Marine low zero carbon fuel engine lean burn emission cooperative control method and system

Abstract

The invention relates to a cooperative control method and system for lean-burn emission of a marine low-zero carbon fuel engine. The method comprises the steps that real-time in-cylinder pressure and temperature data are collected, and an in-cylinder pressure curve and an in-cylinder temperature curve are obtained through time sequence alignment, filtering denoising and interpolation fitting processing; extracting time sequence data based on the curve, calculating average effective pressure and peak temperature in a cylinder to determine a current load interval, and marking a risk tag sequence; calculating the generation rate of the nitrogen oxides and the particulate matters by matching with reaction condition parameters, and extracting reverse variation characteristics to obtain strong mutual exclusion correlation of the nitrogen oxides and the particulate matters; and based on the associated starting model, predicting and controlling the output oil injection quantity and the air inflow adjusting quantity, calculating the transient time sequence lag quantity through a dynamic lag model, and after superposition, calibrating through a combustion phase matching algorithm to generate an oil injection timing and air inlet valve opening control instruction after transient adjustment. The method is adaptive to the combustion characteristics of low-zero-carbon fuel, emission contradictions are accurately captured, transient emission peak values are avoided, and multi-pollutant collaborative control is achieved.

Description

Technical Field

[0001] This invention belongs to the field of emission control technology, and in particular relates to a method and system for coordinated control of lean combustion emissions from marine low-zero carbon fuel engines. Background Technology

[0002] As the global shipping industry accelerates its low-carbon transformation, the application of low-zero carbon fuels such as ammonia and methanol in marine engines has become a core pathway to achieving emission reduction targets. Lean-burn technology, due to its ability to improve fuel economy and reduce carbon emissions, has become a key combustion mode suitable for low-zero carbon fuel engines. However, under lean-burn conditions in marine low-zero carbon fuel engines, nitrogen oxides (NOx)... x There is a significant trade-off between NOx emissions and particulate matter (PM) emissions. Furthermore, low-zero carbon fuels possess unique characteristics such as slow flame propagation speed, high latent heat of vaporization, and delayed combustion, further exacerbating problems like localized lean misfires and localized high-temperature rich combustion, making it difficult to control emission peaks under transient operating conditions. Simultaneously, existing emission control methods are mostly designed for single pollutants or traditional internal combustion engines, lacking adaptability to the combustion characteristics of low-zero carbon fuels and the coupling mechanisms of multiple pollutants, making it difficult to achieve NOx emission control. x The synergistic compliance with PM and hydrocarbon standards is insufficient to meet the increasingly stringent emission regulations of the International Maritime Organization (IMO). Summary of the Invention

[0003] Therefore, it is necessary to provide a method and system for coordinated control of lean combustion emissions from marine low-zero carbon fuel engines that can effectively avoid emission peaks under transient operating conditions and achieve coordinated control of multiple pollutants, in response to the above-mentioned technical problems.

[0004] Firstly, this application provides a method for coordinated control of lean-burn emissions from marine low-zero carbon fuel engines, including:

[0005] Real-time in-cylinder pressure and temperature data are collected and interpolated to obtain the in-cylinder pressure and temperature curves for the combustion process.

[0006] The mean effective pressure and peak temperature in the cylinder are calculated based on the in-cylinder pressure curve and the in-cylinder temperature curve. The current load range is obtained, the combustion zone type to which the current load range belongs is determined, and a risk label sequence is marked.

[0007] By combining the risk label sequence to calculate the nitrogen oxide generation rate and particulate matter generation rate, a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions was obtained.

[0008] Based on strong mutual exclusion correlation, the timing lag in the transient process switching is calculated by a dynamic lag model. The timing lag is then calibrated by a combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command.

[0009] In one embodiment, the mean effective pressure and peak temperature in the cylinder are calculated based on the in-cylinder pressure curve and the in-cylinder temperature curve to obtain the current load range, determine the combustion zone type to which the current load range belongs, and mark a risk label sequence, including:

[0010] Time series data are extracted based on the in-cylinder pressure curve and the in-cylinder temperature curve to obtain in-cylinder pressure time series data and in-cylinder temperature time series data.

[0011] The cylinder pressure time series data is integrated over the engine working cycle to obtain the cylinder average effective pressure.

[0012] The maximum value in the time series data of in-cylinder temperature is extracted to obtain the peak in-cylinder temperature.

[0013] The current load range is determined based on the average effective pressure inside the cylinder, according to the preset load range division threshold.

[0014] The combustion uniformity index is calculated based on the cyclic fluctuation rate or pressure peak variation coefficient of the in-cylinder pressure curve.

[0015] If the current load range is higher than the preset high load threshold and the combustion uniformity index is lower than the preset uniformity threshold, it is determined to be a high load low uniformity combustion area, and the risk of local high temperature and rich combustion is marked.

[0016] If the current load range is lower than the preset medium load threshold and the combustion uniformity index is higher than the uniformity threshold, it is determined to be a medium-low load high uniformity combustion region, and the risk of local lean combustion is marked.

[0017] The combustion zone type is associated with the corresponding locally lean risk or locally high-temperature fuel-rich risk, and the corresponding risk label sequence is output.

[0018] In one embodiment, the combustion uniformity index is calculated using the following formula:

[0019]

[0020]

[0021]

[0022] in, Indicators of combustion uniformity Indicates engine continuous The coefficient of variation of peak in-cylinder pressure per working cycle Indicates engine continuous The coefficient of variation of the slope of the cylinder pressure rise over each working cycle. , This represents the weighting coefficient, calibrated according to the type of low-to-zero carbon fuel and the engine model. This indicates the number of loops. Indicates the first Peak in-cylinder pressure per working cycle express Average peak value of in-cylinder pressure over one working cycle Indicates the first The slope of the cylinder pressure rise over each working cycle is the rate at which the cylinder pressure rises from the minimum pressure of the cycle to... During the process, the ratio of the pressure change to the corresponding crankshaft angle change, express The average slope of the cylinder pressure rise over each working cycle.

