A vehicle-end DPF passive regeneration performance evaluation method, device, equipment and storage medium

Abstract

The application belongs to the technical field of post-processing, and specifically provides a whole vehicle end DPF passive regeneration performance evaluation method, device, equipment and storage medium, the method comprises the following steps: collecting different vehicle operation original emission data; screening the original emission data to obtain effective data; according to the vehicle number, the effective data is divided into several sub data; wherein, the data of each vehicle number is recorded as a sub data; according to the vehicle active regeneration state, the sub data is divided into several sub data segments; the increase rate of carbon load in each sub data segment of each vehicle with the increase of engine running time is calculated; the passive regeneration evaluation coefficient is calculated according to the increase rate of carbon load; the passive regeneration performance in the set state is evaluated according to the passive regeneration evaluation coefficient. By calculating the increase rate of carbon load in each sub data segment of each vehicle with the increase of engine running time, and calculating the passive regeneration evaluation coefficient accordingly, the quantitative evaluation of the passive regeneration performance is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of post-processing, in particular to a whole vehicle end DPF passive regeneration performance evaluation method, device, equipment and storage medium. BACKGROUND

[0002] A diesel particulate filter (DPF) is a post-processing device installed in the exhaust system of a diesel engine, used to capture and store particulate pollutants in engine exhaust. The current mainstream DPF is of wall-flow structure, which is composed of a series of parallel elongated channels inside, which are separated by walls made of porous material. Usually, one end of the channels is open, while the other end is alternately closed, and the wall of each channel is porous, when the exhaust gas passes through the porous wall, most of the particulate matter will be captured in the wall, and the clean gas will continue to flow and pass through the DPF. Over time, the accumulation of particulate matter in the DPF will cause the exhaust back pressure to increase, thereby affecting the engine performance.

[0003] In order to maintain the efficiency of the DPF, it is necessary to eliminate the accumulated particulate matter by regeneration. Regeneration is usually divided into two categories: passive regeneration, which relies on high temperature generated by normal driving and nitrogen oxides in engine exhaust to remove particulate matter; active regeneration, which heats the exhaust gas by injecting additional fuel or burning incomplete engine cylinder gas through the reaction of the post-processing oxidation catalyst to promote the combustion of particulate matter in the DPF. Active regeneration requires additional energy or fuel input, which will increase fuel consumption during vehicle use; high frequency of active regeneration will also accelerate the thermal aging process of the post-processing device, shortening the service life of the equipment. Therefore, in the actual development process, it is more inclined to choose products with strong passive regeneration performance of DPF, in order to improve the power and economy of the whole system. How to accurately and comprehensively evaluate the passive regeneration performance of DPF in use has become the key to optimizing and improving the design and selection of DPF, saving fuel consumption and improving the service life of the post-processing system.

[0004] The industry usually tests the carbon load of DPF under a certain steady-state point or a certain cycle of working conditions on the engine test bench to evaluate the passive regeneration performance of DPF. However, on the one hand, the test bench test conditions cannot accurately simulate the transient working conditions and the corresponding environmental conditions of the vehicle actual driving; on the other hand, only through the carbon load value cannot accurately describe a passive regeneration process changing with time. SUMMARY

[0005] In view of the problems existing in the traditional method for evaluating the passive regeneration performance of DPF, the present application provides a whole vehicle end DPF passive regeneration performance evaluation method, device, equipment and storage medium.

[0006] In a first aspect, the present application provides a method for evaluating passive regeneration performance of a vehicle end DPF, comprising the following steps:

[0007] Collecting original emission data of different vehicles in operation, wherein the original emission data comprises vehicle number, cumulative engine running time, carbon load, and DPF active regeneration state parameters;

[0008] Filtering the original emission data to obtain effective data;

[0009] Dividing the effective data into several sub-data according to the vehicle number, wherein the data of each vehicle number is recorded as a sub-data;

[0010] Dividing the sub-data into several sub-data segments according to the vehicle active regeneration state;

[0011] Calculating the increase rate of carbon load with engine running time in each sub-data segment of each vehicle;

[0012] Calculating the passive regeneration evaluation coefficient according to the increase rate of carbon load;

[0013] Evaluating the passive regeneration performance under the set state according to the passive regeneration evaluation coefficient.

[0014] By collecting original emission data of different vehicles in operation, including vehicle number, cumulative engine running time, carbon load, and DPF active regeneration state parameters, the comprehensiveness and accuracy of the evaluation basis data are ensured, which provides a solid foundation for subsequent analysis and evaluation.

[0015] As a preferred embodiment of the present application, the original emission data further comprises engine model, DPF configuration number, cumulative total mileage, DPF fault parameters, DPF temperature, and exhaust flow.

