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 series-parallel hybrid 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 to run the engine, and calculating a plurality of test fuel consumptions of the vehicle; wherein each set of test fuel injection parameters corresponds to a test fuel consumption; the running time of each set of test fuel injection parameters is a first self-learning time; based on comparing the plurality of test fuel consumptions, 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] A hybrid vehicle is a vehicle whose drive system consists of two or more individual drive systems that can operate simultaneously. The vehicle's driving power is provided individually or jointly by each individual drive system, depending on the actual driving conditions. Hybrid vehicles typically refer to hybrid electric vehicles (HEVs), which use a traditional internal combustion engine (diesel or gasoline engine) and an electric motor as power sources.

[0003] In related technologies, hybrid vehicles experience unstable engine output torque under high-power output conditions such as climbing hills. As the intake air temperature rises, the engine's output torque becomes unstable, leading to a problem where the State of Charge (SOC) cannot reach the equilibrium point. Consequently, the engine speed becomes too high, entering an enriched state, resulting in increased fuel consumption and deterioration of NVH (Noise, Vibration, and Harshness) quality, thus reducing the user experience. 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 reduce fuel consumption 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 a series-parallel hybrid operating 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 test fuel consumptions of the vehicle; wherein each set of test fuel injection parameters corresponds to one test fuel consumption; 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 consumptions based on a comparison; 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 in one working cycle; each set of test injection parameters represents n+1 test injections of the engine in one working cycle; n is greater than or equal to 1; obtaining multiple sets of test injection parameters based on a set of initial injection parameters includes: dividing the initial injection parameters of the i-th initial injection into test injection parameters of two test injections, 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 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.

[0008] In the above scheme, the first self-learning time is 10 seconds; the second self-learning time is 2 minutes.

[0009] In the above scheme, the initial injection parameters include: initial injection angle; the test injection parameters include: test injection angle; dividing the initial injection parameters of the i-th initial injection into the test injection parameters of two test injections includes: taking 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.

[0010] In the above scheme, determining the test injection angle of another test injection between two adjacent initial injection angles includes: taking the median of the initial injection angles of two adjacent initial injections as the test injection angle of the other test injection.

[0011] In the above scheme, the initial injection parameters further include: an initial injection ratio; the test injection parameters further include: a test injection ratio; the step of dividing the initial injection parameters of the i-th initial injection into the test injection parameters of the two test injections further includes: dividing the initial injection ratio of the i-th initial injection into the test injection ratio of the 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.

[0012] In the above scheme, the test injection ratio is equal in both test injections.

[0013] In the above scheme, determining that the vehicle has entered the series-parallel hybrid operating condition includes: recording the vehicle's real-time key parameters; the key parameters include: vehicle speed, fuel consumption, and fuel injection parameters; if the key parameters meet preset conditions, then it is determined that the vehicle has entered the series-parallel hybrid operating condition.

[0014] In the above scheme, before performing fuel injection parameter self-learning, the fuel injection parameter adjustment method further includes: recording the vehicle's real-time environmental parameters; the environmental parameters include: temperature, atmospheric pressure, and intake manifold temperature; the fuel 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 the fuel injection parameter self-learning is performed.

[0015] This application embodiment also provides a fuel injection parameter adjustment device, including: a determining module configured to determine that a vehicle enters a series-parallel hybrid operating 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 test fuel consumptions of the vehicle; and determining the optimal self-learning result among the multiple sets of test fuel consumptions based on a comparison of the multiple sets of test fuel injection parameters; and replacing the initial fuel injection parameters with the self-learning result; wherein each set of test fuel injection parameters corresponds to one test fuel consumption; 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 parameter with the lowest fuel consumption is then determined from these multiple sets, and the injection parameters under the series-parallel hybrid driving condition are updated. This completes the self-learning of the injection parameters under the series-parallel hybrid driving condition. In this way, under the series-parallel hybrid driving condition, the injection parameters can be adjusted in real time according to the specific operating conditions of the vehicle, thereby 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 This is a schematic diagram of the series-parallel hybrid operating condition in an embodiment of this application;

