Construction method of gpf carbon accumulation test offline matching simulation model

Abstract

The present application relates to a kind of GPF carbon accumulation test offline matching simulation model construction method, including the real-time second mass flow of carbon particle emission measured in test cycle and the real carbon accumulation mass of test cycle, a test cycle is divided into multiple test windows and the real carbon accumulation mass of each test window is calculated;According to the test input sequence formed by road spectrum data, input offline matching simulation model, output the model carbon accumulation of test window and test cycle;According to the deviation of model carbon accumulation and real carbon accumulation mass, the parameter of offline matching simulation model is modified.In the present application, based on the whole vehicle GPF carbon accumulation test, the knowledge graph of carbon particle generation is constructed, which provides matching standard for model correction;It can be simulated in offline environment to obtain carbon accumulation test data highly consistent with real vehicle environment, so as to avoid using real vehicle test method when trial and error matching, which can greatly improve the matching efficiency and reduce the number of real vehicle tests and test cost.

Description

Technical Field

[0001] This invention belongs to the field of GPF carbon accumulation test technology, and relates to a method for constructing an offline matching simulation model for GPF carbon accumulation test. Background Technology

[0002] Events where gasoline particulate filters (GPFs) accumulate excessive carbon and significantly impact engine performance primarily occur in extreme low-temperature environments during engine startup, where the coolant temperature is between -10°C and -30°C. To cover typical vehicle usage scenarios, in a laboratory environment, the GPF low-temperature carbon accumulation test requires at least six test cycles, including steady-state and dynamic tests at startup coolant temperatures of -30°C, -20°C, and -10°C. During each test cycle, the accumulated carbon content of the GPF is weighed, and the difference between the carbon load model value in the engine controller's carbon accumulation calculation model and the actual weighed accumulated carbon content of the GPF is compared. If the deviation exceeds the standard range, the corresponding calibration value in the carbon accumulation calculation model needs to be adjusted until the deviation between the model value and the measured value meets the standard range. After the laboratory environment matching is completed, a confirmatory test for winter road use of the GPF is conducted. Traditional matching methods mainly follow a single test-single matching-retest approach. After each real-vehicle test cycle, the calibration value under low-temperature conditions is modified based on the weighing results, and then the model is verified on a real vehicle. The matching data obtained by this testing and matching method often fails to adequately consider the accuracy of the carbon loading model under various operating conditions, and the matching is highly unpredictable. It usually gets stuck in an infinite loop of test-matching-retesting, consuming a lot of testing resources and costs, but failing to obtain ideal test results.

[0003] The main factors affecting engine carbon particulate emissions under low-temperature conditions include engine coolant temperature at startup, real-time engine coolant temperature, engine running time after startup, air-fuel ratio, engine speed, load, and load change rate. Therefore, the amount of carbon particulates accumulated in the GPF is also a multi-dimensional and comprehensive result, and it is difficult to obtain well-covered matching data by matching independent influencing factors. Summary of the Invention

[0004] To address the shortcomings of the existing technology, the technical problem to be solved by the present invention is to provide a method for constructing an offline matching simulation model for GPF carbon accumulation test.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for constructing an offline matching simulation model for GPF carbon accumulation tests includes the following steps: S1. Select multiple low-temperature values ​​as the starting water temperature for the low-temperature test, and conduct test cycles of steady-state test and dynamic test at each starting water temperature; record the road spectrum data during the laboratory GPF carbon accumulation test, and measure the second-by-second mass flow rate of carbon particle emission in real time; after each test cycle, obtain the actual accumulated carbon mass of the GPF for that test cycle by weighing. S2. Divide each test cycle into multiple test windows. Calculate the actual cumulative carbon mass of the GPF in each test window based on the proportion of the second-integrated mass of the test window to the total second-integrated mass of the test cycle and the actual cumulative carbon mass of the GPF in the test cycle. Then, form a knowledge graph of the cumulative carbon mass of the test cycle based on the actual cumulative carbon mass of the GPF in the test cycle and the actual cumulative carbon mass of the GPF in each test window. S3. Form a test input sequence based on the road spectrum data of each test cycle; S4. Model the GPF carbon accumulation model algorithm to obtain the offline matching simulation model of GPF carbon accumulation test; S5. Input each test input sequence, the start and end time of each test window, and each cumulative carbon knowledge graph as input parameters into the offline matching simulation model. The offline matching simulation model calculates the model cumulative carbon amount for each test window and the total model cumulative carbon amount for each test cycle based on the input parameters. S6. Calculate the deviation of the model's accumulated carbon content in each test window from the actual accumulated carbon mass of the GPF in that test window, and the total deviation of the model's accumulated carbon content in each test cycle from the actual accumulated carbon mass of the GPF in that test cycle. If the deviation of each test window is less than the predetermined deviation range of a single measurement window, and the total deviation of each test cycle is less than the predetermined total deviation range of the test cycle, then the matching is successful, and proceed to step S8; otherwise, indicate that the matching data is abnormal, and proceed to step S7. S7. Modify the parameters of the offline matching simulation model and return to execute step S5. S8. Store the parameters of the offline matching simulation model.

