Method for constructing diesel engine original exhaust particulate matter distribution model

Abstract

The invention provides a construction method of a diesel engine original exhaust particulate matter distribution model. The method comprises the following steps: constructing a basis function library, wherein the basis function library comprises a rotating speed power function, a torque power function and a rotating speed and torque combination function; the basis function library is applied to construct a particulate matter quantity model, intercept and coefficients in the particulate matter quantity model are both obtained through multiple regression fitting, and the number of working condition points used during multiple regression fitting is higher than a preset number; and / or, a particle size distribution model is constructed, the particle size distribution model is expressed as the sum of two Gaussian functions, the particle size distribution model reflects the relation between the particle size of the particulate matter and the number of the particulate matter, and the two Gaussian functions are both Gaussian distribution about the particle size of the particulate matter. According to the scheme, the accuracy of the particulate matter distribution model of the diesel engine exhaust aftertreatment system is improved, and / or the problem that the rule of particle size distribution is lacked is solved.

Description

Technical Field

[0001] The present application relates to the technical field of diesel engines, and in particular to a method for constructing a diesel engine original exhaust particulate matter distribution model, a computer-readable storage medium, and an electronic device. Background Art

[0002] As diesel engine emission regulations become increasingly stringent, PM mass limits and particle number (Particle Number, PN) limits are becoming increasingly stringent, and in-engine purification technology can no longer enable diesel engines to meet current limit requirements. Emission post-treatment catalysts, including diesel oxidation catalysts (Diesel Oxidation Catalyst, DOC), diesel particulate filters (Diesel Particulate Filter, DPF) and selective catalytic reduction devices (Selective Catalytic Reduction, SCR), have become one of the necessary configurations for diesel engines.

[0003] Therefore, the structural design optimization of diesel engine exhaust aftertreatment catalysts and their matching with diesel engines have become a hot topic of research in recent years. Usually, the above-mentioned catalyst structural design optimization and matching process requires a large number of bench tests to finally obtain the optimal aftertreatment system structural design scheme that meets different operating emission conditions, which takes a lot of time and costs a lot.

[0004] In order to solve the above-mentioned shortcomings, the prior art CN110261124A proposes a method for constructing and applying a particle distribution model for a diesel engine exhaust aftertreatment system. A multivariate linear regression analysis is applied to obtain a multivariate linear regression analysis equation, and a regression analysis is performed to establish a model for the number and particle size distribution of particles at the inlet of a diesel engine exhaust aftertreatment system with the engine operating conditions as input conditions.

[0005] The solution of the prior art CN110261124A only selected the power exponents of the speed and torque when performing linear fitting to determine the particle quantity model, without considering other factors, resulting in insufficient accuracy of the fitting results. In addition, the solution of the comparative document does not disclose the law of particle size distribution. In addition, it uses fewer operating points for fitting, because there are too few operating points, and does not fully capture the changing characteristics of the number of particles under different operating conditions, resulting in a lack of universality in the fitting results, that is, it is difficult to apply to all operating conditions and has limited applicability. Summary of the invention

[0006] The main purpose of the present application is to provide a method for constructing a diesel engine exhaust particulate matter distribution model, a computer-readable storage medium and an electronic device, so as to at least solve the problem that the diesel engine exhaust aftertreatment system particulate matter distribution model constructed by the prior art solution has low accuracy and lacks a regularity in particle size distribution.

[0007] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for constructing a diesel engine original exhaust particulate matter distribution model is provided, comprising: constructing a basis function library, the basis function library comprising a power function of speed, a power function of torque and a speed-torque combination function; applying the basis function library to construct a particulate matter quantity model, wherein the intercept and coefficient in the particulate matter quantity model are obtained by multivariate regression fitting, and the number of operating points used in the multivariate regression fitting is higher than a preset number; and / or, constructing a particle size distribution model, the particle size distribution model is expressed as the sum of two Gaussian functions, the particle size distribution model reflects the relationship between the particle size and the particle number, and the two Gaussian functions are Gaussian distributions of the particle size.

[0008] Optionally, the particle quantity model is constructed by applying the basis function library, including: determining that the power function of the rotation speed and the power function of the torque include x, y, x 2 ,y 2 、x 3 and 3 , where x represents the speed and y represents the torque; determining the speed-torque combination function includes xy and x 2 y; the particle quantity model constructed by applying the power function of the speed, the power function of the torque and the speed-torque combination function is f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y, where β 0 represents the intercept, β 1 ,β 2 ...β 7 represents the coefficient.

[0009] Optionally, the basis function library further includes a superposition function of a cone sub-function and a Gaussian sub-function, wherein the superposition function of the cone sub-function and the Gaussian sub-function is used to correct an error in calculating a peak value of the number of particles.

[0010] Optionally, applying the basis function library to construct a particle quantity model includes: applying the basis function library including the superposition function of the cone sub-function and the Gaussian sub-function to construct a particle quantity model as follows:

[0011] f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 SF-DCDG, where x represents the speed, y represents the torque, x, y, x 2 ,y 2 、x 3 and 3 is the power function of the rotation speed and the power function of the torque, xy and x 2 y is the speed-torque combination function, SF-DCDG is the superposition function of the cone sub-function and the Gaussian sub-function, SF is the cone sub-function, and DCDG is the Gaussian sub-function.

