Hybrid vehicle fine zoning fuel self-learning process adjustment method and system

Abstract

The application discloses a fine-partition fuel self-learning process adjustment method for a hybrid vehicle, and discloses a fine-partition fuel self-learning process adjustment system for a hybrid vehicle. learn In the fuel self-learning process of the engine, the engine is controlled to perform fuel self-learning according to a corresponding fuel self-learning mode under the current working condition based on the current fuel self-learning progress P learn , the oil tank pressure state and the carbon can load state; the application also discloses a fine-partition fuel self-learning process adjustment system for a hybrid vehicle, which comprises a fuel self-learning progress module, an evaporation system state module, a self-learning process adjustment module and a fuel self-learning module; the calculation of the fuel self-learning progress is introduced, and dynamic calculation updating is maintained; in the case that the progress does not reach the standard, fuel self-learning is quickly completed through a quick fuel self-learning path, the fuel self-learning time can be effectively shortened, and the fine-partition fuel self-learning function is more practical; the oil tank pressure state and the carbon can load state are taken as conditions for controlling the quick fuel self-learning process, and the oil circuit safety is ensured.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a method and system for fine-grained fuel self-learning process adjustment in hybrid vehicles. Background Technology

[0002] Due to manufacturing errors, the characteristics of each fuel injector produced in a batch are not exactly the same. During user operation, the fuel injectors will wear down over time, causing changes in fuel injection characteristics, which in turn deteriorates engine power and emissions. Therefore, the fuel system must have a self-learning function. Compared with gasoline vehicles, hybrid vehicles operate under more stable conditions, which places higher demands on fuel self-learning. This means that the fuel self-learning operating conditions need to be finely divided to achieve fine-grained learning, storage, and retrieval. The fine-grained fuel self-learning function needs to complete the learning of dozens of operating conditions, and the learning time increases by orders of magnitude, which is difficult to complete in the short term.

[0003] Fine-grained fuel self-learning is difficult to achieve, not only due to numerous operating point limitations but also because it is strongly correlated with the operation of the evaporation system. Under traditional coordination mechanisms, to avoid the impact of evaporation system operation (carbon canister flushing) on ​​the accuracy of fuel self-learning, a mutually exclusive logic is mainly adopted between fuel self-learning and carbon canister flushing functions. This means that the two operate independently at predetermined intervals, with carbon canister flushing typically allocating more time to ensure that evaporative emissions meet regulatory requirements. For hybrid vehicles equipped with high-pressure fuel tanks and fuel tank isolation valves, the traditional fuel self-learning process adjustment logic is too time-consuming to meet the requirements of fine-grained fuel self-learning. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the technical problem to be solved by the present invention is to provide a method for adjusting the fine-zone fuel self-learning process of hybrid vehicles that can effectively shorten the fuel self-learning time and make the fine-zone fuel self-learning function more practical.

[0005] To address the aforementioned technical problems, one technical solution adopted by this invention is to provide a method for finely partitioning fuel self-learning process adjustment in hybrid vehicles. During the engine's fuel self-learning process, based on the current fuel self-learning progress P... learn The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions.

[0006] Furthermore, this includes the following steps:

[0007] Create a runtime condition database;

[0008] Fuel self-learning is performed for each operating condition in the operating condition database. Before performing fuel self-learning for each operating condition, the current fuel self-learning progress P is considered. learnThe system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions.

[0009] Furthermore, in the step of creating the runtime condition library, the runtime condition library is created through the following sub-steps:

[0010] Obtain the current operating status;

[0011] Determine whether the speed and load of the current operating condition conform to the characteristics of commonly used operating conditions;

[0012] If the conditions are met, the current operating condition will be considered a common operating condition and stored in the operating condition database.

[0013] Furthermore, the steps for creating the runtime condition library also include the following:

[0014] Determine whether the creation time of the runtime condition library is greater than the set first threshold. If it is greater than the set first threshold, the runtime condition library is considered to have been created.

[0015] The step of performing fuel self-learning for each operating condition in the operating condition database includes the following sub-steps:

[0016] During the creation of the operating condition database, the fuel self-learning progress P will be... learn Set to 0, the engine is controlled to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions based on the fuel tank pressure status and carbon canister load status.

