Particulate filter soot method, model calibration method, apparatus, and electronic device

Abstract

The application discloses a method, a model calibration method, a device and electronic equipment for carbon deposition of a particle trap, and the method comprises the following steps: acquiring at least one set of actual calibration parameters of a carbon load model; performing analysis and calculation on the actual calibration parameters according to preset carbon deposition influence parameters to obtain calibration analysis parameters, wherein the preset carbon deposition influence parameters comprise dynamic carbon deposition influence parameters and static carbon deposition influence parameters, and the calibration analysis parameters correspond to the dynamic carbon deposition influence parameters one by one; acquiring preset evaluation index parameters, wherein the preset evaluation index parameters correspond to the dynamic carbon deposition influence parameters one by one; comparing the calibration analysis parameters with the preset evaluation index parameters, determining calibration parameter correction information according to a comparison result, and outputting a warning signal corresponding to the calibration parameter correction information to a tester. Through the setting of the carbon deposition influence parameters and the evaluation index, the calibration parameters of the carbon load model are quantitatively managed, and the model calibration time and the DPF pretreatment time are shortened.

Description

Technical Field

[0001] This invention relates to the field of engine exhaust gas treatment technology, and in particular to a method for carbon buildup in a particulate filter, a model calibration method, an apparatus, and electronic equipment. Background Technology

[0002] Diesel particulate filters (DPFs) are after-treatment technologies used to reduce carbon emissions from diesel engines. Currently, most particulate filters adopt a wall-flow filter element structure, which forms many small, parallel channels in the axial direction. In the channels adjacent to the filter layer, either end is blocked, forcing the waste through the porous wall to capture particulate matter.

[0003] Figure 1 This is a schematic diagram showing the relationship between the collection efficiency and carbon loading of a particulate filter.

[0004] like Figure 1 As shown, the DPF's capture efficiency η increases with increasing internal carbon loading M. When the DPF carrier is used for the first time, due to the lack of particulate matter obstruction within the carrier, some fine particles can be emitted into the atmosphere through the carrier wall, affecting the particulate number (PN) measurement during vehicle road emissions testing. Therefore, before vehicle road emissions testing, the DPF carrier needs to be pretreated to ensure the accumulation of a certain thickness of carbon layer within the porous media of the DPF, relying on the carbon layer to improve the DPF's particulate matter capture efficiency. Furthermore, during vehicle development, the DPF carbon loading model needs to be finely calibrated to determine the DPF regeneration conditions. Therefore, a reasonable carbon deposition method needs to be developed.

[0005] Existing carbon buildup methods typically involve drivers operating the vehicle under set conditions to induce carbon buildup. The standard driving conditions correspond to a carbon buildup amount of 0.02 g / km. However, existing technologies have the following problems: during actual calibration, the carbon buildup process cannot be monitored, and there are no quantitative indicators, resulting in the inability to quantitatively control the actual carbon buildup amount after DPF pretreatment. Improper driver operation can also lead to a cumulative carbon buildup amount that is less than the theoretical value, and the carbon buildup time is long, affecting the overall vehicle development schedule. Summary of the Invention

[0006] This invention provides a method for carbon deposition in particulate matter traps, a model calibration method, an apparatus, and electronic equipment. By setting carbon deposition influence parameters and evaluation indicators, the carbon loading model calibration parameters are quantitatively managed, thereby shortening the model calibration time.

[0007] According to one aspect of the present invention, a method for calibrating a carbon loading model of a particulate filter is provided, comprising:

[0008] Obtain at least one set of actual calibration parameters for the carbon loading model;

[0009] The actual calibration parameters are analyzed and calculated based on the preset carbon deposit influence parameters to obtain calibration analysis parameters. The preset carbon deposit influence parameters include dynamic carbon deposit influence parameters and static carbon deposit influence parameters. The calibration analysis parameters correspond one-to-one with the dynamic carbon deposit influence parameters.

[0010] Obtain preset evaluation index parameters, which correspond one-to-one with the dynamic carbon deposition influence parameters;

[0011] The calibration analysis parameters are compared with the preset evaluation index parameters. Based on the comparison results, calibration parameter correction information is determined, and a warning signal corresponding to the calibration parameter correction information is output to the tester.

