Fuel injection parameter adjustment method and device, equipment and storage medium
By performing self-learning of fuel injection parameters in hybrid vehicles and selecting the optimal fuel injection parameters, the problems of increased fuel consumption and NVH caused by unstable engine output torque are solved, achieving reduced fuel consumption and improved robustness.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DONGFENG MOTOR GRP
- Filing Date
- 2023-11-24
- Publication Date
- 2026-07-21
AI Technical Summary
In hybrid vehicles, under high-power output conditions such as climbing hills, the engine output torque becomes unstable as the intake air temperature rises, causing the State of Charge (SOC) to fail to reach equilibrium. This leads to increased fuel consumption and NVH (noise, vibration, and harshness) issues, reducing the user experience.
By determining that the vehicle has entered the series-parallel hybrid operating condition, recording the initial fuel injection parameters, and performing self-learning of the fuel injection parameters, multiple sets of test fuel injection parameters are obtained, fuel consumption is calculated, and the optimal fuel injection parameters are selected to update the initial parameters, thereby achieving adaptive adjustment of the fuel injection parameters.
In series-parallel hybrid operation, the fuel injection parameters are adjusted in real time to reduce fuel consumption, improve vehicle robustness, adapt to different driving conditions, and enhance the user experience.
Smart Images
Figure CN117449995B_ABST
Abstract
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 injections of the engine in one working cycle; each set of test injection parameters represents n+1 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 injection into test injection parameters for two 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 injection until the second self-learning time is reached, or until the division of the initial injection parameters of the n-th 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: an initial injection angle; the test injection parameters include: a test injection angle; dividing the initial injection parameters of the i-th injection into the test injection parameters of two injections includes: using the initial injection angle of the i-th injection as the test injection angle of one injection; within the range from the learning start angle to the learning end angle, stepping down by a unit angle amount to obtain at least one test injection angle for another injection; wherein, the difference between the learning end angle and the learning start angle is greater than or equal to the stepping down by the unit angle amount.
[0010] In the above scheme, if i equals 1, the learning start angle is 360°, and the learning end angle is the initial injection angle of the (i+1)th injection; if i is greater than 1 and less than n, the learning start angle is the initial injection angle of the (i-1)th injection, and the learning end angle is the initial injection angle of the (i+1)th injection; if i equals n, the learning start angle is the initial injection angle of the (i-1)th injection, and the learning end angle is 80°.
[0011] In the above scheme, the unit angle amount that is reduced step by step is 5°.
[0012] 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 injection into the test injection parameters of two injections further includes: dividing the initial injection ratio of the i-th injection into the test injection ratio of two injections on average; wherein, the test injection ratios of the two injections are equal; the sum of the test injection ratios of the two injections is equal to the initial injection ratio of the i-th injection.
[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] 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.
[0015] 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.
[0016] 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.
[0017] 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
[0018] Figure 1 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 1 ;
[0019] Figure 2 This is a schematic diagram of the series-parallel hybrid operating condition in an embodiment of this application;
[0020] Figure 3 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 2;
[0021] Figure 4 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 3 ;
[0022] Figure 5 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 4 ;
[0023] Figure 6 A schematic diagram of the implementation process of a fuel injection parameter adjustment method provided in this application embodiment. Figure 5 ;
[0024] Figure 7 This is a schematic diagram of the composition structure of a fuel injection parameter adjustment device provided in an embodiment of this application;
[0025] Figure 8 This is a schematic diagram of the hardware entity of a computer device provided in an embodiment of this application. Detailed Implementation
[0026] 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.
[0027] 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.
[0028] 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.
[0029] 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.
[0030] Figure 1This 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.
[0031] S101. Determine if the vehicle has entered the series-parallel hybrid operating condition.
[0032] 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).
[0033] 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.
[0034] S102. Record a set of initial fuel injection parameters for the engine.
[0035] 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.
