Carburetor control system and control method thereof

Abstract

This invention discloses a carburetor control system and its control method, relating to the field of carburetors, comprising: a data acquisition module for acquiring engine speed, load, intake air temperature, and intake air pressure fluctuation frequency data, and converting the data into electrical signals; and a calculation module for receiving the electrical signals and dynamically fitting the electrical signals to generate engine speed and load fuel demand coefficients and intake air temperature correction factors to calculate the target gasoline atomization amount under corresponding operating conditions. This invention can accurately calculate the target gasoline atomization amount under different operating conditions and, by querying a preset mapping table and combining proportional-integral-derivative adjustment, accurately control the opening degree and opening / closing frequency of the gasoline injection valve.

Description

Technical Field

[0001] This invention relates to the field of carburetor technology, specifically to a carburetor control system and its control method. Background Technology

[0002] A carburetor is a fuel supply device for an internal combustion engine. Its core function is to atomize gasoline, mix it with air in a certain proportion to form a combustible mixture, and deliver it to the engine's combustion chamber.

[0003] Patent application number 202510259348.0 discloses a carburetor control system. This control system is applied to a carburetor with multiple fuel intake channels. The control system includes a fuel switching switch, a fuel type detection circuit, a power detection module, and a control module. The signal input terminals of the fuel switching switch and the fuel type detection circuit are connected. The signal output terminals of the fuel type detection circuit and the power detection module are respectively connected to the signal input terminal of the control module. The signal output terminal of the control module is connected to the signal input terminal of the throttle drive motor. The fuel type detection circuit outputs a corresponding fuel type signal to the control module based on the current gear position of the fuel switching switch. The power detection module detects the current output power signal of the engine and sends it to the control module. The control module is used for... Based on the fuel type signal and the current output power signal, a corresponding throttle opening control signal is output to the drive motor to adjust the throttle opening to the target opening, so that the amount of gas output by the carburetor to the engine block through the multiple gas intake passages can meet the power demand under the current engine load. This application aims to solve the problem that "in the prior art, the carburetor of a multi-fuel engine usually directly inserts the gas intake pipe into the throat of the carburetor. Therefore, it only has one gas intake passage to supply gas, which makes it impossible to accurately match the gas intake volume according to different operating conditions, resulting in unstable engine operation. When the engine is under low load conditions, there will be excessive gas supply, excessive fuel consumption, and low fuel combustion efficiency, which is not conducive to energy saving and emission reduction. When the engine is under high load conditions, there will be insufficient gas intake, which will not meet the power demand."

[0004] However, due to their cost advantages and ease of maintenance, carburetors have not been phased out to this day, which has led to a demand for improvements in their use.

[0005] Therefore, we propose a carburetor control system and its control method. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a carburetor control system and control method, which can effectively solve the problems of the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions;

[0008] This invention discloses a carburetor control system, comprising:

[0009] The system comprises the following modules: a data acquisition module (data acquisition module), a data processing module (data acquisition module), and a data calculation module (data processing module). The data acquisition module collects engine speed, load, intake air temperature, and intake air pressure fluctuation frequency data, converting the data into electrical signals. The calculation module receives these electrical signals and dynamically fits them to generate engine speed and load fuel demand coefficients and intake air temperature correction factors to calculate the target gasoline atomization amount under the corresponding operating conditions. The control module acquires the target gasoline atomization amount and controls the actual gasoline atomization amount by adjusting the opening and closing frequency of the carburetor's built-in gasoline injection quantity regulating valve assembly. The detection module samples the gasoline atomized gas output from the carburetor to obtain parameters such as atomized gas particle size distribution and concentration uniformity, recording these as detection parameters. The adjustment module compares the detection parameters with the target atomization amount standard parameters, calculates the deviation value, generates a dynamic deviation compensation coefficient based on the deviation trend, generates an adjustment command based on the dynamic deviation compensation coefficient, and sends the adjustment command to the control module. The message module receives the output data from each module during system operation, summarizes the output data to form a carburetor control message, and uploads it synchronously to a preset cloud storage in real time.

[0010] The acquisition module is interconnected with a computing module and a control module via a network. The control module is interconnected with a detection module and an adjustment module via a network. The detection module and the adjustment module are interconnected with a message module via a network.

[0011] The network used for interconnection between modules can be any one of a local area network, a wireless network, or a Bluetooth network.

[0012] Furthermore, in the acquisition module, the engine speed is continuously acquired by a speed sensor at a preset fixed sampling period. The load is calculated by fusing the opening signal output by the throttle position sensor and the pressure signal output by the intake manifold absolute pressure sensor according to a preset weighting ratio, which is initially set to 0.6:0.4. The intake air temperature is acquired at the center position of the intake manifold inlet at a preset sampling period, and the measurement accuracy of the sensors used for acquisition is controlled within ±0.5℃. The intake pressure fluctuation frequency is obtained by acquiring the original pressure signal by a pressure sensor at a preset sampling period, and then extracting the main frequency value of the pressure signal through a 1024-point fast Fourier transform. This main frequency value is recorded as the intake pressure fluctuation frequency data.

[0013] After acquiring the raw data, the acquisition module performs adaptive Kalman filtering on the raw data to eliminate sensor noise and operating condition interference.

[0014] ;

[0015] In the formula; The filtered state estimate at time k is the engine speed, load, intake air temperature, or intake air pressure fluctuation frequency data. The state transition matrix is ​​preset according to the dynamic characteristics of engine operating conditions, and is initially set to 4×4. This is the state estimate at time k-1; The control input matrix is ​​4×1; This represents the control input at time k; The Kalman gain at time k; Let k be the sensor observation value at time k; Let $\mathbf{k}$ be the state estimation error covariance matrix at time $k-1$. for The transpose of the matrix; The process noise covariance matrix is ​​3×3. Let k be the state estimation error covariance matrix at time k; It is a 3×3 identity matrix.