[0006] Furthermore, the test window includes a steady-state operating condition test window and a dynamic operating condition test window. The steady-state operating condition test window contains steady-state operating conditions. The dynamic operating condition test window includes dynamic operating conditions and steady-state operating conditions, and the start and end times of each test window are both steady-state operating conditions.

[0007] Furthermore, a test cycle consists of 5 to 10 test windows.

[0008] Furthermore, in step S2, the formula for calculating the actual accumulated carbon mass of the GPF in the test window is as follows:

[0009] The formula for calculating the quality of the second-by-second sampling integral in the test window is as follows:

[0010] Total mass of the second-by-second sampling in the test cycle M sec The calculation formula is as follows:

[0011] Where k represents the test window number, 1≤k≤n; M actk This represents the true cumulative carbon mass of the GPF in the k-th test window; M weigh This indicates the actual accumulated carbon mass of GPF obtained through a weighing test; M rate This represents the mass flow rate of carbon particulate emissions per second, expressed in ug / s. t k This indicates the end time of the k-th test window; t （k-1） This indicates the end time of the (k-1)th test window; n represents the number of test windows included in the test loop. t 0 indicates the start time of the test loop; t n This indicates the end time of the test loop.

[0012] Furthermore, the cumulative carbon knowledge graph for a single test cycle is represented by vectors. Mact express: Mact= [ M act1 , M act2 , ..., M actk , ..., M actn , M weigh ].

[0013] Furthermore, for a test cycle, forming the test input sequence based on the road spectrum data of that test cycle includes the following steps: S301. Create the vector time for the engine's start-up and operation time: time=[time1, time2,…, time(j),…, time(i)];

[0014] in, T0 represents the time interval between two adjacent data elements in the vector time; S302. Extract the engine's start-up time, speed, load, real-time coolant temperature, start-up coolant temperature and air-fuel ratio parameters from the road spectrum data during the test cycle. S303. Using vector time as a reference, obtain the values ​​of the speed, load, real-time water temperature, start-up water temperature and air-fuel ratio parameters corresponding to the time of each data element of vector time, and create the vectors Neng (speed), Leng (load), Teng (real-time water temperature), Tengst (start-up water temperature), and Reng (air-fuel ratio) for this test cycle; form the test input sequence with vectors time, Neng, Leng, Teng, Tengst and Reng.

[0015] Furthermore, the offline matching simulation model includes a thermal engine raw emission calibration table, an air-fuel ratio correction calibration table, an engine real-time coolant temperature correction calibration table, a deviation correction calibration table, and a dynamic load correction calibration table. The thermal engine raw emission calibration table has multiple speed values ​​representing engine speed and load values ​​representing engine load, and a cumulative carbon calibration value is set for each combination of speed value and load value. M c The air-fuel ratio correction calibration table has multiple air-fuel ratio values, and an air-fuel ratio correction coefficient is set for each air-fuel ratio value. S R The deviation correction calibration table has multiple water temperature values ​​representing the start-up water temperature and time values ​​representing the start-up water temperature running time, and a deviation correction coefficient is set for each combination of water temperature value and time value. S d The engine real-time coolant temperature correction calibration table has multiple coolant temperature values ​​representing the engine's real-time coolant temperature, and a real-time coolant temperature correction coefficient is set for each coolant temperature value. S T The dynamic load correction calibration table has multiple load values ​​representing the load under dynamic operating conditions, and a dynamic load correction coefficient is set for each load value. S L .

[0016] Furthermore, the corrected output of the offline matching simulation model M out The calculation formula is: M out =（M c ×S R +S d）×S T ×S L exist t The output of the offline matching simulation model after integration M mod ( t The formula for calculating ) is:

[0017] The formula for calculating the integral quality of the model value in the test window is: M modk =M mod ( t k ) - M mod ( t k-1 ); The formula for calculating the integral quality of the model value in the test cycle is: M mod_all =M mod ( t n ) in, t 0 indicates the start time of the test loop; k indicates the sequence number of the test window; t k This represents the end time of the k-th test window, where 1 ≤ k ≤ n; n represents the number of test windows included in the test loop. t （k-1） This represents the end time of the (k-1)th test window; M modk This represents the integral quality of the model values ​​in the k-th test window; t n Indicates the end time of the test loop; M mod ( t 0) = 0.

