Diesel particulate filter internal carbon load distribution estimation method and system

Abstract

The invention relates to the technical field of diesel engine emission aftertreatment, and particularly discloses a diesel particulate filter internal carbon load distribution estimation method and system.The method comprises the steps that heat dissipation characteristic data of a diesel particulate filter (DPF) under the normal working condition is obtained, and a DPF heat dissipation model is established based on the heat dissipation characteristic data under the normal working condition; establishing a DPF regeneration temperature model according to the law of conservation of energy; extracting temperature change data caused by carbon combustion based on the DPF heat dissipation model and the DPF regeneration temperature model; according to the method, temperature change data is subjected to multi-scale analysis on the basis of db4 wavelet transformation of a Daubechies wavelet system, a carbon distribution estimation function is generated, carbon deposition distribution can be effectively analyzed through multi-scale analysis of wavelet transformation, then local hot spots in the DPF are captured, and the carbon distribution detection precision is improved; by identifying the carbon distribution in the DPF, the aging loss of materials caused by high-temperature hot spots is reduced, the replacement period of the DPF is prolonged, and the use cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of diesel engine exhaust aftertreatment, and in particular to a method and system for estimating carbon load distribution inside a diesel particulate filter. Background Art

[0002] As a key exhaust treatment device for diesel engines, the diesel particulate filter (DPF) captures and burns particulate matter in the exhaust, thereby reducing pollutant emissions. However, the deposition of particulate matter within the DPF is often highly random, and especially after long-term use, the spatial distribution of its carbon load can become significantly non-uniform. This non-uniform distribution can cause a sharp rise in temperature in localized areas during regeneration, forming "hot spots" that exacerbate material loss and shorten the life of the equipment. Therefore, to achieve efficient and safe thermal regeneration, it is crucial to accurately predict the distribution of carbon load within the DPF.

[0003] Due to the complex structure of the DPF, the channel morphology and airflow distribution in different areas will lead to uneven accumulation of carbon particles in the collector, which makes the temperature changes in different parts inconsistent during the regeneration process. Existing technologies are mostly based on average estimation models of the overall temperature field. These models assume that the distribution of carbon deposits inside the DPF is uniform or linear, and the temperature change is only related to the total amount of carbon combustion inside the DPF. However, in actual applications, the spatial distribution of carbon deposits inside the DPF is often random and has significant non-uniform characteristics. Existing methods lack detailed identification of such local differences, and it is especially difficult to effectively monitor the random distribution of high carbon concentration areas inside the DPF. The current mainstream methods for estimating the carbon load inside the DPF are the pressure difference method and the numerical simulation method.

[0004] The differential pressure monitoring method uses differential pressure sensors installed at the inlet and outlet of the DPF to monitor the pressure differential in real time, thereby indirectly inferring the accumulation of particulate matter within the trap. The basic principle is that particulate matter accumulation increases the flow resistance of the DPF, leading to an increase in the inlet and outlet pressure differential. By combining empirical models of the relationship between the pressure differential and the amount of particulate matter accumulated, the overall carbon loading level can be estimated. This method has become one of the most widely used methods for monitoring particulate matter traps due to its simple hardware structure and low cost. However, this method has significant drawbacks. First, it only provides an overall accumulation trend of carbon particles and fails to reflect the spatial distribution characteristics of carbon particles within the DPF. Uneven carbon distribution (such as localized accumulation) can lead to uneven temperatures during regeneration, potentially causing localized overheating, which the differential pressure monitoring method struggles to capture. Second, the differential pressure signal is sensitive to external factors such as exhaust flow rate and environmental conditions. Under real-world dynamic driving conditions, the monitoring results often exhibit significant errors and are less reliable. Furthermore, as the differential pressure characteristics of the DPF change over time due to material aging or structural changes, the measurement accuracy of this method further decreases, making it difficult to meet the requirements of high-precision assessment.