[0021] Figure 3 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 2 ;

[0022] Figure 4 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 3 ;

[0023] Figure 5 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 4 ;

[0024] Figure 6 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 5 ;

[0025] Figure 7 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 6 ;

[0026] Figure 8 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 7 ;

[0027] Figure 9 This is a schematic diagram of the composition structure of a fuel injection parameter adjustment device provided in an embodiment of this application;

[0028] Figure 10 This is a schematic diagram of the hardware entity of a computer device provided in an embodiment of this application. Detailed Implementation

[0029] 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.

[0030] 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.

[0031] 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 limit the scope of this application.

[0032] 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.

[0033] 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.

[0034] S101. Determine if the vehicle has entered the series-parallel hybrid operating condition.

[0035] In this embodiment of the application, reference is made to Figure 2 The series-parallel hybrid mode refers to a hybrid system where the engine and the drive motor can drive the vehicle forward independently (i.e., parallel hybrid), or the engine can drive the generator to generate electricity and provide power to the drive motor, which in turn assists in driving the car forward (i.e., series hybrid).

[0036] Continue to refer to Figure 2 In parallel hybrid systems, both the engine and the drive motor can independently provide power to the transmission system, thus providing good power to the vehicle. However, this results in higher energy consumption, which is not conducive to achieving the State of Charge (SOC) balance point. In series hybrid systems, the engine drives the generator, forming a series powertrain consisting of an engine, generator, and drive motor. This results in lower fuel consumption at low and medium speeds compared to traditional gasoline vehicles, but higher fuel consumption at high speeds.

[0037] S102. Record a set of initial fuel injection parameters for the engine.

[0038] In this embodiment of the application, after determining that the vehicle has entered the series-parallel hybrid operating 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 angle and the injection ratio.

[0039] 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, 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, in a series-parallel hybrid system, if the engine performs 3 injections within one working cycle, the injection angle and injection ratio of these 3 injections can be recorded as a set of initial injection parameters.

[0040] 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.

[0041] 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.

[0042] S104. The engine is run sequentially using multiple sets of test fuel injection parameters, and multiple test fuel consumptions of the vehicle are calculated; each set of test fuel injection parameters corresponds to one test fuel consumption; the running time of each set of test fuel injection parameters is the first self-learning time.

[0043] In this embodiment, multiple sets of test injection parameters can be used sequentially to run the engine and calculate the corresponding multiple test fuel consumptions. 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 test fuel consumption 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 test fuel consumption 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 fuel consumption calculations caused by excessively short engine running time and low self-learning efficiency caused by excessively long engine running time, thus balancing the accuracy of fuel consumption testing and the efficiency of self-learning.

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

[0046] In this embodiment of the application, after obtaining multiple test fuel consumptions, the multiple test fuel consumptions can be compared to determine the lowest test fuel consumption, and the set of test fuel injection parameters corresponding to the lowest test fuel consumption can be used as the self-learning result; that is, the optimal one is determined among multiple sets of test fuel injection parameters to be 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 series-parallel hybrid conditions are updated, thus completing one self-learning of the fuel injection parameters. Accordingly, under series-parallel hybrid conditions, the vehicle can operate according to the new fuel injection parameters, thereby reducing fuel consumption.

[0049] Understandably, in series-parallel hybrid operation, achieving optimal fuel consumption is highly complex due to the selective use of parallel and / or series hybrid systems. In this embodiment, multiple sets of test injection parameters can be obtained based on an initial set of injection parameters. The parameter with the lowest fuel consumption is then determined, and the injection parameters for series-parallel hybrid operation are updated accordingly. This achieves self-learning of the injection parameters for series-parallel hybrid operation. Consequently, the injection parameters can be adjusted in real-time according to the specific operating conditions of the vehicle under series-parallel hybrid conditions, thereby reducing fuel consumption and making the vehicle more adaptable to different conditions (including high temperature, high altitude, and extreme cold), thus improving robustness.

