An injection control method for a diesel-methanol dual-fuel engine

Abstract

The present application provides an injection control method for a diesel-methanol dual-fuel engine, including: obtaining an engine speed signal, a load signal, and a temperature signal, and determining initial diesel injection timing data and initial methanol injection timing data under the current working condition in combination with a preset injection MAP diagram for each of the dual fuels; according to the initial diesel injection timing data and the initial methanol injection timing data, fusing the response times of their respective injectors, constructing a dual-fuel injection timing model under a unified control framework, and obtaining a preliminarily fused injection moment; according to the preliminarily fused injection moment and the actually required injection duration, in combination with a preset injection priority rule, obtaining optimized dual-fuel injection timing control parameters through an adaptive genetic algorithm.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to an injection control method for a diesel-methanol dual-fuel engine. Background Art

[0002] In the field of diesel-methanol dual-fuel engines, the prior art adopts a dual electronic control unit independent control strategy to drive the diesel and methanol injection systems respectively. In this architecture, complex data exchange and collaborative work are required between the two electronic control units to maintain the stability of the engine. To improve efficiency, it is planned to integrate the dual electronic control units into a single electronic control unit to control the diesel and methanol injections simultaneously. However, this integration has brought unique technical problems: the original independent diesel and methanol injection systems have their own hardware response links. After integration, how to coordinate and prioritize the injection timings of the two types of injectors in a single control loop; in addition, the feedback loop of the original diesel control system only needs to focus on the diesel system, but now it needs to consider the methanol supply at the same time, which exacerbates the complexity of the interaction between the two types of injectors. The core of this technical problem is how to ensure that the two completely different injection systems of diesel and methanol can maintain their respective injection accuracies and avoid operation fluctuations caused by injection timing conflicts when sharing a control loop under a unified control framework. Especially when the engine operating conditions change, how to make a single control system coordinate the injection priority and smoothness between the two originally independent sets of hardware actuators is a unique technical problem in this scenario. Summary of the Invention

[0003] The present invention provides an injection control method for a diesel-methanol dual-fuel engine, which mainly includes:

[0004] Obtain the engine speed signal, load signal, and temperature signal, and combine with the preset injection MAP diagrams for the two fuels to determine the initial diesel injection timing data and initial methanol injection timing data under the current working condition; according to the initial diesel injection timing data and initial methanol injection timing data, fuse the response times of their respective injectors, construct a dual-fuel injection timing model under a unified control framework, and obtain the preliminary fused injection moment; obtain the diesel injection pressure and methanol injection pressure signals, calculate the target diesel injection quantity and target methanol injection quantity using the preset injector flow model, and then determine the actual required injection duration data according to the preset differential pressure correction coefficient; according to the preliminary fused injection moment and the actual required injection duration, combine with the preset injection priority rule, and through the adaptive genetic algorithm, obtain the optimized dual-fuel injection timing control parameters; combine the optimized dual-fuel injection timing control parameters with the feedback information of the original diesel control system, calculate through the closed-loop PID algorithm, and obtain the injection timing dynamic adjustment parameters to compensate for the difference between the actual injection quantity and the expected value caused by the interaction interference generated by the supply of the two fuels; correct the current dual-fuel injection timing according to the injection timing dynamic adjustment parameters, predict the dual-fuel injection timing of the next cycle through the pre-established long short-term memory neural network model, and judge whether there is a possibility of overlap or being too close in the injection times of the two fuels. If so, fine-tune the injection start moment and duration to obtain the pre-adjusted injection timing.

[0005] The technical solutions provided in the embodiments of the present invention may include the following beneficial effects:

[0006] The present invention discloses an injection control method for a dual-fuel engine. This method determines the initial injection timing by obtaining the engine working condition signal and combining with the preset MAP diagram, and constructs a dual-fuel injection timing model under a unified control framework. Further, the target injection quantity is calculated according to the injection pressure signal, and the injection timing parameters are optimized using the adaptive genetic algorithm. The present invention also combines the closed-loop PID algorithm and the long short-term memory neural network model to realize the dynamic adjustment and prediction of the injection timing, effectively solving the interaction interference problem caused by the supply of the two fuels. This method can effectively improve the combustion efficiency and emission performance of the dual-fuel engine, and provides a new technical solution for the precise control of the dual-fuel engine. Description of the Drawings

[0007] Figure 1 It is a flowchart of an injection control method for a diesel-methanol dual-fuel engine of the present invention.

[0008] Figure 2 It is a schematic diagram of an injection control method for a diesel-methanol dual-fuel engine of the present invention.

[0009] Figure 3 It is another schematic diagram of an injection control method for a diesel-methanol dual-fuel engine of the present invention. Specific embodiments

[0010] The technical solutions in the embodiments of the present invention will be clearly and detailedly described below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.

[0011] As Figures 1 - 3 , a fuel injection control method for a diesel-methanol dual-fuel engine in this embodiment may specifically include:

[0012] Step S101, obtain the engine speed signal, load signal, and temperature signal, and combine with the preset injection MAP diagrams of the two fuels to determine the initial diesel injection timing data and the initial methanol injection timing data under the current working condition.

[0013] Obtain the engine speed signal, load signal, and temperature signal, and use them as input parameters; according to the preset injection MAP diagrams of the two fuels, look up the table using the speed, load, and temperature parameters to obtain the theoretical injection timings of diesel and methanol under the current working condition; obtain the actual injection timing data collected by the injection timing sensor; if the deviation between the actual injection timing and the theoretical injection timing exceeds the preset threshold, it is determined that the injection timing is abnormal; for the abnormal injection timing, use the Kalman filtering algorithm to filter the actual injection timing data to obtain the corrected injection timing; transmit the corrected injection timing to the electronic control unit of the injection system through the CAN bus as the initial injection timing control parameters for the diesel and methanol injectors; obtain the engine speed, load, and temperature under the historical working conditions as inputs, and the corresponding corrected injection timing as outputs, and use the BP neural network algorithm for training to establish a non-linear mapping relationship between the speed, load, temperature, and injection timing; during the operation of the engine, continuously obtain the speed, load, and temperature parameters in real time, input them into the trained BP neural network model, predict and output the target injection timings of diesel and methanol, and transmit them to the electronic control unit of the injection system to update the injection timing parameters in real time.