[0023] In one embodiment, the formation rates of nitrogen oxides and particulate matter are calculated by combining the risk label sequence to obtain a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions, including:

[0024] Obtain the combustion state characteristics and reaction condition parameters corresponding to the risk label sequence at different time points.

[0025] The combustion state characteristics include in-cylinder pressure time series data, in-cylinder temperature time series data, and in-cylinder peak temperature.

[0026] The reaction condition parameters include air-fuel ratio, EGR rate, and injection timing parameters.

[0027] Combustion state characteristics and reaction condition parameters are input into a pre-trained emission generation rate prediction model to calculate the nitrogen oxide generation rate sequence and particulate matter generation rate sequence.

[0028] The emission generation rate prediction model was trained based on combustion-emission sample data of low-zero carbon fuel engines under different combustion conditions.

[0029] The temporal negative correlation coefficient between the nitrogen oxide formation rate sequence and the particulate matter formation rate sequence was calculated, and the strength of the inverse relationship was obtained by combining the instantaneous variation amplitude weight of the sequence.

[0030] If the strength of the trade-off exceeds the preset mutual exclusion threshold, then a strong mutual exclusion relationship is determined between nitrogen oxide emission control and particulate matter emission control; the mutual exclusion threshold is obtained based on regulatory emission limits and engine operating condition adaptability calibration.

[0031] In one embodiment, the nitrogen oxide generation rate sequence and the particulate matter generation rate sequence are calculated using the following formula:

[0032]

[0033]

[0034] in, Indicates the first time Generation rate, express Generate basic coefficients based on the characteristics of low- to zero-carbon fuels. Indicates the first Instantaneous temperature inside the cylinder at all times. Indicates the first time Efficiency Indicates the first air-fuel ratio at all times Indicates the first time Generation rate, express Generate basic coefficients, Indicates the stoichiometric air-fuel ratio of low- to zero-carbon fuels. Indicates the first The uniformity of combustion at all times Indicates the first Fuel injection timing offset.

[0035] In one embodiment, the timing lag during transient process switching is calculated using a dynamic lag model based on strong mutual exclusion correlation. The timing lag is then calibrated using a combustion phase matching algorithm to obtain transiently adjusted injection timing control commands and intake valve opening control commands, including:

[0036] After determining that there is a strong mutually exclusive correlation between nitrogen oxides and particulate matter emissions, the fuel injection quantity adjustment and intake air quantity adjustment are calculated based on combustion state characteristics, reaction condition parameters, and the strength of the trade-off relationship.

[0037] Based on the combustion ignition delay and flame propagation speed in the combustion state characteristics, the time lag in the transient process switching is calculated by a dynamic lag prediction model.

[0038] The dynamic lag prediction model was trained based on transient test data of a low-zero carbon fuel engine.

[0039] The timing lag is superimposed on the fuel injection quantity adjustment and the intake air quantity adjustment respectively, and calibrated by the combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command.

[0040] By using injection timing control commands to drive the injectors to perform transient injection timing adjustments, the in-cylinder fuel atomization and fuel-air mixing effects are optimized.

[0041] The intake valve opening is adjusted by using intake valve opening control commands to match the intake volume with the adjusted fuel injection timing, thereby calibrating the combustion phase in the cylinder.

[0042] The adjusted in-cylinder combustion phase is monitored in real time and compared with the preset target combustion phase. If the deviation exceeds the preset phase deviation threshold, the combustion phase is determined to be mismatched.

[0043] The updated fuel injection quantity adjustment and intake air quantity adjustment are recalculated based on the phase deviation threshold. The updated fuel injection quantity adjustment and intake air quantity adjustment are then superimposed with the timing lag and calibrated by the combustion phase matching algorithm to obtain the optimized fuel injection timing control command and intake valve opening control command.

[0044] Secondly, this application also provides a lean-burn emission co-control system for marine low-zero carbon fuel engines, the system including:

[0045] The in-cylinder data acquisition module is used to collect real-time in-cylinder pressure data and real-time in-cylinder temperature data, and obtains the in-cylinder pressure curve and in-cylinder temperature curve of the combustion process through interpolation and fitting.

[0046] The load zone determination module is used to calculate the average effective pressure and peak temperature in the cylinder based on the in-cylinder pressure curve and the in-cylinder temperature curve, obtain the current load zone, determine the combustion zone type to which the current load zone belongs, and mark the risk label sequence.

[0047] The emission rate determination module is used to calculate the nitrogen oxide generation rate and particulate matter generation rate by combining the risk label sequence, and obtain a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions.

[0048] The control command generation module is used to calculate the timing lag in the transient process switching based on a dynamic lag model with strong mutual exclusion correlation, and to calibrate the timing lag through a combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command.

[0049] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0050] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method.

[0051] The aforementioned method, system, computer equipment, and storage medium for coordinated control of lean combustion emissions in marine low-zero carbon fuel engines collect real-time in-cylinder pressure and temperature data using in-cylinder pressure and temperature sensors. After time-series alignment and filtering / denoising of the real-time in-cylinder pressure and temperature data, interpolation fitting is used to obtain the in-cylinder pressure and temperature curves for the combustion process. Based on these curves, in-cylinder pressure and temperature time-series data are extracted. The in-cylinder average effective pressure is calculated by integrating the in-cylinder pressure time-series data over the engine's working cycle. The peak in-cylinder temperature is obtained by extracting the maximum value from the in-cylinder temperature time-series data. The current load range is determined by comparing it with a preset load range threshold. The combustion region type of the current load range is determined by combining the fluctuation characteristics of the in-cylinder pressure curve, and a corresponding risk label sequence is marked. Finally, the risk label sequence is combined with reaction condition parameters such as air-fuel ratio and EGR. Based on the emission rate and injection timing parameters, the generation rates of nitrogen oxides (NOx) and particulate matter (PM) are calculated using a simplified formula based on preset emission rates. The inverse variation characteristics of these two generation rates are extracted to obtain a strong mutually exclusive correlation between their emissions. Predictive control is initiated based on this strong mutually exclusive correlation, outputting the injection quantity adjustment and intake volume adjustment. The timing lag during transient switching is calculated using a dynamic lag model, and this timing lag is superimposed on the injection quantity adjustment and intake volume adjustment, respectively. After calibration using a combustion phase matching algorithm, the transiently adjusted injection timing control command and intake valve opening control command are obtained. This method, by adapting parameter calculation and correlation judgment logic to the combustion characteristics of low-zero carbon fuels, accurately captures the inverse relationship between NOx and particulate matter emissions. Combined with transient timing lag calibration and combustion phase matching, it effectively avoids emission peaks under transient operating conditions. This method does not rely on single pollutant control logic and can achieve coordinated management of multiple pollutants, meeting the emission regulations of the International Maritime Organization (IMO) and providing technical support for the market application of marine low-zero carbon fuel engines. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart of a method for coordinated control of lean combustion emissions in marine low-zero carbon fuel engines provided in an embodiment of the present invention;