[0016] Furthermore, the calculation module generates engine speed and load fuel demand coefficients and intake air temperature correction factors based on dynamic fitting of electrical signals, constructing a three-dimensional operating condition coordinate system with engine speed as the horizontal axis, load as the vertical axis, and intake air temperature as the vertical axis, wherein: the dynamic fitting of engine speed and load fuel demand coefficients follows:

[0017] ;

[0018] In the formula: These are the fitting coefficients; Engine speed; For load;

[0019] The dynamic fitting of the intake air temperature correction factor follows:

[0020] When the intake air temperature is less than or equal to 25 degrees Celsius ;

[0021] When the intake air temperature exceeds 25 degrees Celsius ;

[0022] In the formula: Intake air temperature; The correction coefficients are initially set to 1.2, 0.008, 0.8, 0.95, -0.005, and 1.05.

[0023] The calculation module then calculates the target gasoline atomization amount. , This indicates the baseline gasoline atomization quantity of the engine under standard operating conditions. Indicates the frequency of intake pressure fluctuations;

[0024] The standard operating conditions for the engine are set as follows: speed 2000 r / min, load 50%, and intake air temperature 25℃.

[0025] Furthermore, when the control module adjusts the carburetor's built-in gasoline injection quantity regulating valve group, it performs the following steps:

[0026] Determine the reference opening degree of the control valve assembly and the reference opening and closing frequency of the control valve assembly. , Indicates the target gasoline atomization amount. This indicates the injection volume per cycle of the regulating valve assembly. Indicates the number of valves. Indicates the injection efficiency of the regulating valve assembly;

[0027] Opening adjustment:

[0028] ;

[0029] Frequency adjustment:

[0030] ;

[0031] In the formula: This is the amount of fine-tuning for the opening. To set the opening degree, i.e., the reference opening degree; The actual opening degree of the valve assembly is collected in real time by a preset opening degree sensor. , , These are the proportional, integral, and derivative coefficients for adjusting the opening degree.

[0032] This is the amount of fine-tuning for the on / off frequency; To set the start / stop frequency, i.e., the reference start / stop frequency; The actual opening and closing frequency of the regulating valve group is collected in real time by a preset frequency counter; , , These are the proportional, integral, and derivative coefficients for adjusting the on / off frequency.

[0033] Furthermore, when the detection module samples the gasoline atomized gas output from the carburetor, it follows the following rules:

[0034] Six sampling ports are evenly arranged around the inner wall of the carburetor atomizing gas output pipe. Each sampling port is 15cm away from the carburetor outlet, and the inner diameter of the sampling port is set to 2mm.

[0035] During sampling, each sampling port simultaneously collects atomized gas samples, the sampling flow rate is controlled at 50 mL / min, and the sampling cycle is consistent with the adjustment cycle of the control module.

[0036] When the detection module acquires parameters for particle size distribution and concentration uniformity of atomized gas, the particle size distribution is detected using a laser diffraction particle size analyzer on the sampled atomized gas. The volume fraction percentage within the particle size distribution range is obtained, and the median volume diameter is taken as the quantitative parameter characterizing the particle size distribution. The concentration uniformity parameter is characterized by calculating the coefficient of variation of the atomized gas concentration values ​​collected from six sampling ports. The smaller the value, the better the concentration uniformity.

[0037] ;

[0038] In the formula: The coefficient of variation; The standard deviation of the concentration values ​​from the six sampling ports; It is the arithmetic mean of the concentration values ​​from the 6 sampling ports.

[0039] Furthermore, when the adjustment module compares the detection parameters with the target atomization amount standard parameters, it prioritizes presetting the target standard parameters, including: the target median particle size and the target concentration variation coefficient, and then performs the following operations:

[0040] Calculate the deviation between the test parameters and the target standard parameters:

[0041] Particle size deviation , This indicates the actual volumetric median particle size obtained by the detection module. Indicates the median particle size of the target volume;

[0042] Concentration uniformity deviation , This represents the coefficient of variation of the actual concentration obtained by the detection module. Indicates the coefficient of variation of the target concentration;

[0043] Dynamic deviation compensation coefficients are generated based on particle size deviation and concentration uniformity deviation.

[0044] ;

[0045] In the formula: This is the deviation weighting coefficient; This represents the rate of change of the sum of particle size deviation and concentration uniformity deviation; The time interval for calculating the rate of change;

[0046] in, All are greater than zero and follow the rules. ;

[0047] Based on dynamic deviation compensation coefficient Generate adjustment instructions:

[0048] when When <0.5, the command control module maintains the current adjustment parameter;

[0049] When 0.5≤ When <1.2, the instruction control module will adjust the proportional coefficient. , improve 10%;

[0050] when When ≥1.2, the reference opening degree is set. Revised to At the same time, the reference switching frequency Revised to .

[0051] Furthermore, after receiving the output data from each module, the message module classifies and categorizes the data:

[0052] Operating condition data includes: engine speed, load, intake air temperature, intake air pressure fluctuation frequency after filtering by the acquisition module, and acquisition timestamp;

[0053] Control data includes: target gasoline atomization amount, fuel demand coefficient, temperature correction factor generated by the calculation module, and the reference opening degree, reference opening and closing frequency, actual opening degree, and actual opening and closing frequency of the control module.

[0054] The detection and regulation data include: the actual volume median particle size and actual concentration variation coefficient of the detection module, and the deviation value and dynamic deviation compensation coefficient of the regulation module.

[0055] The combination of operating condition data, control data, and detection and regulation data constitutes the carburetor control message.

[0056] Furthermore, it also includes the linkage mechanism between the acquisition module and the calculation module, and the linkage mechanism between the detection module and the adjustment module;

[0057] The linkage mechanism between the data acquisition module and the computing module:

[0058] When the acquisition module detects an engine speed change rate exceeding 500... At the same time, a warning signal of sudden change in operating conditions is sent to the calculation module. After receiving the signal, the calculation module reduces the calculation cycle of dynamic fitting from 50ms to 20ms.

