the i-th initial injection can be any of the n initial injections.

[0054] In other words, a single initial fuel injection of the engine can be divided into two test injections to obtain test injection parameters. Specifically, before performing injection parameter self-learning, the engine performs n initial fuel injections within a working cycle, and these n initial injections correspond to n initial injection parameters. Furthermore, the i-th initial injection of the n initial injections can be divided into two test injections, that is, the initial injection parameters of the i-th initial injection are divided into test injection parameters for the two test injections. Furthermore, the test injection parameters of the two test injections obtained by division, as well as the initial injection parameters of the original n initial injections except for the i-th initial injection, are combined to obtain a set of test injection parameters, that is, n+1 test injection parameters corresponding to the n+1 test injections are obtained.

[0055] For example, before performing injection parameter self-learning, the engine performs three initial injections in one operating cycle, i.e., n=3. Each of the three initial injections has three initial injection parameters. The first initial injection can be divided into two test injections, obtaining two test injection parameters. Furthermore, these two test injection parameters, along with the original initial injection parameters of the second and third initial injections, are combined to obtain a set of test injection parameters, i.e., four test injection parameters corresponding to the four test injections.

[0056] In the embodiment of the present application, the i-th initial fuel injection can be segmented once to obtain a set of test fuel injection parameters. Correspondingly, the i-th initial fuel injection can also be segmented multiple times to obtain multiple sets of test fuel injection parameters.

[0057] It should be noted that the n initial injections or n+1 test injections in this application are sequenced according to the injection time sequence. That is, within a single operating cycle, the engine sequentially performs the first initial injection, the second initial injection, and so on, up to the nth initial injection. Correspondingly, within a single operating cycle, the engine sequentially performs the first test injection, the second test injection, and so on, up to the n+1th test injection, in chronological order. This will not be further elaborated below.

[0058] S202 : Continue segmenting the initial injection parameters of the (i+1)th initial injection until the second self-learning time is reached, or until the segmentation of the initial injection parameters of the nth initial injection is completed.

[0059] In the embodiment of the present application, after the initial injection parameters of the i-th initial injection are segmented, the initial injection parameters of the i+1-th initial injection can be segmented. That is, the i+1-th initial injection among the n-th initial injections can be segmented into two test injections, that is, the initial injection parameters of the i+1-th initial injection can be segmented into test injection parameters of the two test injections; then, the test injection parameters of the two test injections obtained by segmentation, as well as the other initial injection parameters of the original n-th initial injections except the i+1-th initial injection, are combined together to obtain a set of test injection parameters (i.e., n+1 test injection parameters). This cycle is repeated until the second self-learning time is reached, or until the initial injection parameters of the n-th initial injection are segmented.

[0060] For example, before performing injection parameter self-learning, the engine performs three initial injections within a single operating cycle (i.e., n = 3). Each of the three initial injections has three initial injection parameters. After completing segmentation of the first initial injection and obtaining at least one corresponding set of test injection parameters, the second initial injection can be segmented to obtain two test injection parameters. Furthermore, these two test injection parameters, along with the original initial injection parameters of the first and third injections, are combined to obtain a set of test injection parameters. This cycle continues until the second self-learning time is reached, or until the initial injection parameters of the third initial injection are segmented.

[0061] In the embodiment of the present application, after the self-learning of the injection parameters continues for the second self-learning time, the self-learning will end, and the currently obtained set of test injection parameters with the lowest fuel consumption will be used as the self-learning result. The vehicle can then operate in the series-parallel hybrid mode according to the currently obtained self-learning result. Furthermore, when the vehicle enters the series-parallel hybrid mode again, the self-learning of the injection parameters can be continued according to the previous progress. For example, if the previous self-learning divided the initial injection parameters of the original second initial injection, the current self-learning can directly begin dividing the initial injection parameters of the original third initial injection.

[0062] In some embodiments of the present application, the second self-learning time may be 2 minutes. That is, when the self-learning of the injection parameters lasts for 2 minutes, the self-learning will end.

[0063] In the embodiment of the present application, after the segmentation of the initial injection parameters for the nth initial injection is completed, the self-learning process may be terminated, and the currently obtained set of test injection parameters with the lowest fuel consumption may be used as the self-learning result. The vehicle may then operate in the series-parallel hybrid mode according to the currently obtained self-learning result. Furthermore, when the vehicle enters the series-parallel hybrid mode again, the segmentation of the initial injection parameters for the first initial injection may be restarted.