[0050] In some embodiments of this application, it can be achieved through Figure 3 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. Divide the initial injection parameters of the i-th initial injection into test injection parameters for two test injections, 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+1 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 divided into two test fuel injections to obtain test fuel injection parameters. Specifically, before fuel injection parameter self-learning, the engine performs n initial fuel injections in one working cycle, which correspond to n initial fuel injection parameters. Then, the i-th initial fuel injection in the n initial fuel injections can be divided into two test fuel injections, that is, the initial fuel injection parameters of the i-th initial fuel injection are divided into test fuel injection parameters for the two test fuel injections. Then, the test fuel injection parameters of the two test fuel injections obtained from the division, along with the other initial fuel injection parameters from the original n initial fuel injections excluding the i-th initial fuel injection, are combined to obtain a set of test fuel injection parameters, that is, n+1 test fuel injection parameters corresponding to n+1 test fuel injections.

[0054] For example, before the fuel injection parameter self-learning, the engine performs 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 first initial fuel injection can be divided into two test fuel injections, yielding two test fuel injection parameters. Then, these two test fuel injection parameters, along with the original initial fuel injection parameters from the second and third initial fuel injections, are combined to obtain a set of test fuel injection parameters, that is, four test fuel injection parameters corresponding to the four test fuel injections.

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

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

[0057] S202. Continue to segment 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.

[0058] In this embodiment, after segmenting the initial injection parameters of the i-th initial injection, the initial injection parameters of the (i+1)-th initial injection can be further segmented. That is, the (i+1)-th initial injection in the n initial injections can be divided into two test injections, i.e., the initial injection parameters of the (i+1)-th initial injection are divided into two test injection parameters. Then, the two test injection parameters obtained from the segmentation, along with the other initial injection parameters from the original n initial injections excluding the (i+1)-th initial injection, are combined to obtain a set of test injection parameters (i.e., n+1 test injection parameters). This process is repeated until the second self-learning time is reached, or until the initial injection parameters of the n-th initial injection are segmented.

[0059] For example, before the fuel injection parameter self-learning, the engine performs 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. After segmenting the first initial fuel injection and obtaining at least one set of test fuel injection parameters, the second initial fuel injection can be segmented to obtain two test fuel injection parameters. These two test fuel injection parameters, along with the original initial fuel injection parameters from the first and third injections, are then combined to obtain a set of test fuel injection parameters. This process is repeated until the second self-learning time is reached, or until the initial fuel injection parameters for the third initial fuel injection are segmented.

[0060] In this embodiment, after the self-learning of injection parameters has continued for a second self-learning period, the self-learning will end, and the set of test injection parameters with the lowest fuel consumption obtained so far will be taken as the self-learning result. Then, the vehicle can operate in series-parallel hybrid mode according to the currently obtained self-learning result. Furthermore, when the vehicle re-enters series-parallel hybrid mode, the self-learning of injection parameters can continue according to the previous progress. For example, if the previous self-learning segmented the initial injection parameters of the original second initial injection, the current self-learning can directly begin segmenting the initial injection parameters of the original third initial injection.

[0061] In some embodiments of this application, the second self-learning time can be 2 minutes, that is, the self-learning will end after the fuel injection parameters have been self-learned for 2 minutes.

[0062] In this embodiment, once the initial injection parameters for the nth initial injection are segmented, self-learning can end, and the set of test injection parameters with the lowest fuel consumption obtained so far can be used as the self-learning result. Then, the vehicle can operate in series-parallel hybrid mode according to the currently obtained self-learning result. Furthermore, when the vehicle re-enters series-parallel hybrid mode, the segmentation of the initial injection parameters for the first initial injection can begin again.