[0014] Exemplarily, the rotational speed signal, load signal, and temperature signal of the engine are obtained, and these three signals are regarded as the input parameters of the control system. The rotational speed signal can be obtained through a crankshaft position sensor. The load signal reflects the current output power of the engine and can be obtained by measuring the throttle opening, intake pressure, or fuel injection quantity. The temperature signal refers to the coolant temperature or exhaust temperature of the engine and is measured by a temperature sensor. For example, at a certain moment, the engine speed is 2000 revolutions per minute, the load is 60%, and the coolant temperature is 90 degrees Celsius. According to the pre-set dual-fuel injection MAP, look-up table operations are performed using the previously obtained rotational speed, load, and temperature parameters. The dual-fuel injection MAP is calibrated based on bench test data, which specifies the ideal injection timings of diesel and methanol under different operating conditions. For example, based on the above operating conditions, look up the table to obtain that the theoretical injection timing of diesel is 15 degrees before top dead center, and the theoretical injection timing of methanol is 18 degrees before top dead center. At this time, diesel and methanol are injected simultaneously to ensure atomization quality and avoid emission problems. The actual injection timing data is obtained through an injection timing sensor installed on the engine. For example, the sensor detects that the actual injection timing of diesel is 13 degrees before top dead center, and the actual injection timing of methanol is 17 degrees before top dead center. Compare the actual injection timing data with the theoretical injection timing data obtained through look-up table. If the deviation between the two exceeds the preset threshold, it is determined that the injection timing is abnormal. For example, the threshold is set to 2 degrees. Since the deviation between the actual injection timing of diesel and the theoretical value is 2 degrees, and the deviation of methanol is 1 degree, even if only the deviation of the diesel injection timing is equal to the preset threshold, it is also determined that the current injection timing is abnormal. For the determined abnormal injection timing, the Kalman filter algorithm is used to filter the actual injection timing data to obtain the corrected injection timing. The Kalman filter is an optimal estimation algorithm that can estimate a signal containing noise based on the state equation and measurement equation of the system, so as to obtain a more accurate value. This method is more reliable than taking the average value or directly using the actual injection timing, and can correct some errors caused by aging or wear. It can optimize and adjust the injection timing control in real time, and the Kalman filter does not require a long data length to achieve good results, and its real-time performance is very good. After applying the Kalman filter algorithm, the corrected injection timing of diesel is 14.5 degrees before top dead center, and that of methanol is 17.5 degrees before top dead center, which is the required final optimization result. The corrected injection timing data is sent to the ECU through the CAN bus, and these data will be used as the initial injection timing control parameters for the diesel and methanol injectors. Through the CAN bus, the program of the ECU can be quickly rewritten, and the update can be quickly completed.With the control of dual fuel, due to the change of the working medium, it will affect the calibration of the original diesel engine, which may cause the performance of the original machine to fail under the new conditions. Therefore, the calibration in the dual fuel technology needs to pay attention to the changes of both working media simultaneously to achieve the final control goal, such as maintaining the diesel substitution rate and maintaining a better atomization effect. Obtain parameters such as the engine speed, load, and temperature under historical working conditions, and these parameters will be used as the input of the neural network. At the same time, the corrected injection timing corresponding to these working conditions will be used as the output of the neural network. The BP neural network algorithm is used for training to establish a non-linear mapping relationship between the speed, load, temperature, and injection timing. This operation mainly realizes the learning effect, lays a foundation for a better control algorithm, and avoids always executing according to the original control parameters. Through a sufficient amount of historical data, the BP neural network model is trained repeatedly. When the model converges, that is, when the value of the loss function tends to be stable, a model that can accurately predict the injection timing is obtained. During training, the network continuously updates the connection weights between layers through the backpropagation of errors, can simulate the model in the actual physical scenario, and because the neural network has a large number of historical training results as the basis, it is more refined than the MAP. Using the BP neural network can achieve self-adaptation and avoid always using the MAP-based mapping in actual production and life. It can avoid the problem of large deviations in the actual injection device due to various uncontrollable reasons. For example, the decrease in oxygen content in the plateau area may cause abnormal vibration of the engine, and the injection parameters need to be adjusted in time. During the operation of the engine, the speed, load, and temperature parameters are obtained in real time, and these parameters are input into the trained BP neural network model. The model will predict and output the target injection timing of diesel and methanol under the current working conditions according to the established non-linear mapping relationship, and transmit these target injection timing data to the ECU for real-time updating of the injection timing control parameters. This can dynamically adjust the fuel injection strategy according to different engine states, achieve refined control, and reduce emissions.

[0015] Step S102: According to the initial diesel injection timing data and the initial methanol injection timing data, fuse the response times of their respective injectors to construct a dual fuel injection timing model under a unified control framework, and obtain the preliminary fused injection moment.

[0016] Obtain the target injection timing signal of the diesel injection system and the target injection timing signal of the methanol injection system; obtain the response time of the diesel injector and the response time of the methanol injector; construct an injection timing model for the dual-fuel engine according to the target injection timing signal of the diesel engine, the target injection timing signal of the methanol engine, the response time of the diesel injector, and the response time of the methanol injector; collect the operating data of the dual-fuel engine; use the operating data of the dual-fuel engine as training data and train the injection timing model of the dual-fuel engine using the sequential minimal optimization algorithm to obtain the trained injection timing model of the dual-fuel engine; calculate the initial diesel injection moment and the initial methanol injection moment according to the trained injection timing model of the dual-fuel engine; if the time difference between the initial diesel injection moment and the initial methanol injection moment is greater than a preset value, use the initial diesel injection moment and the initial methanol injection moment as the inputs of the Mamdani fuzzy control algorithm and fine-tune them through the Mamdani fuzzy control algorithm to obtain the final diesel injection moment and the final methanol injection moment; if the time difference between the initial diesel injection moment and the initial methanol injection moment is less than the preset value, use the initial diesel injection moment as the final diesel injection moment and the initial methanol injection moment as the final methanol injection moment; generate a diesel injector control signal according to the final diesel injection moment; generate a methanol injector control signal according to the final methanol injection moment; send the diesel injector control signal to the diesel injector; send the methanol injector control signal to the methanol injector.