[0064] It is understandable that the engine's initial fuel injection is divided into two test injections to obtain at least one set of test injection parameters. In this way, the injection parameters can be adjusted in real time according to the specific conditions of vehicle operation under series-parallel hybrid conditions, thereby reducing the vehicle's fuel consumption, making the vehicle more adaptable to different conditions, and improving robustness.

[0065] At the same time, setting the second self-learning time as the end time of each self-learning can effectively control the time of each self-learning. As a result, the user can operate the vehicle according to the self-learning results for most of the time after entering the series-parallel hybrid operating condition, that is, operate the vehicle according to the injection parameters with lower fuel consumption, thereby improving the user experience.

[0066] In some embodiments of the present application, the initial injection parameters further include: an initial injection angle; and the test injection parameters include: a test injection angle. S201 shown in FIG3 can be implemented by S301 to S302 shown in FIG4 , which will be described in conjunction with each step.

[0067] S301: Using the initial injection angle of the i-th initial fuel injection as a test injection angle of a test fuel injection.

[0068] In the embodiment of the present application, in the process of dividing the i-th initial injection into two test injections, the initial injection angle of the i-th initial injection can be used as the test injection angle of one of the two divided test injections.

[0069] S302: Determine a test injection angle for another test injection between the initial injection angles of two adjacent initial injections.

[0070] In the embodiment of the present application, when dividing the i-th initial injection into two test injections, a test injection angle for another test injection can be determined between the initial injection angles of the two adjacent initial injections. The value of the determined test injection angle is between the initial injection angles of the two adjacent initial injections.

[0071] For example, a test injection angle for a test injection may be determined between the initial injection angles of the first and second initial injections, wherein the test injection angle is smaller than the initial injection angle of the first initial injection and larger than the initial injection angle of the second initial injection. Alternatively, a test injection angle for a test injection may be determined between the initial injection angles of the second and third initial injections, wherein the test injection angle is smaller than the initial injection angle of the second initial injection and larger than the initial injection angle of the third initial injection. Similarly, a test injection angle for a test injection may be determined between the initial injection angles of the (n-1)th and n-th initial injections, wherein the test injection angle is smaller than the initial injection angle of the (n-1)th initial injection and larger than the initial injection angle of the n-th initial injection.

[0072] It should be noted that within a working cycle, the engine performs n initial injections in chronological order, and accordingly, the initial injection angles of these n initial injections decrease in sequence. That is, the initial injection angle of the (i-1)th initial injection is greater than the initial injection angle of the (i)th initial injection; and the initial injection angle of the (i+1)th initial injection is greater than the initial injection angle of the (i+1)th initial injection.

[0073] It should be noted that both the initial injection angle and the test injection angle include the injection start angle and the injection end angle. The injection start angle refers to the angle corresponding to the start of each injection, and the injection end angle refers to the angle corresponding to the end of each injection. The injection end angle for each injection is smaller than the injection start angle. Accordingly, each unit angle reduction will reduce both the injection start angle and the injection end angle by that unit angle.

[0074] In the embodiment of the present application, the injection start angle of another test injection can be determined between the injection start angles of two adjacent initial injections; the injection end angle of another test injection can also be determined between the injection end angles of two adjacent initial injections.

[0075] For example, before performing injection parameter self-learning, the engine performs three initial injections within one operating cycle, i.e., n=3. The initial injection angles for these three initial injections are injection start angles SOI1, SOI2, and SOI3, respectively, and the initial injection angles for these three initial injections are injection end angles EOI1, EOI2, and EOI3, respectively. The injection start angle SOI4 and injection end angle EOI4 of another test injection are determined.

[0076] Furthermore, a test injection angle for the test injection may be determined between the initial injection angles of the first and second initial injections, wherein SOI1>SOI4>SOI2, and EOI1>EOI4>EOI2. Alternatively, a test injection angle for the test injection may be determined between the initial injection angles of the second and third initial injections, wherein SOI2>SOI4>SOI3, and EOI2>EOI4>EOI3.