[0012] Optionally, the superposition function of the cone subfunction and the Gaussian subfunction is:

[0013]

[0014] Wherein, x represents the rotation speed, y represents the torque, A 1 represents the amplitude, x c and c are the center coordinates of the cone, corresponding to the speed and torque where the peak value of the number of particles is located, and a and b represent the semi-major axis and semi-minor axis of the ellipse shape, respectively;

[0015] Among them, A 2 represents the amplitude, X c2 and Y c2 They represent the center coordinates of the deformed Gaussian function, corresponding to the speed and torque corresponding to the high value of the number of particles in the high torque and high speed range, and a 2and b 2 Are respectively determined by the expansion ranges of the high values of the particulate matter quantity in the X-axis and Y-axis directions, and σ represents the standard deviation of the Gaussian sub-function.

[0016] Optionally, a particle size distribution model is constructed, and the particle size distribution model is expressed as the sum of two Gaussian functions, including: determining the first Gaussian function as Determining the second Gaussian function as Determining the particle size distribution model as Wherein, x represents the particle size of the particulate matter, f(x) represents the quantity of the particulate matter corresponding to the particle size of the particulate matter, and a 1 , b 1 , c 1 , a 2 , b 2 , c 2 Are coefficients.

[0017] Optionally, the method further includes: using the basis function library including the power function of the rotational speed, the power function of the torque, and the rotational speed-torque combined function to perform polynomial fitting to obtain the coefficients a 1 , b 1 , c 1 , a 2 , b 2 , c 2 .

[0018] Optionally, the method further includes: using the basis function library including the power function of the rotational speed, the power function of the torque, the rotational speed-torque combined function, and the superposition function of the conical sub-function and the Gaussian sub-function to perform polynomial fitting to obtain the coefficients a 1 , b 1 , c 1 , a 2 , b 2 , c 2 , wherein the superposition function of the conical sub-function and the Gaussian sub-function is used to correct the error in the calculation of the particulate matter quantity peak.

[0019] According to another aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored program. Wherein, when the program runs, it controls the device where the computer-readable storage medium is located to execute any one of the methods for constructing the diesel engine raw emission particulate matter distribution model.

[0020] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the diesel engine original exhaust particulate matter distribution models.

[0021] By applying the technical solution of the present application, the basis function library includes not only the power function of the speed and the power function of the torque, but also the speed-torque combination function. The accuracy of the particle quantity model constructed by adding the speed-torque combination function is significantly better than that of the solution that does not include the speed-torque combination function. The above conclusion is obtained by constructing the model and comparing it with the actual number of particles; and / or, the particle size distribution is represented by the sum of two Gaussian functions, that is, the particle size distribution is represented by a model, which solves the defect of lack of a model to represent the particle size distribution in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for constructing a diesel engine original exhaust particulate matter distribution model provided in an embodiment of the present application is shown;

[0024] Figure 2 A schematic flow chart of a method for constructing a diesel engine exhaust particulate matter distribution model provided in accordance with an embodiment of the present application is shown;

[0025] Figure 3 A three-dimensional distribution diagram of rotation speed, torque and measured PN provided according to an embodiment of the present application is shown;

[0026] Figure 4 A first calculated PN distribution diagram provided according to an embodiment of the present application is shown;

[0027] Figure 5 A second calculated PN distribution diagram provided according to an embodiment of the present application is shown;

[0028] Figure 6 A schematic diagram of a particle size distribution curve fitting method using a formula function of two Gaussian functions superimposed on each other according to an embodiment of the present application is shown;

[0029] Figure 7 A comparison diagram of simulated values ​​and measured values ​​of an empirical formula equation group of original PN particle size distribution under a fixed working condition provided in an embodiment of the present application is shown;

[0030] Figure 8 A structural block diagram of a device for constructing a diesel engine exhaust particulate matter distribution model provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0031] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0035] PN (Particulate number): number of particles.

[0036] SS (Skill score) is a commonly used indicator for evaluating the accuracy of model simulation, ranging from 0 to 1. The closer to 1, the better the model simulation result.

[0037] TSS (Taylor Skill score) is an evaluation index that comprehensively considers the correlation between model simulation values ​​and measured values ​​and the consistency of discreteness. It ranges from 0 to 1. The closer it is to 1, the better the model simulation results.

[0038] As introduced in the background technology, the particle matter distribution model of the diesel engine exhaust aftertreatment system constructed in the prior art has low accuracy and lacks the regularity of particle size distribution. In order to solve the problem of low accuracy and lack of regularity of particle size distribution of the particle matter distribution model of the diesel engine exhaust aftertreatment system constructed, the embodiments of the present application provide a method for constructing a diesel engine original exhaust particle matter distribution model, a computer-readable storage medium and an electronic device.

[0039] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0040] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal for constructing a diesel engine original exhaust particulate matter distribution model according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0041] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for constructing the diesel engine original exhaust particulate matter distribution model in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned network specific example may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0042] In this embodiment, a method for constructing a diesel engine exhaust particulate matter distribution model running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] Figure 2 FIG. 1 is a flow chart of a method for constructing a diesel engine exhaust particulate matter distribution model according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0044] Step S201, constructing a basis function library, the basis function library including a power function of a speed, a power function of a torque, and a speed-torque combination function; applying the basis function library to construct a particle quantity model, wherein the intercept and coefficient in the particle quantity model are obtained by multivariate regression fitting, and the number of operating points used in the multivariate regression fitting is higher than a preset number;

[0045] As mentioned above, the base function library includes not only the power function of the speed and the power function of the torque, but also the speed-torque combination function. The accuracy of the particle number model constructed by adding the speed-torque combination function is significantly better than that of the scheme without the speed-torque combination function. The above conclusion is obtained through the construction of the model and the comparison with the actual particle number.