[0018] Furthermore, the formula for calculating the deviation between the model's accumulated carbon content and the actual accumulated carbon mass within the test window is as follows:

[0019] The total deviation between the model carbon accumulation and the actual carbon accumulation mass in the test cycle Error_all The calculation formula is:

[0020] in, Errork This represents the deviation of the k-th test window; M actk This represents the true cumulative carbon mass of the GPF in the k-th test window; M weigh This indicates the true accumulated carbon mass of GPF obtained through a weighing test.

[0021] Furthermore, in step S7, when the real-time engine coolant temperature corresponding to the test window with a deviation exceeding the deviation range is lower than a predetermined value and / or the real-time engine coolant temperature corresponding to the test window with a large deviation is lower than the predetermined value, the real-time coolant temperature correction coefficient in the engine real-time coolant temperature correction calibration table is adjusted. S T Calibration values, deviation correction coefficients in the calibration table S d The calibration values ​​and dynamic load correction coefficients in the dynamic load correction calibration table S L The calibration values ​​are adjusted; when the engine real-time coolant temperature corresponding to the test window with a deviation exceeding the deviation range is higher than the predetermined value and / or the engine real-time coolant temperature corresponding to the test window with a large deviation exceeds the total deviation range, the cumulative carbon calibration value in the original emission calibration table of the thermal engine is adjusted. M c The calibration values ​​and air-fuel ratio correction coefficients in the calibration table S R The calibration value is adjusted.

[0022] In this invention, the GPF carbon accumulation calculation model in the engine controller is modeled with high equivalent value. Based on the vehicle GPF carbon accumulation test, the carbon particle concentration is collected every second and the GPF carbon accumulation weight value is collected simultaneously to construct a knowledge graph of carbon particles generated by the engine in each test cycle, providing a quantitative matching standard for the model correction calculation. It can simulate GPF carbon accumulation test data that is highly consistent with the online operation environment of the vehicle controller in an offline environment, thereby avoiding the use of real vehicle testing methods during exploratory matching, which can significantly improve matching efficiency and reduce the number of real vehicle tests and testing costs. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of a preferred embodiment of a method for constructing an offline matching simulation model for GPF carbon accumulation test according to the present invention.

[0024] Figure 2This is a diagram illustrating how a test loop is divided into six test windows in a specific instance.

[0025] Figure 3 This is a schematic diagram of the computational logic of the offline matching simulation model. Detailed Implementation

[0026] The following specific examples illustrate the implementation of the present invention. The illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0027] like Figure 1 As shown, a preferred embodiment of the method for constructing an offline matching simulation model for GPF carbon accumulation tests according to the present invention includes the following steps: S1. Select multiple low-temperature values ​​as the starting water temperature for the low-temperature test, and perform steady-state and dynamic test cycles at each starting water temperature. In this embodiment, it is preferable to use -30℃, -20℃ and -10℃ as the starting water temperature for the low-temperature test, and perform a total of 6 test cycles.

[0028] During the laboratory GPF carbon accumulation test, road spectrum data (including engine start-up time, speed, load, real-time coolant temperature, start-up coolant temperature, air-fuel ratio, and other parameters) were recorded through the vehicle controller. The carbon particulate concentration acquisition equipment was used to test and record the second-by-second mass flow rate of carbon particulate emissions. m rate The actual cumulative carbon mass of the GPF for each test cycle was obtained by weighing at the end of each test cycle. M weigh Actual accumulated carbon mass M weigh The cumulative carbon mass of the GPF at the end of the test cycle is calculated by subtracting the cumulative carbon mass of the GPF at the beginning of the test cycle. The true cumulative carbon mass of the GPF for this test cycle is obtained by weighing. M weigh The method used is existing technology and will not be elaborated upon here.

[0029] S2. First, divide each test cycle into multiple test windows. Each test window includes a steady-state operating condition test window and a dynamic operating condition test window. The steady-state operating condition test window contains only steady-state operating conditions; the dynamic operating condition test window includes both dynamic and steady-state operating conditions, and the start and end times of each test window are both steady-state operating conditions. The method for determining steady-state and dynamic operating conditions is as follows: if the engine speed and load do not exceed the predetermined range within a predetermined time, it is judged as a steady-state operating condition; otherwise, it is judged as a dynamic operating condition. A test cycle generally includes 5-10 test windows. The first test window consists of the steady-state operating condition during startup and a period after startup, with the engine startup time as the start time. The last continuous steady-state operating condition is the last test window, with the end time of the test cycle as the end time. Between the above two test windows, the dynamic operating conditions are divided according to their fluctuations. Dynamic operating conditions with similar fluctuations are grouped into one test window, and the duration of one test window does not exceed 80 seconds. Figure 2 As shown, a test loop is divided into six test windows in a specific instance.