[0017] Exemplarily, obtaining the target injection timing signals of the diesel injection system and the methanol injection system is a key step in the dual-fuel engine control system. The target injection timing signals are usually calculated by the engine control unit (ECU) according to the current operating conditions. For example, under a certain operating condition, the ECU may calculate that the target diesel injection timing is 15 degrees before top dead center, and the target methanol injection timing is 20 degrees before top dead center. Next, obtain the response times of the diesel injector and the methanol injector. The injector response time refers to the time delay from receiving the control signal to actually starting to inject. Assume that the response time of the diesel injector is 2 milliseconds and the response time of the methanol injector is 3 milliseconds. These data are usually determined through experiments and stored in the ECU. Based on the above data, construct a dual-fuel engine injection timing model. This model comprehensively considers the target injection timing and the injector response time to ensure that the actual injection moment is consistent with the target injection moment. For example, if the target diesel injection timing is 15 degrees before top dead center, considering a 2-millisecond response time, the model will send a control signal 2 milliseconds in advance. Collecting the operating data of the dual-fuel engine is the basis for model training. The operating data includes parameters such as engine speed, load, and temperature. Assume that 100 groups of data under different operating conditions are collected, and each group of data includes speed, load, temperature, and the corresponding actual injection timing. Use data preprocessing methods to clean and extract features from the operating data to obtain normalized training data. According to the training data, construct an SMO optimization objective function, set the SMO algorithm parameters, and determine the SMO iteration termination conditions, such as the KKT condition or the upper limit of the number of iterations. Use the SMO algorithm to train the timing model. In each iteration, select two variables for optimization, and update the Lagrange multipliers by solving the quadratic programming sub-problem until the iteration termination conditions are met, obtaining the trained timing prediction model. Obtain the speed, load, and temperature data under the current operating condition, and calculate the predicted injection timing through the trained timing prediction model. Compare the predicted injection timing with the actual injection timing, calculate the prediction error, and determine whether the model needs to be optimized according to the prediction error. If the prediction error exceeds the preset threshold, retrain and optimize the model; if the prediction error is within the preset threshold range, use the predicted injection timing as the basis for injection control and output the control instruction. For example, after training, the model predicts that at an engine speed of 1800 revolutions per minute, a load of 75%, and a temperature of 88 °C, the diesel injection timing is 16 degrees and the methanol injection timing is 22 degrees. According to the trained model, calculate the initial injection moments of diesel and methanol. Assume that under a certain operating condition, the model calculates that the initial diesel injection moment is 14.5 degrees before top dead center and the initial methanol injection moment is 21.5 degrees before top dead center. If the time difference between the initial diesel injection moment and the initial methanol injection moment is greater than the preset value (such as 5 degrees), fine-tuning is required. Use these two moments as the inputs of the Mamdani fuzzy control algorithm.The Mamdani fuzzy control algorithm performs reasoning through fuzzy rules and outputs the fine-tuned injection timing. For example, when the inputs are 14.5 degrees and 21.5 degrees, after being fine-tuned by the fuzzy control algorithm, the final diesel injection timing is 14.8 degrees and the methanol injection timing is 21.2 degrees. If the time difference is less than the preset value, the initial injection timing is directly adopted. For example, if the time difference is only 3 degrees, the diesel injection timing is 14.5 degrees and the methanol injection timing is 21.5 degrees. An injector control signal is generated according to the final injection timing. The control signal includes the injection timing and the injection duration. For example, when the final diesel injection timing is 14.8 degrees, a corresponding control signal is generated and sent to the diesel injector. Sending the control signal to the injector is a key step in performing the injection. After receiving the control signal, the diesel injector injects fuel at the predetermined timing. Similarly, the methanol injector also injects fuel according to the control signal. The advantage of this method is that through model training and fine-tuning of the fuzzy control algorithm, the injection timing can be controlled more precisely, improving the combustion efficiency and emission performance of the engine. Especially under complex working conditions such as cold start and sudden load changes, the system can quickly adjust the injection timing to meet the dynamic requirements of the engine. By comprehensively applying model training and fuzzy control technology, the injection timing control system of the dual-fuel engine realizes high-precision and self-adaptive injection control, significantly improving the performance and environmental protection indicators of the engine.

[0018] Step S103: Obtain the diesel injection pressure and methanol injection pressure signals, calculate the target diesel injection quantity and target methanol injection quantity by using the preset injector flow model, and then determine the actual required injection duration data according to the preset differential pressure correction coefficient.

[0019] Obtain the diesel injection pressure signal, filter the diesel injection pressure signal through a low-pass filter to obtain the filtered diesel pressure signal; according to the filtered diesel pressure signal, use the least squares method for polynomial fitting to establish a polynomial regression model between the diesel injection pressure and the injection quantity, and calculate the target diesel injection quantity; according to the target diesel injection quantity, combine the preset correspondence table of injector differential pressure and injection duration, and use linear interpolation to determine the first diesel injection duration data; if the first diesel injection duration data exceeds the allowable working range of the injector, generate an alarm signal, and at the same time limit the first diesel injection duration data within the allowable range; send the first diesel injection duration data to the injector control unit; the injector control unit uses the PID control algorithm according to the current engine speed and load condition parameters, takes the first diesel injection duration as the target value and the actual injection duration as the feedback value, adjusts the opening and closing times of the injector, and outputs the second diesel injection duration data; collect the data of the injection pressure sensor and the flow meter to obtain the actual diesel injection pressure and the actual diesel injection quantity; use the least squares method to perform polynomial fitting on the actual diesel injection pressure and the actual diesel injection quantity to obtain the corrected diesel injection pressure-injection quantity polynomial model; if the deviation between the actual diesel injection quantity and the target diesel injection quantity exceeds the preset threshold, substitute the deviation value into the objective function of the gradient descent algorithm, and correct the coefficients of the diesel injection pressure-injection quantity polynomial model through multiple iterations to minimize the objective function; according to the corrected diesel injection pressure-injection quantity polynomial model, recalculate the target diesel injection quantity and update the correspondence table of injector differential pressure and injection duration; continuously cycle the above process to make the diesel injection quantity continuously approach the target value and achieve precise control.

[0020] Exemplarily, the diesel injection pressure signal directly reflects the injection state of the injector and is crucial for the precise control of the fuel injection quantity. For example, assume that under a certain operating condition, the pressure signal measured by the diesel injection pressure sensor is 1500 kPa. Since the sensor signal may contain high-frequency noise, it is filtered through a low-pass filter to remove the high-frequency components, obtaining a smooth filtered pressure signal. Assume the filtered pressure signal is 1480 kPa. Based on the filtered diesel pressure signal, polynomial fitting is performed using the least squares method to establish a polynomial regression model between the diesel injection pressure and the fuel injection quantity. Assume the third-order polynomial model obtained by fitting with experimental data is: Fuel injection quantity = a * Pressure^3 + b * Pressure^2 + c * Pressure + d. Substitute the filtered pressure signal of 1480 kPa into the model to calculate the target diesel fuel injection quantity, assume it is 50 ml. After the target diesel fuel injection quantity is determined, combined with the preset correspondence table of the injector pressure difference and the injection duration, the linear interpolation method is used to determine the first diesel injection duration data. Assume that in the correspondence table, when the pressure difference is 100 kPa, the injection duration is 10 ms; when the pressure difference is 200 kPa, the injection duration is 15 ms. Through linear interpolation, when the pressure difference is 1480 kPa, the calculated first diesel injection duration data is 12.5 ms. If this data exceeds the allowable operating range of the injector (such as 10 - 20 ms), an alarm signal is generated and the data is limited within the allowable range, assume it is limited to 20 ms. Send the first diesel injection duration data to the injector control unit. The injector control unit, based on the current engine speed and load operating condition parameters, uses the PID control algorithm, with the first diesel injection duration as the target value and the actual injection duration as the feedback value, to adjust the opening and closing times of the injector and output the second diesel injection duration data. Assume the current speed is 1800 rpm and the load is 75%. After adjustment by the PID control algorithm, the second diesel injection duration data is 12.8 ms. Collect the data of the injection pressure sensor and the flowmeter to obtain the actual diesel injection pressure and the actual diesel fuel injection quantity. Assume the actually measured pressure is 1470 kPa and the fuel injection quantity is 49 ml. Use the least squares method to perform polynomial fitting on the actual data to obtain a corrected diesel injection pressure - fuel injection quantity polynomial model. If the deviation between the actual fuel injection quantity and the target fuel injection quantity exceeds the preset threshold (such as 1 ml), the deviation value is substituted into the objective function of the gradient descent algorithm, and the coefficients of the polynomial model are corrected through multiple iterations to minimize the objective function. The corrected model can more accurately reflect the relationship between the pressure and the fuel injection quantity, recalculate the target diesel fuel injection quantity, and update the correspondence table of the injector pressure difference and the injection duration. Assume the target fuel injection quantity calculated by the corrected model is 51 ml, update the correspondence table, and the injection duration corresponding to a pressure difference of 1480 kPa is adjusted to 13 ms. Continuously cycle the above process to make the diesel fuel injection quantity continuously approach the target value and achieve precise control.The advantages of this method are that through the comprehensive application of filtering, polynomial fitting, PID control, and gradient descent algorithm, it can effectively reduce noise interference, accurately control the fuel injection quantity, improve the combustion efficiency and emission performance of the engine. Especially under complex working conditions, the system can quickly adjust the fuel injection parameters to meet the dynamic requirements of the engine. For example, under cold start conditions, the engine temperature is low and the fuel atomization effect is poor. By accurately controlling the fuel injection quantity and injection duration, the combustion condition can be effectively improved and the emission pollutants can be reduced. When the load changes suddenly, the system can respond quickly, adjust the fuel injection parameters, and ensure the stable operation of the engine. By continuously optimizing the model and parameters, the dual-fuel engine injection control system realizes high-precision and self-adaptive fuel injection control, significantly improving the overall performance and environmental protection indicators of the engine. This precise control can not only improve fuel economy but also effectively reduce emissions, meeting the increasingly stringent environmental protection requirements.