[0063] Understandably, dividing the engine's initial fuel injection into two test injections to obtain at least one set of test injection parameters allows for real-time adjustment of injection parameters based on the specific operating conditions of the vehicle under series-parallel hybrid operation. This reduces fuel consumption and makes the vehicle more adaptable to different situations, improving robustness.

[0064] Meanwhile, setting a second self-learning time as the end time of each self-learning session can effectively control the time of each self-learning session. This allows users to operate the vehicle according to the self-learning results for most of the time after entering the series-parallel hybrid operating mode, that is, to operate the vehicle according to the fuel injection parameters with lower fuel consumption, thus improving the user experience.

[0065] In some embodiments of this application, the initial injection parameters further include: an initial injection angle; the test injection parameters include: a test injection angle; this can be achieved through... Figure 4 The shown S301 to S302 implement this. Figure 3 The step S201 shown will be explained in conjunction with each step.

[0066] S301. Take the initial injection angle of the i-th initial injection as the test injection angle of the test injection.

[0067] In this embodiment of the application, during 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 test injections after the division.

[0068] S302. Between the initial injection angles of two adjacent initial injections, determine the test injection angle of another test injection.

[0069] In this embodiment of the application, during the process of dividing the i-th initial fuel injection into two test fuel injections, the test injection angle of the other test fuel injection can be determined between the initial injection angles of the two adjacent initial fuel injections. The determined test injection angle value lies between the initial injection angle values ​​of the two adjacent initial fuel injections.

[0070] For example, the test injection angle can be determined between the initial injection angles of the first and second initial injections, wherein the test injection angle is less than the initial injection angle of the first initial injection and greater than the initial injection angle of the second initial injection. Alternatively, the test injection angle can be determined between the initial injection angles of the second and third initial injections, wherein the test injection angle is less than the initial injection angle of the second initial injection and greater than the initial injection angle of the third initial injection. Similarly, the test injection angle can be determined between the initial injection angles of the (n-1)th and nth initial injections, wherein the test injection angle is less than the initial injection angle of the (n-1)th initial injection and greater than the initial injection angle of the nth initial injection.

[0071] It should be noted that within one working cycle, the engine performs n initial fuel injections sequentially in chronological order, and correspondingly, the initial injection angles of the n initial fuel injections decrease sequentially. That is, the initial injection angle of the (i-1)th initial fuel injection is greater than the initial injection angle of the ith initial fuel injection; and the initial injection angle of the ith initial fuel injection is greater than the initial injection angle of the (i+1)th initial fuel injection.

[0072] It should be noted that the initial injection angle or test injection angle includes both the injection start angle and the injection end angle. The injection start angle refers to the angle at the beginning of each injection, and the injection end angle refers to the angle at the end of each injection. The injection end angle for each injection is smaller than the injection start angle. Accordingly, each reduction of a unit angle will simultaneously reduce both the injection start angle and the injection end angle by a unit angle.

[0073] In this embodiment of the application, the injection start angle of another test injection can be determined between the injection start angles of two adjacent initial injections; alternatively, the injection end angle of another test injection can be determined between the injection end angles of two adjacent initial injections.

[0074] For example, before performing self-learning of injection parameters, the engine performed three initial injections within one working cycle, i.e., n=3. The injection start angles of the three initial injections were SOI1, SOI2, and SOI3, and the injection end angles were EOI1, EOI2, and EOI3, respectively. The injection start angle SOI4 and injection end angle EOI4 of another test injection were then determined.

[0075] Furthermore, the test injection angle of the test fuel can be determined between the initial injection angles of the first and second initial fuel injections, where SOI1>SOI4>SOI2 and EOI1>EOI4>EOI2. Alternatively, the test injection angle of the test fuel can be determined between the initial injection angles of the second and third initial fuel injections, where SOI2>SOI4>SOI3 and EOI2>EOI4>EOI3.