[0077] It can be understood that when the i-th initial injection is divided into two test injections, the initial injection angle of the i-th initial injection is used as the test injection angle for one test injection. Simultaneously, the test injection angle for another test injection is determined between the initial injection angles of the two adjacent initial injections. This enables self-learning of injection angles, enabling real-time adjustment of injection parameters based on the specific operating conditions of the vehicle under series-parallel hybrid operation. This reduces fuel consumption, makes the vehicle more adaptable to different conditions, and improves robustness.

[0078] In some embodiments of the present application, S302 shown in FIG. 4 may be implemented by S303 shown in FIG. 5 , which will be described in conjunction with each step.

[0079] S303: Using the median of the initial injection angles of two adjacent initial injections as the test injection angle for another test injection.

[0080] In this embodiment of the present application, when determining the test injection angle for another test injection, the median of the initial injection angles of two adjacent initial injections can be used as the test injection angle for the other test injection. In other words, the injection angle spacing between the test injection and the two initial injections is equal, and the test injection and the two initial injections are considered "equally spaced injections."

[0081] For example, the injection start angle SOI1 of the first initial injection is 340°, and the injection start angle SOI2 of the second initial injection is 260°. The median of SOI1 and SOI2, 300°, can be used as the injection start angle SOI4 of the test injection. The injection angle spacing between SOI4 and SOI1 is 40°, and the injection angle spacing between SOI4 and SOI2 is also 40°. In other words, the test injection is "equally spaced" from the two initial injections.

[0082] It's understandable that using the median of the initial injection angles of two adjacent initial injections as the test injection angle for another test injection ensures that the test injection and the two initial injections are evenly spaced. This results in a more even distribution of injection times and angles for the resulting test injections, ensuring smoother and more consistent engine operation and boosting engine power.

[0083] In some embodiments of the present application, the initial injection parameters further include: an initial injection ratio; the test injection parameters further include: a test injection ratio. S201 shown in FIG3 can also be implemented by S304 shown in FIG6 , which will be described in conjunction with each step.

[0084] S304 : Divide the initial injection ratio of the i-th initial injection into two test injection ratios of test injection.

[0085] In the embodiment of the present application, when splitting the i-th initial injection into two test injections, the initial injection ratio of the i-th initial injection can be evenly divided into the test injection ratios of the two test injections. In other words, the test injection ratios of the two test injections after the split are equal; and the sum of the test injection ratios of the two test injections is equal to the initial injection ratio of the i-th initial injection.

[0086] For example, before performing injection parameter self-learning, the engine performs three initial injections within a single operating cycle (n = 3). The initial injection ratios for these three initial injections are P1, P2, and P3, respectively. If the original first initial injection is split into two injections, the original initial injection ratio P1 is split into two test injection ratios, P1_1 and P1_2, where P1_1 + P1_2 = P1. Similarly, the test injection ratios for the second and third initial injections are split similarly.

[0087] In some embodiments of the present application, the test injection ratios of the two test injections obtained by segmentation are equal. That is, the initial injection ratio of the i-th initial injection can be evenly divided into the test injection ratios of the two test injections. For example, the initial injection ratio P1 of the original first initial injection can be divided into the test injection ratios of the two test injections, P1_1 and P1_2; where P1_1 = P1_2, and P1_1 + P1_2 = P1.

[0088] It can be understood that during the process of splitting the i-th initial injection into two test injections, the initial injection ratio of the i-th initial injection is divided into the test injection ratios of the two test injections. Thus, the sum of the test injection ratios after the split equals the initial injection ratio before the split, thereby achieving adjustment of the injection ratio without changing the sum of all injection ratios within a single operating cycle. Furthermore, combined with self-learning of injection angles, injection parameters can be adjusted in real time based on the specific operating conditions of the vehicle under series-parallel hybrid operation, thereby reducing fuel consumption, making the vehicle more adaptable to different situations, and improving robustness.

[0089] In some embodiments of the present application, S101 shown in FIG. 1 may be implemented through S401 to S402 shown in FIG. 7 , which will be described in conjunction with each step.

[0090] S401. Record key parameters of the vehicle in real time; key parameters include: vehicle speed, fuel consumption and injection parameters.

[0091] S402: If the key parameters meet the preset conditions, it is determined that the vehicle enters the series-parallel hybrid mode.

