Dpf regeneration control method and device based on big data, electronic equipment and vehicle

Abstract

The application discloses a DPF regeneration control method and device based on big data, electronic equipment and vehicle, and comprises the following steps: in the initial state, if regeneration is requested through a diagnostic instrument or a parking regeneration switch, the method jumps to step 3; if regeneration is triggered when the carbon load is greater than a limit value, the method enters step 2; in step 2, after receiving the request, the system determines whether regeneration is suitable based on current GPS position information; if not, the method returns to the initial state of step 1; if suitable, the method enters step 3; in step 3, the system determines whether the current vehicle operating condition is suitable for regeneration; if suitable, the system triggers regeneration; if not suitable, the method returns to the initial state of step 1. The application designs a regeneration state control method based on position information, which is used for determining whether the current position is suitable for regeneration, accurately determining the regeneration entry time, effectively reducing the DPF regeneration interruption probability, improving the DPF regeneration success rate, thereby reducing the DPF regeneration frequency, reducing the DPF regeneration fuel consumption and improving the user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of post-processing control, and particularly relates to a DPF regeneration control method and device based on big data, an electronic device and a vehicle BACKGROUND

[0002] The particulate trap DPF (Diesel Particulate Filter) is a ceramic filter installed in the exhaust system of a diesel engine, which can capture particulate emissions before they enter the atmosphere, thereby reducing the emission of particulate matter in automobile exhaust and reducing the pollution of automobile exhaust to the atmosphere.

[0003] With the upgrading application of the national sixth and future emission regulations, more stringent requirements are put forward for the particulate matter emission of diesel engines, and DPF is the main means to reduce particulate matter. When the carbon load in the DPF reaches the limit value, the carbon in the DPF needs to be eliminated by increasing the exhaust temperature through regeneration.

[0004] The DPF regeneration process is greatly affected by the vehicle operating conditions. Once the operating conditions change, the post-processing temperature is too low or too high, which will cause the DPF regeneration to be interrupted. When the DPF regeneration is started again, the exhaust temperature needs to be raised again. This will increase the number of vehicle regeneration and fuel consumption, and affect the user experience. Therefore, how to reduce the interruption probability of DPF regeneration and reduce the fuel consumption during DPF regeneration is a technical problem to be solved by those skilled in the art. SUMMARY

[0005] In order to overcome the deficiencies in the prior art, the present application provides a regeneration control method based on big data to determine the regeneration entry time, which can effectively reduce the regeneration interruption probability and improve the regeneration success rate.

[0006] To solve the above technical problems, the present application realizes by the following technical scheme:

[0007] A DPF regeneration control method based on big data, comprising the following steps:

[0008] Step 1: In the initial state, if regeneration is requested through a diagnostic instrument or a parked regeneration switch, go to step 3; if regeneration is triggered by carbon load greater than the limit value, go to step 2;

[0009] Step 2: After receiving the request, the system determines whether it is suitable for regeneration based on the current GPS position information. If it is not suitable, go back to step 1; if it is suitable for regeneration, go to step 3;

[0010] Step 3: Determine whether the current vehicle operating condition is suitable for regeneration. If it is suitable, trigger regeneration; if it is not suitable, go back to step 1.

[0011] Preferably, in step 2, the GPS-based regeneration condition determination logic is as follows:

[0012] Step 2-1: Statistics of vehicle and engine data at the same GPS position;

[0013] Step 2-2: Calculate the average value of each data at the same GPS position;

[0014] Step 2-3: Determine whether the average value of each data at the GPS position meets the set value requirement; if yes, go to step 2-4, if no, determine that this GPS is not suitable for regeneration;

[0015] Step 2-4: Determine whether the data of other GPS positions within the set range at the GPS position are within the set range; if yes, determine that this GPS is suitable for regeneration, if no, not suitable for regeneration.

[0016] Further preferably, in steps 2-1 and 2-2, vehicle data collection and average value calculation:

[0017] Parameter average value Wherein:

[0018] P is the collected parameter, including: vehicle speed, engine speed, engine torque, transmission gear position, DOC inlet temperature; when the ECU power-on time exceeds the set value t min , start data collection; N is the number of data collection at the current GPS.

