Method, device and equipment for adjusting self-learning value of engine and storage medium
By adjusting unreasonable self-learning values during engine calibration, the problems of low calibration accuracy and resource waste caused by aging and wear were solved, achieving higher calibration accuracy and resource utilization.
Patent Information
- Application Number
- CN202511141059.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-14
- Publication Date
- 2025-11-18
AI Technical Summary
In the existing technology, the self-learning value is unreasonable due to the aging of engine components and mechanical wear, resulting in low calibration accuracy of engine control parameters and waste of resources.
During the engine control parameter calibration process, it is determined whether the component self-learning value matches the preset self-learning value, and adjustments are made when they do not match to ensure that the self-learning value matches the preset self-learning value and avoid compensation for initial deviations.
It improves the calibration accuracy of engine control parameters, reduces resource waste, and enhances the utilization rate of engine calibration resources.
Smart Images
Figure CN120968930A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle technology, and more specifically to a method, apparatus, device, and storage medium for adjusting engine self-learning values. Background Technology
[0002] With increasingly stringent requirements for engine fuel economy and emissions performance, relevant engine control parameters (such as variable valve timing, fuel injection quantity, and ignition timing) need to be calibrated before mass production. In practical applications, due to aging, mechanical wear, and design and manufacturing issues of various engine components, initial deviations may exist between the actual initial state of each component and the preset calibration reference state. Therefore, the engine control unit typically compensates for these initial deviations using self-learning values corresponding to each component.
[0003] When calibrating the relevant control parameters of an engine, the control parameters of each engine component typically need to be calibrated based on its actual initial state. However, the self-learning values corresponding to each engine component, used to compensate for initial deviations, may mistakenly identify the corrected state as the calibration reference state. In this case, due to the existence of initial deviations, the control parameters calibrated based on the corrected state may cause the engine's performance indicators under the control parameters to fail to accurately reflect the performance indicators corresponding to the control parameters, thus potentially leading to low calibration accuracy of the control parameters.
[0004] In related technologies, the self-learning values of each engine component are typically adjusted manually before calibration to avoid the self-learning values compensating for the initial deviations of each component. However, during the calibration process, as the engine runs, the engine control unit may still perform a self-learning process on each component to obtain self-learning values to eliminate initial deviations, which may result in lower calibration accuracy of control parameters and a waste of engine calibration resources.
[0005] It should be noted that the information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application, and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention
[0006] This application provides a method, apparatus, device, and storage medium for adjusting engine self-learning values, in order to solve the problem in related technologies that unreasonable self-learning values may lead to low calibration accuracy of engine control parameters and waste of engine calibration resources.
[0007] In a first aspect, embodiments of this application provide a method for adjusting engine self-learning values, including: During the calibration of the engine's control parameters, it is determined whether the self-learning value corresponding to the component inside the engine matches the preset self-learning value. The control parameters are used to control the operating state of the engine, and the self-learning value is intended to correct the control error caused by hardware changes of the engine components. When the self-learning value corresponding to a component within the engine does not match the preset self-learning value, the self-learning value corresponding to the component within the engine is adjusted so that the adjusted self-learning value matches the preset self-learning value.
[0008] In one possible implementation, adjusting the self-learning values corresponding to the components within the engine includes: Control the engine to operate at the target speed and target load, and adjust the self-learning values corresponding to the components inside the engine.
[0009] In one possible implementation, before calibrating the engine's control parameters, the following is also included: Determine whether the self-learning values corresponding to the components inside the engine match the preset self-learning values; When the self-learning value corresponding to a component within the engine does not match the preset self-learning value, the self-learning value corresponding to the component within the engine is adjusted.
[0010] In one possible implementation, determining whether the self-learning value corresponding to a component within the engine matches a preset self-learning value includes: When the current operating state of the engine meets the preset conditions, it is determined whether the self-learning value corresponding to the component in the engine matches the preset self-learning value. The preset conditions include: the engine coolant temperature meets the preset coolant temperature range and the engine internal oil temperature meets the preset oil temperature range.
[0011] In one possible implementation, adjusting the self-learning value corresponding to the component within the engine when the self-learning value does not match the preset self-learning value includes: When the self-learning value corresponding to a component within the engine does not match the preset self-learning value, the self-learning value corresponding to the component within the engine is adjusted and a first prompt message is output. The first prompt message is used to output the engine's operating status and / or calibration progress.