[0092] In the embodiment of the present application, key vehicle parameters such as vehicle speed, fuel consumption, and injection parameters can be recorded in real time. Furthermore, the vehicle's entry into the series-parallel hybrid mode can be determined based on these key parameters. That is, if the key parameters meet pre-set conditions, the vehicle is determined to have entered the series-parallel hybrid mode.

[0093] In some embodiments of the present application, S103 shown in FIG. 1 may be implemented through S501 to S502 shown in FIG. 8 , which will be described in conjunction with each step.

[0094] S501. Record the real-time environmental parameters of the vehicle; the environmental parameters include: temperature, atmospheric pressure, and intake manifold temperature.

[0095] S502: If the environmental parameters indicate that the vehicle is in a high temperature environment, a low temperature environment, or a high altitude environment, perform fuel injection parameter self-learning.

[0096] In this embodiment of the present application, prior to performing injection parameter self-learning, environmental parameters such as temperature, atmospheric pressure, and intake manifold temperature may be recorded to determine whether the vehicle is in a special environmental scenario based on these environmental parameters. If the vehicle is in a special environmental scenario such as a high temperature environment, a low temperature environment, or a high altitude environment, and the vehicle is also in a series-parallel hybrid mode, then injection parameter self-learning may be initiated.

[0097] In some embodiments of the present application, when the ambient temperature is greater than 35°C and the intake manifold temperature is greater than 50°C, the vehicle is in a high-temperature environment. When the ambient temperature is less than 0°C, the vehicle is in a low-cold environment. When the atmospheric pressure is less than 80 kPa, the vehicle is in a high-altitude environment.

[0098] It is understandable that the vehicle's environmental scenario is determined based on environmental parameters, and then, when the vehicle is in a special environmental scenario, the fuel injection parameter self-learning is performed. This can improve the vehicle's adaptability to special environmental scenarios and reduce the vehicle's fuel consumption in special environmental scenarios.

[0099] FIG9 is a schematic diagram of the structure of an injection parameter adjustment device provided in an embodiment of the present application. As shown in FIG9 , the injection parameter adjustment device 800 includes a determination module 810, a recording module 820, and a self-learning module 830. Determination module 810 is configured to determine whether the vehicle has entered a series-parallel hybrid operating mode. Recording module 820 is configured to record a set of initial injection parameters for the engine. Self-learning module 830 is configured to perform injection parameter self-learning, obtain multiple sets of test injection parameters based on a set of initial injection parameters, sequentially operate the engine using the multiple sets of test injection parameters, and calculate multiple test fuel consumptions of the vehicle. Based on a comparison of the multiple test fuel consumptions, determine the optimal self-learning result from the multiple sets of test injection parameters. The initial injection parameters are replaced with the self-learning result. Each set of test injection parameters corresponds to a test fuel consumption. The operating time for each set of test injection parameters is a first self-learning time.

[0100] In some embodiments of the present application, a set of initial injection parameters represents n initial injections within an engine operating cycle; each set of test injection parameters represents n+1 test injections within an engine operating cycle; and n is greater than or equal to 1. The self-learning module 830 is further configured to split the initial injection parameters of the i-th initial injection into test injection parameters for two test injections, retain the remaining initial injection parameters, and obtain at least one set of test injection parameters; continue to split the initial injection parameters of the i+1-th initial injection until a second self-learning time is reached, or until the splitting of the initial injection parameters of the n-th initial injection is completed; wherein i is greater than or equal to 1 and less than or equal to n.

[0101] In some embodiments of the present application, the first self-learning time is 10 seconds; the second self-learning time is 2 minutes.

[0102] In some embodiments of the present application, the initial injection parameters include an initial injection angle, and the test injection parameters include a test injection angle. The self-learning module 830 is further configured to use the initial injection angle of the i-th initial injection as the test injection angle for a test injection, and to determine a test injection angle for another test injection between the initial injection angles of two adjacent initial injections.

[0103] In some embodiments of the present application, the self-learning module 830 is further configured to use the median of the initial injection angles of two adjacent initial injections as the test injection angle of another test injection.

[0104] In some embodiments of the present application, the initial injection parameters further include an initial injection ratio, and the test injection parameters further include a test injection ratio. The self-learning module 830 is further configured to split the initial injection ratio of the i-th initial injection into two test injection ratios of the test injections; wherein the sum of the test injection ratios of the two test injections equals the initial injection ratio of the i-th initial injection.