[0012] According to another aspect of the present invention, a method for rapid carbon deposition in a particulate filter is provided, comprising: determining final calibration parameters according to the above method; and performing rapid carbon deposition according to the final calibration parameters.

[0013] According to another aspect of the present invention, a carbon loading model calibration device for a particulate filter is provided, comprising: a calibration condition input module for acquiring at least one set of actual calibration parameters of the carbon loading model; a data analysis module for analyzing and calculating the actual calibration parameters according to preset carbon deposition influence parameters to obtain calibration analysis parameters, wherein the preset carbon deposition influence parameters include dynamic carbon deposition influence parameters and static carbon deposition influence parameters, and the calibration analysis parameters correspond one-to-one with the dynamic carbon deposition influence parameters; an evaluation index acquisition module for acquiring preset evaluation index parameters, wherein the preset evaluation index parameters correspond one-to-one with the dynamic carbon deposition influence parameters; an evaluation module for comparing the calibration analysis parameters with the preset evaluation index parameters and determining calibration parameter correction information based on the comparison result; and an output module for outputting a warning signal corresponding to the calibration parameter correction information to the test personnel.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the carbon loading model calibration method of the particulate filter described above; or to perform the rapid carbon deposition method of the particulate filter described above.

[0015] The technical solution of this invention, by setting preset carbon deposit influence parameters, obtains at least one set of actual calibration parameters for the carbon load model. Based on the preset carbon deposit influence parameters, the actual calibration parameters are analyzed and calculated to obtain calibration analytical parameters. By setting preset evaluation index parameters, the calibration analytical parameters are compared with the preset evaluation index parameters. Based on the comparison results, calibration parameter correction information is determined, and a warning signal corresponding to the calibration parameter correction information is output to the test personnel to remind them to correct the calibration parameters. This solves the problems of existing carbon deposit methods being unable to quantitatively control the amount of carbon deposit and having long carbon deposit time. By setting carbon deposit influence parameters and evaluation indicators, the calibration parameters of the carbon load model are quantitatively managed, which helps to shorten the model calibration time during product development and the DPF preprocessing time during vehicle testing, thus shortening the vehicle development cycle.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram showing the relationship between the collection efficiency and carbon loading of a particulate filter.

[0019] Figure 2 This is a flowchart of a method for calibrating the carbon loading model of a particulate filter, provided in Embodiment 1 of the present invention.

[0020] Figure 3 This is a flowchart of another method for calibrating the carbon loading model of a particulate filter provided in Embodiment 1 of the present invention;

[0021] Figure 4 This is a flowchart of another carbon loading model calibration method for a particulate filter provided in Embodiment 1 of the present invention;

[0022] Figure 5 This is a flowchart of another method for calibrating the carbon loading model of a particulate filter provided in Embodiment 1 of the present invention;

[0023] Figure 6 This is a flowchart of a carbon loading model calibration method for a particulate filter provided in Embodiment 2 of the present invention;

[0024] Figure 7 This is a flowchart of a rapid carbon deposition method for a particulate filter provided in Embodiment 3 of the present invention;

[0025] Figure 8 This is a schematic diagram of the structure of a carbon loading model calibration device for a particulate trap provided in Embodiment 4 of the present invention;

[0026] Figure 9 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 2 This is a flowchart of a carbon loading model calibration method for a particulate filter provided in Embodiment 1 of the present invention. This embodiment can be applied to application scenarios where the calibration parameters of the carbon loading model are evaluated and improved based on the carbon deposition influence parameters of the particulate filter (DPF). The method can be executed by a carbon loading model calibration device, which can be implemented in hardware and / or software and can be configured in an electronic device.

[0031] In embodiments of the present invention, the carbon loading model refers to a model used to predict the mass of carbon particles trapped inside the DPF. During product development, the DPF regeneration conditions can be determined by finely calibrating the DPF carbon loading model. When the DPF carbon loading meets the DPF regeneration conditions, the DPF temperature is increased to ignite and burn the carbon particles inside the DPF, thereby eliminating carbon deposits inside the DPF.

[0032] like Figure 2 As shown, the carbon loading model calibration method for this particulate filter specifically includes the following steps:

[0033] Step S1: Obtain at least one set of actual calibration parameters for the carbon loading model.

[0034] Among them, the actual calibration parameters are the parameters that characterize the operating conditions of the whole vehicle.