[0046] Among them, multivariate regression fitting is performed using data from multiple operating points, and the number of operating points is greater than 100. That is, the intercepts and coefficients obtained by fitting with enough operating point data are universal and can be applied to a variety of operating conditions. As long as there are enough operating point data, the obtained particle quantity model can be applied to all operating conditions.

[0047] The following is a supplementary explanation: the prior art CN110261124A only uses a very small number of operating points, namely 16 operating points, for fitting. Because there are too few operating points, the changing characteristics of PN under different operating conditions are not fully captured, so the fitting result is not representative. The solution of the present application uses more than 100 operating points to overcome this shortcoming.

[0048] Specifically, the particle quantity model is a neural network model. In the initial stage, the diesel engine is used as the research sample, and the original PN value of the diesel engine under universal working conditions and engine state parameters such as speed, torque, injection amount, oil temperature, ERG rate, etc. are obtained through bench experiments. Different variables are first combined in pairs as the input layer of the neural network, and the PN value is used as the output layer; the neural network is trained, and the accuracy of the neural network model established by the input layer of different combinations for PN value prediction is compared, for a total of 119 working points.

[0049] Among all the combinations, it was found that the neural network model established with speed and torque as the input layer had the best prediction effect. The correlation coefficient R between the predicted PN value and the actual PN measured on the bench was between 0.97 and 0.99. The calculation accuracy of the cumulative sum of PN calculated by the neural network under all universal working conditions was about 99%.

[0050] On the basis of the above, in addition to speed and torque, the injection amount, oil temperature, ERG rate and other parameters are added to the input layer in turn. It is found that the accuracy of the predicted PN value is not improved compared with the accuracy of the prediction with only speed and torque as the input layer, and even the prediction accuracy decreases slightly after the input layer variables are increased. Therefore, it is concluded that engine speed and torque are the main factors affecting PN.

[0051] It is confirmed that speed and torque are the main factors affecting the original PN of diesel engines. With speed as the X-axis, torque as the Y-axis, and PN as the Z-axis, a three-dimensional distribution diagram of PN can be drawn. Figure 3A three-dimensional distribution diagram of the speed, torque and measured PN is shown. It can be found that PN is a complex function f(x, y) of the speed (X) and torque (Y), that is, PN = f(x, y).

[0052] and / or,

[0053] Step S202, constructing a particle size distribution model, the particle size distribution model is represented by the sum of two Gaussian functions, the particle size distribution model reflects the relationship between the particle size and the number of particles, and the two Gaussian functions are both Gaussian distributions of the particle size.

[0054] The above-mentioned use of the sum of two Gaussian functions to represent the particle size distribution realizes the use of a model to represent the particle size distribution, thereby solving the defect of the prior art that there is a lack of a model to represent the particle size distribution.

[0055] In a more specific embodiment, the particle quantity model is constructed by applying the above-mentioned basis function library, including:

[0056] The power function for determining the rotational speed and the power function for determining the torque include x, y, x 2 ,y 2 、x 3 and 3 , where x represents the above speed, and y represents the above torque;

[0057] Determine the above speed torque combination function including xy and x 2 y;

[0058] The particle quantity model constructed by applying the power function of the speed, the power function of the torque and the speed-torque combination function is f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y, where β 0 represents the above intercept, β 1 ,β 2 ...β 7 represents the above coefficients.

[0059] In the specific implementation, it can be roughly inferred from the three-dimensional distribution of the original PN that f(x,y) is likely to be a power function of speed and torque (x,y,x2 ,y 2 ...) and its combined function (xy,x 2 y,xy 2 ,x 2 y 2 ……) together to form a polynomial function, and from the change trend of PN, it can be roughly analyzed that the object power function will not exceed the fifth power. Therefore, in order to determine the specific function form of f(x,y), the power function, combination function, exponential function and basis function library of the combination function of speed (x) and torque (y) are first established:

[0060] [x,y,x 2 ,y 2 , x 3 ,y 3 ,x 4 ,y 4 ,x 5 ,y 5 ,xy,x 2 y,xy 2 ,x 2 y 2 ,e x ,e y ,cos(x),sin(x),cos(y),sin(y)…].

[0061] Starting from a simple power function, we can add basis functions one by one to form a new polynomial function f 1 (x,y)…f 20 (x, y), a total of 20; use these polynomial functions to fit the PN value. Calculate the correlation coefficient (R) and model skill score (SS) between the PN values ​​fitted by different basis function combinations and the measured PN values. Through the above process, it is found that: when adding [x 2 ,y 2 , x 3 ,y 3 ,xy,x 2 y], R and SS both increase significantly, while when adding [x 4 ,y 4 ,x 5 ,y 5 ,xy 2 ,x 2 y 2 , cos(x), sin(x), cos(y), sin(y)], R and SS remain basically unchanged; when adding [e x ,e y ] and other basis functions, both R and SS are significantly reduced. As shown below:

[0062] f 1(x,y)=β 0 +b 1 x

[0063] f 2 (x,y)=β 0 +b 1 x+b 2 y

[0064] f 3 (x,y)=β 0 +b 1 x+b 2 y+β 3 x 2

[0065] f 4 (x,y)=β 0 +b 1 x+b 2 y+β 3 x 2 +b 4 y 2 …

[0066] f 20 (x,y)=β 0 +b 1 x+b 2 y+β 3 x 2 +b 4 y 2 +b 5 x 3 +b 6 y 3 +b 7 x 4 +b 8 y 4 +b 9 x 5 +b 10 y 5 +b 11 xy+b 12 x 2 y+β 13 xyz 2 +b 14 x 2 y 2 +b 15 e x +b 16 e y +b 17 cos(x)+β 17 sin(x)+β 18 cos(x)+β 19 sin(y)+β 20cos(y)

[0067] β 0 is the intercept of the function, β 1 ,β 2 ……β 20 are the coefficients of the polynomial, and their values in each polynomial can be obtained by multiple regression fitting.

[0068] Through the above process, it is found that: when adding basis functions such as [y, x 2 , y 2 , x 3 , y 3 , xy, x 2 y], both R and SS increase significantly, while when adding variables such as [x 4 , y 4 , x 5 , y 5 , xy 2 , x 2 y 2 , cos(x), sin(x), cos(y), sin(y)], R and SS remain basically unchanged; when adding basis functions such as [e x , e y , both R and SS decrease significantly. Therefore, a model with relatively high accuracy is obtained: f PN (x, y) = β 0 + β 1 x + β 2 y + β 3 x 2 + β 4 y 2 + β 5 xy + β 6 x 3 + β 7 y 3 + β 7 x 2 y.

[0069] Of course, the particulate matter quantity model in this application can also select the following multiple models:

[0070] f PN (x, y) = β 0 + β 1 x + β 2 y + β 3 x 2 + β 4 y 2 + β 5 xy + β 6 x 3 + β 7 y3 +b 7 x 2 y+β 8 x 4 ;

[0071] f PN (x,y)=β 0 +b 1 x+b 2 y+β 3 x 2 +b 4 y 2 +b 5 xy+b 6 x 3 +b 7 y 3 +b 7 x 2 y+β 8 x 5 ;

[0072] f PN (x,y)=β 0 +b 1 x+b 2 y+β 3 x 2 +b 4 y 2 +b 5 xy+b 6 x 3 +b 7 y 3 +b 7 x 2 y+β 8 y 4 ;

[0073] f PN (x,y)=β 0 +b 1 x+b 2 y+β 3 x 2 +b 4 y 2 +b 5 xy+b 6 x 3 +b 7 y 3 +b 7 x 2 y+β 8 y 5 ;

[0074] f PN (x,y)=β 0 +b 1x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 x 2 y;

[0075] f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 x 2 y 2 .

[0076] For model: f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y, perform multiple linear regression on the total PN value of the original row to obtain the intercept β 0 and β 1 To β 7 , the correlation between the calculated PN value and the measured PN value after substituting into the empirical formula can reach 0.94, and the SS reaches 0.881. The calculated PN is as follows Figure 4 shown.

[0077] Although statistical indicators such as R and SS show that the current empirical formula: f PN (x,y)=β 0 +β1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 The calculation result of y can basically reflect the changing trend of PN. However, compared with the three-dimensional graph of the total PN value calculated by the empirical formula ( Figure 4 ) and the three-dimensional graph of the measured PN total value ( Figure 3 ) It can be found that there are still some problems with the PN value calculated by the empirical formula, especially the simulation effect of the peak value in the PN peak area is poor. For example, under low torque and medium and high speed conditions, the measured PN has an obvious peak, but the calculated PN value lacks the change of this peak; the PN value under high torque and high speed conditions also has the characteristic of rising again, but the calculated PN value cannot be simulated. This may be because the conventional power function and its combination function cannot simulate these special changes. In order to solve this problem, a new basis function is proposed: the superposition function of the cone sub-function and the Gaussian sub-function, wherein the superposition function of the above cone sub-function and the Gaussian sub-function is used to correct the error in the calculation of the peak value of the particle number.

[0078] Furthermore, the particle quantity model is constructed by applying the above-mentioned basis function library, including:

[0079] The particle quantity model is constructed by applying the above-mentioned basis function library including the superposition function of the above-mentioned cone sub-function and Gaussian sub-function as follows:

[0080] f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 SF-DCDG, where x represents the above speed, y represents the above torque, x, y, x 2 ,y 2 、x 3 and 3is the power function of the above speed and the power function of the above torque, xy and x 2 y is the above-mentioned speed-torque combination function, SF-DCDG is the superposition function of the above-mentioned cone sub-function and Gaussian sub-function, SF is the above-mentioned cone sub-function, and DCDG is the above-mentioned Gaussian sub-function.

[0081] That is to say, the superposition function of the cone subfunction and the Gaussian subfunction is added to β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 The particle number model considering the simulation error in the PN peak area is obtained as follows:

[0082] f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 SF-DCDG. Achieve more accurate prediction of the number of particulate matter. Verification found that SF-DCDG not only greatly improves the accuracy of polynomial fitting, but can also be applied to multiple models. This patent is applied to the original PN data of other models for polynomial fitting and calculation, and its calculation accuracy is still very high.