[0005] The numerical simulation method uses computer simulation to predict the accumulation and combustion process of particulate matter by establishing a mathematical model of the physical processes inside the DPF. This method usually combines fluid dynamics (CFD) and thermodynamic models to comprehensively simulate processes such as particle capture inside the DPF, thereby inferring the load distribution of carbon particles. However, this method also has obvious limitations. First, the computational complexity of numerical simulation is high, and the solution involves a large number of nonlinear equations, which is time-consuming and requires high-performance computing. Second, the simulation results are highly dependent on the accuracy of the initial conditions and input parameters (such as flow rate, temperature, carbon combustion rate, etc.). Once the operating conditions deviate or the sensor ages or is damaged, resulting in inaccurate input data, the prediction error will increase significantly, affecting the reliability of the results. Therefore, the numerical simulation method is difficult to apply under actual driving conditions. Summary of the Invention

[0006] The object of the present invention is to provide a method and system for estimating the carbon load distribution inside a diesel particulate filter, so as to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for estimating carbon load distribution inside a diesel particulate filter, the method comprising:

[0008] Acquire heat dissipation characteristic data of the DPF under normal working conditions, and establish a heat dissipation model based on the heat dissipation characteristic data under normal working conditions;

[0009] Establish a DPF regeneration temperature model based on the law of conservation of energy;

[0010] Extract temperature change data caused by carbon combustion based on the carbon-free heat dissipation model and DPF regeneration temperature model;

[0011] Perform multi-scale analysis on temperature change data based on wavelet transform to generate carbon distribution estimation function;

[0012] The carbon load distribution inside the diesel particulate filter is estimated based on the carbon distribution estimation function.

[0013] As a further solution of the present invention, the step of obtaining heat dissipation characteristic data of the DPF on the vehicle under normal working conditions and establishing a heat dissipation model based on the heat dissipation characteristic data under normal working conditions specifically includes:

[0014] Divide the DPF into three sections and collect temperature data and parameters of each section;

[0015] Obtain the heat dissipation characteristic data of the DPF under normal working conditions based on the temperature data and parameters of each section;

[0016] A heat dissipation model is established based on the heat dissipation characteristic data under normal working conditions.

[0017] As a further solution of the present invention, the step of performing multi-scale analysis on the temperature change data based on wavelet transform to generate a carbon distribution estimation function specifically includes:

[0018] The low-frequency components of the temperature change data signal are extracted through a low-pass filter to obtain an approximate coefficient, and the high-frequency components of the temperature change data signal are extracted through a high-pass filter to obtain a detail coefficient;

[0019] Based on the approximate coefficient and detail coefficient, the average and peak values of the temperature difference at large scale and the variance at small scale are obtained;

[0020] The carbon distribution estimation function is generated based on the mean and peak temperature differences at large scales and the variance at small scales.

[0021] As a further solution of the present invention, the wavelet transform adopts the db4 wavelet of the Daubechies wavelet system.

[0022] As a further solution of the present invention, the weight coefficients of the variance, peak value and average value in the carbon distribution estimation are optimized based on a particle swarm optimization algorithm.

[0023] As a further solution of the present invention, it also includes determining the carbon load distribution characteristics under urban conditions, high-speed conditions and other conditions based on the carbon distribution estimation function.

[0024] The present invention also provides a system for estimating the carbon load distribution inside a diesel particulate filter, which is used to implement a method for estimating the carbon load distribution inside a diesel particulate filter. The system comprises:

[0025] a heat dissipation model establishment module, used to obtain heat dissipation characteristic data of the DPF under normal working conditions, and establish a heat dissipation model based on the heat dissipation characteristic data under normal working conditions;

[0026] Temperature model building module, used to build DPF regeneration temperature model based on the law of conservation of energy;

[0027] A data extraction module is used to extract temperature change data caused by carbon combustion based on a carbon-free heat dissipation model and a DPF regeneration temperature model;

[0028] Function generation module, used to perform multi-scale analysis on temperature change data based on wavelet transform and generate carbon distribution estimation function;

[0029] The estimation module is used to estimate the carbon load distribution inside the diesel particulate filter based on the carbon distribution estimation function.

[0030] As a further solution of the present invention, the function generation module includes:

[0031] A low-frequency unit is used to extract the low-frequency component of the temperature change data signal through a low-pass filter to obtain an approximate coefficient;

[0032] A coefficient extraction unit is used to extract the high-frequency components of the temperature change data signal through a high-pass filter to obtain detail coefficients, and is used to obtain the average and peak values of the temperature difference at a large scale and the variance at a small scale based on the approximate coefficients and the detail coefficients;

[0033] The function generation unit is used to generate a carbon distribution estimation function based on the mean and peak of the temperature difference at a large scale and the variance at a small scale.