[0054] Figure 2This is a structural block diagram of the lean combustion emission coordinated control system for marine low-zero carbon fuel engines provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] In one embodiment, such as Figure 1 As shown, this application provides a method for coordinated control of lean combustion emissions from marine low-zero carbon fuel engines, which may include the following steps:

[0057] Step S101: Collect real-time in-cylinder pressure data and real-time in-cylinder temperature data, and obtain the in-cylinder pressure curve and in-cylinder temperature curve of the combustion process through interpolation and fitting.

[0058] Specifically, real-time in-cylinder pressure and temperature data at various moments during the engine's working cycle are synchronously collected using in-cylinder pressure and temperature sensors installed inside the engine cylinders. The collected real-time in-cylinder pressure and temperature data undergo time-series alignment processing to eliminate timing discrepancies caused by differences in the acquisition trigger times. A low-pass filtering algorithm is then used to remove high-frequency interference noise from the data. Based on the filtered effective data, linear or polynomial interpolation fitting methods are employed to fill in missing values ​​within the data acquisition intervals, ultimately generating continuous and complete in-cylinder pressure and temperature curves for the combustion process.

[0059] Step S102: Calculate the average effective pressure and peak temperature in the cylinder based on the in-cylinder pressure curve and the in-cylinder temperature curve to obtain the current load range, determine the combustion zone type to which the current load range belongs, and mark the risk label sequence.

[0060] Based on the in-cylinder pressure and temperature curves, data points are extracted at preset time intervals to form in-cylinder pressure and in-cylinder temperature time-series data. The in-cylinder pressure time-series data is integrated over the crankshaft angle range of one engine working cycle to obtain the average effective in-cylinder pressure. The maximum value in the in-cylinder temperature time-series data is extracted to determine the peak in-cylinder temperature. The current load range is determined based on the average effective in-cylinder pressure, according to preset load range thresholds (calibrated based on engine type and low-zero carbon fuel characteristics). The fluctuation characteristics of the in-cylinder pressure curve are analyzed by calculating its cyclic volatility or peak pressure variation coefficient, and the combustion zone type (low-load lean combustion zone, medium-load transition zone, or high-load rich combustion zone) is determined based on the current load range. For the emission risks corresponding to different combustion zone types, local lean combustion risk or local high-temperature rich combustion risk is marked, forming a risk label sequence corresponding to the time series.

[0061] Step S103: Combine the risk label sequence to calculate the nitrogen oxide generation rate and particulate matter generation rate, and obtain a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions.

[0062] The system calls upon the risk label sequence and combines it with real-time reaction condition parameters (including air-fuel ratio, EGR rate, and injection timing parameters) collected during engine operation. The characteristic information from the risk label sequence, reaction condition parameters, and in-cylinder temperature time-series data are used as input. These are then substituted into preset simplified formulas for nitrogen oxide (NOx) and particulate matter (PM) generation rates to calculate the NOx and PM generation rates, respectively. By calculating the time-series negative correlation coefficient between the two generation rate sequences, the inverse variation characteristics of the two are extracted to obtain the strength value of the inverse relationship between NOx and PM emissions. If the strength value exceeds a preset mutual exclusion threshold, a strong mutual exclusion correlation is determined.

[0063] Step S104: Based on strong mutual exclusion correlation, the timing lag in the transient process switching is calculated through a dynamic lag model. The timing lag is calibrated through a combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command.

[0064] After determining the strong mutually exclusive correlation, model predictive control is initiated and the initial fuel injection quantity adjustment and initial intake air quantity adjustment are output. The combustion ignition delay and flame propagation speed (derived from the in-cylinder pressure and temperature curves) in the combustion state characteristics are input into the dynamic hysteresis model to calculate the timing lag in transient process switching (such as load switching and sudden change in operating conditions). The timing lag is superimposed on the initial fuel injection quantity adjustment and the initial intake air quantity adjustment respectively. The adaptability of the adjustment quantity to the in-cylinder combustion phase is calibrated by the combustion phase matching algorithm to correct the combustion phase offset caused by timing lag. After calibration, the transiently adjusted fuel injection timing control command and intake valve opening control command are generated to drive the engine actuator to adjust parameters.

[0065] The aforementioned method for coordinated control of lean combustion emissions in marine low-zero carbon fuel engines collects real-time in-cylinder pressure and temperature data using in-cylinder pressure and temperature sensors. After time-series alignment, filtering, denoising, and interpolation fitting, in-cylinder pressure and temperature curves are obtained. Based on these curves, time-series data is extracted, and the average effective in-cylinder pressure and peak temperature are calculated to determine the current load range. Combustion zone type is determined by combining pressure curve fluctuation characteristics, and risk label sequences are marked. Combined with reaction condition parameters such as air-fuel ratio, EGR rate, and injection timing, a preset simplified formula is used to calculate the formation rates of nitrogen oxides and particulate matter, extracting inverse variation characteristics to obtain a strong mutually exclusive correlation between the two. Based on this correlation, a start-up model predicts and controls the output injection quantity and intake air quantity adjustment. A dynamic lag model is used to calculate the transient time-series lag, which is then superimposed and calibrated using a combustion phase matching algorithm to generate transiently adjusted injection timing and intake valve opening control commands. This method is adapted to the combustion characteristics of low-zero carbon fuels, accurately captures emission contradictions, avoids transient emission peaks, achieves coordinated control of multiple pollutants, meets International Maritime Organization emission regulations, and provides technical support for the market application of related engines.