[0012] In one possible implementation, after adjusting the self-learning values corresponding to the components within the engine, the method further includes: When the adjusted self-learning value does not match the preset self-learning value, the engine is controlled to stop running and a second prompt message is output. The second prompt message is used to prompt the user to readjust the self-learning value corresponding to the component in the engine so that the readjusted self-learning value matches the preset self-learning value.
[0013] In one possible implementation, the method further includes: When the self-learning value corresponding to a component within the engine matches the preset self-learning value, the control parameters of the engine are calibrated.
[0014] Secondly, embodiments of this application provide an engine self-learning value adjustment device, comprising: The judgment module is used to determine whether the self-learning value corresponding to the component in the engine matches the preset self-learning value during the calibration of the engine's control parameters. The control parameters are used to control the operating state of the engine, and the self-learning value is intended to correct the control error caused by hardware changes of the engine components. The self-learning value adjustment module is used to adjust the self-learning value of the component in the engine when the self-learning value of the component in the engine does not match the preset self-learning value, so that the adjusted self-learning value matches the preset self-learning value.
[0015] Thirdly, embodiments of this application provide an electronic device, including: processor; Memory; And a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, causes the electronic device to perform the method described in any one of the first aspects.
[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of the first aspects.
[0017] In this embodiment, during the calibration of engine control parameters, it is first determined whether the self-learning value corresponding to a component within the engine matches a preset self-learning value. If the self-learning value does not match the preset self-learning value, the self-learning value is adjusted to match the preset self-learning value. Because unreasonable self-learning values are adjusted during calibration, the compensating effect of the self-learning value on the initial deviation can be avoided. This allows the control parameters to be calibrated according to the actual initial state of each component, improving the calibration accuracy of the control parameters and increasing the utilization rate of engine calibration resources. Attached Figure Description
[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a flowchart illustrating a method for adjusting engine self-learning values, provided in an embodiment of this application.
[0020] Figure 2 This is a flowchart illustrating another method for adjusting engine self-learning values provided in an embodiment of this application.
[0021] Figure 3 This is a schematic diagram of the structure of an engine self-learning value adjustment device provided in an embodiment of this application.
[0022] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0023] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0024] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.
[0025] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0026] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0027] To facilitate understanding, the relevant concepts will be explained below.
[0028] The calibration reference state (i.e., ideal state) is the original reference standard used to set the control parameters of various components (e.g., variable valve timing system, throttle body, turbocharger, fuel injectors, etc.) during the calibration of engine control parameters (e.g., variable valve opening, fuel injection quantity, ignition timing, etc.). For example, in the ideal state, the actual opening of the throttle valve is 0% when the engine control unit controls the throttle body to be fully closed.
[0029] Initial deviation refers to the difference between the actual initial state (e.g., mechanical position, physical characteristics, signal output, etc.) of engine components and their calibration reference state, caused by aging, mechanical wear, design and manufacturing issues. For example, in the actual initial state, when the engine control unit controls the throttle valve to be fully closed, due to the aforementioned issues, the actual opening degree of the throttle valve may be 0.5%, meaning the actual opening degree of the throttle valve is 0.5% more than the ideal 0%. Therefore, the initial deviation between the actual initial state of the throttle valve and the calibration reference state is 0.5%.
[0030] The self-learning value is a dynamic correction coefficient that the engine control unit analyzes through various sensors during engine operation to correct initial deviations, in order to adapt to problems such as aging of engine components, mechanical wear, and design processes.
[0031] For example, in an ideal state, the actual opening of the throttle valve when the engine control unit (ECU) controls the throttle valve to be fully closed is 0%; in an actual initial state, the actual opening of the throttle valve when the ECU controls the throttle valve to be fully closed is 0.5%. The ECU controls the throttle valve to be fully closed. Due to the initial deviation, the ECU recognizes that the actual opening in the actual initial state is greater than the actual opening in the ideal state, and obtains a self-learning value of -0.5% through a self-learning process. When the ECU needs to control the throttle valve opening to 2%, the ECU corrects the target throttle valve opening of 2% to 2% - 0.5% = 1.5% based on the self-learning value of -0.5%, causing the throttle valve to rotate 1.5% from the actual initial state of 0.5%, ultimately resulting in an actual opening of 0.5% + 1.5% = 2%.
[0032] With increasingly stringent requirements for engine fuel economy and emissions performance, engine control parameters need to be calibrated before mass production. In practical applications, due to aging, mechanical wear, and design issues of various engine components, initial deviations may exist between the actual initial state of each component and the preset calibration reference state. Therefore, the engine control unit typically compensates for these initial deviations using self-learning values for each component.