[0034] Compared with the existing technology, the present invention has the following beneficial effects: Improved carbon distribution detection accuracy: Utilizing multi-scale analysis of wavelet transform, it is possible to analyze carbon deposition distribution in more detail and effectively capture local hot spots inside the DPF;

[0035] Optimize regeneration process management: Based on accurate carbon distribution estimation, it provides targeted control basis for the regeneration process, effectively reduces the probability of local hot spots, and extends the service life of DPF;

[0036] Extending the service life of DPF: By identifying the carbon distribution inside the DPF, the aging loss of the material caused by high-temperature hot spots is reduced, the replacement cycle of the DPF is extended, and the cost of use is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0038] Figure 1 This is a flowchart of a method for estimating carbon load distribution inside a diesel particulate filter provided by an embodiment of the present invention.

[0039] Figure 2 A flowchart of the steps of obtaining heat dissipation characteristic data of a DPF under normal working conditions and establishing a heat dissipation model based on the heat dissipation characteristic data under normal working conditions is provided in an embodiment of the present invention.

[0040] Figure 3 A schematic diagram of DPF segments provided in an embodiment of the present invention.

[0041] Figure 4 A flowchart of the steps of performing multi-scale analysis on temperature change data based on wavelet transform and generating a carbon distribution estimation function according to an embodiment of the present invention.

[0042] Figure 5 This is an overall flow chart of the DPF carbon load distribution provided by an embodiment of the present invention.

[0043] Figure 6 A schematic diagram of the discrete wavelet transform principle provided by an embodiment of the present invention.

[0044] Figure 7 This is a histogram of carbon distribution inside the DPF under different working conditions provided by an embodiment of the present invention.

[0045] Figure 8 This is a structural block diagram of a system for estimating carbon load distribution inside a diesel particulate filter provided by an embodiment of the present invention.

[0046] Figure 9 This is a structural block diagram of the function generation module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] like Figure 1 、 Figure 5 As shown, in an embodiment of the present invention, a method for estimating carbon load distribution inside a diesel particulate filter includes steps S100 to S500:

[0049] Step S100, obtaining heat dissipation characteristic data of the DPF under normal working conditions, and establishing a DPF heat dissipation model based on the heat dissipation characteristic data under normal working conditions;

[0050] Step S200, establishing a DPF regeneration temperature model according to the law of conservation of energy;

[0051] Step S300, extracting temperature change data caused by carbon combustion based on the DPF heat dissipation model and the DPF regeneration temperature model;

[0052] Step S400, performing multi-scale analysis on the temperature change data based on wavelet transform to generate a carbon distribution estimation function;

[0053] Step S500 : estimating the carbon load distribution inside the diesel particulate filter based on the carbon distribution estimation function.

[0054] In this embodiment, experimental data under carbon unburned conditions are selected for the regeneration process of the diesel particulate filter (DPF). The heat dissipation during the DPF regeneration process under this intake condition is calculated and analyzed in detail. By quantifying the heat dissipation characteristics and change patterns, a DPF heat dissipation model is finally established.

[0055] By comparing the DPF heat dissipation model with experimental data, the temperature changes caused by carbon combustion are extracted. The temperature changes caused by carbon combustion reflect the non-uniform distribution of carbon loading inside the DPF.

[0056] The temperature change characteristics caused by carbon combustion are extracted and decomposed into multi-scale data through wavelet transform to extract low-frequency and high-frequency components and identify the carbon load distribution; the carbon distribution inside the DPF is calculated using the multi-scale analysis results to accurately estimate the carbon distribution.

[0057] The present invention targets the regeneration process of a diesel particulate filter (DPF). Under the condition of no carbon combustion inside the DPF, the heat dissipation characteristics of the DPF are tested. Using the experimental data, the heat dissipation characteristics under no combustion conditions are calculated and analyzed in detail, and a heat dissipation model of the DPF is established. Under the same intake conditions, the DPF in a no-combustion state is simulated, eliminating the influence of combustion, and other parameters are consistent with the experiment. By comparing the simulation and experimental data, the temperature change characteristics are extracted, and multi-scale analysis is performed in combination with wavelet transform. Through large-scale analysis, the trend characteristics of the overall temperature change are identified; through small-scale analysis, transient temperature fluctuations are captured to achieve an estimation of the carbon distribution inside the DPF.