[0021] Step S104, obtain the methanol injection pressure signal, filter the methanol injection pressure signal through a low-pass filter to obtain the filtered methanol pressure signal; according to the filtered methanol pressure signal, use the least squares method for polynomial fitting to establish a polynomial regression model between the methanol injection pressure and the injection quantity, and calculate the target methanol injection quantity; according to the target methanol injection quantity, combine the preset correspondence table of injector pressure difference and injection duration, and use linear interpolation to determine the first methanol injection duration data; if the first methanol injection duration data exceeds the allowable working range of the injector, generate an alarm signal, and at the same time limit the first methanol injection duration data within the allowable range; send the first methanol injection duration data to the injector control unit; the injector control unit uses the PID control algorithm according to the current engine speed and load condition parameters, takes the first methanol injection duration as the target value, and the actual injection duration as the feedback value, adjusts the opening and closing time of the injector, and outputs the second methanol injection duration data; collect the data of the injection pressure sensor and the flow meter to obtain the actual methanol injection pressure and the actual methanol injection quantity; use the least squares method to perform polynomial fitting on the actual methanol injection pressure and the actual methanol injection quantity to obtain the corrected methanol injection pressure-injection quantity polynomial model; if the deviation between the actual methanol injection quantity and the target methanol injection quantity exceeds the preset threshold, substitute the deviation value into the objective function of the gradient descent algorithm, and correct the coefficients of the methanol injection pressure-injection quantity polynomial model through multiple iterations to minimize the objective function; according to the corrected methanol injection pressure-injection quantity polynomial model, recalculate the target methanol injection quantity and update the correspondence table of injector pressure difference and injection duration; continuously cycle the above process to make the methanol injection quantity continuously approach the target value and achieve precise control.

[0022] Exemplarily, the methanol injection pressure signal directly reflects the injection state of the injector and is crucial for the precise control of the methanol injection quantity. For example, assume that under a certain operating condition, the pressure signal measured by the methanol injection pressure sensor is 1400 kPa. Since the sensor signal may contain high-frequency noise, it is filtered through a low-pass filter to remove the high-frequency components, obtaining a smooth filtered pressure signal. Assume the filtered pressure signal is 1390 kPa. Based on the filtered methanol pressure signal, polynomial fitting is performed using the least squares method to establish a polynomial regression model between the methanol injection pressure and the methanol injection quantity. Assume the third-order polynomial model obtained by fitting with experimental data is: Methanol injection quantity = a * Pressure^3 + b * Pressure^2 + c * Pressure + d. Substitute the filtered pressure signal of 1390 kPa into the model to calculate the target methanol injection quantity, assumed to be 40 ml. After the target methanol injection quantity is determined, in combination with the preset correspondence table of the injector pressure difference and the injection duration, the linear interpolation method is used to determine the first methanol injection duration data. Assume that in the correspondence table, when the pressure difference is 100 kPa, the injection duration is 8 ms; when the pressure difference is 200 kPa, the injection duration is 12 ms. Through linear interpolation, when the pressure difference is 1390 kPa, the calculated first methanol injection duration data is 11 ms. If this data exceeds the allowable operating range of the injector (such as 10 - 16 ms), an alarm signal is generated, and the data is limited within the allowable range, assumed to be 16 ms. The first methanol injection duration data is sent to the injector control unit. The injector control unit uses the PID control algorithm based on the current engine speed and load condition parameters, with the first methanol injection duration as the target value and the actual injection duration as the feedback value, to adjust the opening and closing times of the injector and output the second methanol injection duration data. Assume the current speed is 1800 rpm and the load is 75%. After adjustment by the PID control algorithm, the second methanol injection duration data is 11.6 ms. Collect the data of the injection pressure sensor and the flow meter to obtain the actual methanol injection pressure and the actual methanol injection quantity. Assume the actually measured pressure is 1390 kPa and the injection quantity is 38.5 ml. Use the least squares method to perform polynomial fitting on the actual data to obtain a corrected methanol injection pressure - injection quantity polynomial model. If the deviation between the actual injection quantity and the target injection quantity exceeds the preset threshold (such as 1 ml), the deviation value is substituted into the objective function of the gradient descent algorithm, and the coefficients of the polynomial model are corrected through multiple iterations to minimize the objective function. The corrected model can more accurately reflect the relationship between the pressure and the injection quantity, recalculate the target methanol injection quantity, and update the correspondence table of the injector pressure difference and the injection duration. Assume the target injection quantity calculated by the corrected model is 39 ml, and the correspondence table is updated, and the injection duration corresponding to a pressure difference of 1390 kPa is adjusted to 12 ms.Continuously loop the above process to make the methanol injection amount continuously approach the target value and achieve precise control. The advantage of this method is that through the comprehensive application of filtering, polynomial fitting, PID control, and gradient descent algorithm, it can effectively reduce noise interference, precisely control the methanol injection amount, improve the combustion efficiency and emission performance of the engine. Especially under complex working conditions, the system can quickly adjust the injection parameters to meet the dynamic requirements of the engine, and at the same time maximize the methanol injection amount on the premise of ensuring the engine performance, improving the methanol substitution rate. By continuously optimizing the model and parameters, the dual-fuel engine injection control system realizes high-precision and self-adaptive injection control, significantly improving the methanol substitution rate index and environmental protection index of the engine. This precise control can not only improve fuel economy but also effectively reduce emissions, meeting the increasingly stringent environmental requirements.

[0023] Step S105: According to the preliminarily fused injection moment and the actual required injection duration, combined with the preset injection priority rule, obtain the optimized dual-fuel injection timing control parameters through the adaptive genetic algorithm.