[0030] Then, based on the integral quality of the second sampling in each test window, the total integral quality of the second sampling in that test cycle is determined. m sec The proportion and the actual cumulative carbon mass of the GPF in the test cycle M weigh Calculate the true cumulative carbon mass of the GPF in each test window, and then calculate the true cumulative carbon mass of the GPF in that test cycle. M weigh The actual accumulated carbon mass of GPF in each test window forms a knowledge graph of the accumulated carbon mass of this test cycle.

[0031] Total mass of the second-by-second sampling in the test cycle M sec The calculation formula is as follows:

[0032] Where n represents the number of test windows included in the test loop; t 0 indicates the start time of the test loop; t n Indicates the end time of the test cycle; mass flow rate per second. m rate The unit is ug / s.

[0033] The formula for calculating the quality of the second-by-second sampling integral in the test window is as follows:

[0034] The formula for calculating the true cumulative carbon mass of GPF in the test window is as follows:

[0035] in, M seck This represents the quality of the second-by-second sampling integral in the k-th test window; t k This represents the end time of the k-th test window; 1 ≤ k ≤ n. Specifically, The quality of the second-by-second integral in the first test window is: ; The actual accumulated carbon mass of GPF in the first test window is: ; The quality of the second-per-second integral sampling in the second test window is: ; The actual accumulated carbon mass of GPF in the second test window is: ; … The quality of the second-by-second sampling integral in the (n-1)th test window is: ; The actual accumulated carbon mass of the GPF in the (n-1)th test window is: ; The quality of the second-by-second sampling integral in the nth test window is: ; The actual accumulated carbon mass of the GPF in the nth test window is: .

[0036] Knowledge graph of cumulative carbon in a single test cycle using vectors Mact express: Mact= [ M act1 , M act2 , ..., M act(n-1) , M actn , M weigh ].

[0037] S3. Form a test input sequence based on the road spectrum data for each test cycle. For a test cycle, forming the test input sequence based on the road spectrum data of that test cycle includes the following steps: S301. For a test loop, first create a vector `time` representing the engine's start-up and running time: time=[time1, time2,…, time(j),…, time(i)]; Here, time1 represents the moment the engine just starts, i.e., time1=0s. The time value of each subsequent data element in the vector time is equal to the time value of its preceding data element plus... T 0; that is

[0038] T 0 represents the time interval between two adjacent data elements in the vector time. In this embodiment, 0 is taken as... T 0 = 10ms; of course, T 0 can also take other values. It should be noted that the vector `time` uses the moment the engine just starts as 0s, not the moment the car controller starts recording road spectrum data as 0s. Additionally, because the duration of each test loop is different, the value of `i` will also be different in each test loop.

[0039] S302. Extract the engine start-up time, speed, load, real-time water temperature, start-up water temperature and air-fuel ratio parameters corresponding to the test cycle from the road spectrum data.

[0040] S303. Using its vector time as a reference, obtain the values ​​of the engine speed, load, real-time water temperature, start-up water temperature, and air-fuel ratio parameters corresponding to the time of each data element in the vector time, and create vectors Neng (engine speed), Leng (load), Teng (real-time water temperature), Tengst (start-up water temperature), and Reng (air-fuel ratio) for this test cycle: Neng=[Neng1, Neng2,…,Neng(j),…,Neng(i)]; Leng=[Leng1, Leng2,…,Leng(j),…,Leng(i)]; Teng = [Teng1, Teng2, ..., Teng(j), ..., Teng(i)]; Tengst=[Tengst1, Tengst2,…,Tengst(j),…,Tengst(i)]; Reng=[Reng1, Reng2,…,Reng(j),…,Reng(i)].

[0041] Where 1 ≤ j ≤ i; Neng(j) represents the engine speed at time(j) after engine start-up; Leng(j) represents the load at time(j) after engine start-up; Teng(j) represents the real-time coolant temperature at time(j) after engine start-up; Tengst(j) represents the starting coolant temperature at time(j) after engine start-up; Reng(j) represents the air-fuel ratio at time(j) after engine start-up. Since the starting coolant temperature represents the coolant temperature at start-up, and its value does not change with time, therefore: Tengst1=Tengst2=…=Tengst(j)=…=Tengst(i)=Teng1.

[0042] After the vectors are created, a test input sequence is formed using vectors time, Neng, Leng, Teng, Tengst, and Reng, which serves as the input to the offline matching simulation model.