[0017] After the operating condition database is created, the fuel self-learning progress P within the operating condition database is calculated. learn Based on the current fuel self-learning progress P learn The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions.

[0018] Furthermore, the fuel self-learning progress P in the aforementioned operating condition database is calculated. learn Based on the current fuel self-learning progress P learn The steps for controlling the engine to perform fuel self-learning under the current operating conditions according to the corresponding fuel self-learning mode, based on fuel tank pressure status and carbon canister load status, include the following sub-steps:

[0019] Determine the fuel self-learning progress P learn Is it less than the second threshold?

[0020] If the fuel self-learning progress P learn If the value is less than the second threshold, the engine will be controlled to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions based on the fuel tank pressure status and carbon canister load status.

[0021] Otherwise, a periodic fuel self-learning mode is triggered.

[0022] Furthermore, the step of controlling the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating condition based on the fuel tank pressure state and carbon canister load state includes the following sub-steps:

[0023] Determine whether the fuel tank is under low pressure and the carbon canister is under low load.

[0024] If the fuel tank is under low pressure and the carbon canister is under low load, the fuel tank isolation valve and the carbon canister control valve will be closed, triggering the rapid fuel self-learning mode.

[0025] Otherwise, determine whether the conditions of the fuel tank being under low pressure and the carbon canister being under high load are met;

[0026] If the fuel tank is not under low pressure and the carbon canister is under high load, the periodic fuel self-learning mode will be triggered.

[0027] If the fuel tank is under low pressure and the carbon canister is under high load, then close the fuel tank isolation valve, open the carbon canister control valve, and return to continue judging the fuel tank pressure status and carbon canister load status.

[0028] Furthermore, the fuel self-learning progress P learn It is obtained through the following formula:

[0029]

[0030] In equation (1), N learn N represents the number of work conditions that have completed self-learning. total This indicates the total number of operating conditions in the operating condition database.

[0031] Furthermore, the method for determining that the fuel tank is under low pressure and the carbon canister is under low load is as follows:

[0032] Determine if p ≤ p crt -Δp1, where p is the actual pressure in the oil tank, p crt Δp1 is the critical pressure, and Δp1 is the calibrated pressure. If the critical pressure is met, the oil tank is in a low-pressure state; if the critical pressure is not met, the oil tank is in a high-pressure state.

[0033] Determine whether the effective load of the carbon canister is less than or equal to the third threshold. If the effective load of the carbon canister is less than or equal to the third threshold, the carbon canister is in a low-load state; otherwise, the carbon canister is in a high-load state.

[0034] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is: to provide a fine-zone fuel self-learning process adjustment system for hybrid vehicles, comprising:

[0035] The fuel self-learning progress module is used to create a runtime condition library and calculate the fuel self-learning progress P. learn ;

[0036] The evaporation system status module is used to determine the oil tank pressure status and the carbon canister load status.

[0037] The self-learning process adjustment module is used to adjust the fuel self-learning progress P based on the current fuel self-learning progress. learn The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions.

[0038] The fuel self-learning module is used to adjust the control of the adjustment module to perform fuel self-learning for the engine based on the self-learning process.

[0039] Furthermore, the fuel self-learning module includes:

[0040] The fast fuel self-learning module is used to perform fast fuel self-learning of the engine when the self-learning process adjustment module triggers the fast fuel self-learning mode.

[0041] The periodic fuel self-learning module is used to perform periodic fuel self-learning of the engine when the self-learning process adjustment module triggers the periodic fuel self-learning mode.

[0042] The hybrid vehicle fine-zone fuel self-learning process adjustment method and system of the present invention have at least the following beneficial effects: Introducing the calculation of fuel self-learning progress and maintaining dynamic calculation updates; when the progress is not up to standard, achieving rapid completion of fine-zone fuel self-learning through a fast fuel self-learning path, effectively shortening the fuel self-learning time and making the fine-zone fuel self-learning function more practical; not calculating the fuel self-learning progress during the creation of the operating condition library, setting the fuel self-learning progress to 0, allowing the engine to enter the fast fuel self-learning mode as quickly as possible, thereby effectively shortening the fuel self-learning time; using the fuel tank pressure state and carbon canister load state as conditions for controlling the fast fuel self-learning process, ensuring fuel system safety. Attached Figure Description

[0043] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0044] Figure 1 This is a flowchart of one embodiment of the fine-zone fuel self-learning process adjustment method for hybrid vehicles according to the present invention.