[0024] Obtain the preliminarily fused injection moment and the required injection duration, determine the injection amount, injection pressure, and injection times of each fuel to obtain the basic control parameters of dual-fuel injection; obtain the operating parameters of the engine's speed, load, and temperature, query the corresponding priority from the preset dual-fuel injection priority data table, sort the dual-fuel injection control parameters, and obtain the parameter sequence after priority sorting; use the sorted dual-fuel injection control parameters as the initial population of the adaptive genetic algorithm, where each individual in the population contains the injection amount, injection pressure, injection times, and injection moment genes of each fuel; construct a fitness function, with the fuel consumption and emission performance of the engine as the optimization objectives, and through selection, crossover, and mutation genetic operations, iteratively optimize the control parameters; evaluate the fitness value of the current population. If the change in the optimal fitness value for consecutive multiple iterations is less than the preset convergence threshold, output the current optimal individual as the optimal control parameter, otherwise, continue iterative optimization until the preset maximum number of iterations is reached; transmit the optimal dual-fuel injection control parameters to the control unit of the injection actuator through the CAN bus; according to the received optimal dual-fuel injection control parameters, the control unit controls the opening moment and duration of the injector to achieve precise injection of dual fuels; the control unit continuously collects the operating parameters of the engine's speed and load. If the change in the operating parameters exceeds the preset threshold within consecutive multiple working cycles, trigger the re-optimization of the control parameters, use the current parameters as the initial values, and smoothly transition to the new optimized parameters.

[0025] Exemplarily, obtaining the injection timing and required injection duration of the preliminary integration is a key step in the dual-fuel engine control system. For example, under a certain operating condition, the preliminary injection timing obtained by the sensor is 10 degrees crank angle before top dead center, the diesel injection duration is 5 ms, and the methanol injection duration is 4 ms. These basic parameters are the starting point for subsequent optimization. Determining the injection quantity, injection pressure, and injection times of each fuel is the basis for ensuring the efficient operation of the engine. Assume that according to the preliminary calculation, the target diesel injection quantity is 50 mm³, the methanol injection quantity is 40 mm³, the injection pressures are 1500 kPa and 1200 kPa respectively, and the injection times are once per cycle. These parameters directly affect the combustion efficiency and emission performance of the engine. Obtaining the operating parameters of the engine such as speed, load, and temperature is a prerequisite for dynamic adjustment. For example, the current engine speed is 3000 rpm, the load is 80%, and the temperature is 90 °C. Querying from the preset dual-fuel injection priority data table, it is known that under the current operating condition, the diesel injection priority is higher than that of methanol. Therefore, sort the injection control parameters, with the diesel parameters first and the methanol parameters second. Use the sorted dual-fuel injection control parameters as the initial population of the adaptive genetic algorithm. Each individual contains genes such as injection quantity, injection pressure, injection times, and injection timing. For example, an individual may be represented as [50 mm³, 1500 kPa, 1 time, -10 °CA, 40 mm³, 1200 kPa, 1 time, -10 °CA]. Construct a fitness function with the fuel consumption and emission performance of the engine as the optimization objectives. The fitness function may be defined as: Fitness = α * (reciprocal of fuel consumption) + β * (emission performance score), where α and β are weight coefficients. Through genetic operations such as selection, crossover, and mutation, iteratively optimize the control parameters. For example, after multiple iterations, an individual shows lower fuel consumption and better emission performance, and its fitness value is significantly improved. Evaluate the fitness values of the current population. If the change in the optimal fitness value for consecutive multiple iterations is less than the preset convergence threshold (such as 0.01), it is considered that the optimal solution has been found. For example, after 20 iterations, the optimal fitness value stabilizes at 0.95, with a change less than 0.01. At this time, output this optimal individual as the optimal control parameters. Transmit the optimal dual-fuel injection control parameters to the control unit of the injection actuator through the CAN bus. For example, the transmitted parameters are [48 mm³, 1480 kPa, 1 time, -9 °CA, 42 mm³, 1210 kPa, 1 time, -9 °CA], and the control unit precisely controls the opening time and duration of the injector according to these parameters. The control unit real-time collects the operating parameters of the engine such as speed and load. If these parameters change by more than the preset threshold (such as the speed change exceeds 200 rpm) within consecutive multiple working cycles, trigger the re-optimization of the control parameters.For example, when the rotational speed suddenly increases from 3000 rpm to 3200 rpm and the load decreases from 80% to 70%, the system takes the current parameters as the initial values, re-optimizes using the genetic algorithm, and smoothly transitions to the new optimized parameters to ensure that the engine always operates efficiently under different working conditions. In this way, the dual-fuel injection control system not only achieves high-precision injection control but also dynamically adjusts according to the real-time working conditions, significantly improving the performance and environmental protection indicators of the engine.

[0026] Step S106: Combine the optimized dual-fuel injection timing control parameters with the feedback information of the original diesel control system, and calculate through the closed-loop PID algorithm to obtain the dynamic adjustment parameters of the injection timing to compensate for the difference between the actual injection quantity and the expected value caused by the interaction interference generated by the supply of the two fuels.

[0027] Obtain the optimized dual-fuel injection timing control parameters and the feedback information of the original diesel control system, and analyze the influence of the interaction interference of the two fuel supplies on the actual injection quantity. Use the optimized control parameters and feedback information as the input of the closed-loop PID control algorithm to obtain the dynamic adjustment parameters of the injection timing. The feedback information of the original diesel control system and the optimized control parameters generate interaction interference, and the two types of interaction interference affect the actual injection quantity. Limit the dynamic adjustment parameters of the injection timing within the preset safety range. Apply the restricted adjustment parameters to the dual-fuel injection system for real-time compensation of the injection timing. Continuously obtain the feedback information of the original diesel control system during the dual-fuel injection process, input the feedback information of the original diesel control system and the optimized control parameters into the closed-loop PID control algorithm together to obtain the dynamically adjusted injection timing. Compare the deviation between the actual injection quantity and the target value, and adaptively correct the proportional, integral, and differential coefficients of the PID controller using the deviation data. Store the parameters according to the self-adaptively corrected PID controller parameters, and use the stored parameters as the initial parameters for the next dual-fuel injection timing control. Use the dual-fuel injection timing control with the initial parameters to perform injection quantity compensation through closed-loop adaptive control.