[0033] When calibrating the relevant control parameters of an engine, the control parameters of each engine component typically need to be calibrated based on its actual initial state. However, the self-learning values corresponding to each engine component, used to compensate for the initial deviation, may mistakenly identify the corrected state as the calibration reference state. In this case, due to the existence of the initial deviation, the control parameters calibrated based on the corrected state may cause the engine's performance indicators (e.g., power performance, fuel economy, emissions performance, etc.) under the control parameters to fail to accurately reflect the performance indicators corresponding to the control parameters, thus potentially leading to low calibration accuracy of the control parameters.
[0034] For example, if the self-learning value of -0.5% is retained during calibration, as mentioned above, the engine control unit may assume that the actual initial state of the throttle body is consistent with the calibration reference state. Based on the corrected state, the engine performance index reaches its optimal throttle opening of 10% when the accelerator pedal opening is calibrated to be 20%. Theoretically, when the engine control unit controls the target throttle opening to 10%, the actual throttle opening is 0% + 10%. However, in reality, there is a 0.5% deviation in the throttle body. Therefore, when the engine control unit controls the target throttle opening to 10%, the actual throttle opening is 0.5% + 10% = 10.5%. At this point, the engine performance index is the performance index when the actual throttle opening is 10.5%, and does not reflect the performance index when the target throttle opening is 10%. In other words, the engine performance index reaches its optimal throttle opening of 10.5% when the accelerator pedal opening is 20%, not 10%. Therefore, the throttle opening may be calibrated inaccurately when the accelerator pedal opening is 20%.
[0035] In related technologies, the self-learning values for each engine component are typically adjusted manually before calibration to avoid the self-learning values compensating for initial deviations in each component. However, during calibration, as the engine runs, the engine control unit may still perform a self-learning process on each component to obtain self-learning values to compensate for initial deviations. This could lead to the corrected state being mistakenly identified as the calibration reference state.
[0036] Therefore, in this case, the self-learning value during the calibration process may cause the engine's performance indicators under the control parameters to fail to accurately reflect the performance indicators corresponding to the control parameters, which may lead to low calibration accuracy of the control parameters.
[0037] To address the aforementioned issues, in this embodiment, during the calibration of engine control parameters, it is first determined whether the self-learning value corresponding to a component within the engine matches a preset self-learning value. If the self-learning value does not match the preset self-learning value, the self-learning value is adjusted to match the preset self-learning value. Because unreasonable self-learning values are adjusted during the calibration process, the compensating effect of the self-learning value on the initial deviation can be avoided. This allows the control parameters to be calibrated according to the actual initial state of each component, improving the calibration accuracy of the control parameters and increasing the utilization rate of engine calibration resources.
[0038] Specifically, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments.
[0039] See Figure 1 This is a flowchart illustrating a method for adjusting engine self-learning values according to an embodiment of this application. Figure 1 As shown, it specifically includes steps S101 to S102.
[0040] Step S101: During the calibration of the engine control parameters, determine whether the self-learning value corresponding to the component in the engine matches the preset self-learning value.
[0041] In this embodiment, calibrating the engine's control parameters refers to finding the optimal control parameter values for each operating condition of the engine, so that the engine's performance indicators meet the performance standards under each operating condition. Specifically, the calibration of the control parameters can be completed by running a calibration program.
[0042] As is understandable, control parameters are used to control the engine's operating state. Control parameters include, but are not limited to, the phase angle of variable valve timing, throttle opening, turbocharger boost pressure, fuel injection quantity, ignition timing, and other parameters.
[0043] In this embodiment, the self-learning value is intended to correct control errors caused by hardware changes in engine components. These engine components include, but are not limited to, variable valve timing systems, throttle valves, turbochargers, and fuel injectors.
[0044] As mentioned above, the self-learning values within the engine often lead to low calibration accuracy of control parameters and wasted engine calibration resources. Therefore, it is necessary to adjust the engine's self-learning values. Generally, only unreasonable self-learning values need to be adjusted; reasonable self-learning values need not be adjusted to avoid potentially increasing the calibration cycle.
[0045] In this embodiment, the reasonableness of the self-learning value corresponding to a component within the engine is determined by judging whether it matches a preset self-learning value. Specifically, when the self-learning value corresponding to a component within the engine is greater than or less than the preset self-learning value, the self-learning value is considered unreasonable; when the self-learning value corresponding to a component within the engine is equal to the preset self-learning value, the self-learning value is considered reasonable.
[0046] The preset self-learning value is a pre-set self-learning value. Those skilled in the art can set other self-learning values according to actual needs, and this application embodiment does not impose any restrictions on this.