[0059] The linkage mechanism between the detection module and the adjustment module:

[0060] When the detection module detects a concentration variation coefficient exceeding 15% for three consecutive sampling cycles, it sends an early warning signal for uneven uniformity to the adjustment module. Upon receiving this signal, the adjustment module adjusts the dynamic deviation compensation coefficient. Calculation weights The version was increased to 0.8, and the message module was also controlled to prioritize uploading abnormal data.

[0061] On the other hand, a carburetor control method includes:

[0062] The system collects engine speed, load, intake air temperature, and intake air pressure fluctuation frequency data and converts them into electrical signals. Adaptive Kalman filtering is applied to these signals to eliminate sensor noise and operating condition interference. A three-dimensional operating condition coordinate system of engine speed, load, and intake air temperature is constructed. The fuel demand coefficient and intake air temperature correction factor are dynamically fitted, and the target gasoline atomization amount is calculated based on the baseline gasoline atomization amount and intake pressure fluctuation frequency. A preset mapping table is consulted to determine the baseline opening degree of the regulating valve group, the baseline opening and closing frequency is calculated, and the actual opening degree and opening and closing frequency are fine-tuned to control the actual gasoline atomization amount. Atomization occurs in the carburetor. A sampling port is installed around the gas output pipeline to collect samples synchronously. A laser diffraction particle size analyzer is used to detect the particle size distribution and take the median particle size. The coefficient of variation of the sampling concentration is calculated to characterize the uniformity of the atomized gas concentration. The particle size deviation and concentration uniformity deviation are calculated by comparing the detection parameters with the target standard parameters. A dynamic deviation compensation coefficient is generated based on the deviation and deviation trend. Adjustment commands are sent to the control module according to the compensation coefficient and an early warning linkage is triggered in case of abnormality. The operating condition, control, detection and adjustment data are classified and summarized to form the carburetor control message, and the message is uploaded to the preset cloud storage in real time.

[0063] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0064] This invention provides a carburetor control system and its control method. During execution, the system and method accurately collect data on engine speed, load, intake air temperature, and intake air pressure fluctuation frequency. After optimization processing to eliminate sensor noise and operating condition interference, a three-dimensional operating condition coordinate system is constructed based on the data to dynamically fit the fuel demand coefficient and intake air temperature correction factor. The system accurately calculates the target gasoline atomization amount under different operating conditions and precisely controls the opening and closing frequency of the gasoline injection valve by querying a preset mapping table and combining proportional-integral-derivative (PID) adjustment. Simultaneously, multiple points at the atomized gas output end are sampled synchronously to detect the particle size distribution and concentration uniformity of the atomized gas. Deviations are calculated by comparing with target standard parameters, and dynamic compensation coefficients are generated to adjust control parameters. The calculation cycle is shortened when operating conditions change abruptly, and compensation weights are optimized and abnormal data is prioritized when concentration uniformity is abnormal. The system can also summarize operating data in real time and upload it to the cloud, effectively improving fuel atomization accuracy and stability, adapting to various engine operating conditions, enhancing engine operating efficiency, and reducing energy consumption. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0066] Figure 1 A schematic diagram of a carburetor control system;

[0067] Figure 2 This is a flowchart illustrating a carburetor control method. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0069] The present invention will be further described below with reference to embodiments. Example

[0070] This embodiment provides a carburetor control system, such as... Figure 1 As shown, it includes:

[0071] The data acquisition module is used to collect data on engine speed, load, intake air temperature and intake air pressure fluctuation frequency, and convert the data into electrical signals.

[0072] In the data acquisition module, engine speed is continuously acquired by a speed sensor at a preset fixed sampling period. Load is calculated by fusing the opening signal output by the throttle position sensor and the pressure signal output by the intake manifold absolute pressure sensor according to a preset weighting ratio, initially set to 0.6:0.4. Intake temperature is acquired at the center position of the intake manifold inlet at a preset sampling period, and the measurement accuracy of the sensors used for acquisition is controlled within ±0.5℃. The intake pressure fluctuation frequency is obtained by acquiring the original pressure signal by a pressure sensor at a preset sampling period, and then extracting the main frequency value of the pressure signal through a 1024-point fast Fourier transform. This main frequency value is recorded as the intake pressure fluctuation frequency data.

[0073] After acquiring the raw data, the acquisition module performs adaptive Kalman filtering on the raw data to eliminate sensor noise and operating condition interference.

[0074] ;

[0075] In the formula; The filtered state estimate at time k is the engine speed, load, intake air temperature, or intake air pressure fluctuation frequency data. The state transition matrix is ​​preset according to the dynamic characteristics of engine operating conditions, and is initially set to 4×4. This is the state estimate at time k-1; The control input matrix is ​​4×1; This represents the control input at time k; The Kalman gain at time k; Let k be the sensor observation value at time k; Let $\mathbf{k}$ be the state estimation error covariance matrix at time $k-1$. for The transpose of the matrix; The process noise covariance matrix is ​​3×3. Let k be the state estimation error covariance matrix at time k; It is a 3×3 identity matrix.

[0076] Among them, the preset based on the dynamic characteristics of engine operating conditions , The value can be determined through verification using 100 sets of working conditions. , The values ​​can be obtained through bench testing and statistical analysis. ;

[0077] The above formula introduces a state transition matrix, a control input matrix, and a Kalman gain, and combines the state estimate at time k-1 with the sensor observation at time k to achieve filtering of raw data such as engine speed and load. The state transition matrix is ​​preset based on the dynamic characteristics of engine operating conditions, the control input matrix is ​​determined through verification of 100 operating conditions, and the process noise covariance matrix is ​​obtained through bench testing. It can accurately eliminate sensor noise and operating condition interference. Compared with traditional fixed filtering algorithms, it can dynamically adapt to data fluctuations under different engine operating conditions, improve the accuracy and stability of the collected data, and provide a reliable data foundation for subsequent fuel demand calculation.

[0078] The calculation module is used to receive electrical signals and dynamically fit the electrical signals to generate engine speed and load fuel demand coefficients and intake air temperature correction factors, so as to calculate the target gasoline atomization amount under the corresponding operating conditions.