[0058] like Figure 2 、 Figure 3 As shown, as a preferred embodiment of the present invention, the step of obtaining the heat dissipation characteristic data of the DPF on the vehicle under normal working conditions and establishing the DPF heat dissipation model based on the heat dissipation characteristic data under normal working conditions specifically includes:

[0059] Step S101: Divide the DPF into three sections and collect temperature data and parameters of each section;

[0060] Step S102, obtaining heat dissipation characteristic data of the DPF under normal working conditions based on the temperature data and parameters of each segment;

[0061] Step S103 : establishing a DPF heat dissipation model based on the heat dissipation characteristic data under normal working conditions.

[0062] In this embodiment, during the DPF regeneration process, the combustion of particulate matter will significantly increase the temperature inside the filter. In order to accurately describe the heat dissipation behavior during this process, Figure 3The DPF shown was installed on a real-world diesel vehicle for road emissions (RDE) testing. Five temperature sensors were used to divide the interior of the diesel particulate filter (DPF) into three sections to monitor temperature changes in different areas. Experimental data on the DPF's heat dissipation characteristics under normal operating conditions was obtained. Based on this data and the law of conservation of energy, a DPF heat dissipation model was established to simulate the DPF's heat dissipation temperature. All parameters were kept consistent with experimental conditions to eliminate the influence of carbon combustion. In this model, the entire DPF was divided into the following sections: Section 1, from the DOC to the front 1 / 3 of the DPF; Section 2, from the front 1 / 3 to the 2 / 3; Section 3, from the 2 / 3 to the end of the DPF; and Section 3, from the end of the DPF to the SCR inlet, which primarily heats the airflow from the end of the DPF to the SCR. The test results were recorded.

[0063] When DPF is working normally, the energy conservation is: Q DPF，i =Q gas，i +Q evn，i (i=1:3);

[0064] where Q gas，i is the heat transfer between the incoming flow and the DPF substrate in each section, Q evn，i is the heat transfer between the DPF substrate and the external environment, Q DPF,i The heat absorbed by each section of DPF.

[0065] During the operation of the DPF, the heat convection between the gas and the DPF carrier plays an important role in heat dissipation. The heat transfer situation of each section in the gas flow process is described by the classic convection heat transfer equation:

[0066] Q gas，i =h·A(T gas，i -T DPF，i );

[0067] in represents the heat transferred by convection in segment i; h is the convection heat transfer coefficient, which represents the heat transfer capacity between the airflow and the DPF carrier; A is the heat transfer surface area, which represents the effective area of contact between the gas and the DPF carrier; T gas,i 、T DPF，i are the temperatures of the incoming gas and the DPF substrate, respectively.

[0068] After the gas flows through each section, its temperature will change due to heat transfer. The update of the gas temperature can be expressed by the following formula:

[0069]

[0070] The DPF substrate not only exchanges heat with the gas, but also dissipates heat to the external environment through natural convection. This heat transfer process can be described by the following formula:

[0071] Q evn =h·A(T DPF,i -T evn );

[0072] Where Q evn is the heat transfer between the DPF substrate and the environment, the initial temperature of the DPF substrate of each slice is T DPF,i (0) Set to 200℃, ambient temperature T evn It is 27℃.

[0073] Combined with the above heat transfer mechanism, the equation for the temperature change of each section inside the DPF with time when it is working normally is:

[0074]

[0075]

[0076] Where T w (t) represents the temperature of DPF at a certain moment, m is the mass of DPF carrier, c p is the specific heat capacity of the DPF carrier. This heat dissipation equation can be used to dynamically track the temperature change of the DPF carrier at each moment under normal working conditions.

[0077] The energy conservation during DPF regeneration is:

[0078]

[0079] Heat generated by carbon combustion in each section, For the heat transfer between the incoming flow of each section and the DPF carrier, For heat transfer between DPF carrier and external environment, The heat absorbed by each section of DPF.

[0080] Regarding the combustion reaction of soot, during the DPF regeneration process, the oxygen concentration in the exhaust gas is high. It is generally believed that the oxidation reaction inside the DPF is:

[0081] C+O2→CO2;

[0082] When the O2 volume fraction is greater than 5%, the oxidation rate of soot particles tends to be stable. Based on the empirical law of chemical reaction kinetics, the combustion reaction rate of soot can be expressed as:

[0083] R comb =k·m α ;

[0084] where Rcomb is the soot combustion reaction rate, k is the reaction rate constant, m is the mass of carbon particles in the carrier, and α is the order of the combustion reaction.

