An Online Real-time Prediction Method for Fuel Injector Spraying Law and an Evaluation Method for Nozzle Aging State

Abstract

The object of the present invention is to provide a method for online real-time prediction of fuel injector injection law and evaluation of nozzle aging state, belonging to the field of engines. First, by collecting data of excitation current, injector inlet pressure, injection rate and injection quantity under different working conditions, an injection characteristic database when not aged is established. Then, based on the calibrated mathematical model of the fuel injection system, the states of injectors under different nozzle aging degrees are simulated to generate an injection characteristic database in the case of nozzle aging. Next, an injection rate prediction model based on RNN is constructed to realize online prediction of injection characteristics in the non-aged and aged states. Finally, based on the real-time measured excitation current and pressure signals, the prediction model is used to online predict the injection rate and injection quantity, and evaluate the nozzle aging state. The present invention can realize real-time prediction of the injection characteristics of injectors working for a long time and effectively evaluate the nozzle aging state online.

Description

Technical Field

[0001] The present invention relates to an engine injection device, specifically an injection prediction and evaluation method. Background Art

[0002] With the continuous progress of engine technology and the increasingly strict environmental protection requirements, internal combustion engines are gradually developing towards a more efficient, more environmentally friendly, and more reliable direction. As one of the core technologies in the fuel supply system of modern internal combustion engines, the high-pressure fuel system is widely used in various internal combustion engines because it can achieve independent control of fuel injection pressure and injection timing. By precisely controlling the injection quantity and injection timing of the injector, the combustion efficiency of the engine can be effectively improved, emission pollution can be reduced, and the power performance of the engine can be enhanced. However, as the fuel injector works under harsh conditions such as high pressure and high temperature for a long time, its nozzle holes will gradually experience aging phenomena such as wear, carbon deposition, and blockage, which will directly affect the injection law and injection quantity of the injector, and further lead to a decrease in the combustion efficiency of the engine, an increase in emissions, and unstable operation. Therefore, during the operation of the engine, real-time monitoring and prediction of the injection law and evaluation of the nozzle hole aging state have become key technical problems to ensure the long-term stable operation of the engine.

[0003] Traditional injector state detection methods often rely on physical detection or laboratory measurement after regular shutdown, which not only consumes costs and time but also is difficult to reflect the actual working state of the injector in a timely manner. At the same time, due to the complex dynamic characteristics of the injector and the non-linear characteristics of the injector aging process, it is difficult to accurately predict the change of the injection law and the influence of nozzle hole aging using traditional model prediction methods. Summary of the Invention

[0004] The purpose of the present invention is to provide an on-line real-time prediction method for the injection law of a fuel injector and an evaluation method for the nozzle hole aging state, which can solve the problems in the prior art such as inaccurate prediction of the injection law of a long-term operating injector and difficult real-time evaluation of the nozzle hole aging state.

[0005] The purpose of the present invention is achieved as follows:

[0006] An on-line real-time prediction method for the injection law of a fuel injector and an evaluation method for the nozzle hole aging state according to the present invention are characterized by including the following steps:

[0007] (1) Collect the excitation current, injector inlet pressure, injection rate, and injection quantity of single injection, pre-main injection, and pre-main-post injection of the injector under different operating conditions of the engine, and establish an injection characteristic database when not aged;

[0008] (2) Construct a mathematical model of the fuel injection system, calibrate the model based on the injection characteristic database. In the model, change the injector nozzle hole diameter to 90%, 80%, 70%, 60%, and 50% of the original nozzle hole diameter, so as to simulate the injector under different nozzle aging conditions. Using the modified model, calculate and generate the excitation current, injector inlet pressure, injection rate, and injection volume of single injection, pre-main injection, and pre-main-post injection under different working conditions, and establish an injection characteristic database under nozzle aging conditions;

[0009] (3) Taking the excitation current and injector inlet pressure as inputs and the injection rate as the output, construct an RNN-based injection rate prediction model. Use the injection characteristic database when not aging and the injection characteristic database under nozzle aging conditions to train the prediction model simultaneously, and realize the prediction of injection characteristics before and after injector aging;

[0010] (4) Based on the excitation current signal measured in real time by the fuel injection system and the pressure signal at the injector inlet, use the injection rate prediction model established in step (3) to perform online real-time prediction of the injection rate, obtain the real-time injection volume through integration, and evaluate the nozzle aging state.