[0016] As a preferred embodiment of the present application, the step of filtering the original emission data to obtain effective data comprises:

[0017] According to the DPF fault parameters, the data with DPF fault is deleted;

[0018] Selecting data with DPF temperature lower than the first preset temperature, exhaust flow lower than the first preset flow, and carbon load lower than the first preset carbon load from the data after deleting the DPF fault data, and deleting the parameters of DPF failure, DPF differential pressure sensor failure, and DPF temperature sensor failure to obtain effective data.

[0019] As a preferred embodiment of the present application, the step of dividing the sub-data into several sub-data segments according to the vehicle active regeneration state comprises:

[0020] The active regeneration starting moment and ending moment are determined according to the DPF active regeneration state parameter, and the effective data between the active regeneration starting moment and the active regeneration ending moment of each time is recorded as a sub-data segment.

[0021] The sub-data is further divided into a plurality of sub-data segments according to the vehicle active regeneration state, so that the evaluation can be performed on the passive regeneration performance of the vehicle under different active regeneration states, and the evaluation is more detailed and in-depth.

[0022] As a preferred embodiment of the present application, the step of dividing the sub-data into a plurality of sub-data segments according to the vehicle active regeneration state comprises:

[0023] The sub-data segment containing a group of data greater than the set threshold is selected as the effective sub-data segment.

[0024] As a preferred embodiment of the present application, the step of calculating the rate of increase of carbon load in each sub-data segment of each vehicle with the increase of engine running time comprises:

[0025] The effective sub-data segment is sorted in time sequence, the average carbon load of the first percentage of data in the sub-data segment is recorded as the initial carbon load S1, the time midpoint of the first percentage of data is recorded as the time t1, the average carbon load of the last percentage of data in the sub-data segment is recorded as the terminal carbon load S2, and the time midpoint of the last percentage of data is recorded as the time t2.

[0026] The carbon load increase rate v=(S2-S1) / (t2-t1).

[0027] As a preferred embodiment of the present application, the step of evaluating the passive regeneration performance under the set state according to the passive regeneration evaluation coefficient comprises:

[0028] The passive regeneration evaluation coefficients of each sub-data segment with the same engine model, similar cumulative mileage or similar cumulative engine running time are selected, grouped according to the DPF configuration number, the average value of the passive regeneration evaluation coefficients of each group is calculated, and the passive regeneration performance of different DPF configurations is compared;

[0029] The passive regeneration evaluation coefficients of each sub-data segment with the same DPF configuration number, similar cumulative mileage or similar cumulative engine running time are selected, grouped according to the engine model, the average value of the passive regeneration evaluation coefficients of each group is calculated, and the influence of different engine models on the passive regeneration performance is compared;

[0030] The passive regeneration evaluation coefficient of each sub-data segment with the same engine model and DPF configuration number is selected, grouped according to the cumulative mileage or cumulative engine running time, the average value of the passive regeneration evaluation coefficient of each group is calculated, and the influence of aging during vehicle use on the passive regeneration performance is compared.

[0031] In a second aspect, the present application provides a vehicle end DPF passive regeneration performance evaluation device, comprising a data acquisition module, a data screening module, a sub-data division module, a data segment division module, an increase rate calculation module, an evaluation coefficient calculation module and a regeneration performance evaluation module.

[0032] The data acquisition module is used for collecting different vehicle running original emission data; wherein the original emission data includes vehicle number, cumulative engine running time, carbon load, DPF active regeneration state parameters.

[0033] The data screening module is used for screening the original emission data to obtain valid data.

[0034] The sub-data division module is used for dividing the valid data into several sub-data according to the vehicle number; wherein the data of each vehicle number is recorded as a sub-data.

[0035] The data segment division module is used for dividing the sub-data into several sub-data segments according to the vehicle active regeneration state.

[0036] The increase rate calculation module is used for calculating the increase rate of carbon load with engine running time in each sub-data segment of each vehicle.

[0037] The evaluation coefficient calculation module is used for calculating the passive regeneration evaluation coefficient according to the increase rate of carbon load.

[0038] The regeneration performance evaluation module is used for evaluating the passive regeneration performance under the set state according to the passive regeneration evaluation coefficient.

[0039] As a preferred embodiment of the present application, the original emission data further includes engine model, DPF configuration number, cumulative total mileage, DPF fault parameter, DPF temperature and exhaust flow.

[0040] As a preferred embodiment of the present application, the data screening module is used for deleting the data with DPF fault according to the DPF fault parameter; selecting the data with DPF temperature lower than the first preset temperature, exhaust flow lower than the first preset flow and carbon load lower than the first preset carbon load after deleting the DPF fault data, and deleting the parameters of DPF fault, DPF differential pressure sensor fault and DPF temperature sensor fault to obtain valid data.

