Natural gas quality self-learning method, system, device and readable storage medium

Abstract

A method, system, device, and readable storage medium for natural gas gas quality self-learning are disclosed, relating to the field of engine control. In this method, the current natural gas state is determined based on the fuel replenishment ratio to determine if it meets the gas quality self-learning trigger conditions. If so, a gas quality self-learning activation command is generated, and the component aging self-learning process is frozen to avoid mixing of old and new gas qualities and interference from other processes, ensuring a reliable self-learning environment. In response to the gas quality self-learning activation command, if the engine operating conditions meet preset learning conditions, the gas calorific value parameter is determined based on the engine's air-fuel ratio feedback signal, the target reference value, and the effective duration of the current gas quality self-learning. Furthermore, the gas octane number parameter is determined based on the distribution characteristics of the knock detection signal across multiple operating cycles, avoiding invalid data collection under unsteady conditions and achieving differentiated identification of the two types of gas quality parameters, thereby improving the overall operational stability and economy of the natural gas engine.

Description

Technical Field

[0001] This application relates to the field of engine control technology, specifically to a natural gas quality self-learning method, system, device, and readable storage medium. Background Technology

[0002] Currently, the gas quality parameters of vehicle-grade natural gas fluctuate significantly due to factors such as gas source origin, refueling batch, and storage environment. These fluctuations are primarily reflected in differences in calorific value and anti-knock properties. This instability directly leads to deviations in the engine's air-fuel ratio and abnormal combustion, resulting in issues such as fluctuating power output, poor fuel economy, and abnormal knocking tendency. Therefore, accurately identifying real-time gas quality and adaptively correcting engine control parameters is a core technological requirement for ensuring the stable and efficient operation of natural gas engines.

[0003] In related technologies, natural gas engines mostly use fixed gas calibration parameters for control. Some models have basic gas quality adaptive learning functions, which can make simple corrections to gas deviations through engine operation feedback signals to adapt to normal gas quality fluctuations and meet the basic operation and control requirements of the engine.

[0004] However, the existing gas quality learning mechanism is relatively simple and cannot distinguish the differential deviations between the calorific value and the octane number parameter of the anti-knock characteristics of the gas, making it difficult to achieve accurate and independent identification of the two types of gas quality parameters. At the same time, the traditional learning logic lacks effective operating condition constraints and learning data fault tolerance mechanisms, and is prone to collecting invalid data under non-steady-state operating conditions, resulting in low gas quality learning accuracy and poor stability. In addition, the existing technology also has the problems of unreasonable learning timing and interference from the mixing of new and old gas quality, which ultimately causes deviations in the correction of engine control parameters, making it unable to effectively adapt to the complex and ever-changing natural gas quality conditions, and making it difficult to guarantee the stability and economy of engine operation. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a natural gas quality self-learning method, system, device, and readable storage medium.

[0006] In a first aspect, embodiments of this application provide a natural gas quality self-learning method, the natural gas quality self-learning method comprising: The current natural gas status is determined based on the fuel replenishment ratio to determine whether it meets the gas quality self-learning trigger condition. If it does, a gas quality self-learning activation command is generated and the component aging self-learning process is frozen. The fuel replenishment ratio is the ratio of the last fuel added to the total remaining fuel. In response to the gas quality self-learning activation command, if the engine operating conditions meet the preset learning conditions, the gas calorific value parameter is determined based on the engine air-fuel ratio feedback signal, the preset target reference value, and the effective duration of the current gas quality self-learning, and the gas octane number parameter is determined based on the distribution characteristics of the knock detection signal in multiple working cycles.

[0007] In conjunction with the first aspect, in one implementation, determining the calorific value parameters of the fuel gas based on the engine's air-fuel ratio feedback signal, a preset target reference value, and the effective duration of the current gas quality self-learning includes: Based on the engine's air-fuel ratio feedback signal, determine the closed-loop correction coefficient of the engine's front oxygen sensor; The closed-loop correction deviation value is obtained by calculating the absolute value of the difference between the closed-loop correction coefficient and the preset target benchmark value. Based on the cumulative duration of the engine operating conditions meeting the preset learning conditions, the effective duration of the current quality self-learning is obtained, and an integral accumulation calculation is performed based on the closed-loop correction deviation value and the effective duration to obtain the integral result. The integral result is subjected to upper and lower limit processing and combined with the preset target benchmark value to obtain the gas calorific value parameter.