[0035] In this step, the actual calibration parameters can be collected by a specific sensor at a preset acquisition frequency and then transmitted to the engine control unit (ECU).

[0036] Optionally, the actual calibration parameters include, but are not limited to, signals such as throttle, vehicle speed, and gear.

[0037] In this embodiment, the actual calibration parameters can be used as input signals for the carbon loading model. A set of actual calibration parameters corresponds to a set of predicted carbon loading values, and the accumulation rate of carbon particles is different for different calibration parameters.

[0038] Step S2: Analyze and calculate the actual calibration parameters based on the preset carbon deposition influence parameters to obtain the calibration analysis parameters.

[0039] In this embodiment, the preset carbon deposition impact parameters refer to various parameters that will cause changes in the carbon loading of the DPF during the carbon deposition process. The specific content of the preset carbon deposition impact parameters can be obtained through calibration tests and is not limited thereto. Typically, the preset carbon deposition impact parameters include dynamic carbon deposition impact parameters and static carbon deposition impact parameters. Among them, dynamic carbon deposition impact parameters refer to parameters whose values ​​will fluctuate dynamically according to the tester's performance during the calibration test; static carbon deposition impact parameters refer to parameters that remain unchanged during the calibration test.

[0040] Optionally, the dynamic carbon deposit influencing parameters include at least one of the following: average throttle, throttle distribution, throttle change rate, average vehicle speed, vehicle speed change rate, average v*a value, and gear.

[0041] Where, v*a mean represents the vehicle speed greater than 0.1 m / s. 2The product of positive accelerations, v*a, can be used to reflect the degree of fluctuation or smoothness in driving behavior. Average throttle represents the average actual throttle opening during the calibration test; throttle distribution represents the distribution of the actual throttle opening during the calibration test; throttle change rate represents the degree of drastic change in throttle opening during the calibration test; average vehicle speed represents the average actual vehicle speed during the calibration test; and vehicle speed change rate is used to assess the degree of drastic change in vehicle speed during the calibration test.

[0042] In this embodiment, the calibration analysis parameters can be obtained by analyzing a set of data analysis results using the parameters that cause changes in DPF carbon loading (i.e., preset carbon deposition influence parameters) as the analysis target. The data value of any item in the calibration analysis parameters will affect the DPF carbon loading change of the carbon loading model.

[0043] In this embodiment, the calibration analysis parameters can correspond one-to-one with the dynamic carbon deposition influence parameters. That is to say, each dynamic carbon deposition influence parameter can be used as the analysis target to perform data analysis on the actual calibration parameters and obtain the corresponding calibration analysis parameters.

[0044] For example, taking the dynamic carbon deposit influence parameters as average throttle, throttle distribution, and throttle change rate, after analyzing the actual input calibration parameters, the obtained calibration analysis parameters include: average throttle calibration analysis parameters, throttle distribution calibration analysis parameters, and throttle change rate calibration analysis parameters.

[0045] Step S3: Obtain the preset evaluation index parameters. The preset evaluation index parameters correspond one-to-one with the dynamic carbon deposition influence parameters.

[0046] The preset evaluation index parameters refer to the reference indicators set for evaluating the performance of the carbon load model under calibration conditions. The carbon deposition rate under the driving conditions corresponding to the preset evaluation index parameters is faster than the carbon deposition rate under normal driving conditions.

[0047] Optionally, the preset evaluation index parameters may include at least one of the following: average throttle quantitative index, throttle distribution quantitative index, throttle change rate quantitative index, average vehicle speed quantitative index, vehicle speed change rate quantitative index, v*a mean quantitative index, and gear quantitative index.

[0048] In this step, the values ​​of the quantitative indicators in the preset evaluation index parameters can be obtained through calibration or experimentation, and their specific values ​​are not limited.

[0049] Step S4: Compare the calibration analysis parameters with the preset evaluation index parameters, determine the calibration parameter correction information based on the comparison results, and output the corresponding warning signal to the test personnel.

[0050] Among them, calibration parameter correction information and warning information can be used to guide the improvement of actual calibration parameters. After the calibration parameter correction information is corrected, the carbon buildup rate of the driving conditions corresponding to the calibration parameters will be improved.

[0051] Optionally, the calibration parameter correction information includes the target parameter for correction and the correction step size, wherein the target parameter for correction can be any one or a combination of the actual calibration parameters; and the correction step size can be the correction amount when performing parameter correction.