[0047] It should be noted that when searching for the optimal control parameter values for each operating condition, it is necessary to ensure that the self-learning values corresponding to each component in the engine are reasonable, so as to guarantee the calibration accuracy of the control parameters.
[0048] Step S102: When the self-learning value corresponding to the component in the engine does not match the preset self-learning value, adjust the self-learning value corresponding to the component in the engine so that the adjusted self-learning value matches the preset self-learning value.
[0049] In the embodiments of this application, the engine control unit typically stores the correspondence between various engine components and self-learning values. For example, the self-learning value corresponding to the throttle timing system is self-learning value A; the self-learning value corresponding to the throttle body is self-learning value B, etc.
[0050] As mentioned above, self-learning values of engine internal components often lead to low calibration accuracy of control parameters and wasted engine calibration resources. Therefore, it is necessary to adjust unreasonable self-learning values within the engine.
[0051] Specifically, the engine control unit collects operating data of each component through sensors associated with each component, obtains a new self-learning value through a self-learning process, and adjusts the original self-learning value to the new self-learning value so that the adjusted self-learning value matches the preset self-learning value.
[0052] In practical applications, if the self-learning value is not adjusted under appropriate engine speed and load, it may lead to problems such as torque fluctuations, knocking, or pre-ignition. For example, when adjusting the self-learning value under high speed and high load conditions, it may cause control parameter mismatch, with the calibrated ignition advance angle being too large or too small, which may in turn lead to knocking or pre-ignition.
[0053] In addition, the self-learning process of each component of the engine can usually be triggered and the self-learning value adjusted only under the target speed and target load.
[0054] Therefore, in one possible implementation, when the self-learning value corresponding to a component within the engine does not match the preset self-learning value, the engine is controlled to operate at the target speed and target load, and the self-learning value corresponding to the component within the engine is adjusted.
[0055] It is understood that the target speed is a speed within a preset low speed range, and the target load is a load within a preset low load range. It should be noted that those skilled in the art can set other low speed ranges and low load ranges according to actual needs, and the embodiments of this application do not impose specific limitations on this.
[0056] In this application, the engine operates in a stable state under target speed and load. It is understood that under target speed and load, factors such as oil temperature and electromagnetic interference have minimal, even negligible, impact on engine operation. Furthermore, the engine operates within a "safe combustion zone," meaning that even after adjusting the self-learning value, the engine control unit's parameters will not reach the critical values for knocking or pre-ignition. Therefore, operating the engine under target speed and load reduces the likelihood of torque fluctuations, knocking, or pre-ignition to a certain extent.
[0057] It should be noted that when the engine is running at the target speed and target load, the engine control unit can accurately monitor the actual initial state of each engine component. Furthermore, based on the accurate actual initial state of each component, control parameters that match the hardware characteristics of each engine component can be calibrated.
[0058] In practical applications, the self-learning values of various components within the engine may be unreasonable during the initial calibration. If these unreasonable self-learning values are adjusted during the initial calibration, their compensating effect on the initial deviations of the components may cause the engine's performance indicators under the control parameters to fail to accurately reflect the performance indicators corresponding to the control parameters, which could lead to lower calibration accuracy of the control parameters.
[0059] In one possible implementation, before calibrating the engine's control parameters, the method further includes: determining whether the self-learning value corresponding to a component within the engine matches a preset self-learning value; and adjusting the self-learning value corresponding to the component within the engine when the self-learning value does not match the preset self-learning value.
[0060] In this embodiment, before calibrating the engine's control parameters, unreasonable self-learning of various engine components is adjusted to avoid the compensation effect of self-learning values on the initial deviations of each component after calibration begins. This allows the control parameters to be calibrated based on the actual hardware characteristics of each engine component, improving calibration accuracy to a certain extent. Other specific details in this embodiment can be found in the descriptions of the above method embodiments, and will not be repeated here for the sake of brevity.
[0061] In practical applications, when an engine is cold-started and not yet warmed up, its components are not at their optimal operating state, resulting in lower combustion efficiency. In this situation, controlling the engine under calibrated parameters may lead to lower accuracy in the obtained performance indicators, failing to accurately reflect the engine's actual performance under those parameters. Therefore, based on these less accurate performance indicators, the calibration precision of the optimal control parameters for each operating condition is low.
[0062] Therefore, in one possible implementation, when the current operating state of the engine meets the preset conditions, it is determined whether the self-learning value corresponding to the component in the engine matches the preset self-learning value; when the self-learning value corresponding to the component in the engine does not match the preset self-learning value, the self-learning value corresponding to the component in the engine is adjusted.