[0008] In conjunction with the first aspect, in one embodiment, determining the fuel gas octane rating parameter based on the distribution characteristics of the knock detection signal across multiple operating cycles includes: Based on the knock detection signals output by each cylinder of the engine, the knock retraction angle value corresponding to each cylinder in a single working cycle is obtained by analysis. The average knock angle value of all cylinders is taken to obtain the average knock angle value for a single working cycle; Using N consecutive working cycles of the engine as a statistical period, the effective number of cycles in which the average knock retraction angle value falls into each retraction angle interval within a statistical period is calculated, wherein the retraction angle interval is obtained according to multiple preset retraction angle thresholds; The octane rating parameter of the fuel gas is calculated based on the effective number of cycles.

[0009] In conjunction with the first aspect, in one embodiment, before obtaining the knock retraction angle value corresponding to a single working cycle of each cylinder based on the knock detection signals output by each cylinder of the engine, the method further includes: Determine whether the engine operating condition meets the preset learning operating condition conditions, which include the engine speed being within a preset calibration range, the knock sensor being fault-free, and the ignition angle having a preset allowable margin. If satisfied, then proceed with the step of analyzing the knock detection signals output by each cylinder of the engine to obtain the knock retraction angle value corresponding to each cylinder in a single working cycle. If the condition is not met, the preset minimum limit value will be used directly as the octane rating parameter for the fuel gas.

[0010] In conjunction with the first aspect, in one embodiment, calculating the fuel gas octane number parameter based on the effective cycle count includes: Based on the effective number of cycles and the preset correction range corresponding to each setback interval, the actual correction range of each setback interval is determined; At the end of a statistical period, the total correction magnitude is obtained by summing the actual correction magnitudes of all back angle intervals. The octane rating coefficients in the octane rating coefficient table are updated using the total correction magnitude, wherein the octane rating coefficient table is indexed by engine speed and load; Based on the current engine speed and load, the target octane rating is matched from the updated octane rating table, and the target octane rating is constrained by upper and lower limits to obtain the fuel octane rating parameter.

[0011] In conjunction with the first aspect, in one embodiment, after determining the fuel gas octane rating parameter based on the distribution characteristics of the knock detection signal over multiple operating cycles, the method further includes: Based on the mapping relationship between the gas calorific value parameter, the gas octane number parameter, and the preset control parameter, the fuel injection correction coefficient, the gas partial pressure correction coefficient, and the exhaust gas recirculation rate correction coefficient are obtained. Combined with the engine's basic control parameters, the basic fuel injection quantity, the basic gas partial pressure, and the basic exhaust gas recirculation rate are determined. The target fuel injection quantity is calculated based on the fuel injection correction coefficient and the base fuel injection quantity. The target gas partial pressure is calculated based on the gas partial pressure correction coefficient and the base gas partial pressure. The target exhaust gas recirculation rate is calculated based on the exhaust gas recirculation rate correction coefficient and the basic exhaust gas recirculation rate. The target fuel injection quantity, target gas partial pressure, and target exhaust gas recirculation rate are used as the control parameters of the modified engine.

[0012] In conjunction with the first aspect, in one implementation, the method further includes, before determining whether the current natural gas state meets the gas quality self-learning trigger condition based on the fuel replenishment ratio: The amount of fuel added last time and the total amount of fuel remaining are determined based on the fuel tank level signal and historical level data. The fuel replenishment ratio is calculated based on the amount of fuel added in the last refueling and the total amount of remaining fuel.

[0013] Secondly, embodiments of this application provide a natural gas quality self-learning system, the natural gas quality self-learning system comprising: The generation module is used to determine whether the current natural gas status meets the gas quality self-learning trigger condition based on the fuel replenishment ratio. If it does, the gas quality self-learning activation command is generated and the component aging self-learning process is frozen. The fuel replenishment ratio is the ratio of the amount of fuel added last time to the total amount of fuel remaining. The determination module is used to respond to the gas quality self-learning activation command. If the engine operating conditions meet the preset learning operating conditions, it determines the gas calorific value parameter based on the engine air-fuel ratio feedback signal, the preset target reference value, and the effective duration of the current gas quality self-learning. It also determines the gas octane number parameter based on the distribution characteristics of the knock detection signal in multiple working cycles.