[0041] As a preferred technical scheme of the present application, the data segment division module is specifically configured to determine the start time and the end time of active regeneration according to the DPF active regeneration state parameter, and effective data between each active regeneration start time and the last active regeneration end time is recorded as a sub-data segment.

[0042] As a preferred technical scheme of the present application, the device further comprises a data segment screening module configured to select a sub-data segment in which the number of groups containing data is greater than a set threshold as an effective sub-data segment.

[0043] As a preferred technical scheme of the present application, the rate calculation module is configured to sort the effective sub-data segments in chronological order, record the average carbon load of the first percentage of data in the sub-data segment as an initial carbon load S1, record the time midpoint of the first percentage of data as a time t1, record the average carbon load of the first percentage of data after the sub-data segment as a terminal carbon load S2, and record the time midpoint of the first percentage of data after the sub-data segment as a time t2.

[0044] The carbon load increase rate v is equal to (S2-S1) / (t2-t1).

[0045] As a preferred technical scheme of the present application, the regeneration performance evaluation module is configured to select a passive regeneration evaluation coefficient of each sub-data segment with the same engine model, similar cumulative mileage or similar cumulative engine running time, group the passive regeneration evaluation coefficients according to the DPF configuration number, calculate the average value of the passive regeneration evaluation coefficients of each group, and compare the passive regeneration performance of different DPF configurations; select a passive regeneration evaluation coefficient of each sub-data segment with the same DPF configuration number, similar cumulative mileage or similar cumulative engine running time, group the passive regeneration evaluation coefficients according to the engine model, calculate the average value of the passive regeneration evaluation coefficients of each group, and compare the influence of different engine models on the passive regeneration performance; select a passive regeneration evaluation coefficient of each sub-data segment with the same engine model and DPF configuration number, group the passive regeneration evaluation coefficients according to the cumulative mileage or cumulative engine running time, calculate the average value of the passive regeneration evaluation coefficients of each group, and compare the influence of aging during vehicle use on the passive regeneration performance.

[0046] In a third aspect, the present application provides an electronic device, which comprises at least one processor and a memory connected with the at least one processor; the memory stores computer program instructions executable by the at least one processor, and the computer program instructions are executed by the at least one processor to enable the at least one processor to execute the vehicle end DPF passive regeneration performance evaluation method according to the first aspect.

[0047] In a fourth aspect, the present application provides a non-transitory computer readable storage medium storing computer instructions, which cause the computer to perform the whole vehicle end DPF passive regeneration performance evaluation method according to the first aspect.

[0048] From the above technical solutions, it can be seen that the present application has the following advantages: the original emission data is screened to obtain effective data, which effectively eliminates the influence of invalid or abnormal data on the evaluation result, and improves the accuracy and reliability of the evaluation. The effective data is divided into several sub-data according to the vehicle number, and the data of each vehicle number is recorded as a sub-data, so that individual evaluation of each vehicle is realized. This subdivided evaluation method can more accurately reflect the actual passive regeneration performance of each vehicle. The rate of increase of carbon load with engine running time in each sub-data segment of each vehicle is calculated, and the passive regeneration evaluation coefficient is calculated accordingly, so that the quantitative evaluation of passive regeneration performance is realized. This quantitative evaluation method makes the evaluation result more objective, specific, easy to understand and compare.

[0049] According to the passive regeneration evaluation coefficient, the passive regeneration performance in the set state is evaluated, which can provide clear guidance and suggestions for vehicle manufacturers and operators, help them understand the passive regeneration performance of the vehicle in actual operation, and further optimize the vehicle design and operation strategy, and improve the environmental performance and economic benefit of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative labor.

[0051] Figure 1 is a schematic flow chart of the method of one embodiment of the present application.

[0052] Figure 2 is a first sub-data segment diagram of vehicle data A.

[0053] Figure 3 is a schematic flow chart of the method of another embodiment of the present application.

[0054] Figure 4 is a schematic block diagram of the device of one embodiment of the present application. DETAILED DESCRIPTION

[0055] During long-term use, the DPF will cause its trapping efficiency to decrease due to the continuous accumulation of particulate matter, and even may cause the problem of blockage, affecting the normal operation of the engine and the emission performance. In order to solve this problem, the regeneration technology of the DPF emerges as the times require. The regeneration technology is mainly divided into two types: active regeneration and passive regeneration. The active regeneration is to improve the temperature inside the DPF through external heating or fuel injection, etc., so that the accumulated particulate matter is burned and converted into harmless substances. The passive regeneration is to use the oxygen in the engine exhaust and the high temperature condition to make the particulate matter burn by itself inside the DPF. During the passive regeneration process, the change of the carbon load is one of the key indicators for evaluating its performance. The carbon load refers to the mass of the particulate matter accumulated inside the DPF, and its change rate reflects the efficiency and capacity of the passive regeneration. Therefore, how to accurately evaluate the passive regeneration performance of the DPF has important significance for ensuring the emission performance and environmental protection performance of the diesel engine vehicle.