[0036] Here, a set of initial injection parameters represents the n injections performed by the engine in one working cycle. That is, the engine performs n injections during one revolution, and each injection is performed according to 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 in one working cycle, the injection angle and injection ratio of these 3 injections can be recorded as a set of initial injection parameters.
[0037] 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.
[0038] 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.
[0039] 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.
[0040] 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.
[0041] 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.
[0042] S105. Based on the comparison of multiple test fuel consumptions, the optimal self-learning result is determined among multiple sets of test injection parameters.
[0043] 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.
[0044] S106. Replace the initial injection parameters with the self-learning results.
[0045] 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.
[0046] 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.
[0047] 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.
[0048] S201. Divide the initial injection parameters of the i-th injection into test injection parameters for two injections, and retain the other initial injection parameters to obtain at least one set of test injection parameters.
[0049] In this embodiment, a set of initial injection parameters represents n injections of the engine in one working cycle, and each set of test injection parameters represents n+1 injections of the engine in one working cycle. Here, 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.
[0050] In other words, a single fuel injection in the engine can be divided into two injections to obtain test fuel injection parameters. Specifically, before fuel injection parameter self-learning, the engine performs n fuel injections in one working cycle, and these n injections correspond to n initial fuel injection parameters. Then, the i-th injection in the n injections can be divided into two injections, that is, the initial fuel injection parameters of the i-th injection are divided into two test fuel injection parameters. Then, the two test fuel injection parameters obtained from the division, along with the other initial fuel injection parameters from the original n injections excluding the i-th injection, are combined to obtain a set of test fuel injection parameters (i.e., n+1 test fuel injection parameters).
[0051] For example, before the fuel injection parameters are self-learned, the engine performs three fuel injections in one working cycle, resulting in three initial fuel injection parameters. The first fuel injection can be divided into two injections to obtain two test fuel injection parameters. Then, these two test fuel injection parameters, along with the original initial fuel injection parameters for the second and third injections, are combined to obtain a set of test fuel injection parameters (corresponding to four fuel injections).
[0052] In this embodiment, the i-th fuel injection can be divided into one segment to obtain a set of test fuel injection parameters. Correspondingly, the i-th fuel injection can also be divided into multiple different segments to obtain multiple sets of test fuel injection parameters.
[0053] It should be noted that the n fuel injections in this application are all ordered according to the injection time sequence. That is to say, within one working cycle, the engine performs the 1st fuel injection, the 2nd fuel injection, and so on, up to the nth fuel injection, in chronological order. Further details will not be elaborated below.
[0054] S202. Continue to segment the initial injection parameters of the (i+1)th injection until the second self-learning time is reached, or until the segmentation of the initial injection parameters of the nth injection is completed.
[0055] In this embodiment, after segmenting the initial injection parameters of the i-th injection, the initial injection parameters of the (i+1)-th injection can be further segmented. That is, the (i+1)-th injection in the n injections can be divided into two injections, i.e., the initial injection parameters of the (i+1)-th 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 injections excluding the (i+1)-th 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 injection are segmented.
[0056] For example, before the fuel injection parameter self-learning, the engine performed three fuel injections in one working cycle, each with three initial fuel injection parameters. After segmenting the original first fuel injection to obtain at least one set of test fuel injection parameters, the original second fuel injection can be segmented to obtain two test fuel injection parameters. Then, these two test fuel injection parameters, along with the original initial fuel injection parameters from the first and third fuel injections, are combined to obtain a set of test fuel injection parameters (corresponding to four fuel injections).
[0057] 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 injection, the current self-learning can directly begin segmenting the initial injection parameters of the original third injection. In some embodiments of this application, the second self-learning period can be 2 minutes, meaning that the self-learning will end after 2 minutes.
[0058] In this embodiment, once the initial injection parameters for the nth 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 injection can begin again.