[0014] Thirdly, embodiments of this application provide a natural gas quality self-learning device, which includes a processor, a memory, and a natural gas quality self-learning program stored in the memory and executable by the processor. When the natural gas quality self-learning program is executed by the processor, it implements the steps of the natural gas quality self-learning method as described in the first aspect.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing a natural gas quality self-learning program, wherein when the natural gas quality self-learning program is executed by a processor, it implements the steps of the natural gas quality self-learning method as described in the first aspect.

[0016] The beneficial effects of the technical solutions provided in this application include: Based on the fuel replenishment ratio, determine whether the current natural gas state meets the gas quality self-learning trigger conditions. If it does, generate a gas quality self-learning activation command and freeze the component aging self-learning process. Determine the gas quality self-learning trigger timing and freeze the component aging self-learning process to avoid mixing of new and old gas quality and interference from other self-learning processes, ensuring a reliable self-learning environment. In response to the gas quality self-learning activation command, if the engine operating condition meets the preset learning operating condition conditions, determine the gas calorific value parameter based on the engine air-fuel ratio feedback signal, the preset target benchmark value, and the effective duration of the current gas quality self-learning. Determine the gas octane number parameter based on the distribution characteristics of the knock detection signal in multiple working cycles. Avoid invalid data collection under non-steady-state operating conditions, achieve differentiated identification of the two types of gas quality parameters, and improve the overall operating stability and economy of the natural gas engine. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the natural gas quality self-learning method of this application; Figure 2 This is a schematic diagram for judging the quality of natural gas. Figure 3For this application Figure 1 A detailed flowchart of step S20; Figure 4 This is a schematic diagram of the architecture of an embodiment of the natural gas quality self-learning system of this application; Figure 5 This is a schematic diagram of the hardware structure of the natural gas quality self-learning device involved in the embodiments of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0020] In a first aspect, embodiments of this application provide a natural gas quality self-learning method.

[0021] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the natural gas quality self-learning method of this application. Figure 1 As shown, the natural gas quality self-learning method includes: Step S10: Determine whether the current natural gas status meets the gas quality self-learning trigger condition based on the fuel replenishment ratio. If it does, generate a gas quality self-learning activation command and freeze the component aging self-learning process. The fuel replenishment ratio is the ratio of the last fuel added to the total remaining fuel. In this embodiment, the fuel replenishment ratio is used to quantify the proportion of newly added natural gas in the total gas volume in the cylinder. The system has a preset gas quality self-learning trigger threshold. Only when the ratio exceeds the threshold is it determined that there is a significant fluctuation in gas quality after mixing the old and new gas sources, and only then is a gas quality self-learning activation command generated to allow the gas quality self-learning to start. Freezing component aging self-learning is to isolate the correction interference caused by the aging of sensors and valve bodies, and to ensure that the gas quality learning data only reflects the differences in the gas itself, avoiding mutual interference between the two types of self-learning logic.

[0022] Step S20: In response to the gas quality self-learning activation command, if the engine operating condition meets the preset learning operating condition conditions, the gas calorific value parameter is determined based on the engine air-fuel ratio feedback signal, the preset target reference value and the effective duration of the current gas quality self-learning, and the gas octane number parameter is determined based on the distribution characteristics of the knock detection signal in multiple working cycles.

[0023] In this embodiment, the preset learning operating condition is the steady-state operating range calibrated on the test bench, and data collected only within this range can exclude dynamic operating condition interference; the preset target benchmark value is the closed-loop correction benchmark value corresponding to standard component natural gas, used to quantify calorific value deviation; the air-fuel ratio feedback signal is taken from the pre-oxygen sensor and used to extract calorific value-related deviation information; the knock detection signal reflects the gas's anti-knock capability, and the octane number parameter is obtained through multi-cycle statistical distribution characteristics; such as Figure 2 As shown, Figure 2 This diagram illustrates the assessment of natural gas quality. Changes in the internal composition of natural gas will simultaneously alter its calorific value and octane rating: methane is the standard component for fuel gas; an increase in heavy hydrocarbons such as ethane, propane, and butane will increase the calorific value and decrease the octane rating; an increase in inert gases such as nitrogen and carbon dioxide will decrease the calorific value and increase the octane rating; an increase in hydrogen and carbon monoxide will decrease the calorific value and decrease the octane rating. Therefore, it is evident that gas quality fluctuations are two-dimensional variables, and a single parameter cannot fully characterize the gas gas properties. Thus, after the gas quality self-learning function is triggered, only stable and reliable engine steady-state operating conditions are selected for gas quality identification. Two separate paths are used to independently calculate the two core gas quality parameters: calorific value and octane rating. One path relies on closed-loop feedback data from the front oxygen sensor to identify calorific value differences, while the other path relies on the statistical law of full-cycle knock retraction angle to identify differences in gas gas anti-knock performance.