[0066] In one embodiment, the calculation of the average effective pressure and peak temperature in the cylinder based on the in-cylinder pressure curve and the in-cylinder temperature curve to obtain the current load range, determine the combustion zone type to which the current load range belongs, and mark the risk label sequence may include the following steps:

[0067] Step S201: Extract time series data based on the in-cylinder pressure curve and the in-cylinder temperature curve to obtain in-cylinder pressure time series data and in-cylinder temperature time series data.

[0068] Step S202: Integrate the cylinder pressure time sequence data within the engine working cycle to obtain the cylinder average effective pressure.

[0069] Preferably, the calculation range is the crankshaft angle interval corresponding to one complete working cycle of the engine (720°CA for a four-stroke engine, 360°CA for a two-stroke engine, preset according to the engine type); the integral interval is divided into several sub-intervals according to the preset crankshaft angle step size (e.g., 0.1°CA), and the in-cylinder pressure and crankshaft angle change in each sub-interval are integrated using the trapezoidal integral method or Simpson's integral method. The integral results of all sub-intervals are summed to obtain the total integral value of the in-cylinder pressure versus the crankshaft angle in one working cycle; the total integral value is divided by the product of the crankshaft angle range and the cylinder working volume of the working cycle to calculate the average effective pressure in the cylinder, which directly reflects the current working capacity of the engine.

[0070] Step S203: Extract the maximum value from the time series data of in-cylinder temperature to obtain the peak temperature in the cylinder.

[0071] Step S204: Determine the current load range based on the average effective pressure inside the cylinder, referring to the preset load range division threshold.

[0072] Step S205: Calculate the combustion uniformity index based on the cyclic fluctuation rate or pressure peak variation coefficient of the in-cylinder pressure curve.

[0073] Step S206: If the current load range is higher than the preset high load threshold and the combustion uniformity index is lower than the preset uniformity threshold, it is determined to be a high load low uniformity combustion area, and the risk of local high temperature and rich combustion is marked.

[0074] Step S207: If the current load range is lower than the preset medium load threshold and the combustion uniformity index is higher than the uniformity threshold, it is determined to be a medium-low load high uniformity combustion region, and the risk of local lean combustion is marked.

[0075] Step S208: Establish a correlation between the combustion zone type and the corresponding marked local lean risk or local high temperature rich fuel risk, and output the corresponding risk label sequence.

[0076] Specifically, based on the in-cylinder pressure and in-cylinder temperature curves, data points are extracted at preset time intervals to obtain in-cylinder pressure time-series data and in-cylinder temperature time-series data. The in-cylinder pressure time-series data is integrated over the crankshaft angle range of one engine working cycle to obtain the average effective in-cylinder pressure. Simultaneously, the maximum value in the in-cylinder temperature time-series data is extracted to obtain the peak in-cylinder temperature. The current load range is determined based on the average effective in-cylinder pressure, using a preset load range division threshold calibrated according to the engine model and low-zero carbon fuel characteristics. Combustion uniformity is calculated based on the cyclic fluctuation rate or pressure peak variation coefficient of the in-cylinder pressure curve. The system uses a combination of parameters: a high-load threshold, a medium-load threshold, and a uniformity threshold. If the current load range is higher than the high-load threshold and the uniformity index is lower than the uniformity threshold, it is classified as a high-load, low-uniformity combustion region, and a local high-temperature, rich-fuel risk is marked. Conversely, if the current load range is lower than the medium-load threshold and the uniformity index is higher than the uniformity threshold, it is classified as a medium-low load, high-uniformity combustion region, and a local lean-fuel risk is marked. A temporal correlation is established between the identified combustion region type and the corresponding marked local lean-fuel risk or local high-temperature, rich-fuel risk, outputting a risk label sequence containing combustion region information and risk type.

[0077] This embodiment uses in-cylinder pressure and temperature curves as the basic data sources. It forms a complete data link through continuous parameter calculation, interval determination, and risk labeling. All calculations are based on actual engine operating data and preset calibration thresholds to ensure that the output risk label sequence can accurately reflect the combustion state and emission risks under different operating conditions. By combining interval-based risk determination logic with combustion uniformity indicators, it is adapted to the wide operating range of marine low-zero carbon fuel engines from low load to high load. It can specifically identify two types of core emission risks: localized excessive lean fuel and localized high-temperature rich fuel, ensuring the pertinence and effectiveness of subsequent coordinated control strategies.

[0078] In one embodiment, the combustion uniformity index can be calculated using the following formula:

[0079]

[0080]

[0081]

[0082] in, Indicators of combustion uniformity Indicates engine continuous The coefficient of variation of peak in-cylinder pressure per working cycle Indicates engine continuous The coefficient of variation of the slope of the cylinder pressure rise over each working cycle. , This represents the weighting coefficient, calibrated according to the type of low-to-zero carbon fuel and the engine model. This indicates the number of loops. Indicates the first Peak in-cylinder pressure per working cycle express Average peak value of in-cylinder pressure over one working cycle Indicates the first The slope of the cylinder pressure rise over each working cycle is the rate at which the cylinder pressure rises from the minimum pressure of the cycle to... During the process, the ratio of the pressure change to the corresponding crankshaft angle change, express The average slope of the cylinder pressure rise over each working cycle.

[0083] This embodiment calculates the combustion uniformity index using in-cylinder pressure time-series data as the sole data source, eliminating the need for additional sensors. All parameters can be directly extracted or derived from existing data, ensuring a coherent data flow and strong engineering feasibility. By integrating two characteristics—pressure peak stability and pressure rise slope stability—it overcomes the limitations of traditional single-indicator methods, accurately capturing the slow flame propagation speed and unique pressure fluctuations characteristic of low-zero carbon fuel combustion, thus avoiding misjudgments of uniformity caused by a single feature. (Weighting coefficients are also mentioned.) , The calibrable design allows the formula to adapt to the combustion differences of different low-zero carbon fuels, broadening its applicability. The standardized index values ​​(0~100) can be directly linked to the subsequent preset uniformity threshold, providing a quantitative basis for combustion zone type determination and risk labeling, ensuring the accuracy of risk identification, and thus supporting the targeted formulation of subsequent emission coordination control strategies.