[0105] In some embodiments of the present application, the test injection ratios of the two test injections are equal.

[0106] In some embodiments of the present application, the recording module 820 is further configured to record real-time key vehicle parameters; these key parameters include vehicle speed, fuel consumption, and injection parameters. The determination module 810 is further configured to determine that the vehicle has entered a series-parallel hybrid mode if the key parameters meet preset conditions.

[0107] In some embodiments of the present application, the recording module 820 is further configured to record real-time environmental parameters of the vehicle; these parameters include temperature, atmospheric pressure, and intake manifold temperature. The self-learning module 830 is further configured to perform injection parameter self-learning if the environmental parameters indicate that the vehicle is in a high-temperature environment, a low-temperature environment, or a high-altitude environment.

[0108] The description of the above device embodiment is similar to the description of the above method embodiment and has similar beneficial effects as the method embodiment. In some embodiments, the functions or modules included in the device provided in the embodiments of the present application can be used to perform the methods described in the above method embodiments. For technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0109] It should be noted that, in the embodiments of the present application, if the above-mentioned injection parameter adjustment method is implemented in the form of a software function 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 the present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk. In this way, the embodiments of the present application are not limited to any specific hardware, software, or firmware, or any combination of hardware, software, and firmware.

[0110] An embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, some or all of the steps in the above method are implemented.

[0111] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements some or all of the steps in the above method. The computer-readable storage medium may be transient or non-transient.

[0112] An embodiment of the present application provides a computer program, including computer-readable code. When the computer-readable code is run in a computer device, a processor in the computer device executes some or all of the steps for implementing the above method.

[0113] An embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and when the computer program is read and executed by a computer, implements some or all of the steps in the above method. The computer program product can be implemented specifically by hardware, software, or a combination thereof. In some embodiments, the computer program product is embodied as a computer storage medium. In other embodiments, the computer program product is embodied as a software product, such as a software development kit (SDK), etc.

[0114] It should be noted that the descriptions of the various embodiments above tend to emphasize the differences between the various embodiments, and their similarities or similarities can be referenced to each other. The descriptions of the above device, storage medium, computer program, and computer program product embodiments are similar to the descriptions of the above method embodiments and have similar beneficial effects as the method embodiments. 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 description of the method embodiments of this application for understanding.

[0115] Figure 10 is a schematic diagram of the hardware entity of a computer device provided in an embodiment of the present application. As shown in Figure 10, 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 be run on the processor 1101, and when the processor 1101 executes the program, the steps in the method of any of the above embodiments are implemented.

[0116] The memory 1102 stores computer programs that can be run on the processor. The memory 1102 is configured to store instructions and applications executable by the processor 1101. It can also cache data to be processed or processed by the processor 1101 and various modules in the computer device 1100 (for example, image data, audio data, voice communication data, and video communication data). It can be implemented through flash memory (FLASH) or random access memory (RAM).

[0117] When the processor 1101 executes the program, the steps of any of the above-mentioned methods for adjusting fuel injection parameters are implemented. The processor 1101 generally controls the overall operation of the computer device 1100.

[0118] An embodiment of the present application provides a computer storage medium storing one or more programs, which can be executed by one or more processors to implement the steps of the injection parameter adjustment method of any of the above embodiments.

[0119] It should be noted that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0120] The processor may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic device that implements the functions of the processor may also be other electronic devices, which are not specifically limited in the embodiments of the present application.

[0121] The above-mentioned computer storage medium / memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); it can also be various terminals including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0122] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned steps / processes does not mean the order of execution, and 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 the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0123] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0124] 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 schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0125] The units described above as separate components may or may not be physically separated, and the components displayed 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 according to actual needs to achieve the purpose of the scheme of this embodiment.

[0126] In addition, the functional units in the embodiments of the present application can all 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 above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units. It can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiments can be completed by hardware related to program instructions, and the above-mentioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiments; and the above-mentioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), magnetic disks or optical disks.