[0011] The present invention may further include:

[0012] 1. The specific different working conditions in step (1) are as follows:

[0013] In the single injection dataset, the working conditions include pressure conditions and injection pulse width conditions. The pressure conditions include all pressure conditions at intervals of 10 MPa between the idle pressure and the rated pressure. The injection pulse width conditions include all injection pulse width conditions at intervals of 0.2 ms between the minimum injection start pulse width and the rated pulse width;

[0014] The pre-main injection working conditions include pressure conditions, pre-injection pulse width conditions, pre-main injection interval conditions, and main injection pulse width conditions. The pre-main-post injection working conditions include pressure conditions, pre-injection pulse width conditions, pre-main injection interval conditions, main injection pulse width conditions, main-post injection interval conditions, and post-injection pulse width conditions. The pressure conditions include all pressure conditions at intervals of 20 MPa between the idle pressure and the rated pressure. The pre-injection pulse width conditions include all pre-injection pulse width conditions at intervals of 0.05 ms between the minimum injection start pulse width and the maximum pre-injection pulse width. The main injection pulse width conditions include all main injection pulse width conditions at intervals of 0.2 ms between the minimum injection start pulse width and the rated pulse width. The post-injection pulse width conditions include all post-injection pulse width conditions at intervals of 0.05 ms between the minimum injection start pulse width and the maximum post-injection pulse width. The pre-main injection interval conditions include all interval conditions at intervals of 1 ms between the minimum pre-main injection interval and the maximum pre-main injection interval. The main-post injection interval conditions include all interval conditions at intervals of 1 ms between the minimum main-post injection interval and the maximum main-post injection interval.

[0015] 2. The construction method of the injection rate prediction model in step (3) specifically includes:

[0016] (31) Use the exciting current and injector inlet pressure data in the database as the input data set, and the injection rate as the output data set, and perform batch normalization:

[0017]

[0018] In the formula, x * is the processed data; represents the average value of the unnormalized data; represents the data standard deviation; ε is a margin used to prevent division by zero;

[0019] (32) Construct the framework of the injection rate prediction model:

[0020] Establish an input layer, and the input X(t) is a combination of the exciting current and the injector inlet pressure:

[0021]

[0022] In the formula, I drive (t) is the exciting current of the injector at time t, and P in (t) is the value of the injector inlet pressure at time t;

[0023] Establish the hidden state of each time step of the model through a loop structure:

[0024] h t = σ(W h ·h t-1 + W x ·X(t) + b h )

[0025] In the formula, h t is the hidden state at time t, W h is the weight matrix between different hidden states, W x is the weight matrix input to the hidden state, b h is the bias vector of the hidden state, and σ is the tanh activation function;

[0026] Establish an output layer, and define the output Q inj (t) is determined by the current hidden state:

[0027] Q inj (t) = W y ·h t + b y

[0028] In the formula, Wy is the weight matrix from the hidden state to the output layer, b y is the bias vector of the output layer;

[0029] (33) Establish a loss function, defined as the mean square error between the injection rate predicted by the model and the true value:

[0030]

[0031] In the formula, N is the number of samples in the dataset, T is the length of the time series for each sample, is the injection rate predicted at time t i , is the true injection rate at time t i .

[0032] 3. The method for evaluating the aging state of the nozzle holes in step (4) specifically includes:

[0033] Taking the predicted maximum injection rate and the total injection volume and respectively taking the ratios with the maximum injection rate and the total injection volume when not aged, and using the mean value η of the two quantities total to comprehensively evaluate the aging degree of the injector nozzle holes:

[0034]

[0035] When 90% < η total ≤ 100%, the aging state is excellent; when 80% < η total ≤ 90%, the aging state is good; when 70% < η total ≤ 80%, the aging state is medium; when 60% < η total ≤ 70%, the aging state is poor; when η total ≤ 60%, the aging state is extremely poor.

[0036] The advantages of the present invention are as follows:

[0037] (1) The present invention combines the injection characteristic databases in the non-aged and aged cases, enabling real-time online prediction of the injection law under different aging states;

[0038] (2) The present invention can online evaluate the aging state of the nozzle holes without the need for shutdown detection, thereby effectively determining the performance state of the injector, reducing the maintenance cost and improving the reliability of the engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is the flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0040] The present invention will be described in more detail with reference to the accompanying drawings as follows:

[0041] Combined with Figure 1 , a method for online real-time prediction of fuel injection law and evaluation of nozzle aging state of a fuel injector according to the present invention includes the following steps:

[0042] Step S1, install a brand-new injector on the fuel injection system test bench, collect the excitation current, injector inlet pressure, injection rate and injection volume of single injection, pre-main injection, and pre-main-post injection under different working conditions, and establish an injection characteristic database when not aged.

[0043] Step S2, construct a mathematical model of the fuel injection system, calibrate the model based on the injection characteristic database. In the model, change the injector nozzle diameter to 90%, 80%, 70%, 60%, 50% of the original nozzle diameter, so as to simulate the injector under different nozzle aging conditions. Use the modified model to calculate and generate the excitation current, injector inlet pressure, injection rate and injection volume of single injection, pre-main injection, and pre-main-post injection under different working conditions, and establish an injection characteristic database under nozzle aging conditions.