[0063] In this embodiment, the preset conditions include: the engine coolant temperature meets a preset coolant temperature range and the engine internal oil temperature meets a preset oil temperature range. It can be understood that the preset conditions are typically the boundary conditions when the engine is running at a warm-up state.
[0064] Normally, whether an engine is running at warm-up temperature is closely related to the engine coolant temperature and the internal oil temperature. Therefore, it is necessary to first detect the current operating temperature of the engine using a temperature sensor to determine whether the engine is running at warm-up temperature.
[0065] Specifically, when the engine coolant temperature meets the preset coolant temperature range and the engine internal oil temperature meets the preset oil temperature range, the engine can be considered to be in a warm-up state. At this time, the engine is usually in its optimal working condition.
[0066] The preset water temperature range and preset oil temperature range are preset values. Those skilled in the art can set other preset water temperature ranges and preset oil temperature ranges according to actual needs. This application embodiment does not limit this.
[0067] In this embodiment, when the engine reaches a warm-up state, it is determined whether the self-learning values corresponding to the components within the engine match preset self-learning values. Furthermore, when the self-learning values of the components match the preset self-learning values, the control parameters corresponding to each operating condition of the engine are calibrated as each component reaches its optimal working state. This allows for a more accurate reflection of the engine's actual performance under the control parameters. Therefore, based on more accurate performance indicators, more precise optimal control parameters for each operating condition of the engine can be calibrated.
[0068] In practical applications, users may want to know whether the self-learning value, engine operating status, or calibration progress has been adjusted during the calibration process. Therefore, in one possible implementation, when the self-learning value corresponding to a component within the engine does not match the preset self-learning value, the self-learning value corresponding to the component within the engine is adjusted and a first prompt message is output.
[0069] In this embodiment, the first prompt information is used to output the engine's operating status and / or calibration progress. For example, the first prompt information may be "The engine is currently operating at speed A and load A" or "Calibration is 50% complete".
[0070] It should be noted that the first prompt message can also be an abnormality in the self-learning value. For example, the first prompt message could also be "Throttle body self-learning value abnormal" or "Fuel injector self-learning value abnormal," etc. Furthermore, the methods for outputting the first prompt message include, but are not limited to, text descriptions and voice announcements.
[0071] In this embodiment, when the self-learning value corresponding to a component within the engine does not match the preset self-learning value, it indicates that the self-learning value needs to be adjusted. At this time, outputting a first prompt message allows the user to promptly grasp information such as whether the self-learning value has been adjusted, the engine's operating status, or the calibration progress during the calibration process. Furthermore, real-time monitoring of the engine's status can, to some extent, reduce the probability of safety risks to the engine and testing equipment during the experiment.
[0072] In practical applications, the self-learning values within the engine may be stored in both temporary and permanent memory areas. When adjusting unreasonable self-learning values within the engine, due to the protection mechanism for the permanent memory area, only the self-learning values in the temporary memory area may be adjusted. In this case, the adjustment of the self-learning values may be incomplete, potentially leading to unadjusted self-learning values still compensating for initial deviations in components. This could result in the engine's performance indicators under control parameters failing to accurately reflect the performance indicators corresponding to the control parameters, leading to lower calibration accuracy of the control parameters.
[0073] Therefore, in one possible implementation, when the adjusted self-learning value does not match the preset self-learning value, the engine is controlled to stop running and a second prompt message is output.
[0074] Understandably, when the adjusted self-learning value does not match the preset self-learning value, the adjustment of the unreasonable self-learning value within the engine can be considered incomplete. Therefore, it is necessary to control the engine to stop running. Specifically, the engine control unit sends a stop-run control command to the engine, and the engine stops running according to the stop-run control command.
[0075] In this embodiment, a second prompt message is output simultaneously with stopping the engine. This second prompt message prompts the user to readjust the self-learning values corresponding to the components within the engine, so that the readjusted self-learning values match the preset self-learning values. Specifically, the user can manually adjust the self-learning values stored in the components within the engine.
[0076] In this embodiment, after the engine stops running, the user's adjustment of the self-learning value is equivalent to initializing the temporary and permanent storage areas, ensuring that the adjusted self-learning value matches the preset self-learning value. Furthermore, it avoids compensating for initial deviations in various components due to self-learning values that do not match the preset values. This allows for calibration of control parameters based on the actual hardware characteristics of each engine component, improving calibration accuracy to a certain extent.
[0077] In one possible implementation, the method further includes: when the self-learning value corresponding to a component within the engine matches a preset self-learning value, calibrating the control parameters of the engine.