[0056] However, the existing evaluation methods mostly depend on the test data under laboratory conditions, and it is difficult to truly reflect the passive regeneration performance of the vehicle in the actual running process. In addition, due to the differences of different vehicles, different engine models and different running conditions, the evaluation of the passive regeneration performance is more complex and difficult. Therefore, it is necessary to develop an evaluation method which can truly reflect the passive regeneration performance of the vehicle in the actual running process, so as to guide the vehicle manufacturers and operators to optimize the vehicle design and operation strategy, and improve the environmental protection performance and economic benefit of the diesel engine vehicle. The technical scheme of the present application is proposed based on this demand, and aims to provide a whole vehicle end DPF passive regeneration performance evaluation method to solve the problems and deficiencies of the existing evaluation methods.

[0057] In order to enable the personnel in the technical field to better understand the technical scheme in the present application, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the personnel in the field without creative labor should belong to the protection scope of the present application.

[0058] As shown in Figure 1 The embodiment of the present application provides a whole vehicle end DPF passive regeneration performance evaluation method, which comprises the following steps:

[0059] Step 1: Collecting original emission data of different vehicles in operation;

[0060] The original emission data includes: vehicle number (such as license plate number), engine model, DPF configuration number, cumulative engine running time, cumulative total mileage, carbon load, DPF active regeneration state parameter, DPF fault parameter, DPF temperature, exhaust flow, which is collected by the vehicle emission parameter data collection system and exported;

[0061] It should be noted that data collection can also use professional emission data collection equipment, such as on-board diagnostic system (OBD) or special emission monitoring instrument, connected to the emission system of diesel engine vehicle. Start the vehicle and run according to the predetermined driving cycle (such as urban road, highway, mountain road, etc.), while recording the emission data. The collected data should include vehicle number (used to distinguish the data set of different vehicles), cumulative engine running time (record the total running time of engine from start to current time), carbon load (reflect the mass of particles accumulated inside DPF), and DPF active regeneration state parameter (indicate whether DPF is in active regeneration process). Obtain comprehensive and accurate vehicle emission data, which provides a reliable basis for subsequent analysis and evaluation.

[0062] Step 2: screening the original emission data to obtain valid data;

[0063] The collected original data is preliminarily checked to eliminate obviously abnormal or invalid data points, such as data missing, values beyond reasonable range, etc. According to the data quality evaluation standard, further filter the data to ensure the accuracy and consistency of the data. Keep the data that meet the screening conditions as valid data for subsequent analysis and evaluation. Improve the accuracy and reliability of the data, reduce the influence of invalid data on the evaluation results.

[0064] Step 3: according to the vehicle number, divide the valid data into several sub-data; wherein, the data of each vehicle number is recorded as a sub-data; such as the data of the first vehicle is recorded as A, the data of the second vehicle is recorded as B, the data of the third vehicle is recorded as C, etc.

[0065] The valid data is classified and divided into different subsets according to the vehicle number. Each vehicle number corresponds to a sub-data set, which contains all the valid emission data of the vehicle. Realize the individualization of data, which provides convenience for subsequent analysis of each vehicle.

[0066] Step 4: according to the vehicle active regeneration state, divide the sub-data into several sub-data segments;

[0067] Further divide each vehicle numbered sub-data set, divide the data into different sub-data segments according to the DPF active regeneration state parameters. Each sub-data segment corresponds to a specific active regeneration state, such as before active regeneration, during active regeneration, after active regeneration, etc. Make the data more detailed, and can analyze the passive regeneration performance of the vehicle under different active regeneration states.

[0068] Step 5: Calculate the rate of increase of carbon load with engine running time in each sub-data segment of each vehicle;

[0069] Process the data in each sub-data segment, calculate the rate of increase of carbon load with engine running time. Select other mathematical models (such as Gauss model, Logistic model, etc.) for fitting, or use machine learning methods to select a more suitable mathematical model to calculate the rate of increase, to reflect the trend of carbon load with time.

[0070] Step 6: Calculate the passive regeneration evaluation coefficient according to the rate of increase of carbon load;

[0071] The evaluation coefficient can be a dimensionless value, which is used to reflect the passive regeneration performance of the vehicle under different conditions; In the embodiment of the present application, the passive regeneration evaluation coefficient is defined as the reciprocal of the rate of increase of carbon load. The larger the coefficient, the better the passive regeneration performance.

[0072] Any other simple mathematical transformation containing reciprocal, such as 100 / carbon load increase rate, 1 / carbon load increase rate+1, etc. In addition, other suitable mathematical models can also be selected for replacement.

[0073] Step 7: Evaluate the passive regeneration performance under the set state according to the passive regeneration evaluation coefficient.