[0044] Step S3, construct an RNN-based injection rate prediction model with the excitation current and injector inlet pressure as inputs and the injection rate as the output. Use the injection characteristic database when not aged and the injection characteristic database under nozzle aging conditions to train the prediction model at the same time, and realize the prediction of injection characteristics before and after the injector is aged.

[0045] Step S4, based on the excitation current signal measured in real time by the fuel injection system and the pressure signal at the injector inlet, use the injection rate prediction model established in Step S3 to perform online real-time prediction of the injection rate, obtain the real-time injection volume through integration, and evaluate the nozzle aging state.

[0046] The specific different working conditions are as follows:

[0047] In the single injection dataset, the working conditions include pressure and injection pulse width. Among them, the pressure conditions include all pressure conditions at intervals of 10 MPa between the idle pressure and the rated pressure, and the injection pulse width conditions include all injection pulse width conditions at intervals of 0.2 ms between the minimum injection start pulse width and the rated pulse width;

[0048] In the multiple injection dataset, the pre-main injection operating conditions include pressure, pre-injection pulse width, pre-main injection interval, and main injection pulse width. The pre-main-post injection operating conditions include pressure, pre-injection pulse width, pre-main injection interval, main injection pulse width, main-post injection interval, and post-injection pulse width. Among them, the pressure conditions include all pressure conditions at intervals of 20 MPa between the idle pressure and the rated pressure. The pre-injection pulse width conditions include all pre-injection pulse width conditions at intervals of 0.05 ms between the minimum start injection pulse width and the maximum pre-injection pulse width. The main injection pulse width conditions include all main injection pulse width conditions at intervals of 0.2 ms between the minimum start injection pulse width and the rated pulse width. The post-injection pulse width conditions include all post-injection pulse width conditions at intervals of 0.05 ms between the minimum start injection pulse width and the maximum post-injection pulse width. The pre-main injection interval conditions include all interval conditions at intervals of 1 ms between the minimum pre-main injection interval and the maximum pre-main injection interval. The main-post injection interval conditions include all interval conditions at intervals of 1 ms between the minimum main-post injection interval and the maximum main-post injection interval.

[0049] The construction of the injection rate prediction model in step S3 specifically includes:

[0050] Step (a), taking the exciting current and injector inlet pressure data in the database as the input dataset, taking the injection rate as the output dataset, and performing batch normalization:

[0051]

[0052] where x * is the processed data; represents the average value of the unnormalized data; represents the data standard deviation; ε is a margin used to prevent division by zero.

[0053] Step (b), constructing the framework of the injection rate prediction model.

[0054] Establish an input layer, and the input X(t) is a combination of the exciting current and the injector inlet pressure:

[0055]

[0056] where I drive (t) is the exciting current of the injector at time t, and P in (t) is the value of the injector inlet pressure at time t.

[0057] Establish the hidden state of each time step of the model through a loop structure:

[0058] h t =σ(W h ·h t-1 +W x ·X(t)+bh )

[0059] In the formula, h t is the hidden state at time t, W h is the weight matrix between different hidden states, W x is the weight matrix input to the hidden state, b h is the bias vector of the hidden state, and σ is the tanh activation function.

[0060] Build the output layer and define the output Q inj (t) is determined by the current hidden state:

[0061] Q inj (t) = W y ·h t +b y

[0062] In the formula, W y is the weight matrix from the hidden state to the output layer, b y is the bias vector of the output layer.

[0063] Step (c), establish the loss function, which is defined as the mean square error between the predicted injection rate of the model and the true value:

[0064]

[0065] In the formula, N is the number of samples in the dataset, T is the length of the time series for each sample, is the predicted injection rate at time t i , is the true injection rate at time t i .

[0066] The evaluation of the nozzle aging state in step S4 specifically includes:

[0067] Take the ratio of the predicted maximum injection rate and the total injection volume to the maximum injection rate and the total injection volume when not aged respectively, and use the mean value η of the two quantities total to comprehensively evaluate the aging degree of the injector nozzle. The formula is:

[0068]

[0069] The aging state of the nozzle is determined by η total . When 90% < η total ≤100%, the aging state is excellent; when 80% < η total ≤90%, the aging state is good; when 70% < η totalWhen η ≤ 80%, the aging state is medium; when 60% < η total ≤ 70%, the aging state is poor; when η total ≤ 60%, the aging state is extremely poor.