[0028] Exemplarily, obtaining the optimized dual-fuel injection timing control parameters and the feedback information of the original diesel control system is the key to ensuring the efficient operation of the dual-fuel engine. For example, under a certain working condition, the optimized control parameters are a diesel injection volume of 48 mm³, an injection pressure of 1480 kPa, an injection timing of 9 degrees of crankshaft rotation before top dead center, a methanol injection volume of 42 mm³, an injection pressure of 1210 kPa, and the injection timing is also 9 degrees of crankshaft rotation before top dead center. The feedback information of the original diesel control system includes the actual injection volume, injection pressure, injection timing, etc. Assuming the actual diesel injection volume is 47 mm³, the injection pressure is 1475 kPa, and the injection timing is 8.5 degrees of crankshaft rotation before top dead center. Analyzing the influence of the interaction interference between the two fuel supplies on the actual injection volume is to ensure the precise control of the injection volume. For example, during the injection processes of diesel and methanol, due to the slight differences in injection pressure and injection timing, there may be a deviation between the actual injection volume and the target value. Assuming the diesel injection pressure is on the low side, resulting in a decrease in the actual injection volume, and the methanol injection timing is too early, which may cause an increase in the actual injection volume. This kind of interaction interference needs to be dynamically adjusted through a closed-loop PID control algorithm. Taking the optimized control parameters and feedback information as the input of the closed-loop PID control algorithm is to obtain the dynamic adjustment parameters of the injection timing. For example, the PID controller calculates the adjustment amounts of the injection timing and injection pressure according to the deviation between the actual injection volume and the target value. Assuming the actual diesel injection volume is 1 mm³ less than the target value, the PID controller may calculate that the injection timing needs to be advanced by 0.5 degrees of crankshaft rotation and the injection pressure needs to be increased by 10 kPa. The feedback information of the original diesel control system and the optimized control parameters generate interaction interference, and the two kinds of interaction interference affect the actual injection volume. For example, the actual injection volume feedback by the original diesel control system is on the low side, while the optimized control parameters require an increase in the injection volume, and this kind of interference may cause the actual injection volume to be unstable. To ensure safety, the dynamic adjustment parameters of the injection timing are limited within a preset safety range. For example, the adjustment range of the injection timing is limited to ±1 degree of crankshaft rotation, and the adjustment range of the injection pressure is limited to ±50 kPa. Applying the restricted adjustment parameters to the dual-fuel injection system for real-time compensation of the injection timing. For example, according to the adjustment parameters calculated by the PID controller, the opening time and injection pressure of the injector are adjusted in real time to ensure that the actual injection volume is close to the target value. Continuously obtaining the feedback information of the original diesel control system during the dual-fuel injection process is to continuously optimize the injection timing. For example, the actual injection volume, injection pressure, and injection timing are collected every 100 milliseconds, and these information are input into the closed-loop PID control algorithm together with the optimized control parameters to obtain the dynamically adjusted injection timing. Comparing the deviation between the actual injection volume and the target value, and adaptively correcting the proportional, integral, and differential coefficients of the PID controller using the deviation data. For example, if the actual injection volume is continuously on the low side, the PID controller may increase the proportional coefficient to improve the response speed; if the actual injection volume fluctuates greatly, the PID controller may increase the integral coefficient to reduce the steady-state error.The storage of the PID controller parameters after adaptive correction is for use in the next dual-fuel injection timing control. For example, the corrected PID parameters are stored in the memory of the control unit as the initial parameters for the next injection control. The dual-fuel injection timing control using the initial parameters performs injection quantity compensation through closed-loop adaptive control. For example, under new operating conditions, the control unit adjusts the injection timing and injection pressure in real time according to the stored PID parameters to ensure that the actual injection quantity is consistent with the target value. This closed-loop adaptive control can not only improve the accuracy of the injection quantity but also dynamically adjust according to real-time operating conditions, significantly enhancing the performance and environmental protection indicators of the engine. In this way, the dual-fuel injection control system not only achieves high-precision injection control but also can dynamically adjust according to real-time operating conditions to ensure that the engine always operates efficiently under different operating conditions. For example, when the engine speed suddenly increases, the control system quickly adjusts the injection parameters to ensure that the injection quantity matches the new operating conditions, avoiding performance degradation and emission deterioration caused by improper injection quantity. This dynamic adjustment mechanism enables the dual-fuel engine to maintain the best combustion efficiency and emission performance under various complex operating conditions.

[0029] Step S107, dynamically adjust the parameters according to the injection timing to correct the current dual-fuel injection timing. Through a pre-established long short-term memory neural network model, predict the dual-fuel injection timing in the next cycle, and determine whether there is a possibility of overlap or being too close in the injection times of the two fuels. If so, fine-tune the injection start time and duration to obtain a pre-adjusted injection timing.

[0030] Obtain the operating condition parameters and historical operation data of the current dual-fuel engine. The operating condition parameters and historical operation data include the injection start time and duration of each fuel. Use a pre-trained long short-term memory neural network model to predict the dual-fuel injection timing in the next cycle according to the operating condition parameters and historical operation data, and obtain the predicted injection start time and duration of each fuel. Determine whether there is a situation where the injection times of the two predicted fuels partially overlap or the interval is less than a preset time threshold. If so, according to the preset adjustment rules, fine-tune the predicted injection start time and duration to obtain a pre-adjusted dual-fuel injection timing. Obtain the current actually executed dual-fuel injection timing, compare the pre-adjusted timing with the actual timing, calculate the deviation of the start time and duration of each fuel injection timing, and input the deviation as a feedback signal into the long short-term memory neural network model for parameter update. When the deviation meets the preset convergence condition, use the dual-fuel injection timing predicted by the optimized long short-term memory neural network model as the control instruction for the next cycle, and control the opening and closing times and duration of the injector through the engine control unit to achieve precise injection of dual fuels. Monitor the key parameters of the engine speed and load in real time. If they exceed the safe range, adopt a preset emergency injection strategy.

[0031] Exemplarily, obtaining the operating condition parameters and historical operation data of the current dual-fuel engine is the basis for optimizing the injection timing. The operating condition parameters include engine speed, load, temperature, etc., and the historical operation data includes the injection start time and duration in the past cycles. For example, under a certain operating condition, the engine speed is 3000 rpm, the load is 80%, and the temperature is 90 °C. The historical data shows that in the previous cycle, the diesel injection start time was 10 degrees of crankshaft rotation before top dead center, and the duration was 2 ms; the methanol injection start time was 8 degrees of crankshaft rotation before top dead center, and the duration was 1.5 ms. Using a pre-trained long short-term memory neural network model (LSTM), these data are used to predict the injection timing of the next cycle. The LSTM model can predict the injection start time and duration of future cycles by learning the patterns and trends in the historical data. For example, the model predicts that the diesel injection start time in the next cycle will be 9.5 degrees of crankshaft rotation before top dead center, and the duration will be 2.1 ms; the methanol injection start time will be 7.5 degrees of crankshaft rotation before top dead center, and the duration will be 1.6 ms. Judging whether there is an overlap or too small an interval in the predicted injection times is to avoid interference between fuels. For example, if the interval between the predicted diesel and methanol injection times is less than the preset threshold of 1 ms, adjustment is required. According to the preset rules, fine-tune the injection start time and duration. For example, advance the diesel injection start time by 0.2 degrees of crankshaft rotation and delay the methanol injection start time by 0.3 degrees of crankshaft rotation to ensure that the interval between the two is within the safe range. Obtain the actual dual-fuel injection timing currently executed and compare it with the pre-adjusted timing to calculate the deviation. For example, the actual diesel injection start time is 9.8 degrees of crankshaft rotation before top dead center, and the duration is 2.0 ms; the methanol injection start time is 7.8 degrees of crankshaft rotation before top dead center, and the duration is 1.5 ms. The calculated deviation is a 0.3-degree crankshaft rotation deviation in the diesel start time and a 0.1-ms duration deviation; a 0.3-degree crankshaft rotation deviation in the methanol start time and a 0.1-ms duration deviation. Input the deviation as a feedback signal into the LSTM model for parameter update to improve the prediction accuracy of the model. Through continuous iterative optimization, the model can more accurately predict the injection timing. For example, after multiple iterations, the predicted injection start time and duration of the model gradually approach the actual values, and the deviation is reduced to within the preset convergence condition range. When the deviation meets the convergence condition, use the injection timing predicted by the optimized LSTM model as the control instruction. Control the opening and closing times and durations of the injectors through the engine control unit (ECU) to achieve precise injection. For example, the ECU accurately controls the opening and closing of the injectors according to the parameters predicted by the model to ensure that the actual injection quantity is consistent with the target value. Real-time monitoring of key parameters such as the engine speed and load is to ensure operation safety. If the parameters exceed the safe range, adopt the preset emergency injection strategy to prevent engine damage.For example, when the engine speed suddenly exceeds 3500 rpm and the load exceeds 90%, the ECU automatically switches to the emergency injection mode, such as switching to the single-fuel injection mode, or adopting a conservative injection advance angle and injection quantity to ensure the safe operation of the engine until the parameters return to the normal range. This injection timing control method based on the LSTM model can effectively improve the performance and environmental protection indicators of the dual-fuel engine. By combining the LSTM model, real-time monitoring, and emergency strategies, the injection timing control of the dual-fuel engine not only achieves high-precision injection but also has good adaptability and safety, significantly improving the overall performance and environmental protection performance of the engine.