[0079] The calculation module generates engine speed and load fuel demand coefficients and intake air temperature correction factors based on dynamic fitting of electrical signals. It then constructs a three-dimensional operating condition coordinate system with engine speed as the horizontal axis, load as the vertical axis, and intake air temperature as the vertical axis, where:

[0080] The dynamic fit between engine speed and load fuel demand coefficient follows:

[0081] ;

[0082] In the formula: These are the fitting coefficients; Engine speed; For load;

[0083] It should be noted that: The optimal fuel supply data were obtained by collecting no less than 800 sets of data under different speed (500 r / min intervals) and load (10% intervals) conditions through engine bench tests, and the regression fit was no less than 0.98.

[0084] The above formula constructs a two-dimensional fitting relationship with engine speed as the horizontal axis and load as the vertical axis. By introducing fitting coefficients, it realizes the dynamic calculation of fuel demand under different speeds and operating conditions. The fitting coefficients are based on the optimal fuel supply data of bench tests, obtained by least squares regression analysis, and the fitting degree is not less than 0.98. Unlike the traditional fixed fuel coefficient, which cannot adapt to multiple operating conditions, it can accurately output the corresponding fuel demand coefficient according to the real-time changes in engine speed and load, ensuring the dynamic matching of fuel supply and engine power demand.

[0085] The dynamic fitting of the intake air temperature correction factor follows:

[0086] When the intake air temperature is less than or equal to 25 degrees Celsius ;

[0087] When the intake air temperature exceeds 25 degrees Celsius ;

[0088] In the formula: Intake air temperature; The correction coefficients are initially set to 1.2, 0.008, 0.8, 0.95, -0.005, and 1.05.

[0089] in, The results were obtained through regression analysis of fuel correction demand data from pre-conducted engine tests at 5°C intervals within the range of -40°C to 80°C:

[0090] The above formula precisely adjusts the correction factor according to different intake air temperatures in the low-temperature range to meet the fuel correction requirements under normal low-temperature conditions. It dynamically adjusts the correction factor according to the slight changes in intake air temperature in the high-temperature range to accurately compensate for changes in fuel atomization efficiency caused by high temperatures. It is suitable for high-temperature conditions of specific carburetor and engine combinations to ensure the rationality of fuel supply.

[0091] The value was determined by setting test points every 5°C within the range of engine intake air temperature from -40°C to 25°C, collecting the deviation between the actual fuel correction amount and the theoretical value at each point, and using the least squares method to fit the exponential relationship between the deviation and temperature, and minimizing the sum of squared fitting errors to 1.2.

[0092] The value of is determined by using experimental data in the range of -40℃ to 25℃. Based on the slope characteristics of the deviation with temperature, a nonlinear least squares algorithm is used to fit the coefficient of the exponential term and minimize the deviation between the slope of the fitted curve and the slope of the experimental data, and the value is determined to be 0.008.

[0093] The value of is determined by fitting the constant term of the exponential function to compensate for the basic correction deviation in the low temperature range through experimental data in the range of -40℃ to 25℃, so that the theoretical value of the correction factor at 25℃ is consistent with the experimental calibration value (1.0) and is determined to be 0.8.

[0094] The value was determined by setting test points every 5°C within the range of engine intake air temperature from 25°C to 80°C, collecting the deviation between the actual fuel correction amount and the theoretical value at each point, and using the least squares method to fit the exponential relationship between the deviation and temperature, and minimizing the sum of squared fitting errors to 0.95.

[0095] The value of is determined by using experimental data in the range of 25℃ to 80℃, based on the slope characteristics of the deviation with temperature, and by using a nonlinear least squares algorithm to fit the coefficient of the exponential term and minimize the deviation between the slope of the fitted curve and the slope of the experimental data, to be -0.005.

[0096] The value of is determined by fitting the constant term of the exponential function to compensate for the basic correction deviation in the high temperature range, based on experimental data in the range of 25℃ to 80℃, so that the theoretical value of the correction factor at 25℃ is consistent with the experimental calibration value (1.0) and is determined to be 1.05.

[0097] It should be noted that: the above It is particularly suitable for the HR-200 single-chamber downdraft carburetor and 1.6L naturally aspirated four-cylinder gasoline engine (or a carburetor and engine with similar operating conditions). Its calibration process is based on actual operating data of this carburetor and engine combination within an intake air temperature range of -40℃ to 80℃, and can meet the fuel trim requirements of this specific combination under normal urban and suburban driving conditions. For other carburetor and engine models... Needs to be reset;

[0098] The calculation module then calculates the target gasoline atomization amount. , This indicates the baseline gasoline atomization quantity of the engine under standard operating conditions. This parameter represents the frequency of intake pressure fluctuations and is introduced to compensate for the impact of intake pressure fluctuations on the atomization volume requirement.

[0099] The above formula is based on the benchmark gasoline atomization amount under the standard operating conditions of the engine. It combines the speed and load fuel demand coefficient and the intake air temperature correction factor. At the same time, it introduces the intake air pressure fluctuation frequency parameter to compensate for the impact of pressure fluctuation on the atomization amount. It incorporates key influencing factors such as intake air temperature and intake pressure fluctuation into the calculation system, which can fully adapt to the atomization amount requirements of the engine under different operating conditions and ensure the accuracy and comprehensiveness of the target atomization amount calculation.

[0100] The standard operating conditions for the engine are set as follows: speed 2000 r / min, load 50%, and intake air temperature 25℃.

[0101] The control module is used to obtain the target gasoline atomization amount and control the actual gasoline atomization amount by adjusting the opening degree and opening and closing frequency of the gasoline injection quantity regulating valve group built into the carburetor.