[0043] S4. Model the GPF carbon accumulation model algorithm to obtain the offline matching simulation model for the GPF carbon accumulation test. The input parameters of the offline matching simulation model include the engine start-up time, speed, load, real-time water temperature, start-up water temperature and air-fuel ratio, as well as the start and end times of each test window, the actual carbon accumulation mass of the GPF in each test window, and the actual carbon accumulation mass of the GPF in each test window cycle.

[0044] like Figure 3 As shown, the offline matching simulation model includes a thermal engine raw emission calibration table, an air-fuel ratio correction calibration table, an engine real-time coolant temperature correction calibration table, a deviation correction calibration table, and a dynamic load correction calibration table. The thermal engine raw emission calibration table has multiple speed values ​​representing engine speed and load values ​​representing engine load, and a cumulative carbon calibration value is set for each combination of speed value and load value. M c The air-fuel ratio correction calibration table has multiple air-fuel ratio values, and an air-fuel ratio correction coefficient is set for each air-fuel ratio value. S R The deviation correction calibration table has multiple water temperature values ​​representing the start-up water temperature and time values ​​representing the start-up water temperature running time, and a deviation correction coefficient is set for each combination of water temperature value and time value. S d The engine real-time coolant temperature correction calibration table has multiple coolant temperature values ​​representing the engine's real-time coolant temperature, and a real-time coolant temperature correction coefficient is set for each coolant temperature value. S TThe dynamic load correction calibration table has multiple load values ​​representing the load under dynamic operating conditions, and a dynamic load correction coefficient is set for each load value. S L .

[0045] After the vectors time, Neng, Leng, Teng, Tengst, and Reng are input into the offline matching simulation model, a value is calculated sequentially based on each set of data elements of the above input vectors as the corrected output. M out .

[0046] The corrected output of the offline matching simulation model M out The calculation formula is: M out =（M c ×S R +S d ）×S T ×S L The output of the offline matching simulation model after integration at time t. M mod The formula for calculating (t) is: .

[0047] S5. Input each test input sequence, the start and end time of each test window, and each accumulated carbon knowledge graph as input parameters into the offline matching simulation model. The offline matching simulation model calculates the model accumulated carbon for each test window and the total model accumulated carbon for each test cycle based on the input parameters.

[0048] For example, for a test input sequence, the first set of data elements in its input vector is time1, Neng1, Leng1, Teng1, Tengst1, and Reng1. The model looks up the corresponding cumulative carbon calibration value from the original emission calibration table of the heat engine based on the values ​​of Neng1 and Leng1. M c Based on the value of Reng1, look up the corresponding air-fuel ratio correction coefficient from the air-fuel ratio correction calibration table. S R Based on the values ​​of Teng1 and time1, look up the corresponding deviation correction coefficient from the deviation correction calibration table. S dBased on the calculated dynamic load value, the corresponding dynamic load correction factor is looked up from the dynamic load correction calibration table. S L Then according to M out The calculation formula calculates the result at this time. M out The value of time2 is then calculated based on the second set of data elements time2, Neng2, Leng2, Teng2, Tengst2, and Reng2 in the input vector. M out The value of time(i) is calculated; the above process is repeated until the value of time(i) is calculated based on the i-th data elements time(i), Nengi(i), Leng(i), Teng(i), Tengst(i), and Reng(i) of the input vector. M out The value of .

[0049] Then, based on the output of the offline matching simulation model after integration... M mod The formula for calculating (t) is used to calculate the model value integral at the start and end times of each test window.

[0050] ; ; ; … .

[0051] The first set of data elements in the input vector corresponds to t At time 0, the i-th data element of the input vector corresponds to t n time.

[0052] Then, the cumulative carbon content of the model in each test window and the total cumulative carbon content of the model in that test cycle are calculated.

[0053] express t n This indicates that the end time of the test loop corresponds to The formula for calculating the integral quality of the model value in the test window is: M modk =M mod ( t k ) - M mod ( t k-1 ); Right now The integral of the model value in test window 1 is: M mod1 =M mod (t 1 )- M mod (t 0 ) , The integral of the model value in test window 2 is: M mod2 =M mod (t 2 )- M mod (t 1 ) , … The integral of the model values ​​in test window n is: M modn =M mod (t n )- M mod (t n-1 ) , The formula for calculating the integral quality of the model value in the test cycle is: M mod_all =M mod ( t n ).

[0054] After calculating the cumulative carbon amount of the model in each test window and the total cumulative carbon amount of the model in the test cycle, the simulation calculation process of one test input sequence (i.e., one test cycle) is completed; then, the above process is repeated to complete the simulation calculation of all six test input sequences (i.e., six test cycles).