[0024] In this embodiment, the current natural gas state is determined based on the fuel replenishment ratio to determine whether it meets the gas quality self-learning trigger condition. If it does, a gas quality self-learning activation command is generated and the component aging self-learning process is frozen. The timing of the gas quality self-learning trigger is determined, and the component aging self-learning process is frozen to avoid the mixing of new and old gas quality and interference from other self-learning processes, ensuring a reliable self-learning environment. In response to the gas quality self-learning activation command, if the engine operating condition meets the preset learning operating condition conditions, the gas calorific value parameter is determined based on the engine air-fuel ratio feedback signal, the preset target benchmark value, and the effective duration of the current gas quality self-learning. The gas octane number parameter is determined based on the distribution characteristics of the knock detection signal in multiple working cycles, avoiding invalid data collection under non-steady-state operating conditions, realizing differentiated identification of the two types of gas quality parameters, and improving the overall stability and economy of natural gas engine operation.

[0025] Furthermore, in one embodiment, such as Figure 3 As shown, Figure 3 For this application Figure 1A detailed flowchart of step S20 is provided, in which the determination of the gas calorific value parameters based on the engine's air-fuel ratio feedback signal, a preset target reference value, and the effective duration of the current gas quality self-learning includes: Step S201: Determine the closed-loop correction coefficient of the engine front oxygen sensor based on the engine air-fuel ratio feedback signal. Step S202: Obtain the closed-loop correction deviation value based on the absolute value of the difference between the closed-loop correction coefficient and the preset target reference value; Step S203: Based on the cumulative duration of the engine operating condition meeting the preset learning operating condition, the effective duration of the current quality self-learning is obtained, and an integral accumulation calculation is performed based on the closed-loop correction deviation value and the effective duration to obtain the integral result. Step S204: The integral result is subjected to upper and lower limit processing and combined with the preset target benchmark value to obtain the gas calorific value parameter.

[0026] In this embodiment, the preset target benchmark value is the calibration value of the closed-loop correction coefficient corresponding to the standard gas quality of the equivalent combustion natural gas engine. During bench calibration, a value of 1 is preferred, representing that the standard methane gas has no calorific value deviation. The engine front oxygen sensor collects exhaust oxygen concentration in real time to generate an air-fuel ratio feedback signal. The electronic control unit (ECU) calculates the closed-loop correction coefficient through closed-loop control logic. A deviation of 1 indicates a difference between the gas calorific value and the standard gas source. The preset learning operating conditions are preferably: engine speed 800~1500 rpm, load 20%~50%, speed change rate ≤50 rpm / s, and load change rate ≤2% / s. The deceleration fuel cut-off condition is excluded, and the effective duration is only accumulated within this steady-state range. The integral accumulation calculation involves integrating the closed-loop correction deviation value at each time the operating conditions are met. The upper and lower limits of the integral are the bench calibration limits to prevent integral overflow caused by extreme gas quality fluctuations. Finally, the integral limit result is superimposed with the benchmark value of 1 to output the gas calorific value parameter.

[0027] Furthermore, in one embodiment, determining the fuel gas octane number parameter based on the distribution characteristics of the knock detection signal across multiple working cycles includes: Based on the knock detection signals output by each cylinder of the engine, the knock retraction angle value corresponding to each cylinder in a single working cycle is obtained by analysis. The average knock angle value of all cylinders is taken to obtain the average knock angle value for a single working cycle; Using N consecutive working cycles of the engine as a statistical period, the effective number of cycles in which the average knock retraction angle value falls into each retraction angle interval within a statistical period is calculated, wherein the retraction angle interval is obtained according to multiple preset retraction angle thresholds; The octane rating parameter of the fuel gas is calculated based on the effective number of cycles.