[0078] It is understandable that when the self-learning value corresponding to a component within the engine matches the preset self-learning value, the compensation effect of the self-learning value on the initial deviation of each component can be considered to have no impact on the engine control unit's monitoring of the actual initial state of the components within the engine. Therefore, the engine's control parameters can be calibrated based on the actual initial state of each component.
[0079] Specifically, firstly, the calibration data for each operating condition of the engine and the measurement data corresponding to the performance indicators that need to be measured are set; secondly, based on the calibration data for each operating condition, the engine is controlled to operate and the measurement data of the engine under the calibration data are monitored; then, based on the measurement data, the control parameters corresponding to each operating condition are determined from the calibration data.
[0080] The calibration data for each operating condition includes multiple calibration sub-data, and each calibration sub-data includes the set value of each control parameter of the engine when it is running under that operating condition.
[0081] For example, the correspondence between each operating condition and the calibration data is shown in Table 1. The calibration data corresponding to operating condition A includes two calibration sub-data sets: the first calibration sub-data set includes control parameter A1, control parameter B1, control parameter C1, and control parameter D1; the second calibration sub-data set includes control parameter A2, control parameter B2, control parameter C2, and control parameter D2. The calibration data corresponding to operating condition B includes two calibration sub-data sets: the first calibration sub-data set includes control parameter A3, control parameter B3, control parameter C3, and control parameter D3; the second calibration sub-data set includes control parameter A4, control parameter B4, control parameter C4, and control parameter D4. And so on, which will not be elaborated further in this embodiment.
[0082] Table 1: It should be noted that the table above is merely an illustrative example illustrating the relationship between various operating conditions and calibration data. In practical applications, those skilled in the art can set calibration data for other operating conditions. Furthermore, those skilled in the art can also set calibration sub-data for each operating condition; this application does not impose specific limitations on this.
[0083] In this embodiment, when calibrating the control parameters for each operating condition of the engine, the engine is controlled to operate according to the calibration sub-data corresponding to the operating condition, and the measurement data corresponding to the calibration sub-data is monitored. The measurement data is used to characterize the engine's operating performance under the calibration sub-data, such as power, fuel economy, and emissions performance.
[0084] Understandably, after obtaining the measurement data of each operating condition under the corresponding multiple calibration sub-data, for each operating condition, by comparing the measurement data under the multiple calibration sub-data corresponding to that operating condition, the calibration sub-data corresponding to the measurement data with the best engine operating performance is determined as the optimal control parameter under that operating condition.
[0085] In this embodiment, when the self-learning value corresponding to a component within the engine matches a preset self-learning value, the compensation effect of the self-learning value on the initial deviation of each component does not affect the engine control unit's monitoring of the actual initial state of the components within the engine. Therefore, control parameters can be calibrated based on the actual hardware characteristics of each engine component, thus improving calibration accuracy to a certain extent.
[0086] In this embodiment, during the calibration of engine control parameters, it is first determined whether the self-learning value corresponding to a component within the engine matches a preset self-learning value. If the self-learning value does not match the preset self-learning value, the self-learning value is adjusted to match the preset self-learning value. Because unreasonable self-learning values are adjusted during calibration, the compensating effect of the self-learning value on the initial deviation can be avoided. This allows the control parameters to be calibrated according to the actual initial state of each component, improving the calibration accuracy of the control parameters and increasing the utilization rate of engine calibration resources.
[0087] See Figure 2 This is a flowchart illustrating another method for adjusting engine self-learning values provided in an embodiment of this application. Figure 2 As shown, the embodiments of this application are in Figure 1 Based on the illustrated embodiment, it mainly includes the following steps.
[0088] Step S201: The first test device receives the current operating status of the engine sent by the second test device.
[0089] Understandably, the first testing device is typically a terminal device equipped with calibration software. The second testing device is typically a test bench equipped with an engine, engine control unit, engine performance analysis unit, etc. The first and second testing devices are connected via communication.
[0090] In this embodiment, after the engine starts running, the second testing device can monitor the engine's current operating status in real time and send the engine's current operating status to the first testing device. Furthermore, after receiving the engine's current operating status sent by the second testing device, the first testing device can determine whether the engine has reached a warm-up state.
[0091] It should be noted that terminal devices include, but are not limited to, desktop computers, laptop computers, networked computers, handheld computers, personal digital assistants (PDAs), etc.; other hardware, such as emission analyzers and combustion analyzers, may also be configured on the test bench. This application does not impose specific limitations on these aspects.