[0045] Figure 2 for Figure 1 The flowchart for step S1.

[0046] Figure 3 for Figure 1 The flowchart in step S2 describes the fuel self-learning process for each operating condition in the operating condition database.

[0047] Figure 4 for Figure 3 The flowchart in step S21 shows the process of controlling the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions based on the fuel tank pressure state and the carbon canister load state.

[0048] Figure 5 for Figure 3 In step S22, based on the current fuel self-learning progress P learn The flowchart shows the process of controlling the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions, based on the fuel tank pressure status and carbon canister load status.

[0049] Figure 6 for Figure 5 The flowchart for step S223.

[0050] Figure 7 This is a structural block diagram of the fine-zone fuel self-learning process adjustment system for hybrid vehicles according to the present invention. Detailed Implementation

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] The hybrid vehicle fine-zone fuel self-learning process adjustment method of the present invention adjusts the fuel self-learning process based on the current fuel self-learning progress P during the engine's fuel self-learning process. learn The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions.

[0053] Please see Figure 1 This is a flowchart of an embodiment of the fine-zone fuel self-learning process adjustment method for hybrid vehicles according to the present invention. This embodiment specifically includes the following steps:

[0054] S1. Create the runtime condition library.

[0055] Hybrid vehicles operate under stable conditions, placing higher demands on fuel self-learning. This requires finely dividing the operating conditions for fuel self-learning to achieve precise regional learning. Based on engine speed and load, the physical operating conditions of the engine are divided into several small regions. For example, a 10×10 matrix needs to complete self-learning of all 100 regions for fuel self-learning progress to reach 100%. However, in actual operation, the engine of a hybrid vehicle will not operate in all 100 regions. Different driving styles will lead to differences in commonly used operating conditions. Therefore, collecting and extracting statistical data on engine operating conditions, removing unexplored regions, and forming an engine operating condition database before calculating fuel self-learning progress will help to complete precise regional fuel self-learning earlier.

[0056] Please see Figure 2 Step S1 includes the following sub-steps:

[0057] S11. Obtain the current operating status.

[0058] The operating conditions described in this embodiment refer to the engine's working conditions. For example, an engine speed of 1500 rpm and a load of 70% represent one operating condition. The engine speed can be measured by a speed sensor, and the engine load can be calculated based on existing mature technologies.

[0059] S12. Determine whether the speed and load of the current operating condition conform to the characteristics of commonly used operating conditions.

[0060] The characteristics of the common operating conditions here can be set according to the actual situation. For example, a common operating condition can be set as follows: speed 2000 rpm, deviation ±20 rpm, load 80%, deviation ±5%, and change rate 5%. If the actual speed measured under the current operating condition is 2010 rpm, the load is 81%, and the change rate is less than 5%, then the current operating condition is determined to meet the characteristics of the common operating condition.

[0061] S13. If the condition is met, the current operating condition will be considered as a common operating condition and stored in the operating condition library.

[0062] Before storing operating conditions that meet the characteristics of common operating conditions into the operating condition library, it is necessary to determine whether the operating condition library already contains the operating condition. If it already contains the operating condition, it will not be stored again. If it does not contain the operating condition, it will be stored into the operating condition library.

[0063] The system checks if the creation time of the operating condition database exceeds a set first threshold. If it does, the database is considered complete. This first threshold can be set to 80 hours. The database creation begins when the engine first starts running and continues until the database is created after 80 hours of engine operation. The commonly used operating conditions collected during this period are sufficient to characterize the driver's driving habits. New operating conditions generated after 80 hours are stored in the operating condition database if they meet the criteria for commonly used operating conditions.

[0064] S2. Perform fuel self-learning for each operating condition in the operating condition database. Before performing fuel self-learning for each operating condition, the current fuel self-learning progress P is used as a reference. learn The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions.

[0065] Please see Figure 3 This is a flowchart for performing fuel self-learning for each operating condition in the operating condition database in step S2, which includes the following sub-steps:

[0066] S21. During the creation of the operating condition database, the fuel self-learning progress P is... learn When set to 0, the engine is controlled to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions, based on the fuel tank pressure status and carbon canister load status.