[0019] Further preferably, in step 2-3, the single-point GPS average value determination requirement includes:

[0020] 1) The vehicle speed needs to be greater than the set value; 2) The engine speed needs to be within the set value range; 3) The engine torque needs to be greater than the set value; 4) The transmission gear position needs to be greater than the set value; 5) The DOC inlet temperature needs to be greater than the set value; when all the above conditions are met, it is considered that the current GPS single-point set condition is met.

[0021] Further preferably, in step 2-4, the condition determination of other position points within the range corresponding to the current GPS:

[0022] The average vehicle speed at the current GPS position is multiplied by the set regeneration time t rgn to get the distance L; draw a circle with the current GPS as the center and L as the radius, and perform the condition determination in step 2-3 on all GPS points within the circle; when all the conditions of all GPS positions within the range are met, it is considered that the conditions within the set range of the current GPS position are met.

[0023] Preferably, in step 3, the regeneration condition determination based on the current vehicle operating state includes:

[0024] 1) vehicle speed needs to be greater than a set value; 2) engine speed needs to be within a set value range; 3) engine torque needs to be greater than a set value; 4) gearbox gear needs to be greater than a set value; 5) DOC inlet temperature needs to be greater than a set value; 6) intercooled water temperature is within a set range; when the above conditions are met, it is considered that the current vehicle regeneration condition is met.

[0025] A regeneration control device based on big data, comprising:

[0026] A carbon load acquisition module is configured to acquire the carbon load of the particulate trap in real time.

[0027] A data acquisition module is configured to acquire the data of the whole vehicle and the engine in real time.

[0028] A first judgment module is configured to judge whether the carbon load is greater than a limit value.

[0029] A second judgment module is configured to judge whether the system is suitable for regeneration based on the current GPS position information.

[0030] A third judgment module is configured to judge whether the current whole vehicle operating condition is suitable for regeneration.

[0031] A trigger module is configured to trigger the vehicle to perform DPF regeneration when the third judgment module judges that the current whole vehicle operating condition is suitable for regeneration.

[0032] An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the DPF regeneration control method based on big data as described above.

[0033] A computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the DPF regeneration control method based on big data as described above.

[0034] A vehicle comprising a regeneration control device based on big data as described above.

[0035] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0036] The present application is based on big data, analyzes the operating parameters of the whole vehicle and the engine at each GPS position, and marks each position point as suitable for regeneration or unsuitable for regeneration. When it is confirmed that no "unsuitable for regeneration" position will be passed in a complete regeneration cycle, the regeneration is allowed to enter. In this way, the regeneration interruption can be effectively avoided, the regeneration success rate can be improved, the DPF regeneration frequency can be reduced, the DPF regeneration fuel consumption can be reduced, and the user experience can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 Flow chart of the DPF regeneration control method based on big data of the present application.

[0038] Figure 2 Flow chart of Step 2 of the present application.

[0039] Figure 3 Actual performance trend chart of the DPF regeneration process of the verification example of the present application. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0041] The accompanying drawings are referred to in the following description of the embodiments of the present application. Figures 1-2 The embodiments of the present application are further described in detail.

[0042] The embodiments of the present application provide a DPF regeneration control method, device, electronic equipment, storage medium, and vehicle based on big data, to effectively reduce the interruption probability of DPF regeneration.

[0043] To achieve the above technical effects, the general idea of the present application is as follows:

[0044] A DPF regeneration control method based on big data, comprising the following steps:

[0045] Step 1: In the initial state, if regeneration is requested through a diagnostic instrument or a stationary regeneration switch, go to Step 3; if regeneration is triggered by the carbon load being greater than the limit value, go to Step 2;

[0046] Step 2: After receiving the request, the system determines whether it is suitable for regeneration based on the current GPS position information. If it is not suitable, go back to Step 1; if it is suitable for regeneration, go to Step 3;

[0047] Step 3: Determine whether the current vehicle operating condition is suitable for regeneration. If it is suitable, trigger regeneration; if it is not suitable, go back to Step 1.