[0085] The reaction rate constant k can be calculated using the Arrhenius equation:

[0086]

[0087] Where A is the frequency factor, E a is the activation energy of the combustion reaction, R is the universal gas constant of 8.314 J / (mol·K), and T is the absolute temperature in Kelvin. Using the above formula, the combustion rate of soot particles at different temperatures can be calculated, and the heat generated by carbon combustion can be obtained as:

[0088]

[0089] in is the heat released by the combustion of soot, and ΔH is the enthalpy change of the combustion reaction, which represents the heat released when each unit mass of carbon particles burns.

[0090] In summary, the calculation equation for the temperature change of each DPF thermal regeneration section over time is:

[0091]

[0092] Where k is the reaction rate constant, m is the mass of carbon particles in the carrier, α is the order of combustion reaction, β is the ratio of carbon loading in each stage, E a is the activation energy of the combustion reaction, R is the universal gas constant; E a is the activation energy of the combustion reaction, R is the universal gas constant; is the convective heat transfer coefficient between the DPF carrier and the environment, is the heat transfer surface area between DPF and the environment; is the convective heat transfer coefficient between the incoming flow and the DPF carrier, is the heat transfer surface area between the incoming flow and the DPF.

[0093] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of performing multi-scale analysis on temperature change data based on wavelet transform to generate a carbon distribution estimation function specifically includes:

[0094] Step S401, extracting low-frequency components of the temperature change data signal through a low-pass filter to obtain approximate coefficients, and extracting high-frequency components of the temperature change data signal through a high-pass filter to obtain detail coefficients;

[0095] Step S402: obtaining the average value and peak value of the temperature difference at a large scale and the variance at a small scale based on the approximation coefficient and the detail coefficient;

[0096] Step S403: Generate a carbon distribution estimation function based on the mean and peak values of the temperature difference at a large scale and the variance at a small scale.

[0097] In this example, based on the DPF heat dissipation model established above, the DPF interior was simulated under identical intake conditions, assuming a carbon-free state, i.e., no carbon particle oxidation reaction. Excluding the effects of carbon combustion, other operating parameters were identical to the experimental conditions during actual carbon regeneration. By comparing the simulation results with the experimental data, the temperature changes caused by carbon combustion were extracted. Based on this temperature change, multi-scale analysis combined with wavelet transforms was performed to further deduce the internal carbon distribution of this DPF structure.

[0098] In the technical solution of the present invention, in order to more accurately describe the distribution characteristics of the carbon load inside the DPF, the time series temperature signal is segmented and each segment of data is subjected to wavelet transform analysis.

[0099] By applying a wavelet transform to the temperature differences at three measurement points within the DPF, the wavelet transform can identify the more gradual changes in the temperature signal at larger scales. These slow temperature changes reflect the overall trend of the system. Conversely, at smaller scales, the wavelet transform can capture transient fluctuations in the temperature signal. These rapid temperature changes correspond to the dramatic temperature fluctuations generated during carbon combustion.

[0100] The principle of Discrete Wavelet Transform (DWT) is as follows Figure 6 shown.

[0101] DWT performs convolution operation on the signal x[n] through a pair of low-pass filters g[n] and high-pass filters h[n]. The low-pass filter extracts the low-frequency components of the signal to obtain the approximation coefficient cAn; the high-pass filter extracts the high-frequency components of the signal to obtain the detail coefficient cDn.

[0102]

[0103] The data is then downsampled, and the above steps are continued for the approximate coefficient cAn to decompose it into a lower frequency approximate part and a detail part.

[0104] Temperature change ΔT caused by carbon combustion in each DPF section carbon,i Can be broken down into:

[0105]

[0106] Among them, cD i It is the detail coefficient of different scales, capturing the transient changes in the temperature signal and reflecting the rapid temperature fluctuations generated during carbon combustion. N It is an approximate coefficient that reflects the overall trend or slow change of temperature change.

[0107] Combining the above analysis, a simple carbon distribution estimation function is obtained based on the mean and peak values of the temperature difference at a large scale (i.e., cA5) and the variance at a small scale (i.e., cD1):

[0108]

[0109] where p i is the carbon load distribution of the DPF in section i, w σ 、w peak 、 are the weights of variance, peak value and mean in carbon distribution estimation, is the variance at small scale, P i is the peak value at large scale, is the mean value at large scale.

[0110] As a preferred embodiment of the present invention, the wavelet transform adopts the db4 wavelet of the Daubechies wavelet system.