[0055] S6. Compare the model carbon accumulation in each test window and the total model carbon accumulation in each test cycle with the data from the carbon accumulation knowledge graph. Calculate the deviation of the model carbon accumulation in each test window from the actual carbon accumulation mass of the GPF calculated in step S2, and the total deviation of the total model carbon accumulation in each test cycle from the actual carbon accumulation mass of the GPF obtained by weighing in step S1. The formula for calculating the deviation between the model carbon accumulation and the actual carbon accumulation mass in a test window is as follows:

[0056] in, Errork This represents the deviation of the k-th test window.

[0057] The total deviation between the model carbon accumulation and the actual carbon accumulation mass in the test cycle Error_all The calculation formula is:

[0058] If the deviation values ​​of each test window are all less than the predetermined deviation range for a single window, and the total deviation of each test cycle is less than the predetermined total deviation range for the test cycle, then the matching passes, and step S8 is executed; otherwise, an error message indicating abnormal matching data is displayed, and step S7 is executed. At this point, the deviation value of each test window and the total deviation value of each test cycle will be output to facilitate adjustment of the model parameters based on the output results. The predetermined deviation range for a single test window can be 0–6%, and the predetermined total deviation range for the test cycle can be 0–25%. It should be noted that the deviation value of a single test window and the total deviation of the test cycle... Error_all Negative values ​​also fall outside the deviation range.

[0059] S7. Modify the parameters of the offline matching simulation model and return to step S5. The method for modifying the parameters of the offline matching simulation model is as follows: (1) When the real-time engine coolant temperature corresponding to the test window where the deviation exceeds the deviation range is lower than the predetermined value (e.g., 80°C), or the total deviation Error_all When the engine coolant temperature corresponding to the test window that exceeds the total deviation range and has a large deviation is lower than the predetermined value, or when both of the above situations exist simultaneously, the real-time coolant temperature correction coefficient in the engine real-time coolant temperature correction calibration table should be adjusted. S T Calibration values, deviation correction coefficients in the calibration table S d The calibration values ​​and dynamic load correction coefficients in the dynamic load correction calibration table S L The calibration value is adjusted.

[0060] (2) When the real-time engine coolant temperature corresponding to the test window where the deviation exceeds the deviation range is higher than the predetermined value (e.g., 80°C), or the total deviation... Error_all When the engine coolant temperature corresponding to the test window that exceeds the total deviation range and has a large deviation is higher than the predetermined value, or when both of the above situations exist simultaneously, the cumulative carbon calibration amount in the original emission calibration table of the hot engine will be adjusted. M c The calibration values ​​and air-fuel ratio correction coefficients in the calibration table S R The calibration value is adjusted.

[0061] When both conditions in point (1) and point (2) above exist simultaneously, adjustments can be made for one condition first. For example, adjustments can be made to the real-time coolant temperature correction coefficient in the engine real-time coolant temperature correction calibration table for the condition in point (1). S T Calibration values, deviation correction coefficients in the calibration table S d The calibration values ​​and dynamic load correction coefficients in the dynamic load correction calibration table S L The calibration value is adjusted, and after the adjustment is completed, the cumulative carbon calibration value in the original emission calibration table of the heat engine is adjusted according to the situation in point (2). M c The calibration values ​​and air-fuel ratio correction coefficients in the calibration table S R The calibration value is adjusted until a match is found.

[0062] S8. Store the parameters of the offline matching simulation model. Once the matching is successful, it indicates that the simulation results of the offline matching simulation model are very close to the actual test results. The offline matching simulation model can be used to simulate the actual vehicle test cycle until the output results of the offline matching simulation model meet the requirements. Then, a GPF carbon accumulation test is conducted on actual roads. Because the carbon particle concentration acquisition equipment used for second-by-second data acquisition will experience reduced measurement accuracy at temperatures below 0°C, and due to limitations in testing methods and the outdoor environment, actual road tests can only perform GPF carbon accumulation weighing, not second-by-second mass flow rate data acquisition. However, in this embodiment, second-by-second mass flow rate data acquisition is performed in the laboratory. When the engine is operating at low temperatures, the carbon particle concentration acquisition equipment can operate at a higher temperature, thus achieving better coverage.