[0032] Although the present invention has been described in detail with general descriptions and specific embodiments above, based on the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention.

Claims

1. A diesel-methanol dual-fuel engine injection control method, characterized in that: The method comprises: The engine speed signal, load signal, and temperature signal are obtained, and combined with the preset dual-fuel respective injection MAP diagrams, the initial diesel injection timing data and the initial methanol injection timing data under the current working condition are determined; according to the initial diesel injection timing data and the initial methanol injection timing data, the respective injector response times are integrated, and a dual-fuel injection timing model under a unified control framework is constructed to obtain a preliminary fusion injection moment; the diesel injection pressure and methanol injection pressure signals are obtained, and the target diesel injection amount and the target methanol injection amount are calculated using the preset injector flow model, and then the actual required injection duration data is determined according to the preset pressure difference correction coefficient; according to the preliminary fusion injection moment and the actual required injection duration, the preset injection The optimized dual-fuel injection timing control parameters are obtained through an adaptive genetic algorithm based on the injection priority rule; the optimized dual-fuel injection timing control parameters are combined with the feedback information of the original diesel control system, and the injection timing dynamic adjustment parameters are calculated through a closed-loop PID algorithm to compensate for the difference between the actual injection amount and the expected value caused by the mutual interference generated by the two fuel supplies; the current dual-fuel injection timing is corrected according to the injection timing dynamic adjustment parameters, and the dual-fuel injection timing of the next cycle is predicted through a pre-established long short-term memory neural network model to determine whether there is a possibility that the injection time of the two fuels overlaps or is too close. If so, the injection start time and duration are fine-tuned to obtain the pre-adjusted injection timing.

2. The method according to claim 1, characterized in that The method of acquiring the engine speed signal, the load signal, and the temperature signal, and combining the preset dual-fuel respective injection MAP diagrams to determine the initial diesel injection timing data and the initial methanol injection timing data under the current working condition includes: Obtaining engine speed signal, load signal and temperature signal as input parameters; According to the preset dual-fuel injection MAP diagram, the speed, load and temperature parameters are used to look up the table to obtain the theoretical injection timing of diesel and methanol under the current working conditions; Acquire actual injection timing data collected by the injection timing sensor; If the deviation between the actual injection timing and the theoretical injection timing exceeds a preset threshold, it is judged as an injection timing abnormality; In view of the abnormal injection timing, the Kalman filter algorithm is used to filter the actual injection timing data to obtain the corrected injection timing; The corrected injection timing is transmitted to the electronic control unit of the injection system through the CAN bus as the initial injection timing control parameter of the diesel and methanol injectors; The engine speed, load and temperature under historical working conditions are obtained as input, and the corresponding corrected injection timing is used as output. The BP neural network algorithm is used for training to establish a nonlinear mapping relationship between the speed, load and temperature and the injection timing. During engine operation, the speed, load and temperature parameters are acquired in real time and input into the trained BP neural network model to predict and output the target injection timing of diesel and methanol, which are then transmitted to the electronic control unit of the injection system to update the injection timing parameters in real time.

3. The method according to claim 1, characterized in that The method of fusing the initial diesel injection timing data and the initial methanol injection timing data, fusing the respective injector response times, constructing a dual-fuel injection timing model under a unified control framework, and obtaining a preliminary fusion injection time includes: obtaining a target injection timing signal of a diesel injection system and a target injection timing signal of a methanol injection system; Get the diesel injector response time and the methanol injector response time; According to the diesel engine target injection timing signal, the methanol engine target injection timing signal, the diesel injector response time and the methanol injector response time, a dual-fuel engine injection timing model is constructed; Collect dual-fuel engine operation data; The dual-fuel engine operation data is used as training data, and a sequence minimum optimization algorithm is used to train the dual-fuel engine injection timing model to obtain a trained dual-fuel engine injection timing model; According to the trained dual-fuel engine injection timing model, the initial injection time of diesel and the initial injection time of methanol are calculated; If the time difference between the initial injection time of diesel and the initial injection time of methanol is greater than a preset value, the initial injection time of diesel and the initial injection time of methanol are used as inputs of the Mamdani fuzzy control algorithm, and the final diesel injection time and the final methanol injection time are obtained through fine-tuning of the Mamdani fuzzy control algorithm; If the time difference between the initial diesel injection time and the initial methanol injection time is less than a preset value, the initial diesel injection time is used as the final diesel injection time, and the initial methanol injection time is used as the final methanol injection time; generating a diesel injector control signal according to a final diesel injection timing; generating a methanol injector control signal according to a final methanol injection time; sending a diesel injector control signal to the diesel injector; The methanol injector control signal is sent to the methanol injector.

4. The method according to claim 1, characterized in that The method of obtaining the diesel injection pressure and methanol injection pressure signals, calculating the target diesel injection amount and the target methanol injection amount using a preset injector flow model, and then determining the actual required injection duration data according to a preset pressure difference correction coefficient includes: Acquire a diesel injection pressure signal, filter the diesel injection pressure signal through a low-pass filter, and obtain a filtered diesel pressure signal; According to the filtered diesel pressure signal, the least square method is used to perform polynomial fitting, a polynomial regression model between diesel injection pressure and injection amount is established, and the target diesel injection amount is calculated; According to the target diesel injection amount, combined with the preset correspondence table between the injector pressure difference and the injection duration, the first diesel injection duration data is determined by using a linear interpolation method; If the first diesel injection duration data exceeds the allowable operating range of the injector, an alarm signal is generated, and the first diesel injection duration data is limited to the allowable range; sending first diesel injection duration data to an injector control unit; The injector control unit uses a PID control algorithm according to the current engine speed and load condition parameters, takes the first diesel injection duration as the target value and the actual injection duration as the feedback value, adjusts the opening and closing time of the injector, and outputs the second diesel injection duration data; Collect data from the injection pressure sensor and flow meter to obtain the actual diesel injection pressure and actual diesel injection amount; The actual diesel injection pressure and the actual diesel injection amount are fitted with polynomials by using the least square method to obtain a modified diesel injection pressure-injection amount polynomial model. If the deviation between the actual diesel injection amount and the target diesel injection amount exceeds a preset threshold, the deviation value is substituted into the objective function of the gradient descent algorithm, and the coefficients of the diesel injection pressure-injection amount polynomial model are corrected through multiple iterations to minimize the objective function; According to the modified diesel injection pressure-injection quantity polynomial model, the target diesel injection quantity is recalculated, and the corresponding relationship table between the injector pressure difference and the injection duration is updated; The above process is repeated continuously, so that the diesel injection amount continues to approach the target value and precise control is achieved.