[0127] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function 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 present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0128] The above is only an implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for adjusting fuel injection parameters, characterized in that: include: Determine that the vehicle enters a series-parallel hybrid operating state; Record a set of initial injection parameters of the engine; Performing fuel injection parameter self-learning to obtain multiple groups of test fuel injection parameters based on a group of initial fuel injection parameters; sequentially using a plurality of groups of the test fuel injection parameters, running the engine, and calculating a plurality of test fuel consumptions of the vehicle; wherein each group of the test fuel injection parameters corresponds to one test fuel consumption; and the running time of each group of the test fuel injection parameters is the first self-learning time; Based on comparing the plurality of test fuel consumptions, determining the best self-learning result among the plurality of groups of test fuel injection parameters; The initial injection parameters are replaced by the self-learning results.

2. The method for adjusting fuel injection parameters according to claim 1, characterized in that: A set of initial injection parameters characterizes n initial injections of the engine in one working cycle; Each group of the test injection parameters represents n+1 test injections of the engine in one working cycle; n is greater than or equal to 1; The method of obtaining a plurality of test injection parameters based on a set of the initial injection parameters includes: The initial injection parameters of the i-th initial injection are divided into the test injection parameters of two test injections, and the other initial injection parameters are retained 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; Continue to segment the initial injection parameters of the i+1th initial injection until the i+1th initial injection is reached.

2. Self-learning time, or until the segmentation of the initial injection parameters of the nth initial injection is completed.

3. The method for adjusting fuel injection parameters according to claim 2, characterized in that: The first self-learning time is 10s; The second self-learning time is 2 minutes.

4. The method for adjusting fuel injection parameters according to claim 2, characterized in that: The initial injection parameters include: initial injection angle; the test injection parameters include: test injection angle; The step of dividing the initial injection parameters of the i-th initial injection into the test injection parameters of two test injections comprises: Using the initial injection angle of the i-th initial injection as the test injection angle of the test injection; The test injection angle of another test injection is determined between the initial injection angles of two adjacent initial injections.

5. The method for adjusting fuel injection parameters according to claim 4, characterized in that: The step of determining the test injection angle of another test injection between the initial injection angles of two adjacent initial injections includes: The median of the initial injection angles of two adjacent initial injections is used as the test injection angle of another test injection.

6. The method for adjusting fuel injection parameters according to claim 2, characterized in that: The initial injection parameters also include: initial injection ratio; the test injection parameters also include: test injection ratio; The step of dividing the initial injection parameters of the i-th initial injection into the test injection parameters of two test injections further includes: The initial injection ratio of the i-th initial injection is divided into the test injection ratios of two test injections; wherein the sum of the test injection ratios of the two test injections is equal to the initial injection ratio of the i-th initial injection.

7. The method for adjusting fuel injection parameters according to claim 6, characterized in that: The test injection ratio of the two test injections is equal.

8. The method for adjusting fuel injection parameters according to claim 1, characterized in that: The step of determining that the vehicle enters the series-parallel hybrid operating state includes: Recording the real-time key parameters of the vehicle; the key parameters include: vehicle speed, fuel consumption and injection parameters; If the key parameters meet the preset conditions, it is determined that the vehicle enters the series-parallel hybrid operating state.

9. The method for adjusting fuel injection parameters according to claim 1, characterized in that: Before the fuel injection parameter self-learning is performed, the fuel injection parameter adjustment method further includes: Recording the real-time environmental parameters of the vehicle; the environmental parameters include: temperature, atmospheric pressure and intake manifold temperature; The fuel injection parameter self-learning includes: If the environmental parameter indicates that the vehicle is in a high temperature environment, a low temperature environment or a high altitude environment, the injection parameter self-learning is performed.

10. A fuel injection parameter adjustment device, characterized in that: include: A determination module is configured to determine that the vehicle enters a series-parallel hybrid operating state; a recording module configured to record a set of initial fuel injection parameters of the engine; A self-learning module is configured to perform self-learning of injection parameters, and obtain multiple groups of test injection parameters based on a group of initial injection parameters; and, sequentially using a plurality of sets of the test fuel injection parameters, operating the engine, and calculating a plurality of test fuel consumptions of the vehicle; And, based on comparing the multiple test fuel consumptions, determining the best self-learning result among the multiple groups of test injection parameters; and replacing the initial injection parameters with the self-learning results; wherein each group of the test injection parameters corresponds to one test fuel consumption; and the running time of each group of the test injection parameters is the first self-learning time.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the program, the steps in the method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 9 are implemented.

Images