[0084] In one embodiment, calculating the nitrogen oxide generation rate and particulate matter generation rate by combining the risk label sequence to obtain a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions may include the following steps:

[0085] Step S301: Obtain the combustion state characteristics and reaction condition parameters corresponding to the risk label sequence at the time sequence.

[0086] The combustion state characteristics include in-cylinder pressure time series data, in-cylinder temperature time series data, and in-cylinder peak temperature.

[0087] The reaction condition parameters include air-fuel ratio, EGR rate, and injection timing parameters.

[0088] Step S302: Input the combustion state characteristics and reaction condition parameters into the pre-trained emission generation rate prediction model to calculate the nitrogen oxide generation rate sequence and particulate matter generation rate sequence.

[0089] The emission generation rate prediction model was trained based on combustion-emission sample data of low-zero carbon fuel engines under different combustion conditions.

[0090] Preferably, the acquired combustion state characteristics and reaction condition parameters are first normalized and preprocessed to eliminate the impact of differences in the magnitude of different parameters on the model's calculation accuracy. The combustion state characteristics include in-cylinder pressure time series data, in-cylinder temperature time series data, and in-cylinder peak temperature. The reaction condition parameters include air-fuel ratio, EGR rate, and injection timing parameters. Both types of parameters are derived from the previous data acquisition and calculation process. The preprocessed combustion state characteristics and reaction condition parameters are synchronously input into a pre-trained emission generation rate prediction model. This model is trained based on combustion-emission sample data of a low-zero carbon fuel engine under different combustion conditions (covering low load, medium load, high load, and transient switching conditions). The sample data contains a one-to-one correspondence between combustion state characteristics, reaction condition parameters, and nitrogen oxide generation rate and particulate matter generation rate, which can accurately adapt to the combustion characteristics of low-zero carbon fuel. Through feature mapping and regression calculation within the model, the nitrogen oxide generation rate sequence and particulate matter generation rate sequence corresponding to the input parameter time series are output synchronously. The time interval of the two sequences is consistent with the acquisition interval of the combustion state characteristics and reaction condition parameters.

[0091] Step S303: Calculate the time-series negative correlation coefficient between the nitrogen oxide generation rate sequence and the particulate matter generation rate sequence, and combine it with the weight of the instantaneous change amplitude of the sequence to obtain the strength value of the inverse relationship.

[0092] Step S304: If the strength of the offsetting relationship exceeds the preset mutual exclusion threshold, it is determined that there is a strong mutual exclusion relationship between nitrogen oxide emission control and particulate matter emission control; the mutual exclusion threshold is obtained based on regulatory emission limits and engine operating condition adaptability calibration.

[0093] Specifically, the combustion state characteristics and reaction condition parameters corresponding to the risk label sequence are obtained. The combustion state characteristics include in-cylinder pressure time series data, in-cylinder temperature time series data, and in-cylinder peak temperature. The reaction condition parameters include air-fuel ratio, EGR rate, and injection timing parameters. The above combustion state characteristics and reaction condition parameters are used as inputs to a pre-trained emission generation rate prediction model. The model calculates the nitrogen oxide generation rate sequence and particulate matter generation rate sequence. This emission generation rate prediction model is trained based on combustion-emission sample data of low-zero carbon fuel engines under different combustion conditions and can accurately adapt to the combustion characteristics of low-zero carbon fuels. Then, the time-series negative correlation coefficient between the nitrogen oxide generation rate sequence and the particulate matter generation rate sequence is calculated. Combined with the instantaneous change magnitude weights of the two sequences, a weighted fusion is performed to obtain a normalized mutual exclusion relationship strength value. A preset mutual exclusion correlation threshold is set. This threshold is obtained based on regulatory emission limits and engine operating condition adaptability calibration. If the mutual exclusion relationship strength value exceeds the preset mutual exclusion correlation threshold, it is determined that there is a strong mutual exclusion correlation between nitrogen oxide emission control and particulate matter emission control.

[0094] This embodiment uses the risk label sequence time series as a benchmark to achieve a precise temporal correspondence between combustion state characteristics, reaction condition parameters, and emission generation rates. The data flow is clear and coherent, and all input parameters come from previous collection and calculation results, requiring no additional data sources, thus demonstrating strong engineering feasibility. The emission generation rate prediction model is trained based on dedicated sample data for low-zero carbon fuel engines, exhibiting strong adaptability and accurately outputting the generation rate sequences of the two types of pollutants. By combining the time-series negative correlation coefficient with the instantaneous change amplitude weighting, the inverse relationship between nitrogen oxide and particulate matter emissions can be accurately captured. Combined with mutually exclusive correlation thresholds based on regulations and operating conditions, the existence of a strong mutually exclusive correlation between the two can be accurately determined, ensuring the targetedness and effectiveness of emission coordinated control.

[0095] In one embodiment, the nitrogen oxide generation rate sequence and the particulate matter generation rate sequence can be calculated using the following formula:

[0096]

[0097]

[0098] in, Indicates the first time Generation rate, express Generate basic coefficients based on the characteristics of low- to zero-carbon fuels. Indicates the first Instantaneous temperature inside the cylinder at all times. Indicates the first time Efficiency Indicates the first air-fuel ratio at all times Indicates the first time Generation rate, express Generate basic coefficients, Indicates the stoichiometric air-fuel ratio of low- to zero-carbon fuels. Indicates the first The uniformity of combustion at all times Indicates the first Fuel injection timing offset.