[0083] In some embodiments, the superposition function of the above-mentioned cone sub-function and Gaussian sub-function is:

[0084]

[0085] Wherein, x represents the above speed, y represents the above torque, A 1 represents the amplitude, which determines the overall scaling of the deformation cone; x c and care the center coordinates of the cone, corresponding to the speed and torque at the peak of the particle number, a and b represent the semi-major axis and semi-minor axis of the ellipse, respectively; the value of a will be determined by the extent of expansion of the ellipsoid in the x-axis direction; similarly, the value of b will be determined by the extent of expansion of the ellipsoid in the y-axis direction.

[0086] Among them, A 2 Represents the amplitude, which determines the overall scaling of the "3D deformation Gaussian function"; its value is related to A 1 About, A 1 : A 2 This is consistent with the ratio of the PN peak value to the second highest value. c2 and Y c2 They represent the center coordinates of the deformed Gaussian function, corresponding to the speed and torque corresponding to the high value of the number of particles in the high torque and high speed range, and a 2 and b 2 They are respectively determined by the extension range of the high value of the number of particles in the X-axis and Y-axis directions, and σ represents the standard deviation of the Gaussian subfunction, which is determined by the range corresponding to the high value of PN in the high torque and high speed working conditions. That is, in the SF-DCDG function, the above parameters are all constant values, and SF-DCDG is only a function of x and y.

[0087] f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 SF-DCDG in SF-DCDG is selected as

[0088] Perform multiple linear regression to obtain the intercept β 0 and coefficient (β 1 …β 8) is substituted into the above empirical formula, and the correlation R between the calculated diesel engine original exhaust PN and the measured PN value is 0.98, and SS is 0.96; both R and SS have been significantly improved. The diesel original exhaust PN calculated by the new empirical formula is plotted into a three-dimensional distribution diagram, and it can be found that the three-dimensional distribution of PN calculated by the current empirical formula is very consistent with the measured PN distribution. The addition of the SF-DCDG basis function enables the empirical formula constructed in this paper to calculate the total original exhaust PN value very well, which conforms to all the laws and characteristics of the original exhaust PN distribution. Figure 5 The calculated PN obtained after adding SF-DCDG is shown.

[0089] To verify f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 SF-DCDG, using the WP10H diesel engine as the research sample, uses the 49-point universal data of another diesel engine model, WP13H, to compare the empirical formula f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 SF-DCDG performs polynomial regression fitting to obtain the intercept β 0 and coefficient (β 1 …β 8) and then the empirical formula is entered to calculate the total PN value under universal conditions. Although the operating range of the WP13H universal condition is quite different from that of the WP10H. However, the empirical formula of this patent can still calculate the PN values ​​of different operating points of the WP13H universal condition very well. The correlation coefficient R between the calculated value and the actual test bench value reached 0.961, the model skill score SS reached 0.923, and the cumulative total accuracy reached 99.99%. In addition, the three-dimensional graph of the total original exhaust PN value of the WP13H model calculated by the empirical formula is very close to the three-dimensional spatial distribution of the measured PN, and the bimodal characteristics are also consistent. The above results show that the empirical formula of this patent can adapt to the calculation of original exhaust PN of different models of diesel engines.

[0090] In addition, in order to verify the applicability of this patent under different working conditions, this patent further uses the bench test data of the WP13H model under WHTC and WHSC conditions to further verify the diesel engine original exhaust PN empirical formula. The results show that the calculated PN cumulative sum accuracy can well reflect the changing characteristics of the PN value of the WP13H model. This shows that the present invention has a very wide range of applications, and multiple models of diesel engines are still verified under multiple working conditions. In addition, the data under the universal working conditions of the gas engine are also used for verification, and it is found that this method is also applicable to the PN calculation of the gas engine.

[0091] The measurement interval of the EEPS particle size analyzer is 5.6 to 560 nm, and the measurement range is also limited. Therefore, it is necessary to first find the law of particle size distribution and find a formula that can describe the particle size distribution before calculating the particle size distribution of any particle size range; and the particle size distribution under any working conditions. In the embodiment of the present application, a particle size distribution model is constructed, and the above particle size distribution model is expressed as the sum of two Gaussian functions including:

[0092] Determine the first Gaussian function as

[0093] Determine the second Gaussian function as

[0094] The above particle size distribution model is determined as Wherein, x represents the particle size of the above-mentioned particles, f(x) represents the number of the above-mentioned particles corresponding to the particle size of the above-mentioned particles, and a 1 , b 1 、c 1 、a 2 , b 2 、c 2 The specific particle size distribution model is: Fit the particle size distribution under multiple conditions (more than 100), see Figure 6 It is found that the fitting results are very good (R>0.99) under all working conditions.

[0095] Furthermore, the method further comprises: using the base function library including the power function of the speed, the power function of the torque and the speed-torque combination function to perform polynomial fitting to obtain the coefficient a 1 , b 1 、c 1 、a 2 , b 2 、c 2 .

[0096] Furthermore, the method further comprises: using the basis function library including the power function of the speed, the power function of the torque, the speed torque combination function, and the superposition function of the cone sub-function and the Gaussian sub-function to perform polynomial fitting to obtain the coefficient a 1 , b 1 、c 1 、a 2 , b 2 、c 2 , wherein the superposition function of the above-mentioned cone sub-function and Gaussian sub-function is used to correct the error in the calculation of the peak value of the particle number.

[0097] Under different working conditions, the particle size distribution a 1 , b 1 、c 1 、a 2 , b 2 、c 2 It changes with the speed and torque, and is obviously strongly correlated with the speed and torque. Therefore, the particle size distribution characteristics of diesel engines, which were originally difficult to find a regularity, are simplified to finding The six parameters change with speed and torque.