[0074] In some embodiments, the step of selecting valid data from the original emission data includes:

[0075] Step 21: According to the DPF fault parameter judgment, delete the data with DPF fault;

[0076] Step 22: Select the data with DPF temperature lower than the first preset temperature, exhaust flow lower than the first preset flow, and carbon load lower than the first preset carbon load in the data after deleting the DPF fault data, and delete the parameters of DPF fault, DPF differential pressure sensor fault, and DPF temperature sensor fault, to obtain valid data.

[0077] The first preset temperature is generally the highest temperature that the DPF can reach in a non-active regeneration condition, and the embodiment of the application can adopt 450°C. The first preset flow is generally the maximum exhaust flow when the vehicle or engine is running normally, and the embodiment of the application can adopt 3000 kg / h. The first preset carbon load is generally the maximum carbon load determined when the DPF is designed and calibrated, and the embodiment of the application can adopt 50 g.

[0078] In some embodiments, the step of dividing the sub-data into a plurality of sub-data segments according to the active regeneration state of the vehicle comprises:

[0079] The active regeneration start time and end time are determined according to the DPF active regeneration state parameter, and the effective data between each active regeneration start time and the last active regeneration end time is recorded as a sub-data segment.

[0080] For example, the data of the first vehicle records all the data of the vehicle from the cumulative engine running time t0 to t5, and after screening, the effective data forms data A, which contains two active regenerations: normal driving from time t0 to time t1, first active regeneration from time t1 to time t2, normal driving from time t2 to time t3, second active regeneration from time t3 to time t4, and normal driving from time t4 to time t5. The data between times t0 to t1, t2 to t3 and t3 to t4 is three sub-data segments, which are recorded as A1, A2 and A3 respectively.

[0081] In some embodiments, the step of dividing the sub-data into a plurality of sub-data segments according to the active regeneration state of the vehicle comprises:

[0082] The sub-data segment containing a group of data greater than the set threshold is selected as the effective sub-data segment. Here, the effective sub-data segment needs to contain at least 100 groups of data. The effective data is obtained, and the interference of other faults or abnormal states is excluded.

[0083] Correspondingly, the step of calculating the increase rate of the carbon load in each sub-data segment of each vehicle with the increase of the engine running time comprises:

[0084] The effective sub-data segments are sorted in time sequence, the average carbon load of the first 1% data of the sub-data (if less than 10 groups of data, the first 10 groups of data are selected) is recorded as the initial carbon load S1, the time midpoint is recorded as time t1, the average carbon load of the last 1% data of the sub-data (if less than 10 groups of data, the last 10 groups of data are selected) is recorded as the terminal carbon load S2, and the time midpoint is recorded as time t2. Then the carbon load increase rate v=(S2-S1) / (t2-t1). For example, the first 1% data of the sub-data A1 is 10 groups of data, and the last 1% data of the sub-data A1 is 10 groups of data. The average carbon load of the first 10 groups of data is 50 g, and the average carbon load of the last 10 groups of data is 60 g. The time interval between the first 10 groups of data and the last 10 groups of data is 10 hours. Therefore, the carbon load increase rate v=(60-50) / 10=1 g / h. Figure 2The first sub-data segment A1 of the first vehicle data A is shown, containing 1200 data points, the horizontal axis is the cumulative engine running time (unit: second, s), and the vertical axis is the carbon load (unit: gram, g). The first 1% data has 12 data, S1=9.4, t1=5110; the last 1% data has 12 data, S2=15.08, t2=24705. Then the carbon load increase rate v=(15.08-9.4) / (24705-5110)=0.00028987 g / s. Based on the above, the passive regeneration evaluation coefficient of the sub-data segment A1 of the vehicle is 1 / 0.00028987=3450.

[0085] The passive regeneration evaluation coefficient calculated by the above method can be used to evaluate the passive regeneration performance of the specified DPF configuration of the specified engine on the specified vehicle in a certain state. According to actual needs or evaluation targets, the final evaluation result can be obtained through the following processing mode, such as Figure 3 As shown in the figure, the step of evaluating the passive regeneration performance in the set state according to the passive regeneration evaluation coefficient includes:

[0086] The passive regeneration evaluation coefficients of each sub-data segment with the same engine model, similar cumulative mileage or similar cumulative engine running time are selected, grouped according to the DPF configuration number, and the average value of the passive regeneration evaluation coefficients of each group is calculated for comparing the passive regeneration performance of different DPF configurations;

[0087] The passive regeneration evaluation coefficients of each sub-data segment with the same DPF configuration number, similar cumulative mileage or similar cumulative engine running time are selected, grouped according to the engine model, and the average value of the passive regeneration evaluation coefficients of each group is calculated for comparing the influence of different engine models on the passive regeneration performance;

[0088] The passive regeneration evaluation coefficients of each sub-data segment with the same engine model and DPF configuration number are selected, grouped according to the cumulative mileage or cumulative engine running time, and the average value of the passive regeneration evaluation coefficients of each group is calculated for comparing the influence of aging during vehicle use on the passive regeneration performance. The passive regeneration evaluation coefficient is calculated by the carbon load increase rate. In fact, the carbon load increase rate itself can be used to evaluate the passive regeneration performance of the DPF, that is, the higher the carbon load increase rate, the worse the passive regeneration performance.