[0067] During the creation of the operating condition database, fuel self-learning is continuously performed, but the fuel self-learning progress is not calculated. The fuel self-learning progress P is then recorded. learn The setting is 0 because there are a lot of operating conditions that have not been learned in the early stages. This setting is to allow the engine to enter the fast fuel self-learning mode as soon as possible, thereby effectively shortening the fuel self-learning time.

[0068] Please see Figure 4 This is a flowchart of step S21, which involves controlling the engine to perform fuel self-learning under the current operating condition according to the corresponding fuel self-learning mode based on the fuel tank pressure state and the carbon canister load state. It includes the following sub-steps:

[0069] S211. Determine whether the conditions of the fuel tank being under low pressure and the carbon canister being under low load are met.

[0070] The specific method for determining whether the fuel tank is under low pressure and the carbon canister is under low load is as follows: determine whether p ≤ p crt -Δp1, where p is the actual pressure in the oil tank, p crtThe critical pressure is the maximum pressure the fuel tank can withstand, and Δp1 is the calibrated pressure. If this condition is met, the fuel tank is in a low-pressure state with a large safe space to store fuel vapor. If not, the fuel tank is in a high-pressure state. The effective load of the carbon canister is then checked against a third threshold. If the effective load is less than or equal to the third threshold, the carbon canister is in a low-load state with a large safe space to store fuel vapor released from the fuel tank. Otherwise, the carbon canister is in a high-load state. In this embodiment, the third threshold is a calibrable quantity with a calibration range of 5 to 8.

[0071] Because the calculation of the canister load stops updating when the engine is stopped, but continues to update while the engine is running and the canister valve is open, the canister load when the engine is running and the canister valve is open is a more accurate indicator of the canister's true condition. The canister load calculation utilizes existing mature technology. Only when the engine is running and the integral value of the canister flushing flow is greater than or equal to the calibrated value (calibration range 4–7) will the canister effective load be updated synchronously with the canister load. That is, at this time, the canister effective load and the canister load are the same; otherwise, the canister effective load remains unchanged from the last update.

[0072] S212. If the fuel tank is under low pressure and the carbon canister is under low load, then close the fuel tank isolation valve and the carbon canister control valve to trigger the rapid fuel self-learning mode.

[0073] The rapid fuel self-learning mode differs from the traditional periodic fuel self-learning mode. The traditional periodic fuel self-learning mode alternates between carbon canister flushing and fuel self-learning, but the rapid fuel self-learning mode only performs fuel self-learning without alternating with carbon canister flushing. This effectively shortens the fuel self-learning time and completes the fuel self-learning process as quickly as possible.

[0074] S213. Otherwise, determine whether the conditions of the fuel tank being under low pressure and the carbon canister being under high load are met.

[0075] S214. If the fuel tank is not under low pressure and the carbon canister is under high load, the periodic fuel self-learning mode will be triggered.

[0076] S215. If the oil tank is under low pressure and the carbon canister is under high load, close the oil tank isolation valve, open the carbon canister control valve, and return to continue judging the oil tank pressure status and carbon canister load status.

[0077] When the fuel tank is under low pressure and the carbon canister is under high load, the fuel tank has a large safe space to store fuel vapor, while the carbon canister has no safe space to store fuel vapor released from the fuel tank. Considering the safety of the fuel system, the fuel tank isolation valve is closed at this time to restrict fuel vapor in the fuel tank from entering the carbon canister. At the same time, the carbon canister control valve is opened to flush the carbon canister. Only when the fuel tank is under low pressure and the carbon canister is under low load can the fuel tank isolation valve and the carbon canister control valve be closed to trigger the rapid fuel self-learning mode.

[0078] S22. After the operating condition database is created, calculate the fuel self-learning progress P within the operating condition database. learn Based on the current fuel self-learning progress P learn The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions.

[0079] The fuel self-learning progress P learn The calculation formula is as follows:

[0080]

[0081] Where, N learn N represents the number of work conditions that have completed self-learning. total This represents the total number of operating conditions in the operating condition database. After the operating condition database is created, the fuel self-learning progress P is calculated. learn At this point, the fuel self-learning progress P learn It is no longer a fixed value; it will be dynamically updated as fuel self-learning progresses and the total number of operating conditions in the operating condition database changes, making it a real-time changing value.