[0076] Understandably, in the process of dividing the i-th initial fuel injection into two test fuel injections, the initial injection angle of the i-th initial fuel injection is used as the test injection angle of the first test fuel injection. Simultaneously, the test injection angle of the second test fuel injection is determined between the initial injection angles of two adjacent initial fuel injections. This achieves self-learning of the injection angle, meaning that under series-parallel hybrid conditions, the fuel injection parameters can be adjusted in real time according to the specific operating conditions of the vehicle. This reduces fuel consumption and makes the vehicle more adaptable to different situations, improving robustness.

[0077] In some embodiments of this application, it can be achieved through Figure 5 The S303 shown implements Figure 4 The step S302 shown will be explained in conjunction with each step.

[0078] S303. Take the median of the initial injection angles of two consecutive initial injections as the test injection angle for another test injection.

[0079] In this embodiment of the application, during the process of determining the test injection angle of another test injection, the median of the initial injection angles of two adjacent initial injections can be used as the test injection angle of the other test injection. That is, the interval between the injection angles of the test injection and the two initial injections is equal, and the test injection and the two initial injections are "equal interval injections".

[0080] For example, the injection initiation angle SOI1 for the first initial injection is 340°, and the injection initiation angle SOI2 for the second initial injection is 260°. The median of SOI1 and SOI2, 300°, can be used as the injection initiation angle SOI4 for the test injection. The injection angle interval between SOI4 and SOI1 is 40°, and the injection angle interval between SOI4 and SOI2 is also 40°. That is, the test injection and the two initial injections are "equal interval injections".

[0081] Understandably, the median of the initial injection angles of two consecutive initial injections is used as the test injection angle for another test injection, ensuring that the test injection and the two initial injections are "equally spaced." This results in a more uniform distribution of the test injection in terms of injection time and angle, leading to smoother and more continuous engine operation, which is beneficial for enhancing engine power.

[0082] In some embodiments of this application, the initial injection parameters further include: an initial injection ratio; the test injection parameters further include: a test injection ratio; and can also be obtained through... Figure 6 The S304 shown implements Figure 3 The step S201 shown will be explained in conjunction with each step.

[0083] S304. Divide the initial injection ratio of the i-th initial injection into two test injection ratios.

[0084] In this embodiment of the application, during the process of dividing the i-th initial fuel injection into two test fuel injections, the initial injection ratio of the i-th initial fuel injection can be equally divided into the test injection ratios of the two test fuel injections. That is, the test injection ratios of the two divided test fuel injections are equal; at the same time, the sum of the test injection ratios of the two test fuel injections is equal to the initial injection ratio of the i-th initial fuel injection.

[0085] For example, before the fuel injection parameter self-learning, the engine performed three initial fuel injections in one working cycle, i.e., n=3; the initial injection ratios of the three initial fuel injections were P1, P2, and P3, respectively. If the original first initial fuel injection is divided into two injections, then the original initial injection ratio P1 of the first initial fuel injection is divided into two test injection ratios P1_1 and P1_2; where P1_1 + P1_2 = P1. The test injection ratios obtained by dividing the original second and third initial fuel injections are similar.

[0086] In some embodiments of this 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 divided equally into two test injection ratios. For example, the original initial injection ratio P1 of the first initial injection can be divided into two test injection ratios P1_1 and P1_2; where P1_1 = P1_2, and P1_1 + P1_2 = P1.

[0087] Understandably, in the process of dividing the i-th initial fuel injection into two test fuel injections, the initial injection ratio of the i-th initial fuel injection is divided into the test injection ratios of the two test fuel injections. Thus, the sum of the test injection ratios after the division equals the initial injection ratio before the division. Therefore, the injection ratio is adjusted without changing the sum of all injection ratios within a single work cycle. Simultaneously, combined with self-learning of the injection angle, fuel injection parameters can be adjusted in real time according to the specific operating conditions of the vehicle under series-parallel hybrid conditions. This reduces fuel consumption and makes the vehicle more adaptable to different situations, improving robustness.