[0089] The present application needs to collect a sufficient amount of original data, and the selection of effective sub-data segments includes the requirement for the amount of data. A large amount of data can ensure that the rules summarized finally are reasonable and the interference of abnormal data is reduced as much as possible.

[0090] The passive regeneration evaluation coefficient in the specified state (different vehicles, engines, DPF configurations, use time, etc.) is accurately calculated by creating a set of sub-data and sub-data segments, and adjustment space is provided for subsequent calculation of different actual requirements or evaluation targets.

[0091] The increase of carbon load is the result of the accumulation of emitted particles, and the role of passive regeneration is to prevent the increase of carbon load to some extent, and the increase rate of carbon load can directly reflect the level of passive regeneration.

[0092] As shown in Figure 4 The embodiment of the present application provides a whole vehicle end DPF passive regeneration performance evaluation device, which comprises a data acquisition module, a data screening module, a sub-data division module, a data segment division module, an increase rate calculation module, an evaluation coefficient calculation module and a regeneration performance evaluation module.

[0093] The data acquisition module is used for acquiring original emission data of different vehicles; wherein the original emission data comprises vehicle number, cumulative engine running time, carbon load and DPF active regeneration state parameter.

[0094] The data screening module is used for screening the original emission data to obtain effective data.

[0095] The sub-data division module is used for dividing the effective data into several sub-data according to the vehicle number; wherein the data of each vehicle number is recorded as a sub-data.

[0096] The data segment division module is used for dividing the sub-data into several sub-data segments according to the active regeneration state of the vehicle.

[0097] The increase rate calculation module is used for calculating the increase rate of carbon load with engine running time in each sub-data segment of each vehicle.

[0098] The evaluation coefficient calculation module is used for calculating the passive regeneration evaluation coefficient according to the increase rate of carbon load.

[0099] The regeneration performance evaluation module is used for evaluating the passive regeneration performance in the specified state according to the passive regeneration evaluation coefficient.

[0100] The original emission data further comprises engine model, DPF configuration number, cumulative total mileage, DPF fault parameter, DPF temperature and exhaust flow.

[0101] In some embodiments, the data screening module is configured to delete data in which DPF failure is determined according to the DPF failure parameter; select data in which DPF temperature is lower than a first preset temperature, exhaust flow is lower than a first preset flow, and carbon loading is lower than a first preset carbon loading from the data after the DPF failure data is deleted, and delete parameters in which DPF failure, DPF pressure difference sensor failure, and DPF temperature sensor failure occur, to obtain valid data.

[0102] In some embodiments, the data segment division module is configured to determine active regeneration start time and end time according to the DPF active regeneration state parameter, and valid data between each active regeneration start time and the last active regeneration end time is recorded as a sub-data segment.

[0103] In some embodiments, the device further comprises a data segment screening module configured to select a sub-data segment in which the number of groups containing data is greater than a set threshold as a valid sub-data segment. Correspondingly, the rate calculation module is configured to sort the valid sub-data segments in chronological order, record the average carbon loading of the first percentage of data in the sub-data segment as an initial carbon loading S1, record the middle point of the time of the first percentage of data as time t1, record the average carbon loading of the first percentage of data after the sub-data segment as a terminal carbon loading S2, and record the middle point of the time of the first percentage of data after the sub-data segment as time t2.

[0104] The carbon loading increase rate v = (S2-S1) / (t2-t1).

[0105] In some embodiments, the regeneration performance evaluation module is configured to select a passive regeneration evaluation coefficient of each sub-data segment with the same engine model, similar cumulative mileage, or similar cumulative engine running time, group the passive regeneration evaluation coefficients according to the DPF configuration number, calculate the average value of the passive regeneration evaluation coefficients of each group, and compare the passive regeneration performance of different DPF configurations; select a passive regeneration evaluation coefficient of each sub-data segment with the same DPF configuration number, similar cumulative mileage, or similar cumulative engine running time, group the passive regeneration evaluation coefficients according to the engine model, calculate the average value of the passive regeneration evaluation coefficients of each group, and compare the influence of different engine models on passive regeneration performance; select a passive regeneration evaluation coefficient of each sub-data segment with the same engine model and DPF configuration number, group the passive regeneration evaluation coefficients according to the cumulative mileage or cumulative engine running time, calculate the average value of the passive regeneration evaluation coefficients of each group, and compare the influence of aging during vehicle use on passive regeneration performance.