[0052] Specifically, preset carbon deposit impact parameters can be set based on parameters affecting DPF carbon load changes (e.g., dynamic carbon deposit impact parameters such as average throttle, throttle distribution, throttle change rate, average vehicle speed, vehicle speed change rate, average v*a value and gear position, and static carbon deposit impact parameters such as engine model and particulate filter model). Preset evaluation index parameters can also be set based on carbon deposit speed under different driving conditions (e.g., average throttle quantitative index, throttle distribution quantitative index, throttle change rate quantitative index, average vehicle speed quantitative index, vehicle speed change rate quantitative index, average v*a value quantitative index and gear position quantitative index).

[0053] During the carbon load model calibration process, actual calibration parameters are collected in real time. These parameters are then analyzed according to the parameter types defined by the preset carbon deposition influence parameters to obtain calibration analysis parameters. Taking actual calibration parameters including throttle, vehicle speed, and gear as an example, the calibration analysis parameters may include average throttle analysis parameters, throttle distribution analysis parameters, throttle change rate analysis parameters, average vehicle speed analysis parameters, vehicle speed change rate analysis parameters, average v*a value analysis parameters, and gear analysis parameters. The quantitative indicators of each preset evaluation index parameter are used as reference benchmark values. The obtained calibration analysis parameters are compared with the corresponding preset evaluation index parameters to determine whether improvements to the actual calibration parameters are needed. If improvements are required, calibration parameter correction information is output to remind testers to adjust the calibration analysis parameters to the quantitative indicators defined by the preset evaluation index parameters.

[0054] Therefore, the technical solution of this invention, by setting preset carbon deposition influence parameters and preset evaluation index parameters, quantitatively manages the carbon load model calibration parameters, which solves the problems of existing carbon deposition methods being unable to quantitatively control the amount of carbon deposition and having a long carbon deposition time. This is beneficial to shorten the model calibration time during product development and the DPF pretreatment time during vehicle testing, thereby shortening the vehicle development cycle.

[0055] Optionally, Figure 3 This is a flowchart of another method for calibrating the carbon loading model of a particulate filter according to Embodiment 1 of the present invention. Figure 2 Based on the illustrated embodiment, an exemplary implementation method is shown for data analysis of actual calibration parameters according to preset carbon deposition influence parameters.

[0056] like Figure 3 As shown, step S2 above includes:

[0057] Step S201: Obtain any target influence parameter from the dynamic carbon deposition influence parameters.

[0058] The target influence parameter can be any one of the dynamic carbon deposition influence parameters.

[0059] For example, the target influencing parameter may be any of the following: average throttle, throttle distribution, throttle change rate, average vehicle speed, vehicle speed change rate, mean v*a, and gear.

[0060] Step S202: Perform a traversal analysis of the actual calibration parameters based on the target influence parameters to obtain the traversal results.

[0061] Step S203: Determine the calibration analysis parameters corresponding to the target influence parameters based on the traversal results.

[0062] For example, taking the throttle distribution as the target influencing parameter, when traversing and analyzing all actual calibration parameters in the calibration test based on the throttle distribution, the throttle opening in all actual calibration parameters is searched, and the actual throttle opening is classified and summarized according to the set step size until the classification and summarization of all actual calibration parameters is completed, and the throttle distribution result is determined as the throttle distribution calibration analysis parameter.

[0063] Optionally, Figure 4 This is a flowchart of another method for calibrating the carbon loading model of a particulate filter provided in Embodiment 1 of the present invention. Figure 2 Based on the illustrated embodiment, an exemplary implementation method for obtaining preset evaluation index parameters is shown.

[0064] like Figure 4 As shown, step S3 above includes:

[0065] Step S301: Determine the parameter threshold of any dynamic carbon deposition influence parameter based on the static carbon deposition influence parameter.

[0066] Among them, the static carbon deposit impact parameters include any one or more combinations of engine model and particulate filter model.

[0067] Step S302: Determine the preset evaluation index parameters corresponding to the dynamic carbon deposition influence parameters based on the parameter thresholds.