[0092] Step S202: The first testing device determines whether the current operating state of the engine meets the preset conditions. If the first testing device determines that the current operating state of the engine does not meet the preset conditions, step S203 is executed; otherwise, step S204 is executed.
[0093] In this embodiment, after the first testing device receives the current operating status of the engine from the second testing device, it can determine whether the engine has reached a warm-up state. When the first testing device determines that the current operating status of the engine does not meet the preset conditions, it can be considered that the engine has not yet reached a warm-up state. Therefore, the first testing device does not send any instructions to the second testing device.
[0094] Step S203: The second test equipment controls the engine to continue running.
[0095] In this embodiment of the application, when the first test device determines that the current operating state of the engine does not meet the preset conditions, the second test device controls the engine to continue running so that the engine runs to the warm-up state.
[0096] Step S204: The first testing device determines whether the self-learning value corresponding to the component inside the engine matches the preset self-learning value. If the first testing device determines that the self-learning value corresponding to the component inside the engine does not match the preset self-learning value, proceed to step S205; otherwise, proceed to step S207.
[0097] In this embodiment of the application, when the first test device determines that the current operating state of the engine meets the preset conditions, the first test device determines whether the self-learning value corresponding to the component in the engine matches the preset self-learning value, and then determines whether it is necessary to adjust the self-learning value corresponding to the component in the engine.
[0098] Step S205: The first test device sends a self-learning adjustment command to the second test device.
[0099] In this embodiment of the application, when the first test device determines that the self-learning value corresponding to the component in the engine does not match the preset self-learning value, the first test device sends a self-learning adjustment command to the second test device.
[0100] It is understandable that the self-learning adjustment command is used to instruct the second test equipment to trigger the engine's self-learning process so as to adjust the self-learning values corresponding to the components within the engine.
[0101] Step S206: The second test device controls the engine to run at the target speed and target load according to the self-learning adjustment command sent by the first test device, and adjusts the self-learning values corresponding to the components in the engine.
[0102] In this embodiment, when the second testing device receives a self-learning adjustment command from the first testing device, the second testing device controls the engine to operate at the target speed and target load according to the command, and adjusts the self-learning values corresponding to the components within the engine. The second testing device then sends the adjusted self-learning values to the first testing device so that the first testing device can adjust its stored self-learning values.
[0103] Step S207: The first test equipment calibrates the control parameters of the engine.
[0104] In this embodiment of the application, when the first test device determines that the self-learning value corresponding to the component in the engine matches the preset self-learning value, the first test device controls the calibration of the engine's control parameters.
[0105] Specifically, the first testing device sets calibration data for each operating condition of the engine and measurement data corresponding to the performance indicators that need to be measured; then, it sends the calibration data for each operating condition to the second testing device. The second testing device controls the engine to operate and monitors the engine's measurement data under the calibration data according to the calibration data for each operating condition, and sends the measurement data to the first measurement data. Then, the first measurement data determines the control parameters corresponding to each operating condition from the calibration data based on the measurement data.
[0106] Step S208: During the calibration of the engine's control parameters, the first testing device determines whether the self-learning value corresponding to the component inside the engine matches the preset self-learning value. If the first testing device determines that the self-learning value corresponding to the component inside the engine does not match the preset self-learning value, step S205 is executed; otherwise, step S207 is executed.
[0107] In this embodiment of the application, during the calibration of the engine's control parameters, the first testing device can determine whether the self-learning value corresponding to the component inside the engine matches the preset self-learning value. This determines whether it is necessary to adjust the self-learning value corresponding to the component inside the engine during the calibration of the engine's control parameters to eliminate the compensating effect of the self-learning value on the initial deviation.
[0108] For details regarding the implementation of this application, please refer to the descriptions in the above embodiments. For the sake of brevity, these details will not be repeated here.
[0109] Corresponding to the above embodiments, this application also provides an engine self-learning value adjustment device.
[0110] See Figure 3 This is a schematic diagram of the structure of an engine self-learning value adjustment device provided in an embodiment of this application. Figure 3As shown, the engine self-learning value adjustment device 300 includes: a judgment module 301 and a self-learning value adjustment module 302.
[0111] Specifically, the judgment module 301 is used to determine whether the self-learning value corresponding to the component inside the engine matches the preset self-learning value during the calibration of the engine's control parameters. The control parameters are used to control the operating state of the engine, and the self-learning value is intended to correct the control error caused by hardware changes of the engine component. The self-learning value adjustment module 302 is used to adjust the self-learning value corresponding to the component inside the engine when the self-learning value does not match the preset self-learning value, so that the adjusted self-learning value matches the preset self-learning value.