[0099] This embodiment uses pre-acquired in-cylinder temperature time-series data, reaction condition parameters, and combustion uniformity indicators as the core data source, eliminating the need for additional sensors and data acquisition stages. The data flow is clear and highly feasible for engineering implementation. The formula structure is simplified, eliminating complex exponential and integral terms, resulting in low computational load and adaptability to the real-time control requirements of marine low-zero carbon fuel engines, avoiding control delays caused by complex calculations. , and Its adaptive design is tailored to the combustion characteristics of low-zero carbon fuels, accurately reflecting the impact of factors such as in-cylinder temperature, EGR rate, air-fuel ratio, combustion uniformity, and injection timing on the formation of both types of pollutants. The calculated nitrogen oxide formation rate sequence and particulate matter formation rate sequence are synchronized with previous parameters in time, and can be directly used to calculate the strength of the subsequent inverse relationship, providing an accurate and reliable quantitative basis for determining the strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions.

[0100] In one embodiment, the timing lag during transient process switching is calculated based on a dynamic lag model using a strong mutually exclusive correlation. The timing lag is then calibrated using a combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command. This process may include the following steps:

[0101] Step S401: After determining that there is a strong mutually exclusive correlation between nitrogen oxides and particulate matter emissions, the fuel injection quantity adjustment amount and intake air quantity adjustment amount are calculated based on the combustion state characteristics, reaction condition parameters, and the strength value of the mutual attrition relationship.

[0102] Step S402: Based on the combustion ignition delay and flame propagation speed in the combustion state characteristics, the time lag in the transient process switching is calculated using a dynamic lag prediction model.

[0103] The dynamic lag prediction model was trained based on transient test data of a low-zero carbon fuel engine.

[0104] Step S403: The timing lag is superimposed on the fuel injection quantity adjustment and the intake air quantity adjustment respectively, and calibrated by the combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command.

[0105] Preferably, the timing lag, fuel injection quantity adjustment, and intake air quantity adjustment obtained in the early stage are used as inputs. The timing lag is superimposed on the fuel injection quantity adjustment and intake air quantity adjustment respectively to obtain the initial fuel injection quantity adjustment and the initial intake air quantity adjustment. The combustion phase matching algorithm is started. Based on the current combustion phase derived from the in-cylinder pressure curve and the in-cylinder temperature curve, the initial fuel injection quantity adjustment and the initial intake air quantity adjustment are matched with the current combustion phase to correct the deviation between the adjustment amount and the actual combustion phase caused by the superposition of timing lag. After calibration, the transiently adjusted fuel injection timing control command and intake valve opening control command are generated. The fuel injection timing control command includes specific fuel injection timing parameters, and the intake valve opening control command includes specific opening parameters. The timing interval of the two types of commands is consistent with the previous parameter acquisition interval to ensure synchronization with the real-time operating conditions of the engine.

[0106] Step S404: The fuel injector is driven to perform transient fuel injection timing adjustment by the fuel injection timing control command to optimize the fuel atomization and fuel-air mixing effect in the cylinder.

[0107] The obtained fuel injection timing control command is transmitted to the fuel injector actuator of the engine fuel injection system, driving the fuel injector to perform adjustment actions according to the transient fuel injection timing set in the command. During the adjustment process, the opening and closing timing of the fuel injector is precisely controlled according to the atomization characteristics of low-carbon fuel to reduce fuel injection lag error. By optimizing the fuel injection timing, the timing of fuel injection into the cylinder is adapted to the airflow movement and temperature distribution in the cylinder, thereby optimizing the fuel atomization effect in the cylinder, making the fuel particles more uniform, improving the fuel-air mixture, and reducing the generation of local lean or rich combustion areas.

[0108] Step S405: The intake valve opening of the intake system is adjusted by using the intake valve opening control command to match the intake volume with the adjusted injection timing, thereby calibrating the in-cylinder combustion phase.

[0109] Step S406: Monitor the adjusted in-cylinder combustion phase in real time and compare it with the preset target combustion phase. If the deviation exceeds the preset phase deviation threshold, the combustion phase is determined to be mismatched.

[0110] Step S407: Based on the phase deviation threshold, the updated fuel injection quantity adjustment and intake air quantity adjustment are recalculated. The updated fuel injection quantity adjustment and intake air quantity adjustment are then superimposed with the timing lag and calibrated by the combustion phase matching algorithm to obtain the optimized fuel injection timing control command and intake valve opening control command.

[0111] Once a strong mutually exclusive correlation between nitrogen oxides and particulate matter emissions is established, the fuel injection quantity adjustment and intake air quantity adjustment are calculated using combustion state characteristics, reaction condition parameters, and the strength of their trade-off as inputs. Based on the combustion ignition delay and flame propagation speed in the combustion state characteristics, the timing lag during transient process switching is calculated using a dynamic lag prediction model. This dynamic lag prediction model is trained based on transient operating condition test data of a low-zero carbon fuel engine and can accurately adapt to transient operating condition characteristics. The timing lag is then superimposed on the fuel injection quantity adjustment and intake air quantity adjustment, respectively. The compatibility between the adjustment and the in-cylinder combustion phase is calibrated using a combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command. The fuel injection timing control... The command drives the injector to perform transient injection timing adjustment, optimizing in-cylinder fuel atomization and air-fuel mixture. Simultaneously, the intake valve opening control command adjusts the intake valve opening of the intake system to match the intake air volume with the adjusted injection timing, thereby calibrating the in-cylinder combustion phase. The adjusted in-cylinder combustion phase is monitored in real time and compared with the preset target combustion phase. A preset phase deviation threshold is set. If the deviation exceeds the threshold, it is determined that the combustion phase is mismatched. When combustion phase mismatch is determined, the updated injection quantity adjustment and intake air volume adjustment are recalculated based on the phase deviation. The updated adjustment is then superimposed with the timing lag, and after calibration by the combustion phase matching algorithm, the optimized injection timing control command and intake valve opening control command are obtained.