[0102] When adjusting the carburetor's built-in gasoline injection quantity regulating valve assembly using the control module, follow these steps:

[0103] The reference opening degree of the regulating valve assembly is determined by consulting a preset "target gasoline atomization amount - reference opening degree" mapping table. This mapping table is generated by linear interpolation after experimentally measuring the actual gasoline atomization amount under at least 100 sets of different regulating valve opening degrees (0.9° intervals), and the error of the mapping table is ≤±2%. The reference opening and closing frequency of the regulating valve assembly is then determined. , Indicates the target gasoline atomization amount. This indicates the injection volume per cycle of the regulating valve assembly. Indicates the number of valves. Indicates the injection efficiency of the regulating valve assembly;

[0104] Opening adjustment:

[0105] ;

[0106] The above formula introduces proportional coefficient, integral coefficient, and derivative coefficient, and combines them with the deviation between the set opening degree and the actual opening degree to construct a PID control relationship. The proportional coefficient is determined through experiments to achieve a rapid approach of the opening degree to the set value with an overshoot of ≤5%. The integral coefficient is obtained by statistically analyzing the static deviation under stable operating conditions to calibrate the control deviation within ±0.2°. The derivative coefficient is determined by analyzing the dynamic overshoot trend to control the stabilization time within 200ms. Thus, through the synergistic effect of the three links of PID, it can not only respond quickly to the opening degree deviation, but also eliminate static errors and suppress regulation oscillations, thereby achieving precise and stable regulation of the valve opening degree.

[0107] Frequency adjustment:

[0108] ;

[0109] In the formula: This is the amount of fine-tuning for the opening. To set the opening degree, i.e., the reference opening degree; The actual opening degree of the valve assembly is collected in real time by a preset opening degree sensor. , , These are the proportional, integral, and derivative coefficients for adjusting the opening degree.

[0110] The above formula introduces proportional coefficient, integral coefficient, and derivative coefficient. Based on the deviation between the set opening and closing frequency and the actual opening and closing frequency, a PID control formula is constructed. The proportional coefficient is determined by step response test in the range of 5-30Hz to optimize the response speed. The integral coefficient is obtained by test under 20%-100% load to eliminate steady-state error and avoid integral saturation calibration. The derivative coefficient is determined by simulating sudden changes in operating conditions to suppress overshoot. This formula achieves dynamic fine-tuning of frequency through PID control, which can adapt to different load and sudden change in operating conditions, ensuring that the opening and closing frequency quickly and stably approaches the set value, and ensuring accurate control of gasoline injection quantity.

[0111] This is the amount of fine-tuning for the on / off frequency; To set the start / stop frequency, i.e., the reference start / stop frequency; The actual opening and closing frequency of the regulating valve group is collected in real time by a preset frequency counter; , , These are the proportional, integral, and derivative coefficients for adjusting the on / off frequency.

[0112] The proportional coefficient was determined by experimentally measuring the actual response speed and overshoot of the carburetor regulating valve assembly under different engine operating conditions, with the goal of achieving a rapid approach of the opening to the set value and an overshoot ≤5%. The integral coefficient is set to 2.5; it is obtained by statistically analyzing the cumulative static deviation of the regulating valve opening under stable engine operating conditions (speed fluctuation ≤ 50 r / min, load fluctuation ≤ 3%), with the goal of minimizing this static deviation (controlled within ±0.2°). The differential coefficient was set to 0.3; it was determined through experimental analysis of the overshoot trend during the dynamic change of the valve opening, with the goal of suppressing oscillations during opening adjustment and controlling the settling time of the adjustment process within 200ms. Set to 0.1;

[0113] The proportional gain was determined through step response tests on the carburetor regulating valve assembly at different target opening and closing frequencies (covering a range of 5-30Hz), with the goal of optimizing the frequency regulation response speed while ensuring no significant overshoot. Set to 3.0.

[0114] The integral coefficient is calibrated by testing the steady-state deviation of the starting and stopping frequencies under different engine loads (range 20%-100%), with the goal of eliminating steady-state errors in frequency regulation and avoiding integral saturation. Set it to 0.2.

[0115] The differential coefficients were determined experimentally by simulating the fluctuations in the starting and stopping frequency during sudden changes in engine operating conditions (such as a sudden increase / decrease in load) with the goal of suppressing overshoot during frequency regulation and improving system stability. Set to 0.08;

[0116] The detection module is used to sample the gasoline atomized gas output from the carburetor to obtain parameters such as the particle size distribution and concentration uniformity of the atomized gas, which are recorded as detection parameters.

[0117] When the detection module samples the gasoline atomized gas output from the carburetor, it follows the following rules:

[0118] Six sampling ports are evenly arranged around the inner wall of the carburetor atomizing gas output pipe. Each sampling port is 15cm away from the carburetor outlet, and the inner diameter of the sampling port is set to 2mm.

[0119] During sampling, each sampling port simultaneously collects atomized gas samples, the sampling flow rate is controlled at 50 mL / min, and the sampling cycle is consistent with the adjustment cycle of the control module.

[0120] When the detection module acquires parameters for particle size distribution and concentration uniformity of atomized gas, the particle size distribution is detected using a laser diffraction particle size analyzer on the sampled atomized gas. The volume fraction percentage within the particle size distribution range is obtained, and the median volume diameter is taken as the quantitative parameter characterizing the particle size distribution. The concentration uniformity parameter is characterized by calculating the coefficient of variation of the atomized gas concentration values ​​collected from six sampling ports. The smaller the value, the better the concentration uniformity.

[0121] ;

[0122] In the formula: The coefficient of variation; The standard deviation of the concentration values ​​from the six sampling ports; The arithmetic mean of the concentration values ​​from the six sampling ports;

[0123] The adjustment module is used to compare the detection parameters with the target atomization amount standard parameters, calculate the deviation value, generate a dynamic deviation compensation coefficient based on the deviation trend, generate an adjustment command based on the dynamic deviation compensation coefficient, and send the adjustment command to the control module.