[0112] For details regarding the implementation of this application, please refer to the descriptions in the above embodiments. For the sake of brevity, these details will not be repeated here.
[0113] Corresponding to the above embodiments, this application also provides an electronic device.
[0114] See Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 may include a processor 401, a memory 402, and a communication unit 403. These components communicate via one or more buses. Those skilled in the art will understand that the electronic device structure shown in the figures does not constitute a limitation on the embodiments of this application. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0115] The communication unit 403 is used to establish a communication channel, thereby enabling the electronic device to communicate with other devices.
[0116] The processor 401 serves as the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes software programs and / or modules stored in the memory 402, and calls data stored in the memory to perform various functions and / or process data. The processor may be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 401 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.
[0117] Memory 402 is used to store the execution instructions of processor 401. Memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.
[0118] When the execution instructions in memory 402 are executed by processor 401, the electronic device 400 is able to perform some or all of the steps in the above method embodiments.
[0119] Corresponding to the above embodiments, this application also provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, wherein when the program runs, it can control the device where the computer-readable storage medium is located to execute some or all of the steps in the above method embodiments. In specific implementation, the computer-readable storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0120] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.
[0121] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0122] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0123] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can 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 the prior art, or a part of the technical solution, 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 steps 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, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0124] The above description is merely a specific embodiment of this application. Any variations 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 protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.
Claims
1. An engine self-learning value adjustment method characterized by, The method comprises the following steps: In the process of calibrating the control parameter of the engine, it is judged whether the self-learning value corresponding to the component in the engine matches the preset self-learning value, the control parameter being used to control the running state of the engine, and the self-learning value being intended to correct the control error caused by the hardware change of the engine component; When the self-learning value corresponding to the component in the engine does not match the preset self-learning value, the self-learning value corresponding to the component in the engine is adjusted so as to match the preset self-learning value.
2. The method of claim 1, wherein, The adjustment of the self-learning value corresponding to the component in the engine comprises: The engine is controlled to run at a target speed and a target load, and the self-learning value corresponding to the component in the engine is adjusted.
3. The method of claim 1, wherein, Before the calibration of the control parameter of the engine, the method further comprises the following steps: It is judged whether the self-learning value corresponding to the component in the engine matches the preset self-learning value; When the self-learning value corresponding to the component in the engine does not match the preset self-learning value, the self-learning value corresponding to the component in the engine is adjusted.
4. The method of claim 3, wherein, The judgment of whether the self-learning value corresponding to the component in the engine matches the preset self-learning value comprises: When the current running state of the engine meets a preset condition, it is judged whether the self-learning value corresponding to the component in the engine matches the preset self-learning value; The preset condition comprises that the engine water temperature meets a preset water temperature range and the engine internal oil temperature meets a preset oil temperature range.
5. The method of claim 1, wherein, The adjustment of the self-learning value corresponding to the component in the engine when the self-learning value corresponding to the component in the engine does not match the preset self-learning value comprises: When the self-learning value corresponding to the component in the engine does not match the preset self-learning value, the self-learning value corresponding to the component in the engine is adjusted and a first prompt information is output, the first prompt information being used to output the running state of the engine and / or the calibration progress.
6. The method of claim 1, wherein, After the adjustment of the self-learning value corresponding to the component in the engine, the method further comprises the following steps: When the adjusted self-learning value does not match the preset self-learning value, the engine is controlled to stop running and a second prompt information is output, the second prompt information being used to prompt the user to re-adjust the self-learning value corresponding to the component in the engine so as to match the preset self-learning value.
7. The method of claim 1, wherein, The method further comprises the following steps: When the self-learning value corresponding to the component in the engine matches the preset self-learning value, the calibration of the control parameter of the engine is controlled.
8. An engine self-learning value adjustment device characterized by comprising: The method comprises the following steps: A judgment module is configured to judge whether the self-learning value corresponding to the component in the engine matches the preset self-learning value in the process of calibrating the control parameter of the engine, the control parameter being used to control the running state of the engine, and the self-learning value being intended to correct the control error caused by the hardware change of the engine component; A self-learning value adjustment module is configured to adjust the self-learning value corresponding to the component in the engine when the self-learning value corresponding to the component in the engine does not match the preset self-learning value, so as to match the preset self-learning value.
9. An electronic device, comprising: The method comprises the following steps: a processor; a memory; and a computer program, wherein the computer program is stored in the memory and, when executed by the processor, causes the electronic device to perform the method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the method of any one of claims 1 to 7 is implemented.