[0088] In some embodiments of this application, it can be achieved through Figure 7 The shown S401 to S402 implement this. Figure 1 S101, shown below, will be explained in conjunction with each step.

[0089] S401, Records real-time key parameters of the vehicle; key parameters include: vehicle speed, fuel consumption and fuel injection parameters.

[0090] S402. If the key parameters meet the preset conditions, then the vehicle is determined to enter the series-parallel hybrid operating condition.

[0091] In this embodiment, key vehicle parameters, such as vehicle speed, fuel consumption, and fuel injection parameters, can be recorded in real time. Furthermore, the system uses these key parameters to determine if the vehicle has entered a series-parallel hybrid operating condition; that is, if the key parameters meet preset conditions, the system determines that the vehicle has entered a series-parallel hybrid operating condition.

[0092] In some embodiments of this application, it can be achieved through Figure 8 The shown S501 to S502 implement this. Figure 1 S103, shown below, will be explained in conjunction with each step.

[0093] S501, Records real-time environmental parameters of the vehicle; environmental parameters include: temperature, atmospheric pressure and intake manifold temperature.

[0094] S502. If the environmental parameters indicate that the vehicle is in a high-temperature environment, a low-temperature environment, or a high-altitude environment, then the fuel injection parameters will be self-learned.

[0095] In this embodiment, before performing fuel injection parameter self-learning, environmental parameters such as temperature, atmospheric pressure, and intake manifold temperature can be recorded to determine whether the vehicle is in a special environmental scenario. 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 has entered a series-parallel hybrid operating condition, then fuel injection parameter self-learning can begin.

[0096] In some embodiments of this 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-temperature environment. When the atmospheric pressure is less than 80 kPa, the vehicle is in a high-altitude environment.

[0097] Understandably, environmental parameters are used to determine the vehicle's environment, and then, when the vehicle is in a special environment, it learns its fuel injection parameters. This improves the vehicle's adaptability to special environmental scenarios and reduces fuel consumption in those scenarios.

[0098] Figure 9 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 9 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 a series-parallel hybrid operating 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 test fuel consumptions of the vehicle; determining the optimal self-learning result among the multiple sets of test fuel consumptions based on a comparison of the multiple sets of test fuel injection parameters; and replacing the initial fuel injection parameters with the self-learning result; wherein each set of test fuel injection parameters corresponds to one test fuel consumption; and the running time of each set of test fuel injection parameters is the first self-learning time.

[0099] In some embodiments of this application, a set of initial injection parameters represents n initial injections of the engine in one working cycle; each set of test injection parameters represents n+1 test injections of the engine in one working cycle; n is greater than or equal to 1. The self-learning module 830 is further configured to divide 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; continue 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; wherein i is greater than or equal to 1 and less than or equal to n.

[0100] In some embodiments of this application, the first self-learning time is 10 seconds; the second self-learning time is 2 minutes.

[0101] In some embodiments of this application, the initial injection parameters include: an initial injection angle; 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 of a test injection; and to determine the test injection angle of another test injection between the initial injection angles of two adjacent initial injections.

[0102] In some embodiments of this 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.

[0103] In some embodiments of this application, the initial injection parameters further include: an initial injection ratio; the test injection parameters further include: a test injection ratio. The self-learning module 830 is also configured to divide 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.

[0104] In some embodiments of this application, the test injection ratios for the two test injections are equal.

[0105] 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, fuel consumption, and fuel injection parameters. The determining module 810 is further configured to determine that the vehicle has entered a series-parallel hybrid operating condition if the key parameters meet preset conditions.