[0059] Understandably, dividing the engine's single fuel injection into two injections allows for the acquisition of at least one set of test injection parameters. This enables real-time adjustment of injection parameters based on the specific operating conditions of the vehicle under series-parallel hybrid operation, thereby reducing fuel consumption and making the vehicle more adaptable to different situations, thus improving robustness.
[0060] 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.
[0061] In some embodiments of this application, the initial injection parameters include: an initial injection angle; the test injection parameters include: a test injection angle; and can be determined by... Figure 4 The shown S301 to S302 implement this. Figure 3 The step S201 shown will be explained in conjunction with each step.
[0062] S301. Take the initial injection angle of the i-th injection as the test injection angle of the first injection.
[0063] In this embodiment of the application, during the process of dividing the i-th fuel injection into two fuel injections, the initial injection angle of the i-th fuel injection can be used as the test injection angle of one of the two fuel injections after the division.
[0064] S302. Within the range from the learning start angle to the learning end angle, the angle is decreased step by step by a unit angle amount to obtain at least one test injection angle for another injection; wherein the difference between the learning end angle and the learning start angle is greater than or equal to the step-decreasing unit angle amount.
[0065] In this embodiment of the application, during the process of dividing the i-th fuel injection into two fuel injections, the unit angle amount can be gradually reduced within the range of the learning start angle to the learning end angle to obtain at least one test injection angle for the other of the two fuel injections. The difference between the learning start angle and the learning end angle needs to be greater than the unit angle amount to ensure that at least one step adjustment can be performed to obtain at least one test injection angle.
[0066] 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.
[0067] Furthermore, the learning start angle is a constraint on the injection start angle, and the learning end angle is a constraint on the injection end angle. In other words, in determining the test injection angle of the other injection in the two split injections, the injection start angle of the test injection angle cannot exceed the constraint of the learning start angle (i.e., the injection start angle of the test injection angle needs to be less than the learning start angle), and the injection end angle of the test injection angle cannot exceed the constraint of the learning end angle (i.e., the injection end angle of the test injection angle needs to be greater than the learning end angle).
[0068] In some embodiments of this application, if i equals 1, the learning start angle is 360° and the learning end angle is the initial injection angle of the (i+1)th injection; if i is greater than 1 and less than n, the learning start angle is the initial injection angle of the (i-1)th injection and the learning end angle is the initial injection angle of the (i+1)th injection; if i equals n, the learning start angle is the initial injection angle of the (i-1)th injection and the learning end angle is 80°.
[0069] It should be noted that within one working cycle, the engine performs n fuel injections sequentially in chronological order, and correspondingly, the initial injection angles of the n fuel injections decrease sequentially. That is, the initial injection angle of the (i-1)th fuel injection is greater than the initial injection angle of the ith fuel injection; and the initial injection angle of the ith fuel injection is greater than the initial injection angle of the (i+1)th fuel injection.
[0070] In some embodiments of this application, the unit angle amount of the step reduction is 5°.
[0071] The following example illustrates this: Before the fuel injection parameters were self-learned, the engine performed 3 fuel injections in one working cycle, i.e., n=3; the initial injection angles of the 3 fuel injections were SOI1, SOI2 and SOI3, and the final injection angles of the 3 fuel injections were EOI1, EOI2 and EOI3, respectively.
[0072] If the original first fuel injection is divided into two fuel injections, then the injection start angle SOI1 and injection end angle EOI1 of the original first fuel injection are used as the injection start angle SOI1_1 and injection end angle EOI1_1 of one of the two fuel injections. Simultaneously, within the range of the learning start angle 360° to the learning end angle SOI2, the angle is decreased by a unit angle of 5° to obtain at least one test injection angle for the other of the two fuel injections. Each test injection angle includes the injection start angle SOI1_2 and the injection end angle EOI1_2.