[0124] When the adjustment module compares the detection parameters with the target atomization amount standard parameters, it prioritizes the preset target standard parameters, including: target median particle size and target concentration coefficient of variation, and then performs the following operations:

[0125] Calculate the deviation between the test parameters and the target standard parameters:

[0126] Particle size deviation , This indicates the actual volumetric median particle size obtained by the detection module. Indicates the median particle size of the target volume;

[0127] Concentration uniformity deviation , This represents the coefficient of variation of the actual concentration obtained by the detection module. Indicates the coefficient of variation of the target concentration;

[0128] Dynamic deviation compensation coefficients are generated based on particle size deviation and concentration uniformity deviation.

[0129] ;

[0130] In the formula: This is the deviation weighting coefficient; This represents the rate of change of the sum of particle size deviation and concentration uniformity deviation; The time interval for calculating the rate of change;

[0131] in, All are greater than zero and follow the rules. The initial values ​​were set to 0.8, 0.5, and 0.3.

[0132] It should be noted that: When determining their values, particle size deviation and concentration uniformity are both dimensionless parameters in percentage form, and the summation is performed.

[0133] The above formula integrates particle size deviation, concentration uniformity deviation and their rate of change, and constructs the calculation relationship of compensation coefficient through deviation weight coefficient. At the same time, the rate of change of deviation is introduced to reflect the deviation trend. By taking the deviation trend into consideration, it can dynamically adapt to the changes in deviation, generate compensation coefficients that are more in line with actual needs, provide a scientific basis for the generation of adjustment instructions, and improve the system's response and correction capabilities to deviation.

[0134] Based on dynamic deviation compensation coefficient Generate adjustment instructions:

[0135] when When <0.5, the command control module maintains the current adjustment parameter;

[0136] When 0.5≤ When <1.2, the instruction control module will adjust the proportional coefficient. , improve 10%;

[0137] when When the value is ≥1.2, the control module re-queries the "target gasoline atomization amount - reference opening degree" mapping table and sets the reference opening degree accordingly. Revised to At the same time, the reference switching frequency Revised to The message module is used to receive the output data from each module during system operation, summarize the output data to form carburetor control messages, and upload them to the preset cloud storage in real time.

[0138] After receiving the output data from each module, the message module classifies and categorizes the data:

[0139] Operating condition data includes: engine speed, load, intake air temperature, intake air pressure fluctuation frequency after filtering by the acquisition module, and acquisition timestamp;

[0140] Control data includes: target gasoline atomization amount, fuel demand coefficient, temperature correction factor generated by the calculation module, and the reference opening degree, reference opening and closing frequency, actual opening degree, and actual opening and closing frequency of the control module.

[0141] The detection and regulation data include: the actual volume median particle size and actual concentration variation coefficient of the detection module, and the deviation value and dynamic deviation compensation coefficient of the regulation module.

[0142] Among them, the combination of operating condition data, control data, and detection and regulation data constitutes the carburetor control message;

[0143] It also includes the linkage mechanism between the acquisition module and the calculation module, and the linkage mechanism between the detection module and the adjustment module;

[0144] The linkage mechanism between the data acquisition module and the computing module:

[0145] When the acquisition module detects an engine speed change rate exceeding 500... At the same time, a warning signal of sudden change in operating conditions is sent to the calculation module. After receiving the signal, the calculation module reduces the calculation cycle of dynamic fitting from 50ms to 20ms.

[0146] The linkage mechanism between the detection module and the adjustment module:

[0147] When the detection module detects a concentration variation coefficient exceeding 15% for three consecutive sampling cycles, it sends an early warning signal for uneven uniformity to the adjustment module. Upon receiving this signal, the adjustment module adjusts the dynamic deviation compensation coefficient. Calculation weights The version was improved to 0.8, and the message module was also controlled to prioritize uploading abnormal data.

[0148] The acquisition module is connected to the computing module and the control module via a network. The control module is connected to the detection module and the adjustment module via a network. The detection module and the adjustment module are connected to the message module via a network.

[0149] The network used for interconnection between modules can be any one of a local area network, a wireless network, or a Bluetooth network.

[0150] In this embodiment, the acquisition module collects data on engine speed, load, intake air temperature, and intake air pressure fluctuation frequency, converts the data into electrical signals, and the calculation module receives these electrical signals. Based on the dynamic fitting of the electrical signals, it generates engine speed and load fuel demand coefficients and intake air temperature correction factors to calculate the target gasoline atomization amount under the corresponding operating conditions. The control module then obtains the target gasoline atomization amount and controls the actual gasoline atomization amount by adjusting the opening and closing frequency of the carburetor's built-in gasoline injection quantity regulating valve group. The detection module further samples the gasoline atomized gas output by the carburetor to obtain parameters such as atomized gas particle size distribution and concentration uniformity, which are recorded as detection parameters. The adjustment module compares the detection parameters with the target atomization amount standard parameters, calculates the deviation value, and generates a dynamic deviation compensation coefficient based on the deviation trend. Based on the dynamic deviation compensation coefficient, an adjustment command is generated and sent to the control module. Finally, the message module receives the output data from each module during system operation, summarizes the output data to form a carburetor control message, and uploads it to a preset cloud storage in real time.

[0151] The system described in the above embodiments can accurately capture engine operating condition data, dynamically adapt to different operating states to calculate and adjust the gasoline atomization amount, optimize injection accuracy and frequency, and simultaneously monitor the uniformity of atomized gas particles and concentration in real time, promptly compensate for deviations, and synchronously store operating data. This makes the engine more fuel-efficient under various operating conditions, reduces fuel consumption and emissions, improves operational stability, facilitates subsequent maintenance, and provides a more sensitive response to sudden changes in operating conditions.

[0152] Example 2:

[0153] At the implementation level, based on Example 1, this example refers to... Figure 2 A further detailed description of a carburetor control system in Example 1 is provided below:

[0154] A carburetor control method, comprising:

[0155] The engine speed, load, intake air temperature and intake air pressure fluctuation frequency data are collected and converted into electrical signals. Adaptive Kalman filtering is performed on the electrical signals to eliminate sensor noise and operating condition interference.