[0098] Therefore, by using the "basis function library" proposed in the previous article to perform multiple regression on each particle size, we can get each a 1 , b 1 、c 1 、a 2 , b 2 、c 2 These 6 empirical formulas plus A total of 7 empirical formulas constitute the empirical formula equation group for diesel engine particle size distribution.

[0099] Specifically, 75% of the working conditions are used as training data sets to fit a 1 , b 1 、c 1 、a 2 , b 2 、c 2The empirical formula of the remaining working conditions is used to verify the accuracy of the "Empirical Formula Equation Group of Diesel Engine Original Exhaust PN Particle Size Distribution". With this equation group, the particle size distribution under this working condition can be obtained by inputting the speed and torque, such as Figure 7 The figure shows the comparison between the simulated value and the measured value of the particle size distribution empirical formula equation group at a speed of 1000 rpm and a torque of 200 N·m. Figure 7 It can be concluded that the difference between the simulated values ​​of the empirical formula group and the measured values ​​is very small, which proves that the empirical formula equation group for the original PN particle size distribution of diesel engines proposed in this patent is relatively accurate.

[0100] Different speeds and torques can also be substituted to obtain the distribution of a certain particle size at different speeds and torques, proving that this formula has strong flexibility.

[0101] In addition, the method for constructing the diesel engine original exhaust particulate matter distribution model in the present application has good simulation results and low computing resource requirements, and can be deployed in the ECU as a virtual sensor.

[0102] The embodiment of the present application also provides a device for constructing a diesel engine original exhaust particulate matter distribution model. It should be noted that the device for constructing a diesel engine original exhaust particulate matter distribution model in the embodiment of the present application can be used to execute the method for constructing a diesel engine original exhaust particulate matter distribution model provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0103] The following is an introduction to the device for constructing the diesel engine original exhaust particulate matter distribution model provided in the embodiments of the present application.

[0104] Figure 8 Schematic diagram of a device for constructing a diesel engine exhaust particulate matter distribution model according to an embodiment of the present application. Figure 8 As shown, the device includes a first building unit 81 and / or a second building unit 82,

[0105] A first construction unit 81 is used to construct a basis function library, wherein the basis function library includes a power function of a speed, a power function of a torque, and a speed-torque combination function; a particle quantity model is constructed by using the basis function library, wherein the intercept and coefficient in the particle quantity model are obtained by multivariate regression fitting, and the number of operating points used in the multivariate regression fitting is higher than a preset number;

[0106] As mentioned above, the base function library includes not only the power function of the speed and the power function of the torque, but also the speed-torque combination function. The accuracy of the particle number model constructed by adding the speed-torque combination function is significantly better than that of the scheme without the speed-torque combination function. The above conclusion is obtained through the construction of the model and the comparison with the actual particle number.

[0107] The second construction unit 82 is used to construct a particle size distribution model, which is represented by the sum of two Gaussian functions. The particle size distribution model reflects the relationship between the particle size and the number of particles. The two Gaussian functions are Gaussian distributions of the particle size.

[0108] The above-mentioned use of the sum of two Gaussian functions to represent the particle size distribution realizes the use of a model to represent the particle size distribution, thereby solving the defect of the prior art that there is a lack of a model to represent the particle size distribution.

[0109] In some embodiments, the first construction unit includes a first determining module, a second determining module and a construction module.

[0110] The first determination module is used to determine the power function of the rotation speed and the power function of the torque, including x, y, x 2 ,y 2 、x 3 and 3 , where x represents the above speed, and y represents the above torque;

[0111] The second determination module is used to determine that the speed torque combination function includes xy and x 2 y;

[0112] The construction module is used to apply the power function of the speed, the power function of the torque and the speed-torque combination function to construct the particle quantity model f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y, where β 0 represents the above intercept, β 1 ,β 2 ...β 7 represents the above coefficients.

[0113] In some embodiments, the basis function library further includes a superposition function of a cone sub-function and a Gaussian sub-function, wherein the superposition function of the cone sub-function and the Gaussian sub-function is used to correct errors in the calculation of the peak value of the number of particles.

[0114] In some embodiments, the first construction unit is further used to apply the above-mentioned basis function library including the superposition function of the above-mentioned cone sub-function and Gaussian sub-function to construct the particle quantity model as follows:

[0115] f PN (x,y)=β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 SF-DCDG, where x represents the above speed, y represents the above torque, x, y, x 2 ,y 2 、x 3 and 3 is the power function of the above speed and the power function of the above torque, xy and x 2 y is the above-mentioned speed-torque combination function, SF-DCDG is the superposition function of the above-mentioned cone sub-function and Gaussian sub-function, SF is the above-mentioned cone sub-function, and DCDG is the above-mentioned Gaussian sub-function.

[0116] That is to say, the superposition function of the cone subfunction and the Gaussian subfunction is added to β 0 +β 1 x+β 2 y+β 3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 The particle number model considering the simulation error in the PN peak area is obtained as follows:

[0117] f PN (x,y)=β 0 +β 1 x+β 2 y+β3 x 2 +β 4 y 2 +β 5 xy+β 6 x 3 +β 7 y 3 +β 7 x 2 y+β 8 SF-DCDG. Achieve more accurate prediction of the number of particulate matter. Verification found that SF-DCDG not only greatly improves the accuracy of polynomial fitting, but can also be applied to multiple models. This patent is applied to the original PN data of other models for polynomial fitting and calculation, and its calculation accuracy is still very high.