[0112] This embodiment uses strong mutual exclusion correlation as the core triggering condition. All input parameters are derived from previous data acquisition and calculation results, resulting in a clear and coherent data flow. No additional sensors or data acquisition links are required, making it highly feasible in engineering. The dynamic lag prediction model is adapted to the transient operating characteristics of low-zero carbon fuel engines. The application of a combustion phase matching algorithm based on the superposition of time lag effectively corrects control deviations during transient process switching, avoiding combustion instability and emission peaks caused by time lag. Through the coordinated execution of fuel injection and intake control commands, the fuel-air mixture effect is optimized and the combustion phase is calibrated. Combined with real-time monitoring and closed-loop adjustment of the combustion phase, it ensures that the in-cylinder combustion phase always matches the target combustion phase, effectively mitigating the mutual exclusion contradiction between nitrogen oxides and particulate matter emissions. The entire process achieves precise control under transient and steady-state conditions, ensuring that multiple pollutants meet standards in a coordinated manner, satisfying the emission regulations of the International Maritime Organization, and adapting to the operational requirements of marine low-zero carbon fuel engines.

[0113] In one embodiment, such as Figure 2 As shown, this application also provides a lean-burn emission co-control system for marine low-zero carbon fuel engines, the system may include:

[0114] The in-cylinder data acquisition module 501 is used to acquire real-time in-cylinder pressure data and real-time in-cylinder temperature data, and obtain the in-cylinder pressure curve and in-cylinder temperature curve of the combustion process through interpolation and fitting.

[0115] The load zone determination module 502 is used to calculate the average effective pressure and peak temperature in the cylinder based on the in-cylinder pressure curve and the in-cylinder temperature curve, obtain the current load zone, determine the combustion zone type to which the current load zone belongs, and mark the risk label sequence.

[0116] The emission rate determination module 503 is used to calculate the nitrogen oxide generation rate and particulate matter generation rate by combining the risk label sequence, and obtain a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions.

[0117] The control command generation module 504 is used to calculate the timing lag in the transient process switching based on a dynamic lag model with strong mutual exclusion correlation, and to calibrate the timing lag through a combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command.

[0118] The aforementioned lean-burn emission co-control system for marine low-zero carbon fuel engines comprises a cylinder data acquisition module, a load region determination module, an emission rate determination module, and a control command generation module, which work collaboratively to form the core chain of the system. The cylinder data acquisition module collects real-time cylinder pressure and temperature data during engine operation using cylinder pressure and temperature sensors. After time-series alignment and noise reduction, the acquired data is interpolated to obtain the cylinder pressure and temperature curves for the combustion process. The load region determination module receives the cylinder pressure and temperature curves output by the cylinder data acquisition module, extracts time-series data based on these curves, calculates the average effective cylinder pressure and peak cylinder temperature, determines the current load range by comparing it with preset thresholds, and determines the combustion region type based on the cylinder pressure curve fluctuation characteristics, marking a risk label sequence corresponding to the time series. The emission rate determination module… The block calls the risk label sequence output by the load area judgment module, and calculates the nitrogen oxide generation rate and particulate matter generation rate by combining them with the reaction condition parameters of the engine in real time. By analyzing the inverse variation characteristics of the two types of generation rates, a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions is obtained. The control command generation module is based on the strong mutually exclusive correlation determined by the emission rate judgment module. It inputs the relevant parameters in the combustion state characteristics into the dynamic hysteresis model, calculates the time lag in the transient process switching, and calibrates the time lag through the combustion phase matching algorithm. Finally, it generates transiently adjusted fuel injection timing control commands and intake valve opening control commands to drive the engine actuators to adjust parameters.

[0119] In this embodiment, each module has a clear division of labor and progresses step by step. The in-cylinder data acquisition module provides the foundation for accurate in-cylinder curves, ensuring the accuracy of subsequent parameter calculations and risk assessments. The risk label sequence of the load area judgment module provides targeted preliminary basis for emission rate calculation, improving the accuracy of pollutant generation rate prediction. The emission rate determination module accurately captures the strong mutually exclusive correlation between the two types of pollutants, providing core logical support for control command generation. The control command generation module, through dynamic hysteresis models and combustion phase matching algorithms, adapts to the transient operating characteristics of low-zero carbon fuel engines, ensuring the timeliness and adaptability of control commands. The collaborative work of each module effectively avoids the limitations of independent operation of a single module, realizing the integration of combustion state monitoring, risk identification, pollutant correlation determination, and control command generation. It can accurately alleviate the mutual contradiction between nitrogen oxides and particulate matter emissions, achieve multi-pollutant collaborative management, meet the emission regulations of the International Maritime Organization, adapt to the operational needs of marine low-zero carbon fuel engines, and provide system-level technical support for their market application.

[0120] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0121] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the previously described method for coordinated control of lean-burn emissions from a marine low-zero carbon fuel engine.

[0122] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0123] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0124] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for coordinated control of lean combustion emissions in marine low-zero carbon fuel engines, characterized in that, The method includes: Real-time in-cylinder pressure and temperature data are collected and interpolated to obtain the in-cylinder pressure and temperature curves of the combustion process. Based on the in-cylinder pressure curve and the in-cylinder temperature curve, the average effective pressure and peak temperature in the cylinder are calculated to obtain the current load range, determine the combustion zone type to which the current load range belongs, and mark the risk label sequence. By combining the risk label sequences, the formation rates of nitrogen oxides and particulate matter were calculated, resulting in a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions. Based on the strong mutual exclusion correlation, the timing lag in the transient process switching is calculated by the dynamic lag model. The timing lag is then calibrated by the combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command.

2. The method according to claim 1, characterized in that, The process involves calculating the average effective pressure and peak temperature in the cylinder based on the in-cylinder pressure curve and the in-cylinder temperature curve, obtaining the current load range, determining the combustion zone type to which the current load range belongs, and marking a risk label sequence, including: Based on the in-cylinder pressure curve and the in-cylinder temperature curve, time-series data are extracted to obtain in-cylinder pressure time-series data and in-cylinder temperature time-series data. The cylinder pressure time sequence data is integrated over the engine working cycle to obtain the cylinder average effective pressure. Extract the maximum value from the in-cylinder temperature time series data to obtain the in-cylinder peak temperature; The current load range is determined based on the average effective pressure inside the cylinder, according to the preset load range division threshold. The combustion uniformity index is calculated based on the cyclic fluctuation rate or pressure peak variation coefficient of the in-cylinder pressure curve. If the current load range is higher than the preset high load threshold and the combustion uniformity index is lower than the preset uniformity threshold, it is determined to be a high load low uniformity combustion area, and the risk of local high temperature and rich combustion is marked. If the current load range is lower than the preset medium load threshold and the combustion uniformity index is higher than the uniformity threshold, it is determined to be a medium-low load high uniformity combustion region, and the risk of local lean combustion is marked. The combustion zone type is associated with the corresponding locally lean risk or locally high-temperature fuel-rich risk, and the corresponding risk label sequence is output.