[0068] Specifically, differences in the filter element structure of different particulate filters lead to variations in their ability to capture carbon particles; differences in carbon emissions from different engine models result in variations in the flow rate of carbon particles through the particulate filter. Therefore, static carbon buildup parameters can influence changes in DPF carbon load. Thresholds for dynamic carbon buildup parameters can be set based on different static carbon buildup parameters, and these thresholds can be used as quantitative indicators for preset evaluation parameters. By introducing static factors affecting DPF carbon load changes and establishing different quantitative indicators for different vehicle models and DPF structures, it is beneficial to optimize the quantitative indicators and improve the reliability of calibration parameter evaluation results.

[0069] Optionally, Figure 5 This is a flowchart of another method for calibrating the carbon loading model of a particulate filter according to Embodiment 1 of the present invention. Figure 2 Based on the illustrated embodiment, an exemplary implementation method for parameter evaluation of calibration conditions is shown.

[0070] like Figure 5 As shown, step S4 above includes:

[0071] Step S401: Determine whether the calibration analysis parameters match the preset evaluation index parameters.

[0072] If any of the calibration analysis parameters does not match the preset evaluation index parameters, then proceed to step S402; if all the calibration analysis parameters match the preset evaluation index parameters, then proceed to step S403.

[0073] Step S402: Determine the calibration parameter correction information based on the actual calibration parameters corresponding to the mismatch, and output the corresponding warning signal to the test personnel.

[0074] Step S403: Remind the test personnel to perform the calibration test according to the current actual calibration parameters.

[0075] For example, the quantitative indicators of the preset evaluation index parameters meet the following conditions:

[0076] The average throttle quantification index meets the following requirement: average throttle opening > 30%;

[0077] The quantitative indicators for throttle distribution meet the following requirements: the proportion of data with throttle opening <10% and throttle opening >90% is more than 70%, and the ratio of data with throttle opening <10% to data with throttle opening >90% is 2:1.

[0078] The quantitative indicator of throttle change rate meets the following requirements: the proportion of data with a throttle opening change rate exceeding 1% and the proportion of data with a throttle opening change rate below -1% are both above 10%, and the mean of the throttle change rate is above 1%.

[0079] The quantitative indicator for average vehicle speed must meet the following requirement: average vehicle speed < 20 km / h;

[0080] The quantitative indicator of vehicle speed change rate meets the following requirements: the proportion of data with vehicle speed change > 0.1 and the proportion of data with vehicle speed change < 0.1 are within the range of 20% ± 5%.

[0081] The v*a mean quantification indicator satisfies: v*a mean > 0.04m 2 / s 3 ;

[0082] The gear quantification indicator meets the following requirement: the proportion of data in gear 3 and above is less than 5%.

[0083] After obtaining the quantitative indicators, carbon load model calibration is performed. Data analysis is conducted on the actual calibration parameters. The average throttle analysis parameters, throttle distribution analysis parameters, throttle change rate analysis parameters, average vehicle speed analysis parameters, vehicle speed change rate analysis parameters, mean v*a analysis parameters, and gear analysis parameters are compared with the quantitative indicators of the aforementioned preset evaluation indicators. If any of the calibration analysis parameters does not match the corresponding quantitative indicator, for example, average throttle opening < 30%, the actual calibration parameter corresponding to the mismatch (e.g., throttle) is determined as the correction target parameter. When determining the calibration parameter correction information, the correction target parameter can be corrected according to a preset correction step size until the corrected calibration parameter meets the quantitative indicators of the preset evaluation indicators.

[0084] Optionally, the warning signal includes at least one of the following: an audible warning signal, an image warning signal, and a visual warning signal. That is, the evaluation result can be output through any one or a combination of audible, image, and visual warning signals to remind the tester to adjust the actual calibration parameters until the corrected calibration parameters meet the quantitative indicators of the preset evaluation index parameters.

[0085] Therefore, by setting quantitative indicators to evaluate the parameters of the calibration conditions and providing correction reminders, it is beneficial to shorten the model calibration time and save manpower and time costs.

[0086] Example 2

[0087] Optionally, Figure 6 This is a flowchart of a carbon loading model calibration method for a particulate filter provided in Embodiment 2 of the present invention. Based on Embodiment 1, a self-learning function for quantitative indicators is added.

[0088] like Figure 6 As shown, the carbon loading model calibration method for this particulate filter specifically includes the following steps:

[0089] Step S1: Obtain at least one set of actual calibration parameters for the carbon loading model.

[0090] Step S2: Analyze and calculate the actual calibration parameters based on the preset carbon deposition influence parameters to obtain the calibration analysis parameters.