[0118] In some embodiments, the superposition function of the above-mentioned cone sub-function and Gaussian sub-function is:

[0119]

[0120] Wherein, x represents the above speed, y represents the above torque, A 1 represents the amplitude, x c and c are the center coordinates of the cone, corresponding to the speed and torque where the peak value of the number of particles is located, and a and b represent the semi-major axis and semi-minor axis of the ellipse shape, respectively;

[0121] Among them, A 2 represents the amplitude, X c2 and Y c2 They represent the center coordinates of the deformed Gaussian function, corresponding to the speed and torque corresponding to the high value of the number of particles in the high torque and high speed range, and a 2 and b 2 They are respectively determined by the extension range of the above-mentioned high value of the number of particles in the X-axis and Y-axis directions, and σ represents the standard deviation of the Gaussian subfunction.

[0122] In some embodiments, the second construction unit includes a third determination module, a fourth determination module and a fifth determination module.

[0123] The third determination module is used to determine that the first Gaussian function is

[0124] The fourth determination module is used to determine that the second Gaussian function is

[0125] The fifth determination module is used to determine the particle size distribution model as follows: Wherein, x represents the particle size of the above-mentioned particles, f(x) represents the number of the above-mentioned particles corresponding to the particle size of the above-mentioned particles, and a 1, b 1 、c 1 、a 2 , b 2 、c 2 The specific particle size distribution model is: Fit the particle size distribution under multiple conditions (more than 100), see Figure 6 It is found that the fitting results are very good (R>0.99) under all working conditions.

[0126] In some embodiments, the apparatus further comprises a first fitting unit, the first fitting unit being configured to use the basis function library comprising the power function of the speed, the power function of the torque and the speed-torque combination function to perform polynomial fitting to obtain the coefficient a 1 , b 1 、c 1 、a 2 , b 2 、c 2 .

[0127] In some embodiments, the apparatus further comprises a second fitting unit, the second fitting unit being configured to use the basis function library comprising the power function of the speed, the power function of the torque, the speed torque combination function, and the superposition function of the cone sub-function and the Gaussian sub-function to perform polynomial fitting to obtain the coefficient a 1 , b 1 、c 1 、a 2 , b 2 、c 2 , where the superposition function of the above-mentioned cone subfunction and Gaussian subfunction is used to correct the error in the calculation of the peak value of the number of particles. Under different working conditions, the particle size distribution a 1 , b 1 、c 1 、a 2 , b 2 、c 2 It changes with the speed and torque, and is obviously strongly correlated with the speed and torque. Therefore, the particle size distribution characteristics of diesel engines, which were originally difficult to find a regularity, are simplified to finding The six parameters change with speed and torque.

[0128] The above-mentioned diesel engine original exhaust particulate matter distribution model construction device includes a processor and a memory, the above-mentioned first construction unit and the second construction unit are stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0129] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the accuracy of the diesel engine original exhaust particle number model is improved by adjusting the kernel parameters, and a particle size distribution model is proposed.

[0130] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0131] An embodiment of the present invention provides an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the above-mentioned diesel engine original exhaust particulate matter distribution models.

[0132] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for constructing the diesel engine original exhaust particulate matter distribution model.

[0133] Specifically, the method for constructing the diesel engine original exhaust particulate matter distribution model includes:

[0134] Step S201, constructing a basis function library, the basis function library including a power function of a speed, a power function of a torque, and a speed-torque combination function; applying the basis function library to construct a particle quantity model, wherein the intercept and coefficient in the particle quantity model are obtained by multivariate regression fitting, and the number of operating points used in the multivariate regression fitting is higher than a preset number;

[0135] and / or,

[0136] Step S202, constructing a particle size distribution model, the particle size distribution model is represented by the sum of two Gaussian functions, the particle size distribution model reflects the relationship between the particle size and the number of particles, and the two Gaussian functions are both Gaussian distributions of the particle size.

[0137] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the method for constructing the diesel engine original exhaust particulate matter distribution model when running.

[0138] Specifically, the method for constructing the diesel engine original exhaust particulate matter distribution model includes:

[0139] Step S201, constructing a basis function library, the basis function library including a power function of a speed, a power function of a torque, and a speed-torque combination function; applying the basis function library to construct a particle quantity model, wherein the intercept and coefficient in the particle quantity model are obtained by multivariate regression fitting, and the number of operating points used in the multivariate regression fitting is higher than a preset number;

[0140] and / or,

[0141] Step S202, constructing a particle size distribution model, the particle size distribution model is represented by the sum of two Gaussian functions, the particle size distribution model reflects the relationship between the particle size and the number of particles, and the two Gaussian functions are both Gaussian distributions of the particle size.

[0142] An embodiment of the present invention provides a device, the device including a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0143] Step S201, constructing a basis function library, the basis function library including a power function of a speed, a power function of a torque, and a speed-torque combination function; applying the basis function library to construct a particle quantity model, wherein the intercept and coefficient in the particle quantity model are obtained by multivariate regression fitting, and the number of operating points used in the multivariate regression fitting is higher than a preset number;

[0144] and / or,

[0145] Step S202, construct a particle size distribution model, the above particle size distribution model is expressed as the sum of two Gaussian functions, the above particle size distribution model reflects the relationship between the particle size and the number of particles, and the two above Gaussian functions are both The device in this article can be a server, PC, PAD, mobile phone, etc.