[0111] In this example, the Daubechies4 (db4) wavelet was selected as the basis function for signal analysis due to its excellent performance in processing non-stationary signals. Compared to other wavelets, db4 offers higher resolution across various frequency and time scales, making it well-suited for processing temperature signal variations under complex operating conditions. This is particularly true for capturing transient temperature fluctuations during DPF thermal regeneration, providing a reliable signal analysis foundation for estimating carbon particle distribution within the DPF.

[0112] As a preferred embodiment of the present invention, the weight coefficients of the variance, peak value and average value in carbon distribution estimation are optimized based on a particle swarm optimization algorithm.

[0113] In this embodiment, to optimize the selection of weight coefficients, the present invention introduces a particle swarm optimization (PSO) algorithm. The PSO algorithm optimizes the weight coefficients by combining global and local searches. The objective function is to minimize the error between the regeneration temperature value calculated by carbon load estimation and the actual regeneration temperature measurement, thereby ensuring the rationality and accuracy of the weight combination. The pseudo code for the implementation process is shown below:

[0114]

[0115]

[0116] like Figure 7 As shown, as a preferred embodiment of the present invention, it also includes determining the carbon load distribution characteristics under urban conditions, high-speed conditions and other conditions based on the carbon distribution estimation function.

[0117] In this embodiment, the optimization algorithm implemented by the above pseudo code can avoid local optimal traps while searching for the global optimal solution, thereby ensuring the rationality and accuracy of the weight combination.

[0118] In order to predict the carbon load distribution characteristics under different working conditions, the carbon load distribution characteristics under urban and highway working conditions are calculated based on a simple carbon distribution estimation function, which are P 城市 、P 高速 The average speed of the city and the average speed of the highway are v 城市 、v 高速 The carbon loading distribution under other working conditions is:

[0119]

[0120] Where P is the carbon load distribution under actual working conditions, and v is the average vehicle speed under actual working conditions.

[0121] The distribution of internal carbon loading is estimated using wavelet transform to obtain the disturbance of regeneration temperature caused by uneven carbon distribution. Figure 7 The specific characteristics of carbon load distribution in the three sections of the DPF under highway and urban conditions are shown:

[0122] Looking at the carbon load distribution across the three zones, Segment 2 has a higher carbon load ratio than Segments 1 and 3 under all operating conditions. This suggests that Segment 2 may be the primary location for carbon deposition and combustion, while Segments 1 and 3 have relatively less carbon accumulation. Segment 3, located at the tail end of the DPF, exhibits even more random carbon distribution.

[0123] The carbon load distribution is roughly the same under highway and urban conditions, with carbon mainly concentrated in the rear and middle sections. The carbon load in Segment 1 under highway conditions is slightly lower.

[0124] Table 1 Carbon distribution inside DPF under different working conditions

[0125]

[0126] In the above calculations, the average vehicle speeds for urban and suburban conditions were 32 km / h and 81 km / h, respectively. The average vehicle speed for suburban conditions was 54 km / h. Based on the above formula, the carbon load distribution of each DPF segment under suburban conditions is as follows: 28.6% for segment 1, 37.1% for segment 2, and 34.2% for segment 3. These results are highly consistent with those obtained using the carbon load distribution estimation method (28.6% for segment 1, 36.9% for segment 2, and 34.5% for segment 3), validating the accuracy of the calculation method.

[0127] like Figure 8 As shown, an embodiment of the present invention further provides a system for estimating carbon load distribution inside a diesel particulate filter, the system comprising:

[0128] The heat dissipation model establishment module 100 is used to obtain the heat dissipation characteristic data of the DPF under normal working conditions and establish a DPF heat dissipation model based on the heat dissipation characteristic data under normal working conditions;

[0129] The temperature model building module 200 is used to build a DPF regeneration temperature model according to the law of conservation of energy;

[0130] A data extraction module 300 is used to extract temperature change data caused by carbon combustion based on a DPF heat dissipation model and a DPF regeneration temperature model;

[0131] Function generation module 400, for performing multi-scale analysis on temperature change data based on wavelet transform to generate a carbon distribution estimation function;

[0132] The estimation module 500 is configured to estimate the carbon load distribution inside the diesel particulate filter based on the carbon distribution estimation function.