[0091] Step S3: Obtain the preset evaluation index parameters. The preset evaluation index parameters correspond one-to-one with the dynamic carbon deposition influence parameters.

[0092] Step S501: Obtain the actual carbon loading change rate corresponding to the calibration analysis parameters.

[0093] Among them, the carbon loading change rate can be used to characterize the rate of carbon deposition.

[0094] Step S502: Determine whether the actual carbon loading change rate is higher than the preset change rate threshold.

[0095] The preset change rate threshold can be the carbon loading change rate under the calibration conditions corresponding to the preset evaluation index parameters.

[0096] If the actual carbon loading change rate is higher than the preset change rate threshold, then proceed to step S503; otherwise, proceed to step S4.

[0097] Step S503: Update the preset evaluation index parameters according to the calibration analysis parameters, and continue to execute step S4.

[0098] Step S4: Compare the calibration analysis parameters with the preset evaluation index parameters, determine the calibration parameter correction information based on the comparison results, and output the corresponding warning signal to the test personnel.

[0099] Specifically, before comparing the calibration analysis parameters with the preset evaluation index parameters, it can be determined whether the currently set preset evaluation index parameters need to be updated. Referring to the implementation methods described in steps S501 to S503, the actual carbon load change rate of the current actual calibration parameters (or the calibration analysis parameters corresponding to the actual calibration parameters) can be obtained. If the actual carbon load change rate is higher than the preset change rate threshold, that is, the carbon deposition rate of the actual calibration parameters given under the current calibration condition is faster than the carbon deposition rate under the calibration condition corresponding to the preset evaluation index parameters, then the current preset evaluation index parameters will be replaced by the calibration analysis parameters, and the updated preset evaluation index parameters will be used to perform subsequent parameter evaluation. By setting a self-learning function for quantitative indicators and optimizing the quantitative indicators in combination with the actual calibration given values, it is beneficial to improve the reliability of the calibration parameter evaluation results, further shorten the model calibration time and DPF preprocessing time, and shorten the vehicle development cycle.

[0100] Example 3

[0101] Based on any of the above embodiments, according to Embodiment 3 of the present invention, a method for rapid carbon deposition in a particulate filter is provided. This rapid carbon deposition method is implemented based on the carbon loading model calibration method provided in any of the above embodiments and has the corresponding beneficial effects of the calibration method.

[0102] Figure 7 This is a flowchart of a rapid carbon deposition method for a particulate filter provided in Embodiment 3 of the present invention.

[0103] like Figure 7 As shown, this DPF rapid carbon deposition method includes:

[0104] Step S701: Determine the final calibration parameters according to the carbon loading model calibration method described above.

[0105] Step S702: Perform rapid carbon deposition according to the final calibration parameters.

[0106] The technical solution of this invention, by setting preset carbon deposit influence parameters, obtains at least one set of actual calibration parameters for the carbon load model. Based on the preset carbon deposit influence parameters, the actual calibration parameters are analyzed and calculated to obtain calibration analytical parameters. By setting preset evaluation index parameters, the calibration analytical parameters are compared with the preset evaluation index parameters. Based on the comparison results, calibration parameter correction information is determined, and a warning signal corresponding to the calibration parameter correction information is output to the test personnel to remind them to correct the calibration parameters. This solves the problems of existing carbon deposit methods being unable to quantitatively control the amount of carbon deposit and having long carbon deposit time. By setting carbon deposit influence parameters and evaluation indicators, the calibration parameters of the carbon load model are quantitatively managed, which is beneficial for reducing carbon deposit time and shortening the vehicle development cycle.

[0107] Example 4

[0108] Based on any of the above embodiments, Embodiment 4 of the present invention provides a carbon loading model calibration device for a particulate filter. The carbon loading model calibration device provided in this embodiment of the present invention can execute the carbon loading model calibration method provided in any embodiment of the present invention, and possesses the corresponding functional modules and beneficial effects of the method.

[0109] Figure 8 This is a schematic diagram of the structure of a carbon loading model calibration device for a particulate trap provided in Embodiment 4 of the present invention.