[0146] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0147] Step S201, constructing a basis function library, the basis function library including a power function of a speed, a power function of a torque, and a speed-torque combination function; applying the basis function library to construct a particle quantity model, wherein the intercept and coefficient in the particle quantity model are obtained by multivariate regression fitting, and the number of operating points used in the multivariate regression fitting is higher than a preset number;

[0148] and / or,

[0149] Step S202: Construct a particle size distribution model. The above particle size distribution model is expressed as the sum of two Gaussian functions. The above particle size distribution model reflects the relationship between the particle size of particulate matter and the number of particulate matter. Both of the above Gaussian functions are Gaussian distributions with respect to the above particle size of particulate matter.

[0150] Obviously, those skilled in the art should understand that the various modules or steps of the present invention described above can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.

[0151] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0152] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0153] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0155] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0156] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0157] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0158] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0159] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for constructing a diesel engine exhaust particulate matter distribution model, characterized in that: include: Constructing a basis function library, wherein the basis function library includes a power function of a rotation speed, a power function of a torque, and a rotation speed and torque combination function; The particle quantity model is constructed by applying the basis function library, wherein the intercept and coefficient in the particle quantity model are obtained by multivariate regression fitting, and the number of operating points used in the multivariate regression fitting is higher than a preset number; and / or, A particle size distribution model is constructed, wherein the particle size distribution model is represented as the sum of two Gaussian functions, and the particle size distribution model reflects the relationship between the particle size and the number of particles, and the two Gaussian functions are Gaussian distributions of the particle size.

2. The method according to claim 1, characterized in that The particle quantity model is constructed by applying the basis function library, including: Determine the power function of the rotation speed and the power function of the torque including x, y, x 2 ,y 2 、x 3 and 3 ,in, x represents the rotation speed, and y represents the torque; Determine the speed torque combination function including xy and x 2 y; The particle quantity model constructed by applying the power function of the speed, the power function of the torque and the speed-torque combination function is f PN (x,y)=β0+β1x+β2y+β3x 2 +β4y 2 +β5xy+β6x 3 +β7y 3 +β7x 2 y, wherein β0 represents the intercept, and β1, β2...β7 represent the coefficients.

3. The method according to claim 1, characterized in that The basis function library also includes a superposition function of a cone sub-function and a Gaussian sub-function, wherein the superposition function of the cone sub-function and the Gaussian sub-function is used to correct the error of the calculation of the peak value of the number of particles.

4. The method according to claim 3, characterized in that The particle quantity model is constructed by applying the basis function library, including: The particle quantity model is constructed by applying the basis function library including the superposition function of the cone sub-function and the Gaussian sub-function: f PN (x,y)=β0+β1x+β2y+β3x 2 +β4y 2 +β5xy+β6x 3 +β7y 3 +β7x 2 y+β8·SF-DCDG, Wherein, x represents the rotation speed, y represents the torque, x, y, x 2 ,y 2 、x 3 and 3 is the power function of the rotation speed and the power function of the torque, xy and x 2 y is the speed-torque combination function, SF-DCDG is the superposition function of the cone sub-function and the Gaussian sub-function, SF is the cone sub-function, and DCDG is the Gaussian sub-function.

5. The method according to claim 3 or 4, characterized in that: The superposition function of the cone subfunction and the Gaussian subfunction is: Wherein, x represents the rotation speed, y represents the torque, A1 represents the amplitude, and x c and c are the center coordinates of the cone, corresponding to the speed and torque where the peak value of the number of particles is located, and a and b represent the semi-major axis and semi-minor axis of the ellipse shape, respectively; Among them, A2 represents the amplitude, X c2 and Y c2 They respectively represent the central coordinates of the deformed Gaussian function, corresponding to the speed and torque corresponding to the high value of the number of particles within the high torque and high speed operating range. a2 and b2 are determined by the expansion range of the high value of the number of particles in the X-axis and Y-axis directions, respectively. σ represents the standard deviation of the Gaussian sub-function.

6. The method according to claim 1, characterized in that A particle size distribution model is constructed, wherein the particle size distribution model is expressed as the sum of two Gaussian functions including: Determine the first Gaussian function as Determine the second Gaussian function as The particle size distribution model is determined as Among them, x represents the particle size of the particle, f(x) represents the number of the particles corresponding to the particle size of the particle, and a1, b1, c1, a2, b2, and c2 are coefficients.

7. The method according to claim 6, characterized in that The method further comprises: The basis function library including the power function of the rotational speed, the power function of the torque and the rotational speed-torque combination function is used to perform polynomial fitting to obtain coefficients a1, b1, c1, a2, b2 and c2.

8. The method according to claim 6, characterized in that The method also includes: using the basis function library including the power function of the speed, the power function of the torque, the speed-torque combination function, and the superposition function of the cone sub-function and the Gaussian sub-function to perform polynomial fitting to obtain coefficients a1, b1, c1, a2, b2, and c2, wherein the superposition function of the cone sub-function and the Gaussian sub-function is used to correct the error in the calculation of the peak value of the particle number.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for constructing a diesel engine original exhaust particulate matter distribution model according to any one of claims 1 to 8.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the construction method of the diesel engine original exhaust particulate matter distribution model as described in any one of claims 1 to 8.

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