Claims

1. A method for online real-time prediction of fuel injector injection law and evaluation of nozzle aging state, characterized by: The following steps are involved: (1) Collect the excitation current, injector inlet pressure, injection rate and injection amount of the injector for single injection, pre-main injection and pre-main-post injection under different engine operating conditions to establish an injection characteristic database before aging; (2) Construct a mathematical model of the fuel injection system and calibrate the model based on the injection characteristic database. In the model, the injector nozzle diameter is changed to 90%, 80%, 70%, 60%, and 50% of the original nozzle diameter to simulate the injector under different nozzle aging conditions. The modified model is used to calculate the excitation current, injector inlet pressure, injection rate, and injection amount of single injection, pre-main injection, and pre-main-post injection under different working conditions, and establish an injection characteristic database under nozzle aging conditions; (3) With the excitation current and the injector inlet pressure as input and the injection rate as output, an RNN-based injection rate prediction model is constructed. The prediction model is trained simultaneously using the injection characteristic database before aging and the injection characteristic database after nozzle aging to achieve the injection characteristic prediction of the injector before and after aging. (4) Based on the excitation current signal and the pressure signal at the injector inlet measured in real time by the fuel injection system, the injection rate prediction model established in step (3) is used to perform online real-time prediction of the injection rate, and the real-time injection amount is obtained by integration to evaluate the nozzle aging status.

2. The method for online real-time prediction of fuel injector injection law and evaluation of nozzle hole aging state according to claim 1 is characterized by: The different working conditions in step (1) are specifically: In the single injection data set, the operating conditions include pressure conditions and injection pulse width conditions. The pressure conditions include all pressure conditions with an interval of 10 MPa between the idle pressure and the rated pressure. The injection pulse width conditions include all injection pulse width conditions with an interval of 0.2 ms between the minimum start-up pulse width and the rated pulse width. The pre-main injection working condition includes pressure condition, pre-injection pulse width condition, pre-main injection interval condition and main injection pulse width condition, the pre-main-post injection working condition includes pressure condition, pre-injection pulse width condition, pre-main injection interval condition, main injection pulse width condition, main-post injection interval condition and post injection pulse width condition, the pressure condition includes all pressure conditions between idle pressure and rated pressure with an interval of 20MPa, the pre-injection pulse width condition includes all pre-injection pulse width conditions between the minimum start-injection pulse width and the maximum pre-injection pulse width with an interval of 0.05ms The main-injection pulse width condition includes all main-injection pulse width conditions with an interval of 0.2ms between the minimum start-injection pulse width and the rated pulse width, the post-injection pulse width condition includes all post-injection pulse width conditions with an interval of 0.05ms between the minimum start-injection pulse width and the maximum post-injection pulse width, the pre-main-injection interval condition includes all interval conditions with an interval of 1ms between the minimum pre-main-injection interval and the maximum pre-main-injection interval, and the main-post-injection interval condition includes all interval conditions with an interval of 1ms between the minimum main-post-injection interval and the maximum main-post-injection interval.

3. The method for online real-time prediction of fuel injector injection law and evaluation of nozzle hole aging state according to claim 1 is characterized by: The method for constructing the injection rate prediction model in step (3) specifically includes: (31) The excitation current and injector inlet pressure data in the database are used as input data sets, and the injection rate is used as output data sets, and batch normalization is performed: In the formula, x * is the processed data; represents the mean of unnormalized data; represents the standard deviation of the data; ε is the margin used to prevent division by zero; (32) Constructing the framework of injection rate prediction model: The input layer is established, and the input X(t) is the combination of the excitation current and the injector inlet pressure: In the formula, I drive (t) is the excitation current of the injector at time t, P in (t) is the value of the injector inlet pressure at time t; The hidden state of each time step of the model is established through a loop structure: h t =σ(W h ·h t-1 +W x ·X(t)+b h ) In the formula, h t is the hidden state at time t, W h is the weight matrix between different hidden states, W x is the weight matrix input to the hidden state, b h is the bias vector of the hidden state, σ is the tanh activation function; Establish the output layer and define the output Q inj (t) Determined by the current implicit state: Q inj (t)=W y ·h t +b y Where W y is the weight matrix from hidden state to output layer, b y is the bias vector of the output layer; (33) A loss function is established, which is defined as the mean square error between the injection rate predicted by the model and the true value: In the formula, N is the number of samples in the data set, T is the time series length of each sample, For time t i The predicted injection rate, For time t i The actual injection rate.

4. The method for online real-time prediction of fuel injector injection law and evaluation of nozzle hole aging state according to claim 1 is characterized by: The method for evaluating the aging state of the nozzle in step (4) specifically includes: The maximum injection rate will be predicted and total injection volume The maximum injection rate before aging and total injection volume As a ratio, we use the mean of the two quantities η total Comprehensive evaluation of the aging degree of the injector nozzle: When 90%<η total When ≤100%, the aging state is excellent; when 80%<η total When ≤90%, the aging state is good; when 70%<η total When ≤80%, the aging state is medium; when 60%<η total When ≤70%, the aging state is poor; when η total When ≤60%, the aging state is extremely poor.

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