3. The method according to claim 2, characterized in that, The combustion uniformity index is calculated using the following formula: in, Indicators of combustion uniformity Indicates engine continuous The coefficient of variation of peak in-cylinder pressure per working cycle Indicates engine continuous The coefficient of variation of the slope of the cylinder pressure rise over each working cycle. , This represents the weighting coefficient, calibrated according to the type of low-to-zero carbon fuel and the engine model. This indicates the number of loops. Indicates the first Peak in-cylinder pressure per working cycle express Average peak value of in-cylinder pressure over one working cycle Indicates the first The slope of the cylinder pressure rise over each working cycle is the rate at which the cylinder pressure rises from the minimum pressure of the cycle to... During the process, the ratio of the pressure change to the corresponding crankshaft angle change, express The average slope of the cylinder pressure rise over each working cycle.

4. The method according to claim 1, characterized in that, The calculation of nitrogen oxide generation rate and particulate matter generation rate by combining the risk label sequence to obtain a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions includes: Obtain the combustion state characteristics and reaction condition parameters corresponding to the time sequence of the risk label sequence; The combustion state characteristics include in-cylinder pressure time series data, in-cylinder temperature time series data, and in-cylinder peak temperature; The reaction condition parameters include air-fuel ratio, EGR rate, and injection timing parameters; The combustion state characteristics and reaction condition parameters are input into a pre-trained emission generation rate prediction model to calculate the nitrogen oxide generation rate sequence and particulate matter generation rate sequence. The emission generation rate prediction model is trained based on combustion-emission sample data of a low-zero carbon fuel engine under different combustion conditions. The time-series negative correlation coefficient between the nitrogen oxide generation rate sequence and the particulate matter generation rate sequence is calculated, and the strength value of the inverse relationship is obtained by combining the instantaneous variation amplitude weight of the sequence. If the strength of the mutually exclusive relationship exceeds a preset threshold, it is determined that there is a strong mutually exclusive relationship between nitrogen oxide emission control and particulate matter emission control; the mutually exclusive relationship threshold is obtained based on regulatory emission limits and engine operating condition adaptability calibration.

5. The method according to claim 4, characterized in that, The nitrogen oxide formation rate sequence and the particulate matter formation rate sequence were calculated using the following formulas: in, Indicates the first time Generation rate, express Generate basic coefficients based on the characteristics of low- to zero-carbon fuels. Indicates the first Instantaneous temperature inside the cylinder at all times. Indicates the first time Efficiency Indicates the first air-fuel ratio at all times Indicates the first time Generation rate, express Generate basic coefficients, Indicates the stoichiometric air-fuel ratio of low- to zero-carbon fuels. Indicates the first The uniformity of combustion at all times Indicates the first Fuel injection timing offset.

6. The method according to claim 1, characterized in that, The process involves calculating the timing lag during transient switching based on the strong mutually exclusive correlation using a dynamic lag model, calibrating the timing lag using a combustion phase matching algorithm, and obtaining transiently adjusted injection timing control commands and intake valve opening control commands, including: After confirming the strong mutually exclusive correlation between nitrogen oxides and particulate matter emissions, the fuel injection quantity adjustment and intake air quantity adjustment are calculated based on combustion state characteristics, reaction condition parameters, and the strength of the trade-off relationship. Based on the combustion ignition delay and flame propagation speed in the combustion state characteristics, the time lag in the transient process switching is calculated by a dynamic lag prediction model. The dynamic hysteresis prediction model is trained based on transient operating condition test data of a low-zero carbon fuel engine. The timing lag is superimposed on the fuel injection quantity adjustment and the intake air quantity adjustment respectively, and calibrated by the combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command. The injection timing control command drives the injector to perform transient injection timing adjustment, thereby optimizing the in-cylinder fuel atomization and fuel-air mixing effect. The intake valve opening is adjusted by the intake valve opening control command to match the intake volume with the adjusted injection timing, thereby calibrating the in-cylinder combustion phase. The adjusted in-cylinder combustion phase is monitored in real time and compared with the preset target combustion phase. If the deviation exceeds the preset phase deviation threshold, the combustion phase is determined to be mismatched. The updated fuel injection quantity adjustment and intake air quantity adjustment are recalculated based on the phase deviation threshold. The updated fuel injection quantity adjustment and intake air quantity adjustment are then superimposed with the timing lag and calibrated by the combustion phase matching algorithm to obtain the optimized fuel injection timing control command and intake valve opening control command.

7. A lean-burn emission co-control system for marine low-zero carbon fuel engines, characterized in that, The system includes: The in-cylinder data acquisition module is used to collect real-time in-cylinder pressure data and real-time in-cylinder temperature data, and obtains the in-cylinder pressure curve and in-cylinder temperature curve of the combustion process through interpolation and fitting. The load zone determination module is used to calculate the average effective pressure and peak temperature in the cylinder based on the cylinder pressure curve and the cylinder temperature curve, obtain the current load zone, determine the combustion zone type to which the current load zone belongs, and mark the risk label sequence. The emission rate determination module is used to calculate the nitrogen oxide generation rate and particulate matter generation rate by combining the risk label sequence, and obtain a strong mutually exclusive correlation between nitrogen oxide and particulate matter emissions. The control command generation module is used to calculate the timing lag in the transient process switching based on the strong mutual exclusion correlation through a dynamic lag model, and to calibrate the timing lag through a combustion phase matching algorithm to obtain the transiently adjusted fuel injection timing control command and intake valve opening control command.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images