[0063] In this embodiment, based on the multi-source measurement results of the simultaneous acquisition of carbon particle concentration per second and GPF carbon accumulation weight value during the whole vehicle GPF carbon accumulation test, a knowledge graph of carbon particles generated by the engine operation in each test cycle is constructed. The engine operating conditions are divided into several test windows according to the running time after startup, and quantitative matching standards are provided for various correction calculations of the carbon load model based on the weight of carbon particle mass in each test window. In addition, the GPF carbon accumulation calculation model in the engine controller is highly equivalently modeled. Its operation logic and running cycle in the offline environment are highly consistent with those in the online operating environment of the controller. The road spectrum data collected by the real vehicle test can be directly imported into the offline environment for offline matching and simulation calculation, thereby avoiding the use of real vehicle testing methods during exploratory matching and saving real vehicle testing costs. Meanwhile, in GPF equivalent modeling, the test window corresponding to the knowledge graph automatically calculates the carbon loading model value weights and determines whether the model value weights in each test window and the carbon particle mass weights in the knowledge graph meet the preset deviation range. When the total weight deviation of the six test cycles and the weight deviation of each test window are both within the preset deviation range, the matching data is the final experimental data. This embodiment, by combining the GPF carbon accumulation knowledge graph with offline matching, can significantly improve matching efficiency and reduce the number of real-vehicle tests and testing costs.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for constructing an offline matching simulation model for GPF carbon accumulation tests, characterized in that, Includes the following steps: S1. Select multiple low-temperature values ​​as the starting water temperature for low-temperature testing, and conduct steady-state and dynamic testing cycles at each starting water temperature; record road spectrum data during the laboratory GPF carbon accumulation test, and measure the second-by-second mass flow rate of carbon particle emissions in real time. After each test cycle, the actual accumulated carbon mass of the GPF for that test cycle is obtained by weighing; the low temperature value is a temperature value between -10 and -30℃; S2. Each test cycle is divided into multiple test windows. The actual cumulative carbon mass of the GPF in each test window is calculated based on the proportion of the second-integrated mass of the test window to the total second-integrated mass of the test cycle and the actual cumulative carbon mass of the GPF in the test cycle. A knowledge graph of the cumulative carbon mass of the test cycle is formed based on the actual cumulative carbon mass of the GPF in the test cycle and the actual cumulative carbon mass of the GPF in each test window. The test window includes a steady-state operating condition test window and a dynamic operating condition test window. The steady-state operating condition test window contains steady-state operating conditions. The dynamic operating condition test window includes dynamic operating conditions and steady-state operating conditions. The start and end times of each test window are both steady-state operating conditions. S3. Form a test input sequence based on the road spectrum data of each test cycle; S4. Model the GPF carbon accumulation model algorithm to obtain the offline matching simulation model of GPF carbon accumulation test; S5. Input each test input sequence, the start and end time of each test window, and each cumulative carbon knowledge graph as input parameters into the offline matching simulation model. The offline matching simulation model calculates the model cumulative carbon amount for each test window and the total model cumulative carbon amount for each test cycle based on the input parameters. S6. Calculate the deviation of the model carbon accumulation in each test window from the actual carbon accumulation mass of the GPF in that test window, and the total deviation of the model carbon accumulation in each test cycle from the actual carbon accumulation mass of the GPF in that test cycle. If the deviation of each test window is less than the predetermined deviation range of a single measurement window, and the total deviation of each test cycle is less than the predetermined total deviation range of the test cycle, then the matching is successful, and proceed to step S8. Otherwise, an error message indicating abnormal matching data will be displayed, and step S7 will be executed. S7. Modify the parameters of the offline matching simulation model and return to execute step S5. S8. Store the parameters of the offline matching simulation model.

2. The method for constructing the offline matching simulation model for GPF carbon accumulation test according to claim 1, characterized in that: A test cycle consists of 5 to 10 test windows.

3. The method for constructing the offline matching simulation model for GPF carbon accumulation test according to claim 1, characterized in that: In step S2, the formula for calculating the true cumulative carbon mass of the GPF in the test window is as follows: The formula for calculating the quality of the second-by-second sampling integral in the test window is as follows: Total mass of the second-by-second sampling in the test cycle M sec The calculation formula is as follows: Where k represents the test window number, 1≤k≤n; M actk This represents the true cumulative carbon mass of the GPF in the k-th test window; M weigh This indicates the actual accumulated carbon mass of GPF obtained through a weighing test; M rate This represents the mass flow rate of carbon particulate emissions per second, expressed in ug / s. t k This indicates the end time of the k-th test window; t （k-1） This indicates the end time of the (k-1)th test window; n represents the number of test windows included in the test loop. t 0 indicates the start time of the test loop; t n This indicates the end time of the test loop.