5. The method according to claim 4, characterized in that Also includes: Acquire a methanol injection pressure signal, and filter the methanol injection pressure signal through a low-pass filter to obtain a filtered methanol pressure signal; According to the filtered methanol pressure signal, a polynomial fitting is performed using the least square method to establish a polynomial regression model between the methanol injection pressure and the injection amount, and the target methanol injection amount is calculated. According to the target methanol injection amount, combined with a preset correspondence table between the injector pressure difference and the injection duration, the first methanol injection duration data is determined by using a linear interpolation method; If the first methanol injection duration data exceeds the allowable operating range of the injector, an alarm signal is generated, and the first methanol injection duration data is limited to the allowable range; sending first methanol injection duration data to an injector control unit; The injector control unit uses a PID control algorithm according to the current engine speed and load condition parameters, takes the first methanol injection duration as the target value and the actual injection duration as the feedback value, adjusts the opening and closing time of the injector, and outputs the second methanol injection duration data; Collect data from the injection pressure sensor and flow meter to obtain actual methanol injection pressure and actual methanol injection amount; The actual methanol injection pressure and the actual methanol injection amount are fitted with polynomials by using the least square method to obtain a modified methanol injection pressure-injection amount polynomial model. If the deviation between the actual methanol injection amount and the target methanol injection amount exceeds a preset threshold, the deviation value is substituted into the objective function of the gradient descent algorithm, and the coefficients of the methanol injection pressure-injection amount polynomial model are corrected through multiple iterations to minimize the objective function; According to the modified methanol injection pressure-injection amount polynomial model, the target methanol injection amount is recalculated, and the corresponding relationship table between the injector pressure difference and the injection duration is updated; The above process is repeated continuously, so that the methanol injection amount continues to approach the target value and precise control is achieved.

6. The method according to claim 1, characterized in that The optimized dual fuel injection timing control parameters are obtained by an adaptive genetic algorithm based on the preliminary fusion injection time and the actual required injection duration, combined with the preset injection priority rule, including: Obtain the initial fusion injection time and the required injection duration, determine the injection amount, injection pressure and injection number of each fuel, and obtain the basic control parameters of dual fuel injection; Obtaining the speed, load and temperature operating parameters of the engine, querying the corresponding priority from a preset dual-fuel injection priority data table, sorting the dual-fuel injection control parameters, and obtaining a priority-sorted parameter sequence; The sorted dual-fuel injection control parameters are used as the initial population of the adaptive genetic algorithm, wherein each individual in the population contains the injection amount, injection pressure, injection number and injection time gene of each fuel; Construct a fitness function that takes the fuel consumption and emission performance of the engine as the optimization target and iteratively optimizes the control parameters through selection, crossover and mutation genetic operations; Evaluate the fitness value of the current population. If the change in the optimal fitness value of multiple consecutive iterations is less than the preset convergence threshold, the current optimal individual is output as the optimal control parameter. Otherwise, continue iterative optimization until the preset maximum number of iterations is reached. Transmitting optimal dual fuel injection control parameters to a control unit of an injection actuator via a CAN bus; According to the received optimal dual-fuel injection control parameters, the control unit controls the opening time and duration of the injector to achieve accurate dual-fuel injection; The control unit collects the engine's speed and load operating parameters in real time. If the operating parameter changes exceed the preset threshold within multiple consecutive working cycles, it triggers the re-optimization of the control parameters, uses the current parameters as the initial values, and smoothly transitions to the new optimized parameters.

7. The method according to claim 6, characterized in that The optimized dual-fuel injection timing control parameters are combined with the feedback information of the original diesel control system, and the injection timing dynamic adjustment parameters are calculated by a closed-loop PID algorithm to compensate for the difference between the actual injection amount and the expected value caused by the mutual interference generated by the two fuel supplies, including: Obtain optimized dual-fuel injection timing control parameters and original diesel control system feedback information, and analyze the impact of the mutual interference of the two fuel supplies on the actual injection quantity; The optimized control parameters and feedback information are used as inputs of the closed-loop PID control algorithm to obtain the dynamic adjustment parameters of the injection timing; The original diesel control system feedback information and the optimized control parameters produce mutual interference, and the two mutual interferences affect the actual injection amount; Limit the dynamic adjustment parameters of the injection timing to a preset safety range; Applying the limited post-adjustment parameters to the dual fuel injection system to perform real-time compensation of injection timing; Continuously obtain the original diesel control system feedback information during the dual-fuel injection process, and input the original diesel control system feedback information and the optimized control parameters into the closed-loop PID control algorithm to obtain the injection timing after dynamic adjustment; Compare the deviation between the actual injection amount and the target value, and use the deviation data to adaptively correct the proportional, integral and differential coefficients of the PID controller; The PID controller parameters after the adaptive correction are stored, and the stored parameters are used as initial parameters for the next dual fuel injection timing control; The dual-fuel injection timing control with initial parameters is adopted to compensate the injection quantity through closed-loop adaptive control.

8. The method according to claim 1, characterized in that The method of dynamically adjusting the parameters according to the injection timing to correct the current dual-fuel injection timing, predicting the dual-fuel injection timing of the next cycle through the pre-established long short-term memory neural network model, and judging whether there is a possibility that the injection times of the two fuels overlap or are too close, and if so, fine-tuning the injection start time and duration to obtain the pre-adjusted injection timing, includes: Acquire the current operating parameters and historical operating data of the dual-fuel engine, wherein the operating parameters and historical operating data include the injection start time and duration of each fuel; Using a pre-trained long short-term memory neural network model, based on operating parameters and historical operating data, the injection timing of the dual fuels in the next cycle is predicted to obtain the predicted injection start time and duration of each fuel; Determine whether the predicted injection times of the two fuels partially overlap or the interval is less than a preset time threshold, and if so, fine-tune the predicted injection start time and duration according to a preset adjustment rule to obtain a pre-adjusted dual-fuel injection timing; Obtain the currently actually executed dual fuel injection timing, compare the pre-adjusted timing with the actual timing, calculate the start time and duration deviation of each fuel injection timing, and input the deviation as a feedback signal into the long short-term memory neural network model for parameter update; When the deviation meets the preset convergence condition, the dual-fuel injection timing predicted by the optimized long short-term memory neural network model is used as the control instruction for the next cycle, and the opening and closing time and duration of the injector are controlled by the engine control unit to achieve accurate dual-fuel injection; The engine speed and load key parameters are monitored in real time. If they exceed the safe range, the preset emergency injection strategy is adopted.

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