[0028] In this embodiment, the preset back angle threshold bench calibration is preferably 0°, 2°, and 4°, which divides the test into four back angle intervals: 0° (high octane I zone), 0°~2° (lower octane II zone), 2°~4° (low octane III zone), and >4° (very low octane IV zone); N is the preset number of statistical cycles, optionally, 100 engine working cycles are calibrated as a complete statistical cycle; the knock sensor collects the vibration signal of each cylinder, and the ECU converts the ignition retarding angle of each cylinder in a single cycle into the knock back angle value; the arithmetic mean of the back angles of multiple cylinders is used to eliminate the combustion differences of a single cylinder; the average back angle of each cycle within the statistical cycle entering the corresponding interval is counted as one effective cycle, and the number of effective cycles in the four intervals is counted separately as the input for subsequent correction magnitude calculation.

[0029] Furthermore, in one embodiment, before analyzing the knock detection signals output by each cylinder of the engine to obtain the knock retraction angle value corresponding to a single working cycle of each cylinder, the method further includes: Determine whether the engine operating condition meets the preset learning operating condition conditions, which include the engine speed being within a preset calibration range, the knock sensor being fault-free, and the ignition angle having a preset allowable margin. If satisfied, then proceed with the step of analyzing the knock detection signals output by each cylinder of the engine to obtain the knock retraction angle value corresponding to each cylinder in a single working cycle. If the condition is not met, the preset minimum limit value will be used directly as the octane rating parameter for the fuel gas.

[0030] In this embodiment, the preset calibration range is preferably 1000~2200rpm, which has the highest knock detection sensitivity; the preset allowable calibration is preferably ≥2° ignition angle to ensure that the system has adjustment space for delayed ignition when knock occurs; knock sensor faults include open circuit, short circuit, and signal drift faults, and the knock intensity cannot be accurately identified under fault conditions; the preset minimum limit is the lowest octane number coefficient of bench calibration, which is suitable for inferior natural gas with high heavy hydrocarbon content and high knockability, and avoids the risk of engine knock damage; when the operating conditions are not up to standard or the sensor is faulty, the value is directly assigned, skipping the knock acquisition and range statistics process.

[0031] Further, in one embodiment, calculating the fuel gas octane number parameter based on the effective cycle count includes: Based on the effective number of cycles and the preset correction range corresponding to each setback interval, the actual correction range of each setback interval is determined; At the end of a statistical period, the total correction magnitude is obtained by summing the actual correction magnitudes of all back angle intervals. The octane rating coefficients in the octane rating coefficient table are updated using the total correction magnitude, wherein the octane rating coefficient table is indexed by engine speed and load; Matching a corresponding target octane number coefficient from the updated octane number coefficient table according to the current engine speed and load, and performing upper and lower limit amplitude constraint processing on the target octane number coefficient to obtain the gas octane number parameter.

[0032] In this embodiment, optionally, the preset correction amplitude for each retard angle interval is calibrated on a bench: Zone Ⅰ a1=0.1, Zone Ⅱ a2=-0.05, Zone Ⅲ a3=-0.08, Zone Ⅳ a4=-0.1, which satisfies a4<a3<a2<a1; when there is no valid cycle number in a certain interval, the actual correction amplitude of this interval is directly set to 0; the total correction amplitude is obtained by accumulating the amplitudes of all intervals only after the complete 100-cycle statistical period ends, the total correction amplitude is 0 when the period is not completed, and the table is not updated; the octane number coefficient table is a two-dimensional table built in the ECU, with engine speed as the horizontal axis and engine load as the vertical axis, covering the full working condition range; the total correction amplitude is superimposed on the original octane coefficient of the corresponding working condition point to complete iterative update; upper and lower limit amplitude limiting is performed after matching working conditions and reading the coefficient to prevent correction out of range; the final gas octane number parameter is output after amplitude limiting.

[0033] Further, in one embodiment, after determining the gas octane number parameter based on the distribution characteristics of the knock detection signal in a plurality of working cycles, the method further comprises: Obtaining a fuel injection correction coefficient, a gas partial pressure correction coefficient and an exhaust gas recirculation rate correction coefficient according to the gas heating value parameter, the gas octane number parameter and a preset control parameter mapping relationship, and determining a basic fuel injection amount, a basic gas partial pressure and a basic exhaust gas recirculation rate in combination with basic control parameters of the engine; Calculating a target fuel injection amount according to the fuel injection correction coefficient and the basic fuel injection amount; Calculating a target gas partial pressure according to the gas partial pressure correction coefficient and the basic gas partial pressure; Calculating a target exhaust gas recirculation rate according to the exhaust gas recirculation rate correction coefficient and the basic exhaust gas recirculation rate; Taking the target fuel injection amount, the target gas partial pressure and the target exhaust gas recirculation rate as corrected engine control parameters.