[0073] If the original second fuel injection is divided into two injections, the injection start angle SOI2 and injection end angle EOI2 of the original second fuel injection are used as the injection start angle SOI2_1 and injection end angle EOI2_1 of one of the two injections. Simultaneously, within the range of the learned start angle EOI1 to the learned end angle SOI3, the angle is decreased by a unit angle of 5° to obtain at least one test injection angle for the other injection in the two injections. Each test injection angle includes the injection start angle SOI2_2 and the injection end angle EOI2_2.
[0074] If the original third injection is divided into two injections, the injection start angle SOI3 and injection end angle EOI3 of the original third injection are used as the injection start angle SOI3_1 and injection end angle EOI3_1 of one of the two injections. Simultaneously, within the range of the learned start angle EOI2 to the learned end angle of 80°, the angle is decreased by a unit angle of 5° to obtain at least one test injection angle for the other injection in the two injections. Each test injection angle includes the injection start angle SOI3_2 and the injection end angle EOI3_2.
[0075] Understandably, in the process of dividing the i-th fuel injection into two injections, the initial injection angle of the i-th injection is used as the test injection angle for one of the two injections. Simultaneously, within the range from the learning start angle to the learning end angle, the angle is progressively reduced by a unit amount to obtain at least one test injection angle for the other of the two 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.
[0076] 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 5 The S303 shown implements Figure 3 The step S201 shown will be explained in conjunction with each step.
[0077] S303. Divide the initial injection ratio of the i-th injection into two test injection ratios.
[0078] In this embodiment of the application, during the process of dividing the i-th fuel injection into two fuel injections, the initial injection ratio of the i-th fuel injection can be divided equally into two test injection ratios. That is, the test injection ratios of the two divided fuel injections are equal; at the same time, the sum of the test injection ratios of the two divided fuel injections is equal to the original initial injection ratio of the i-th fuel injection.
[0079] For example, before the fuel injection parameter self-learning, the engine performed three fuel injections in one working cycle, i.e., n=3; the initial injection ratios for the three injections were P1, P2, and P3, respectively. If the original first fuel injection is divided into two injections, then the initial injection ratio of the original first fuel injection is equally divided into two test injection ratios, P1_1 and P1_2, i.e., P1_1 = P1_2, P1_1 + P1_2 = P1. The test injection ratios for the original second and third fuel injections are obtained by dividing them in the same way.
[0080] Understandably, in the process of dividing the i-th fuel injection into two injections, the initial injection ratio of the i-th injection is equally divided into two test injection ratios. Thus, the sum of the test injection ratios after division equals the initial injection ratio before 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 in series-parallel hybrid operation. This reduces fuel consumption and makes the vehicle more adaptable to different situations, improving robustness.
[0081] In some embodiments of this application, it can be achieved through Figure 6 The shown S401 to S402 implement this. Figure 1 S101, shown below, will be explained in conjunction with each step.
[0082] S401, Records real-time key parameters of the vehicle; key parameters include: vehicle speed, fuel consumption and fuel injection parameters.
[0083] S402. If the key parameters meet the preset conditions, then the vehicle is determined to enter the series-parallel hybrid operating condition.
[0084] In this embodiment, key vehicle parameters, such as vehicle speed, fuel consumption, and fuel injection parameters, can be recorded in real time. Furthermore, the vehicle's entry into a series-parallel hybrid operating condition can be determined based on these key parameters; that is, if the key parameters meet preset conditions, the vehicle is confirmed to be in a series-parallel hybrid operating condition.
[0085] Figure 7 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 7 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.
[0086] In some embodiments of this application, a set of initial injection parameters represents n injections of the engine in one working cycle; each set of test injection parameters represents n+1 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 injection into test injection parameters for two injections, and retain 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; continue to divide the initial injection parameters of the (i+1)-th injection until the second self-learning time is reached, or until the division of the initial injection parameters of the n-th injection is completed.
[0087] In some embodiments of this application, the first self-learning time is 10 seconds. The second self-learning time is 2 minutes.