[0082] Please see Figure 5 This is the self-learning progress P based on the current fuel consumption in step S22. learn The flowchart for controlling the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions, based on fuel tank pressure status and carbon canister load status, includes the following sub-steps:

[0083] S221. Determine the fuel self-learning progress P learn Is it less than the second threshold?

[0084] In this embodiment, the second threshold is 100%. If the fuel self-learning progress reaches 100%, it means that all the operating conditions in the operating condition library have been learned. However, as long as the engine is running, fuel self-learning is always in progress. The engine performs rapid fuel self-learning in order to complete fuel self-learning as soon as possible. Therefore, once all the operating conditions in the operating condition library have been learned, there is no need to continue rapid fuel self-learning. Fuel self-learning can be performed according to the periodic fuel self-learning mode.

[0085] S222, If the fuel self-learning progress P learn If the value is less than the second threshold, the engine will be controlled to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions, based on the fuel tank pressure status and carbon canister load status.

[0086] The step of controlling the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating condition based on the fuel tank pressure state and the carbon canister load state in this step can be found in step S21, which is not repeated here.

[0087] S223. Otherwise, trigger the periodic fuel self-learning mode.

[0088] Please see Figure 6 Step S223 includes the following sub-steps:

[0089] S2231, The oil tank isolation valve is opened or closed under the control of the oil tank pressure, and the carbon canister control valve is opened.

[0090] When the fuel tank is under high pressure, the fuel tank isolation valve opens; when the fuel tank is under low pressure, the fuel tank isolation valve closes. The periodic fuel self-learning mode alternates between carbon canister flushing and fuel self-learning, so the carbon canister control valve is opened to flush the carbon canister during this time.

[0091] S2232. Perform carbon canister rinsing, and record the rinsing timer.

[0092] S2233. When the flushing time exceeds the set flushing time threshold, close the fuel tank isolation valve and the carbon canister control valve to perform fuel self-learning. The self-learning timer records the learning time.

[0093] S2234. When the learning time exceeds the set learning time threshold, end the fuel self-learning process and continue to calculate and update the fuel self-learning progress P. learn This allows for the control of the fuel self-learning mode for the next operating condition.

[0094] Please see Figure 7 This is a structural block diagram of the fine-zone fuel self-learning process adjustment system for hybrid vehicles of the present invention. The fine-zone fuel self-learning process adjustment system for hybrid vehicles includes:

[0095] Fuel self-learning progress module 110 is used to create a runtime condition library and calculate the fuel self-learning progress P. learn .

[0096] The evaporation system status module 120 is used to determine the oil tank pressure status and the carbon canister load status.

[0097] The self-learning process adjustment module 130 is used to adjust the current fuel self-learning progress P based on the current fuel self-learning progress P. learn The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions.

[0098] The fuel self-learning module 140 is used to perform fuel self-learning of the engine according to the control of the self-learning adjustment module 130.

[0099] In this embodiment, the fuel self-learning module 140 includes the following sub-modules:

[0100] The fast fuel self-learning module 141 is used to perform fast fuel self-learning of the engine when the self-learning process adjustment module 130 triggers the fast fuel self-learning mode.

[0101] The periodic fuel self-learning module 142 is used to perform periodic fuel self-learning of the engine when the self-learning process adjustment module 130 triggers the periodic fuel self-learning mode.