[0106] In some embodiments of this application, the recording module 820 is further configured to record real-time environmental parameters of the vehicle; the environmental parameters include temperature, atmospheric pressure, and intake manifold temperature. The self-learning module 830 is further configured to perform fuel 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Figure 10 This application provides a hardware entity diagram of a computer device as an embodiment of the present application, such as... Figure 10 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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: Determine if the vehicle has entered the series-parallel hybrid operating condition; 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 fuel injection parameters, and multiple test fuel consumptions of the vehicle are calculated; wherein, each set of the aforementioned test fuel injection parameters corresponds to one test fuel consumption; the running time of each set of the aforementioned test fuel injection parameters is the first self-learning time; Based on the comparison of multiple test fuel consumptions, the optimal self-learning result is determined among multiple sets of test fuel 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 in one working cycle; each set of 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 a set of initial injection parameters, multiple sets of test injection parameters are obtained, including: The initial injection parameters of the i-th initial injection are divided into the test injection parameters of the 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 segmenting the initial injection parameters for the (i+1)th initial injection until the second self-learning time is reached, or until the segmentation of the initial injection parameters for the nth initial injection is completed.

2. The method for adjusting injection parameters according to claim 1, characterized in that, The first self-learning time is 10 seconds; The second self-learning time is 2 minutes.

3. The method for adjusting injection parameters according to claim 1, 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 the two test injections includes: The initial injection angle of the i-th initial injection is taken as the test injection angle of the test injection. Between the initial injection angles of two consecutive initial injections, the test injection angle of another test injection is determined.

4. The method for adjusting injection parameters according to claim 3, characterized in that, Determining the test injection angle of another test injection between two adjacent initial injection angles includes: The median of the initial injection angles of two consecutive initial injections is taken as the test injection angle of the next test injection.

5. The method for adjusting injection parameters according to claim 1, characterized in that, The initial injection parameters also include: the initial injection ratio; the test injection parameters also include: the test injection ratio; The step of dividing the initial injection parameters of the i-th initial injection into the test injection parameters of the two test injections further includes: The initial injection ratio of the i-th initial injection is divided into two test injection ratios; wherein the sum of the two test injection ratios is equal to the initial injection ratio of the i-th initial injection.

6. The method for adjusting injection parameters according to claim 5, characterized in that, The test injection ratios were equal in both test injections.

7. The method for adjusting injection parameters according to claim 1, characterized in that, The determination that the vehicle has entered the series-parallel hybrid operating condition includes: Record the vehicle's key parameters in real time; these key parameters include: vehicle speed, fuel consumption, and fuel injection parameters. If the key parameters meet the preset conditions, then the vehicle is determined to enter the series-parallel hybrid operating condition.

8. The method for adjusting injection parameters according to claim 1, characterized in that, Before performing the self-learning of injection parameters, the method for adjusting injection parameters further includes: Record the vehicle's real-time environmental parameters; these environmental parameters include: temperature, atmospheric pressure, and intake manifold temperature. The process of performing self-learning of fuel injection parameters 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 fuel injection parameters will be self-learned.

9. A fuel injection parameter adjustment device, characterized in that, include: The determination module is configured to determine when a vehicle enters a series-parallel hybrid operating condition; 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 sequentially using multiple sets of the aforementioned test injection parameters, and multiple test fuel consumptions of the vehicle are calculated. Furthermore, based on the comparison of multiple test fuel consumptions, the optimal self-learning result is determined among multiple sets of test fuel injection parameters; and the initial fuel injection parameters are replaced with the self-learning result; wherein each set of test fuel injection parameters corresponds to one test fuel consumption; and the running time of each set of test fuel injection parameters is the first self-learning time. Wherein, a set of initial injection parameters represents n initial injections of the engine in one working cycle; each set of test injection parameters represents n+1 test injections of the engine in one working cycle; n is greater than or equal to 1; The self-learning module is further configured to divide the initial injection parameters of the i-th initial injection into the test injection parameters of two test injections, and retain the other initial injection parameters to obtain at least one set of test injection parameters; and to continue dividing 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; 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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