[0088] 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 injection as the test injection angle for one injection; within the range from the learning start angle to the learning end angle, it incrementally decreases by a unit angle amount to obtain at least one test injection angle for another injection; wherein the difference between the learning end angle and the learning start angle is greater than or equal to the incrementally decreased unit angle amount.
[0089] In some embodiments of this application, if i equals 1, the learning start angle is 360° and the learning end angle is the initial injection angle of the (i+1)th injection; if i is greater than 1 and less than n, the learning start angle is the initial injection angle of the (i-1)th injection and the learning end angle is the initial injection angle of the (i+1)th injection; if i equals n, the learning start angle is the initial injection angle of the (i-1)th injection and the learning end angle is 80°.
[0090] In some embodiments of this application, the unit angle amount of the step reduction is 5°.
[0091] 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 injection into two test injection ratios; wherein the test injection ratios of the two injections are equal; and the sum of the test injection ratios of the two injections is equal to the initial injection ratio of the i-th injection.
[0092] In some embodiments of this application, the determining module 810 is also configured to record key parameters of the vehicle in real time; the key parameters include: vehicle speed, fuel consumption and fuel injection parameters; if the key parameters meet preset conditions, it is determined that the vehicle has entered the series-parallel hybrid operating condition.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] Figure 8 This application provides a hardware entity diagram of a computer device as an embodiment of the present application, such as... Figure 8 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.
[0101] 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).
[0102] 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.
[0103] 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.
[0104] 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.
[0105] 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.
[0106] 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.
[0107] 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.
[0108] 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.
[0109] 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.
[0110] 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.
[0111] 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.
[0112] 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.
[0113] 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 injections of the engine within one working cycle; each set of test injection parameters represents n+1 injections of the engine within 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 injection are divided into test injection parameters for two 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 for the (i+1)th injection until the second self-learning time is reached, or until the segmentation of the initial injection parameters for the nth injection is completed; The initial injection parameters include: initial injection angle; the test injection parameters include: test injection angle; the test injection parameters for dividing the initial injection parameters of the i-th injection into two injections include: The initial injection angle of the i-th injection is taken as the test injection angle of the first injection. Within the range from the learning start angle to the learning end angle, the angle is decreased stepwise by a unit angle amount to obtain at least one test injection angle for another injection; wherein the difference between the learning end angle and the learning start angle is greater than or equal to the stepwise decrease in the unit angle amount.
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, If i equals 1, then the learning start angle is 360°, and the learning end angle is the initial injection angle of the (i+1)th injection. If i is greater than 1 and less than n, then the learning start angle is the initial injection angle of the (i-1)th injection, and the learning end angle is the initial injection angle of the (i+1)th injection. If i equals n, then the learning start angle is the initial injection angle of the (i-1)th injection, and the learning end angle is 80°.
4. The method for adjusting injection parameters according to claim 1, characterized in that, The unit angle amount that is decreased step by step is 5°.
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 injection into the test injection parameters of two injections further includes: The initial injection ratio of the i-th injection is divided equally into two test injection ratios; wherein the test injection ratios of the two injections are equal; and the sum of the test injection ratios of the two injections is equal to the initial injection ratio of the i-th injection.
6. 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.
7. 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 injections of the engine in one working cycle; each set of test injection parameters represents n+1 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 injection into test injection parameters for two 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 injection until the second self-learning time is reached, or until the division of the initial injection parameters of the n-th injection is completed; i is greater than or equal to 1 and less than or equal to n; The initial injection parameters include: initial injection angle; the test injection parameters include: test injection angle; The self-learning module is further configured to take the initial injection angle of the i-th injection as the test injection angle of the first injection; and to decrease the learning start angle by a unit angle amount within the range of the learning start angle to the learning end angle to obtain at least one test injection angle of another injection; wherein the difference between the learning end angle and the learning start angle is greater than or equal to the unit angle amount decreased by the step.
8. 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 6.
9. 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 6.