[0110] like Figure 8As shown, the carbon loading model calibration device 00 for the particulate filter includes: a calibration condition input module 101, used to acquire at least one set of actual calibration parameters of the carbon loading model; a data analysis module 102, used to analyze and calculate the actual calibration parameters according to preset carbon deposition influence parameters to obtain calibration analysis parameters, wherein the preset carbon deposition influence parameters include dynamic carbon deposition influence parameters and static carbon deposition influence parameters, and the calibration analysis parameters correspond one-to-one with the dynamic carbon deposition influence parameters; an evaluation index acquisition module 103, used to acquire preset evaluation index parameters, wherein the preset evaluation index parameters correspond one-to-one with the dynamic carbon deposition influence parameters; an evaluation module 104, used to compare the calibration analysis parameters with the preset evaluation index parameters and determine calibration parameter correction information based on the comparison results; and an output module 105, used to output a warning signal corresponding to the calibration parameter correction information to the test personnel.

[0111] Optionally, the data parsing module 102 is used to obtain any target influence parameter among the dynamic carbon deposition influence parameters; to perform traversal parsing on the actual calibration parameters according to the target influence parameter to obtain the traversal result; and to determine the calibration parsing parameter corresponding to the target influence parameter according to the traversal result.

[0112] Optionally, the dynamic carbon deposit influencing parameters include at least one of the following: average throttle, throttle distribution, throttle change rate, average vehicle speed, vehicle speed change rate, average v*a value, and gear; the static carbon deposit influencing parameters include at least one of the following: engine model and particulate filter model.

[0113] Optionally, the evaluation index acquisition module 103 is used to determine the parameter threshold of any dynamic carbon deposition influence parameter based on the static carbon deposition influence parameter; and to determine the preset evaluation index parameter corresponding to the dynamic carbon deposition influence parameter based on the parameter threshold.

[0114] Optionally, the evaluation module 104 is used to determine whether the calibration analysis parameters match the preset evaluation index parameters, and when any item in the calibration analysis parameters does not match the preset evaluation index parameters, it determines the calibration parameter correction information based on the actual calibration parameters corresponding to the mismatch item; wherein, the calibration parameter correction information includes the correction target parameter and the correction step size.

[0115] Optionally, the evaluation index acquisition module 103 is also used to acquire the actual carbon loading change rate corresponding to the calibration analysis parameters; determine whether the actual carbon loading change rate is higher than the preset change rate threshold; and update the preset evaluation index parameters according to the calibration analysis parameters when the actual carbon loading change rate is higher than the preset change rate threshold.

[0116] Optionally, the warning signal includes at least one of the following: audible warning signal, visual warning signal, and visual warning signal.

[0117] Example 5

[0118] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the carbon loading model calibration method of the particulate filter described above; or to perform the rapid carbon deposition method of the particulate filter described above.

[0119] Figure 9 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0120] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0121] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0122] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the carbon loading model calibration method for the particulate trap described above; or, performs the rapid carbon deposition method for the particulate trap described above.

[0123] In some embodiments, the carbon loading model calibration method for the particulate filter described above, or the method for rapid carbon deposition in the particulate filter described above, can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, the carbon loading model calibration method for the particulate filter described above, or one or more steps of the rapid carbon deposition method for the particulate filter, can be executed. Alternatively, in other embodiments, processor 11 can be configured by any other suitable means (e.g., by means of firmware) to execute the carbon loading model calibration method for the particulate filter; or to execute the rapid carbon deposition method for the particulate filter described above.