[0102] This invention introduces a method and system for adjusting the fine-zone fuel self-learning process of hybrid vehicles. It calculates and dynamically updates the fuel self-learning progress. If the progress is not met, a fast fuel self-learning path is used to quickly complete the fine-zone fuel self-learning process, effectively shortening the fuel self-learning time and making the fine-zone fuel self-learning function more practical. During the creation of the operating condition database, the fuel self-learning progress is not calculated; it is set to 0, allowing the engine to enter the fast fuel self-learning mode as quickly as possible, thus effectively shortening the fuel self-learning time. The fuel tank pressure and carbon canister load are used as conditions to control the fast fuel self-learning process, ensuring fuel system safety.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for fine-grained fuel self-learning process adjustment in hybrid vehicles, characterized in that: During the engine's fuel self-learning process, based on the current fuel self-learning progress... The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions. This step includes the following steps: Create a runtime condition database; For each operating condition in the operating condition database, fuel self-learning is performed. This step includes: During the creation of the operating condition database, the fuel self-learning progress will be recorded. The setting is 0, and the engine is controlled to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions based on the fuel tank pressure and carbon canister load status; this step includes: Determine whether the fuel tank is under low pressure and the carbon canister is under low load. If the fuel tank is under low pressure and the carbon canister is under low load, the fuel tank isolation valve and the carbon canister control valve will be closed, triggering the rapid fuel self-learning mode. Otherwise, determine whether the conditions of the fuel tank being under low pressure and the carbon canister being under high load are met; If the fuel tank is not under low pressure and the carbon canister is under high load, the periodic fuel self-learning mode will be triggered. If the fuel tank is under low pressure and the carbon canister is under high load, then close the fuel tank isolation valve, open the carbon canister control valve, and return to continue judging the fuel tank pressure status and carbon canister load status. After the operating condition database is created, the fuel self-learning progress within the database is calculated. Based on the current progress of fuel self-learning The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions. This step includes: Determine fuel self-learning progress Is it less than the second threshold? If fuel self-learning progress If the value is less than the second threshold, the engine will be controlled to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions based on the fuel tank pressure status and carbon canister load status. Otherwise, a periodic fuel self-learning mode is triggered; Among them, the periodic fuel self-learning mode refers to the alternation of carbon canister flushing and fuel self-learning, while the rapid fuel self-learning mode refers to fuel self-learning only, without alternation with carbon canister flushing.

2. The method for fine-grained fuel self-learning process adjustment of hybrid vehicles as described in claim 1, characterized in that, The process of creating the runtime condition library involves the following sub-steps: Obtain the current operating status; Determine whether the speed and load of the current operating condition conform to the characteristics of commonly used operating conditions; If the conditions are met, the current operating condition will be considered a common operating condition and stored in the operating condition database.

3. The method for fine-grained fuel self-learning process adjustment of hybrid vehicles as described in claim 2, characterized in that, The steps involved in creating the runtime condition library also include the following: Determine if the creation time of the runtime condition library is greater than the set first threshold. If it is greater than the set first threshold, the runtime condition library is considered to have been created.

4. The method for fine-grained fuel self-learning process adjustment of hybrid vehicles as described in claim 1, characterized in that, The fuel self-learning progress It is obtained through the following formula: (one) In formula (1), This indicates the number of work conditions that have completed self-learning. This indicates the total number of operating conditions in the operating condition database.

5. The method for fine-zone fuel self-learning process adjustment of hybrid vehicles as described in claim 1, characterized in that, The method to determine if the fuel tank is under low pressure and the carbon canister is under low load is as follows: Determine if it satisfies ,in This is the actual pressure in the fuel tank. The critical pressure. To calibrate the pressure, if the condition is met, the oil tank is in a low-pressure state; if the condition is not met, the oil tank is in a high-pressure state. Determine whether the effective load of the carbon canister is less than or equal to the third threshold. If the effective load of the carbon canister is less than or equal to the third threshold, the carbon canister is in a low-load state; otherwise, the carbon canister is in a high-load state.

6. A fine-zone fuel self-learning process adjustment system for hybrid vehicles, characterized in that, The method for adjusting the fine-grained fuel self-learning process of a hybrid vehicle as described in any one of claims 1 to 5 includes: The fuel self-learning progress module is used to create a runtime condition library and calculate the fuel self-learning progress. ; The evaporation system status module is used to determine the oil tank pressure status and the carbon canister load status. The self-learning process adjustment module is used to adjust the fuel self-learning progress based on the current fuel self-learning progress. The system monitors fuel tank pressure and carbon canister load, and controls the engine to perform fuel self-learning according to the corresponding fuel self-learning mode under the current operating conditions. The fuel self-learning module is used to adjust the control of the adjustment module to perform fuel self-learning for the engine based on the self-learning process.

7. The hybrid vehicle fine-zone fuel self-learning process adjustment system as described in claim 6, characterized in that, The fuel self-learning module includes: The fast fuel self-learning module is used to perform fast fuel self-learning of the engine when the self-learning process adjustment module triggers the fast fuel self-learning mode. The periodic fuel self-learning module is used to perform periodic fuel self-learning of the engine when the self-learning process adjustment module triggers the periodic fuel self-learning mode.

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