[0156] Construct a three-dimensional coordinate system of engine speed-load-intake air temperature, dynamically fit the fuel demand coefficient and intake air temperature correction factor, and calculate the target gasoline atomization amount by combining the benchmark gasoline atomization amount and intake pressure fluctuation frequency.

[0157] The reference opening degree of the regulating valve group is determined by querying the preset mapping table, the reference opening and closing frequency is calculated, and the actual opening degree and opening and closing frequency are finely adjusted to control the actual amount of gasoline atomization.

[0158] Sampling ports are set up around the atomizing gas output pipe of the carburetor to collect samples simultaneously. The particle size distribution is detected by a laser diffraction particle size analyzer and the median particle size is taken. The sampling concentration variation coefficient is calculated to characterize the uniformity of atomizing gas concentration.

[0159] The particle size deviation and concentration uniformity deviation are calculated by comparing the detection parameters with the target standard parameters. A dynamic deviation compensation coefficient is generated based on the deviation and deviation trend. Adjustment commands are sent to the control module according to the compensation coefficient, and an early warning linkage is triggered when there is an abnormality.

[0160] The carburetor control messages are categorized and summarized into operating condition, control, and detection / adjustment data, and then uploaded to a preset cloud storage in real time.

[0161] In summary, the system and method described in the above embodiments can accurately collect data on engine speed, load, intake air temperature, and intake air pressure fluctuation frequency during execution. After optimization processing to eliminate sensor noise and operating condition interference, a three-dimensional operating condition coordinate system is constructed based on the data to dynamically fit the fuel demand coefficient and intake air temperature correction factor. The target gasoline atomization amount under different operating conditions is accurately calculated. By querying a preset mapping table and combining proportional-integral-derivative adjustment, the opening degree and opening and closing frequency of the gasoline injection valve are precisely controlled. At the same time, multiple points are sampled simultaneously at the atomized gas output end to detect the particle size distribution and concentration uniformity of the atomized gas. The deviation is calculated by comparing with the target standard parameters and a dynamic compensation coefficient is generated to adjust the control parameters. When encountering sudden changes in operating conditions, the calculation cycle is shortened. When encountering abnormal concentration uniformity, the compensation weight is optimized and abnormal data is uploaded first. The system can also summarize the operating data in real time and upload it to the cloud, effectively improving the accuracy and stability of fuel atomization, adapting to various engine operating conditions, enhancing engine operating efficiency, and reducing energy consumption.

[0162] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A carburetor control system characterized by, The method comprises the following steps: A collection module is used to collect engine speed, load, intake temperature and intake pressure fluctuation frequency data, and convert the data into electrical signals; A calculation module is used to receive the electrical signals, and generate engine speed and load fuel demand coefficient and intake temperature correction factor based on dynamic fitting of the electrical signals, to calculate the target gasoline atomization amount under the corresponding working condition; A control module is used to obtain the target gasoline atomization amount, and control the actual gasoline atomization amount by adjusting the opening degree and on-off frequency of the built-in gasoline injection amount adjusting valve group of the carburetor; A detection module is used to sample the gasoline atomization gas output by the carburetor to obtain the particle size distribution and concentration uniformity parameters of the atomization gas, which are denoted as detection parameters; An adjustment module is used to compare the detection parameters with the target atomization amount standard parameters, calculate the deviation value, generate a dynamic deviation compensation coefficient according to the deviation trend, generate an adjustment instruction based on the dynamic deviation compensation coefficient, and send the adjustment instruction to the control module; A message module is used to receive the output data in each module during system operation, and to aggregate the output data to form a carburetor control message and upload it to a preset cloud storage in real time.

2. A carburettor control system according to claim 1, wherein In the collection module, the engine speed is continuously collected by a speed sensor at a preset fixed sampling period, the load is calculated by fusing the opening degree signal output by the throttle position sensor and the pressure signal output by the intake manifold absolute pressure sensor according to a preset weighting ratio, the weighting ratio is initially set to 0.6:0.4, the intake temperature is collected at the center position of the intake manifold inlet at a preset sampling period, and the measurement accuracy of the sensor used for collection is controlled within ±0.5℃; the intake pressure fluctuation frequency is collected by a pressure sensor at a preset sampling period, and the main frequency value of the pressure signal is extracted by 1024-point fast Fourier transform after the original pressure signal is collected, which is denoted as the intake pressure fluctuation frequency data; After collecting the original data, the collection module performs adaptive Kalman filtering on the original data to eliminate sensor noise and working condition interference; ； In the formula; The filtered state estimate at time k is the engine speed, load, intake air temperature, or intake air pressure fluctuation frequency data. The state transition matrix is ​​preset according to the dynamic characteristics of engine operating conditions, and is initially set to 4×4. This is the state estimate at time k-1; The control input matrix is ​​4×1; This represents the control input at time k; The Kalman gain at time k; Let k be the sensor observation value at time k; Let $\mathbf{k}$ be the state estimation error covariance matrix at time $k-1$. for The transpose of the matrix; It is a 4×4 process noise covariance matrix; Let k be the state estimation error covariance matrix at time k; It is a 4×4 identity matrix.

3. A carburetor control system as defined in claim 1 wherein, In the calculation module, the engine speed and load fuel demand coefficient and the intake temperature correction factor are dynamically fitted based on the electrical signals, and a three-dimensional working condition coordinate system is constructed with the engine speed as the horizontal axis, the load as the vertical axis, and the intake temperature as the vertical axis, wherein: The dynamic fitting of the engine speed and the load fuel demand coefficient is subject to: ； In the formulae: are fitting coefficients; is the engine speed; is the load; The dynamic fitting of the intake temperature correction factor is subject to: When the intake air temperature is less than or equal to 25 degrees Celsius, ; When the intake air temperature exceeds 25 degrees Celsius, ; In the formula: is the intake air temperature; is a correction coefficient, initially set to 1.2, 0.008, 0.8, 0.95, -0.005, 1.05; The calculation module calculates the target gasoline atomization amount , represents the reference gasoline atomization amount of the engine under the standard operating condition, represents the intake pressure fluctuation frequency; Wherein, the standard working condition of the engine is set as: speed 2000r / min, load 50%, intake temperature 25℃.