[0133] like Figure 9 As shown, as a preferred embodiment of the present invention, the function generation module includes:

[0134] The coefficient extraction unit 401 is used to extract the low-frequency components of the temperature change data signal through a low-pass filter to obtain approximate coefficients, and is used to extract the high-frequency components of the temperature change data signal through a high-pass filter to obtain detail coefficients;

[0135] A calculation unit 402 is configured to obtain an average value and a peak value of the temperature difference at a large scale and a variance at a small scale based on the approximation coefficient and the detail coefficient;

[0136] The function generating unit 403 is used to generate a carbon distribution estimation function based on the mean and peak of the temperature difference at a large scale and the variance at a small scale.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for estimating carbon load distribution inside a diesel particulate filter, characterized in that: The method comprises: Acquiring heat dissipation characteristic data of the DPF under normal working conditions, and establishing a DPF heat dissipation model based on the heat dissipation characteristic data under normal working conditions; Establish a DPF regeneration temperature model based on the law of conservation of energy; Extract temperature change data caused by carbon combustion based on the DPF heat dissipation model and DPF regeneration temperature model; Perform multi-scale analysis on temperature change data based on wavelet transform to generate carbon distribution estimation function; The carbon load distribution inside the diesel particulate filter is estimated based on the carbon distribution estimation function.

2. The method for estimating carbon load distribution inside a diesel particulate filter according to claim 1, characterized in that: The step of obtaining heat dissipation characteristic data of the DPF on the vehicle under normal working conditions and establishing a DPF heat dissipation model based on the heat dissipation characteristic data under normal working conditions specifically includes: Divide the DPF into three sections and collect temperature data and parameters of each section; Obtain the heat dissipation characteristic data of the DPF under normal working conditions based on the temperature data and parameters of each section; A DPF heat dissipation model is established based on the heat dissipation characteristic data under normal working conditions.

3. The method for estimating carbon load distribution inside a diesel particulate filter according to claim 1, characterized in that: The step of performing multi-scale analysis on the temperature change data based on wavelet transform to generate a carbon distribution estimation function specifically includes: The low-frequency components of the temperature change data signal are extracted through a low-pass filter to obtain an approximate coefficient, and the high-frequency components of the temperature change data signal are extracted through a high-pass filter to obtain a detail coefficient; Based on the approximate coefficient and detail coefficient, the average and peak values of the temperature difference at large scale and the variance at small scale are obtained; The carbon distribution estimation function is generated based on the mean and peak temperature differences at large scales and the variance at small scales.

4. A method for estimating carbon load distribution inside a diesel particulate filter according to claim 1 or 3, characterized in that: The wavelet transform adopts the db4 wavelet of the Daubechies wavelet system.

5. The method for estimating carbon load distribution inside a diesel particulate filter according to claim 1, characterized in that: It also includes optimizing the weight coefficients of variance, peak value and average value in carbon distribution estimation based on particle swarm optimization algorithm.

6. The method for estimating carbon load distribution inside a diesel particulate filter according to claim 1, characterized in that: It also includes determining the carbon load distribution characteristics under urban conditions, high-speed conditions and other conditions based on the carbon distribution estimation function.

7. A system for estimating the carbon load distribution inside a diesel particulate filter, used to implement the method for estimating the carbon load distribution inside a diesel particulate filter according to any one of claims 1 to 6, characterized in that: The system comprises: a heat dissipation model establishment module, used to obtain heat dissipation characteristic data of the DPF under normal working conditions, and establish a heat dissipation model based on the heat dissipation characteristic data under normal working conditions; Temperature model building module, used to build DPF regeneration temperature model based on the law of conservation of energy; A data extraction module is used to extract temperature change data caused by carbon combustion based on a carbon-free heat dissipation model and a DPF regeneration temperature model; Function generation module, used to perform multi-scale analysis on temperature change data based on wavelet transform and generate carbon distribution estimation function; The estimation module is used to estimate the carbon load distribution inside the diesel particulate filter based on the carbon distribution estimation function.

8. The system for estimating carbon load distribution inside a diesel particulate filter according to claim 7, characterized in that: The function generation module includes: A coefficient extraction unit, configured to extract low-frequency components of the temperature change data signal through a low-pass filter to obtain approximate coefficients, and to extract high-frequency components of the temperature change data signal through a high-pass filter to obtain detail coefficients; A calculation unit, used to obtain the average value and peak value of the temperature difference at a large scale and the variance at a small scale based on the approximate coefficient and the detail coefficient; The function generation unit is used to generate a carbon distribution estimation function based on the mean and peak of the temperature difference at a large scale and the variance at a small scale.

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