[0106] The embodiment of the present application also provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus. The communication bus can be used for information transmission between the electronic device and the sensor. The processor can call the logical instructions in the memory to execute the following method: step 1: collecting different vehicle running original emission data; wherein the original emission data comprises vehicle number, cumulative engine running time, carbon load and DPF active regeneration state parameter; step 2: screening the original emission data to obtain effective data; step 3: dividing the effective data into several sub-data according to the vehicle number; wherein the data of each vehicle number is recorded as a sub-data; step 4: dividing the sub-data into several sub-data segments according to the vehicle active regeneration state; step 5: calculating the increase rate of the carbon load with the increase of the engine running time in each sub-data segment of each vehicle; step 6: calculating the passive regeneration evaluation coefficient according to the increase rate of the carbon load; and step 7: evaluating the passive regeneration performance in the set state according to the passive regeneration evaluation coefficient.

[0107] In addition, the logical instructions in the memory described above can be realized in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk and various program code storage media.

[0108] The embodiment of the present application provides a non-transitory computer readable storage medium storing computer instructions, which causes a computer to execute the method provided by the method embodiment, for example, comprising: step 1, collecting different vehicle running original emission data; wherein the original emission data comprises vehicle number, cumulative engine running time, carbon load, DPF active regeneration state parameter; step 2, screening the original emission data to obtain effective data; step 3, according to the vehicle number, the effective data is divided into several sub-data; wherein the data of each vehicle number is recorded as a sub-data; step 4, according to the vehicle active regeneration state, the sub-data is divided into several sub-data segments; step 5, calculating the increase rate of carbon load with the increase of engine running time in each sub-data segment of each vehicle; step 6, calculating the passive regeneration evaluation coefficient according to the increase rate of carbon load; and step 7, evaluating the passive regeneration performance under the state according to the passive regeneration evaluation coefficient.

[0109] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0110] The embodiment of the whole vehicle end DPF passive regeneration performance evaluation device provided by the embodiment of the present application, the device and the whole vehicle end DPF passive regeneration performance evaluation method of each embodiment belong to the same inventive concept, and the details not described in detail in the embodiment of the whole vehicle end DPF passive regeneration performance evaluation device can be referred to the embodiment of the whole vehicle end DPF passive regeneration performance evaluation method.

[0111] The whole vehicle end DPF passive regeneration performance evaluation device is the unit and algorithm steps of each example described in combination with the embodiments disclosed herein, which can be realized by electronic hardware, computer software or combination of both, in order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether the functions are executed in hardware or software mode depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0112] Those skilled in the art can understand that each aspect of the whole vehicle end DPF passive regeneration performance evaluation method can be realized as a system, a method or a program product. Therefore, each aspect of the present disclosure can be embodied as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuit", "module" or "system" here.

[0113] Although the present application has been described in detail with reference to the preferred embodiments, the application is not limited to such but encompasses any modifications or alternatives within the scope of the application as disclosed in the appended claims.

Claims

1. A method for evaluating passive regeneration performance of a vehicle-end DPF, characterized by, The method comprises the following steps: Collecting original emission data of different vehicles; wherein the original emission data comprises vehicle number, cumulative engine running time, carbon load, and DPF active regeneration state parameter; Filtering the original emission data to obtain effective data; Dividing the effective data into several sub-data according to the vehicle number; wherein the data of each vehicle number is recorded as a sub-data; Dividing the sub-data into several sub-data segments according to the active regeneration state of the vehicle; specifically including: determining the start time and end time of active regeneration according to the DPF active regeneration state parameter, and recording the effective data between each active regeneration start time and the previous active regeneration end time as a sub-data segment; Selecting a sub-data segment containing a group of data greater than a set threshold as an effective sub-data segment; Calculating the increase rate of carbon load with the increase of engine running time in each sub-data segment of each vehicle; specifically including: Sorting the effective sub-data segments in chronological order, recording the average carbon load of the first percentage of data before the sub-data segment as the initial carbon load S1, recording the time midpoint of the first percentage of data as the time t1, recording the average carbon load of the first percentage of data after the sub-data segment as the terminal carbon load S2, and recording the time midpoint of the first percentage of data as the time t2; the carbon load increase rate v=(S2-S1) / (t2-t1); Calculating the passive regeneration evaluation coefficient according to the increase rate of carbon load; Evaluating the passive regeneration performance under the set state according to the passive regeneration evaluation coefficient; The original emission data further comprises engine model, DPF configuration number, cumulative total mileage, DPF fault parameter, DPF temperature, and exhaust flow; The step of evaluating the passive regeneration performance under the set state according to the passive regeneration evaluation coefficient comprises: Selecting the passive regeneration evaluation coefficient of each sub-data segment with the same engine model, similar cumulative mileage, or similar cumulative engine running time, grouping according to the DPF configuration number, calculating the average value of the passive regeneration evaluation coefficient of each group, and comparing the passive regeneration performance of different DPF configurations; Selecting the passive regeneration evaluation coefficient of each sub-data segment with the same DPF configuration number, similar cumulative mileage, or similar cumulative engine running time, grouping according to the engine model, calculating the average value of the passive regeneration evaluation coefficient of each group, and comparing the influence of different engine models on the passive regeneration performance; Selecting the passive regeneration evaluation coefficient of each sub-data segment with the same engine model and DPF configuration number, grouping according to the cumulative mileage or cumulative engine running time, calculating the average value of the passive regeneration evaluation coefficient of each group, and comparing the influence of vehicle aging during use on the passive regeneration performance.