4. A carburetor control system as defined in claim 1 wherein, When the control module adjusts the built-in gasoline injection amount adjusting valve group of the carburetor, the following steps are performed: determining a reference opening degree of the adjustment valve group, determining a reference opening / closing frequency of the adjustment valve group , denotes a target gasoline atomization amount, denotes a single-cycle injection amount of the adjustment valve group, denotes the number of valves, denotes an injection efficiency of the adjustment valve group; Opening degree adjustment: ； On-off frequency adjustment: ； In the formula: is the opening fine adjustment amount; is the set opening, i.e. the reference opening; is the actual opening of the valve group, which is collected in real time by the preset opening sensor; , , is the proportional, integral, and differential coefficient of the opening adjustment; For the opening and closing frequency fine-tuning amount; For setting the opening and closing frequency, i.e. the reference opening and closing frequency; For adjusting the actual opening and closing frequency of the valve group, which is collected in real time by the preset frequency counter; , , For the opening and closing frequency adjustment proportional, integral, and differential coefficients.

5. A carburetor control system as defined in claim 1 wherein, When the detection module samples the gasoline atomization gas output by the carburetor, it is subject to: Six sampling ports are arranged uniformly on the inner wall of the carburetor atomization gas output pipeline, each sampling port is 15cm away from the outlet of the carburetor, and the inner diameter of the sampling port is 2mm; During sampling, the sampling ports synchronously collect atomization gas samples, the sampling flow rate is controlled to be 50mL / min, and the sampling period is consistent with the adjustment period of the control module; When the detection module acquires the atomized gas particle size distribution and concentration uniformity parameters, the particle size distribution is detected by a laser diffraction particle size analyzer on the sampled atomized gas sample to acquire the volume fraction ratio in the particle size distribution interval, and the volume median particle size is taken as the quantitative parameter representing the particle size distribution, and the concentration uniformity parameter is represented by calculating the variation coefficient of the atomized gas concentration values collected by the six sampling ports, and the smaller the value, the better the concentration uniformity: ； wherein: is the coefficient of variation; is the standard deviation of the 6 port concentration values; is the arithmetic mean of the 6 port concentration values.

6. A carburettor control system according to claim 4, wherein When the adjustment module compares the detection parameters with the target atomization amount standard parameters, the target standard parameters are preferentially preset, including: target volume median particle size, target concentration variation coefficient, and the following operations are then performed: Calculate the deviation value of the detection parameters and the target standard parameters: Particle size deviation , represents the actual volume median particle size obtained by the detection module, represents the target volume median particle size; Concentration uniformity bias , represents the actual concentration variation coefficient obtained by the detection module, represents the target concentration variation coefficient; Generate a dynamic deviation compensation coefficient based on the particle size deviation and the concentration uniformity deviation: ； In the formula: is a deviation weight coefficient; represents a change rate of the sum of the particle size deviation and the concentration uniformity deviation; is a time interval for calculating the change rate; wherein are all greater than zero and subject to ; Dynamic bias compensation coefficient-based Generating conditioning instructions: When When <0.5, the instruction control module maintains the current adjustment parameter; When 0.5≤ When <1.2, the instruction control module will adjust the proportional coefficient. , improve 10%; When ≥ 1.2, the reference opening degree is corrected to , while the reference opening and closing frequency is corrected to .

7. A carburetor control system as defined in claim 1 wherein, After the message module receives the output data of each module, the data is classified and divided: The working condition class data includes: the filtered engine speed, load, intake temperature, intake pressure fluctuation frequency of the collection module, and the collection timestamp; The control class data includes: the target gasoline atomization amount generated by the calculation module, the fuel demand coefficient, the temperature correction factor, the reference opening degree of the control module, the reference opening and closing frequency, the actual opening degree, and the actual opening and closing frequency; The detection and adjustment class data includes: the actual volume median particle size of the detection module, the actual concentration variation coefficient, the deviation value of the adjustment module, and the dynamic deviation compensation coefficient; Wherein, the combination of working condition class data, control class data and detection and adjustment class data is the carburetor control message.

8. A carburetor control system as defined in claim 1 wherein, The collection module is connected with the calculation module and the control module through network interaction, the control module is connected with the detection module and the adjustment module through network interaction, and the detection module and the adjustment module are connected with the message module through network interaction; Wherein, the network used for mutual connection between the modules is any one of a local area network, a wireless network or a Bluetooth network.

9. A carburetor control method, which is a method of implementing the carburetor control system according to any one of claims 1 to 8, characterized by, Including: Collecting engine speed, load, intake temperature and intake pressure fluctuation frequency data and converting them into electrical signals, performing adaptive Kalman filtering on the electrical signals to eliminate sensor noise and working condition interference; Constructing an engine speed-load-intake temperature three-dimensional working condition coordinate system, dynamically fitting the fuel demand coefficient and the intake temperature correction factor, and combining the reference gasoline atomization amount and the intake pressure fluctuation frequency to calculate the target gasoline atomization amount; Querying a preset mapping table to determine the reference opening degree of the adjustment valve group, calculating the reference opening and closing frequency, and fine-tuning the actual opening degree and the opening and closing frequency to control the actual gasoline atomization amount; Synchronously collecting samples through the sampling ports arranged circumferentially on the carburetor atomized gas output pipeline, detecting the particle size distribution by a laser diffraction particle size analyzer, and taking the volume median particle size, and calculating the sampling concentration variation coefficient to represent the atomized gas concentration uniformity; Comparing the detection parameters with the target standard parameters to calculate the particle size deviation and the concentration uniformity deviation, generating a dynamic deviation compensation coefficient based on the deviation and the deviation trend, sending an adjustment instruction to the control module according to the compensation coefficient, and triggering an early warning linkage in an abnormal situation; Classifying and summarizing the working condition class, control class, detection and adjustment class data to form the carburetor control message, and uploading the message to a preset cloud storage in real time.

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