[0048] The regeneration condition determination logic based on GPS (Step 2) is as follows:

[0049] Step 2-1: Statistics of vehicle and engine data at the same GPS position;

[0050] Step 2-2: Calculate the average value of each data point at the same GPS location;

[0051] Step 2-3: Determine if the average value of each data point at this GPS location meets the set value requirements; if yes, proceed to Step 2-4; otherwise, determine that this GPS is not suitable for regeneration.

[0052] Steps 2-4: Determine if other GPS location data within the set range are within the set range; if so, determine if this GPS is suitable for regeneration; otherwise, it is not suitable for regeneration.

[0053] Implementation Case 1 (Step 2): Vehicle Data Collection and Mean Calculation

[0054] Parameter mean in:

[0055] P represents the parameters collected, including but not limited to: vehicle speed, engine speed, engine torque, transmission gear, DOC inlet temperature, etc.; when the ECU power-on time exceeds the set value t... min Data collection begins at time N; N represents the number of data collection cycles under the current GPS.

[0056] Implementation Case 2 (Step 2-3): Requirements for determining the average GPS value at a single point, including but not limited to:

[0057] 1) The vehicle speed must be greater than the set value;

[0058] 2) The engine speed must be within the set range;

[0059] 3) The engine torque must be greater than the set value;

[0060] 4) The gearbox gear must be higher than the set value;

[0061] 5) The DOC inlet temperature must be greater than the set value;

[0062] When all of the above conditions are met, the current GPS single-point setting conditions can be considered satisfied.

[0063] Implementation Case 3 (Step 2-4): Condition determination for other location points within the current GPS range:

[0064] Multiply the average vehicle speed at the current GPS location by the set regeneration time t rgn Obtain the distance L; draw a circle with the current GPS as the center and L as the radius, and perform the condition judgment in Case 2 on all GPS points within the circle; when all conditions of all GPS locations within the range are satisfied, it can be considered that the conditions within the set range of the current GPS location are satisfied.

[0065] Case 4 (Step 3): Regeneration condition determination based on current vehicle operating state, including but not limited to:

[0066] 1) Vehicle speed needs to be greater than a set value;

[0067] 2) Engine speed needs to be within a set value range;

[0068] 3) Engine torque needs to be greater than a set value;

[0069] 4) Transmission gear needs to be greater than a set value;

[0070] 5) DOC inlet temperature needs to be greater than a set value;

[0071] 6) Intercooled water temperature is within a set range;

[0072] When all the above conditions are met, it can be considered that the current vehicle regeneration condition is met.

[0073] The set values described in the implementation cases are obtained through bench and vehicle bench tests. The set values described in Case 2 should be more relaxed than the set values described in Case 3 to exclude the difference between vehicles, ensuring that all vehicles can normally enter the subsequent regeneration determination.

[0074] Based on the same inventive concept as the DPF regeneration control method based on big data, the embodiment of the application provides a regeneration control device based on big data, comprising:

[0075] A carbon load acquisition module for acquiring the carbon load of the particulate trap in real time;

[0076] A data acquisition module for acquiring the data of the vehicle and the engine in real time;

[0077] A first judgment module for judging whether the carbon load is greater than a limit value;

[0078] A second judgment module for judging whether the system is suitable for regeneration based on the current GPS position information;

[0079] A third judgment module for judging whether the current vehicle operating condition is suitable for regeneration;

[0080] A trigger module for triggering the vehicle to perform DPF regeneration when the third judgment module judges that the current vehicle operating condition is suitable for regeneration.

[0081] Based on the same inventive concept as the DPF regeneration control method based on big data, the embodiment of the application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize a DPF regeneration control method based on big data as described above.

[0082] Based on the same inventive concept as the embodiment of the DPF regeneration control method based on big data, this embodiment of the invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned DPF regeneration control method based on big data.

[0083] Based on the same inventive concept as the big data-based regeneration control device embodiment, the present invention provides a vehicle including a big data-based regeneration control device as described above.