4. The method for constructing the offline matching simulation model for GPF carbon accumulation test according to claim 3, characterized in that: Knowledge graph of cumulative carbon in a single test cycle using vectors Mact express: Mact= [ M act1 ， M act2 ，…， M actk ，…， M actn ， M weigh ]。 5. The method for constructing the offline matching simulation model for GPF carbon accumulation test according to claim 1, characterized in that: For a test cycle, forming the test input sequence based on the road spectrum data of that test cycle includes the following steps: S301. Create the vector time for the engine's start-up and operation time: time=[time1, time2,…, time(j),…, time(i)]; Where time1 is the moment when the engine just starts; T 0 represents the time interval between two adjacent data elements in the vector time; S302. Extract data on engine running time, speed, load, real-time coolant temperature, start-up coolant temperature and air-fuel ratio parameters from the road spectrum data during the test cycle. S303. Using vector time as a reference, obtain the values ​​of the speed, load, real-time water temperature, start-up water temperature and air-fuel ratio parameters corresponding to the time of each data element of vector time, and create vectors Neng (speed), Leng (load), Teng (real-time water temperature), Tengst (start-up water temperature), and Reng (air-fuel ratio) for this test cycle; form a test input sequence with vectors time, Neng, Leng, Teng, Tengst and Reng.

6. The method for constructing an offline matching simulation model for GPF carbon accumulation test according to any one of claims 1 to 5, characterized in that: The offline matching simulation model includes a heat engine raw emission calibration table, an air-fuel ratio correction calibration table, an engine real-time coolant temperature correction calibration table, a deviation correction calibration table, and a dynamic load correction calibration table. The heat engine raw emission calibration table has multiple speed values ​​representing engine speed and load values ​​representing engine load, and a cumulative carbon calibration value is set for each combination of speed and load values. M c The air-fuel ratio correction calibration table has multiple air-fuel ratio values, and an air-fuel ratio correction coefficient is set for each air-fuel ratio value. S R The deviation correction calibration table has multiple water temperature values ​​representing the start-up water temperature and time values ​​representing the start-up water temperature running time, and a deviation correction coefficient is set for each combination of water temperature value and time value. S d The engine real-time coolant temperature correction calibration table has multiple coolant temperature values ​​representing the engine's real-time coolant temperature, and a real-time coolant temperature correction coefficient is set for each coolant temperature value. S T The dynamic load correction calibration table has multiple load values ​​representing the load under dynamic operating conditions, and a dynamic load correction coefficient is set for each load value. S L .

7. The method for constructing the offline matching simulation model for GPF carbon accumulation test according to claim 6, characterized in that: The corrected output of the offline matching simulation model M out The calculation formula is: M out =（M c ×S R +S d ）×S T ×S L exist t The output of the offline matching simulation model after integration M mod ( t The formula for calculating ) is: The formula for calculating the integral quality of the model value in the test window is: M modk =M mod ( t k ) - M mod ( t k-1 )； The formula for calculating the integral quality of the model value in the test cycle is: M mod_all =M mod ( t n ) in, t 0 indicates the start time of the test loop; k indicates the sequence number of the test window; t k This represents the end time of the k-th test window, where 1 ≤ k ≤ n; n represents the number of test windows included in the test loop. t （k-1） This represents the end time of the (k-1)th test window; M modk This represents the integral quality of the model values ​​in the k-th test window; t n Indicates the end time of the test loop; M mod ( t 0) = 0.

8. The method for constructing the offline matching simulation model for GPF carbon accumulation test according to claim 7, characterized in that: The formula for calculating the deviation between the model's accumulated carbon content and the actual accumulated carbon mass in the test window is as follows: The total deviation between the model carbon accumulation and the actual carbon accumulation mass in the test cycle Error_all The calculation formula is: in, Error k This represents the deviation of the k-th test window; M actk This represents the true cumulative carbon mass of the GPF in the k-th test window; M weigh This indicates the true accumulated carbon mass of GPF obtained through a weighing test.

9. The method for constructing the offline matching simulation model for GPF carbon accumulation test according to claim 1, characterized in that: In step S7, when the engine real-time coolant temperature corresponding to the test window with a deviation exceeding the deviation range is lower than a predetermined value and / or the engine real-time coolant temperature corresponding to the test window with a large deviation is lower than the predetermined value, the real-time coolant temperature correction coefficient in the engine real-time coolant temperature correction calibration table is adjusted. S T Calibration values, deviation correction coefficients in the calibration table S d The calibration values ​​and dynamic load correction coefficients in the dynamic load correction calibration table S L The calibration values ​​are adjusted; when the engine real-time coolant temperature corresponding to the test window with a deviation exceeding the deviation range is higher than the predetermined value and / or the engine real-time coolant temperature corresponding to the test window with a large deviation exceeds the total deviation range, the cumulative carbon calibration value in the original emission calibration table of the thermal engine is adjusted. M c The calibration values ​​and air-fuel ratio correction coefficients in the calibration table S R The calibration value is adjusted.

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