2. The method according to claim 1, wherein, The step of filtering the original emission data to obtain effective data comprises: According to the DPF fault parameter, the data with DPF fault is deleted. The data in which the DPF temperature is lower than the first preset temperature, the exhaust flow is lower than the first preset flow, and the carbon load is lower than the first preset carbon load is selected from the data after the DPF fault data is deleted, and parameters of DPF failure, DPF pressure difference sensor failure, and DPF temperature sensor failure are deleted, to obtain valid data.

3. A device for evaluating passive regeneration performance of a vehicle-end DPF, characterized by comprising: The system comprises a data collection module, a data screening module, a sub-data division module, a data segment division module, a data segment screening module, an increase rate calculation module, an evaluation coefficient calculation module, and a regeneration performance evaluation module. The data collection module is configured to collect original emission data of different vehicles, wherein the original emission data comprises a vehicle number, cumulative engine running time, carbon load, and DPF active regeneration state parameters. The data screening module is configured to screen the original emission data to obtain valid data. The sub-data division module is configured to divide the valid data into a plurality of sub-data according to the vehicle number, wherein the data of each vehicle number is recorded as a sub-data. The data segment division module is configured to divide the sub-data into a plurality of sub-data segments according to the vehicle active regeneration state, and specifically configured to determine the start time and end time of active regeneration according to the DPF active regeneration state parameters, and record the valid data between the start time of each active regeneration and the end time of the previous active regeneration as a sub-data segment. The data segment screening module is configured to select a sub-data segment in which the number of groups containing data is greater than a set threshold as a valid sub-data segment. The increase rate calculation module is configured to calculate the increase rate of the carbon load with the engine running time in each sub-data segment of each vehicle, and specifically configured to sort the valid sub-data segments in chronological order, record the average carbon load of the first percentage of data in the sub-data segment as the initial carbon load S1, record the time midpoint of the first percentage of data as the time t1, record the average carbon load of the last percentage of data in the sub-data segment as the terminal carbon load S2, and record the time midpoint of the last percentage of data as the time t2. The carbon load increase rate v=(S2-S1) / (t2-t1). The evaluation coefficient calculation module is configured to calculate the passive regeneration evaluation coefficient according to the increase rate of the carbon load. The regeneration performance evaluation module is configured to evaluate the passive regeneration performance under a set state according to the passive regeneration evaluation coefficient, and specifically configured to select the passive regeneration evaluation coefficient of each sub-data segment with the same engine model, similar cumulative mileage, or similar cumulative engine running time, group the passive regeneration evaluation coefficients according to the DPF configuration number, calculate the average value of the passive regeneration evaluation coefficients of each group, and compare the passive regeneration performance of different DPF configurations. The regeneration performance evaluation module is configured to select the passive regeneration evaluation coefficient of each sub-data segment with the same DPF configuration number, similar cumulative mileage, or similar cumulative engine running time, group the passive regeneration evaluation coefficients according to the engine model, calculate the average value of the passive regeneration evaluation coefficients of each group, and compare the influence of different engine models on the passive regeneration performance. The passive regeneration evaluation coefficient of each sub-data segment with the same engine model and DPF configuration number is selected, grouped according to the cumulative mileage or cumulative engine running time, the average value of the passive regeneration evaluation coefficient of each group is calculated, and the influence of aging during vehicle use on the passive regeneration performance is compared.

4. An electronic device, comprising: The electronic device comprises at least one processor and a memory connected in communication with the at least one processor; the memory stores computer program instructions executable by the at least one processor, and the computer program instructions are executed by the at least one processor to enable the at least one processor to perform the vehicle-end DPF passive regeneration performance evaluation method according to any one of claims 1 to 2.

5. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium stores computer instructions, and the computer instructions enable the computer to perform the vehicle-end DPF passive regeneration performance evaluation method according to any one of claims 1 to 2.

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