[0084] Based on the above regeneration control method, the present invention also provides the following verification examples:

[0085] like Figure 3 As shown, in intervals 1 and 2, regeneration stops because the DOC inlet temperature is below the minimum limit. Using the method of this invention, since the DOC inlet temperature in intervals 1 and 2 does not meet the set requirements, regeneration is not triggered at these intervals. Upon entering interval 3, the DOC inlet temperature is above the limit, thus the position condition is met, and regeneration can begin. In interval 3, although the DOC inlet temperature fluctuates significantly, the DPF inlet temperature remains constant, allowing for normal regeneration. Testing shows that the probability of DPF regeneration interruption is reduced after adopting the regeneration control method of this invention.

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Furthermore, any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory.

[0087] The above are merely specific embodiments of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A DPF regeneration control method based on big data, characterized in that, Includes the following steps: Step 1: In the initial state, if regeneration is requested through the diagnostic tool or the parking regeneration switch, proceed to step 3; if regeneration is triggered by the carbon load exceeding the limit, proceed to step 2. Step 2: Upon receiving the request, the system determines whether regeneration is suitable based on the current GPS location information. If not, it returns to the initial state in Step 1; if regeneration is suitable, it proceeds to Step 3. Step 3: Determine whether the current vehicle operating conditions are suitable for regeneration. If suitable, trigger regeneration; otherwise, return to the initial state of Step 1. In step 2, the GPS-based regeneration condition determination logic is as follows: Step 2-1: Collect data on the whole vehicle and engine at the same GPS location; Step 2-2: Calculate the average value of each data point at the same GPS location; Step 2-3: Determine whether the average value of each data point at this GPS location meets the set value requirements; if yes, proceed to step 2-4; otherwise, determine that this GPS location is not suitable for regeneration. Steps 2-4: Determine whether other GPS location data within the set range of this GPS location are within the set range; if yes, then this GPS location is suitable for regeneration; otherwise, it is not suitable for regeneration. In steps 2-1 and 2-2, the whole vehicle data is collected and the mean is calculated: Parameter mean ,in: P represents the parameters collected, including: vehicle speed, engine speed, engine torque, transmission gear, and DOC inlet temperature; when the ECU power-on time exceeds the set value... Data collection begins at the specified time; N represents the number of data collection attempts at the current GPS location. In steps 2-3, the requirements for determining the average GPS position of a single point include: 1) Vehicle speed must be greater than the set value; 2) Engine speed must be within the set value range; 3) Engine torque must be greater than the set value; 4) Gearbox gear position must be greater than the set value; 5) DOC inlet temperature must be greater than the set value; When all of the above conditions are met, the current GPS single-point setting conditions are considered to be met. In steps 2-4, the conditions for determining other location points within the current GPS location's range are as follows: Multiply the average vehicle speed at the current GPS location by the set regeneration time. Get distance With the current GPS as the center, Draw a circle with a radius and perform the condition judgment in steps 2-3 on all GPS points within the circle; when all conditions of all GPS locations within the range are met, it is considered that the conditions within the current GPS location setting range are met.

2. The DPF regeneration control method based on big data according to claim 1, characterized in that: Step 3, the determination of regeneration conditions based on the current vehicle operating status, includes: 1) The vehicle speed must be greater than the set value; 2) The engine speed must be within the set value range; 3) The engine torque must be greater than the set value; 4) The gearbox gear must be greater than the set value; 5) The DOC inlet temperature must be greater than the set value; 6) The intercooler temperature must be within the set range. When all of the above conditions are met, the current vehicle regeneration conditions are considered to be met.

3. A big data-based regeneration control device based on the DPF regeneration control method according to claim 1 or 2, characterized in that, include: The carbon loading acquisition module is used to acquire the carbon loading of the particulate filter in real time. The data acquisition module is used to acquire real-time data from the vehicle and engine. The first judgment module is used to determine whether the carbon loading is greater than the limit value; The second judgment module is used to determine whether the system is suitable for regeneration based on the current GPS location information; The third judgment module is used to determine whether the current operating conditions of the vehicle are suitable for regeneration; The trigger module is used to trigger the vehicle to perform DPF regeneration when the third judgment module determines that the current vehicle operating conditions are suitable for regeneration.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the DPF regeneration control method as described in claim 1 or 2.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the DPF regeneration control method as described in claim 1 or 2.

6. A vehicle, characterized in that: Includes the regeneration control device as described in claim 3.

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