[0124] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0128] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0129] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0130] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0131] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for calibrating a carbon loading model for a particulate filter, wherein the carbon loading model is used to predict the mass of carbon particles captured inside a DPF, characterized in that, include: Step S1: Obtain at least one set of actual calibration parameters for the carbon load model, wherein the actual calibration parameters are parameters characterizing the vehicle's operating conditions; Step S2: Analyze and calculate the actual calibration parameters based on the dynamic carbon deposit influence parameters in the preset carbon deposit influence parameters to obtain calibration analysis parameters. The preset carbon deposit influence parameters include dynamic carbon deposit influence parameters and static carbon deposit influence parameters, and the calibration analysis parameters correspond one-to-one with the dynamic carbon deposit influence parameters. The dynamic carbon deposit influence parameters include at least one of the following: average throttle, throttle distribution, throttle change rate, average vehicle speed, vehicle speed change rate, average VA value, and gear. The static carbon deposit influence parameters include at least one of the following: engine model and particulate filter model. The average VA value represents a speed greater than 0.1 m / s. 2 The product of positive accelerations; Step S3: Obtain preset evaluation index parameters, which correspond one-to-one with the dynamic carbon deposition influence parameters; the preset evaluation index parameters are reference indicators for evaluating the quality of the carbon loading model calibration conditions. Step S4: Compare the calibration analysis parameters with the preset evaluation index parameters, determine the correction information for the actual calibration parameters based on the comparison results, and output the warning signal corresponding to the calibration parameter correction information to the test personnel; The analysis and calculation of the actual calibration parameters based on the dynamic carbon deposition influence parameters in the preset carbon deposition influence parameters includes: Obtain any target influence parameter from the dynamic carbon deposition influence parameters; wherein, the target influence parameter is any one of the dynamic carbon deposition influence parameters; The actual calibration parameters are traversed and analyzed based on the target influence parameters to obtain the traversal results; Based on the traversal results, determine the calibration and analysis parameters corresponding to the target influence parameters; Before comparing the calibration analysis parameters with the preset evaluation index parameters, steps S501 to S503 are performed. Step S501: Obtain the actual carbon loading change rate corresponding to the calibration analysis parameters; wherein, the carbon loading change rate is used to characterize the carbon deposition rate; Step S502: Determine whether the actual carbon loading change rate is higher than the preset change rate threshold; wherein, the preset change rate threshold is the carbon loading change rate under the calibration condition corresponding to the preset evaluation index parameter; If the actual carbon loading change rate is higher than the preset change rate threshold, then proceed to step S503; otherwise, proceed to step S4. Step S503: Update the preset evaluation index parameters according to the calibration analysis parameters, and continue to execute step S4.

2. The method according to claim 1, characterized in that, The process of obtaining preset evaluation index parameters includes: Determine the parameter threshold for any of the dynamic carbon deposition influence parameters based on the static carbon deposition influence parameters; The preset evaluation index parameter corresponding to the dynamic carbon deposition impact parameter is determined based on the parameter threshold.

3. The method according to claim 1, characterized in that, The calibration analysis parameters are compared with the preset evaluation index parameters, and the calibration parameter correction information is determined based on the comparison results, including: Determine whether the calibration analysis parameters match the preset evaluation index parameters; If any of the calibration analysis parameters does not match the preset evaluation index parameters, then calibration parameter correction information is determined based on the actual calibration parameters corresponding to the mismatched items. The calibration parameter correction information includes the target correction parameter and the preset correction step size.

4. The method according to any one of claims 1-3, characterized in that, Before comparing the calibration analysis parameters with the preset evaluation index parameters, the following steps are also included: Obtain the actual carbon loading change rate corresponding to the calibration analysis parameters; Determine whether the actual carbon loading change rate is higher than a preset change rate threshold; If the actual carbon loading change rate is higher than the preset change rate threshold, the preset evaluation index parameters are updated according to the calibration analysis parameters.

5. The method according to any one of claims 1-3, characterized in that, The warning signal includes at least one of the following: audible warning signal, visual warning signal, and visual warning signal.

6. A method for rapid carbon deposition in a particulate filter, characterized in that, include: The final calibration parameters are determined by the method according to any one of claims 1-5; Perform rapid carbon deposition based on the final calibration parameters.

7. A carbon loading model calibration device for a particulate filter, used to perform the method of claim 1, characterized in that, include: The calibration condition input module is used to obtain at least one set of actual calibration parameters of the carbon loading model; The data analysis module is used to analyze and calculate the actual calibration parameters based on the preset carbon deposition influence parameters to obtain calibration analysis parameters. The preset carbon deposition influence parameters include dynamic carbon deposition influence parameters and static carbon deposition influence parameters. The calibration analysis parameters correspond one-to-one with the dynamic carbon deposition influence parameters. The evaluation index acquisition module is used to acquire preset evaluation index parameters, which correspond one-to-one with the dynamic carbon deposition influence parameters. The evaluation module is used to compare the calibration analysis parameters with the preset evaluation index parameters, and determine the calibration parameter correction information based on the comparison results; The output module is used to output warning signals corresponding to the calibration parameter correction information to the test personnel.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the carbon loading model calibration method for the particulate trap according to any one of claims 1-5; Alternatively, the rapid carbon deposition method for the particulate filter as described in claim 6 may be implemented.

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