[0034] In this embodiment, the preset control parameter mapping relationship is a three-dimensional look-up table calibrated on a bench, which takes heating value and octane number as double input parameters and outputs three types of correction coefficients; the basic control parameters are the factory-calibrated injection amount, gas supply partial pressure and exhaust gas recirculation (EGR) rate of the engine under a standard gas source; target value = basic value × corresponding correction coefficient, completing gas property adaptive compensation; the medium is uniformly corrected to gas injection amount, and the three types of target parameters are directly output to the gas injection valve, pressure regulating valve and EGR valve actuator to complete adaptive correction of the whole machine control parameters, so as to adapt to the current gas heating value and anti-knock characteristics.

[0035] Furthermore, in one embodiment, before determining whether the current natural gas state meets the gas quality self-learning trigger condition based on the fuel replenishment ratio, the method further includes: The amount of fuel added last time and the total amount of fuel remaining are determined based on the fuel tank level signal and historical level data. The fuel replenishment ratio is calculated based on the amount of fuel added in the last refueling and the total amount of remaining fuel.

[0036] In this embodiment, the historical liquid level data is the liquid level stored in the gas cylinder before refueling, and the real-time liquid level is the liquid level after refueling. The difference between the two is used to calculate the volume and obtain the amount of newly added fuel. The remaining total fuel is the total amount of gas remaining in the gas cylinder after refueling. The fuel replenishment ratio = newly added fuel amount ÷ remaining total fuel amount. The system's preset trigger threshold is preferably 0.3. Only when the ratio is greater than 0.3 is it determined that there has been a significant change in gas quality, and the gas quality self-learning process is activated. When the refueling amount ratio is too low, the gas quality fluctuation after mixing the new and old gas is minimal, and there is no need to start self-learning, thus avoiding ineffective learning that would consume ECU computing power.

[0037] Secondly, embodiments of this application also provide a natural gas quality self-learning system.

[0038] In one embodiment, reference is made to Figure 4 , Figure 4 This is a schematic diagram of the architecture of an embodiment of the natural gas quality self-learning system of this application. Figure 4 As shown, the natural gas quality self-learning system includes: The generation module is used to determine whether the current natural gas status meets the gas quality self-learning trigger condition based on the fuel replenishment ratio. If it does, the gas quality self-learning activation command is generated and the component aging self-learning process is frozen. The fuel replenishment ratio is the ratio of the amount of fuel added last time to the total amount of fuel remaining. The determination module is used to respond to the gas quality self-learning activation command. If the engine operating conditions meet the preset learning operating conditions, it determines the gas calorific value parameter based on the engine air-fuel ratio feedback signal, the preset target reference value, and the effective duration of the current gas quality self-learning. It also determines the gas octane number parameter based on the distribution characteristics of the knock detection signal in multiple working cycles.

[0039] Furthermore, in one embodiment, the determining module is used to: Based on the engine's air-fuel ratio feedback signal, determine the closed-loop correction coefficient of the engine's front oxygen sensor; The closed-loop correction deviation value is obtained by calculating the absolute value of the difference between the closed-loop correction coefficient and the preset target benchmark value. Based on the cumulative duration of the engine operating conditions meeting the preset learning conditions, the effective duration of the current quality self-learning is obtained, and an integral accumulation calculation is performed based on the closed-loop correction deviation value and the effective duration to obtain the integral result. The integral result is subjected to upper and lower limit processing and combined with the preset target benchmark value to obtain the gas calorific value parameter.

[0040] Furthermore, in one embodiment, the determining module is used to: Based on the knock detection signals output by each cylinder of the engine, the knock retraction angle value corresponding to each cylinder in a single working cycle is obtained by analysis. The average knock angle value of all cylinders is taken to obtain the average knock angle value for a single working cycle; Using N consecutive working cycles of the engine as a statistical period, the effective number of cycles in which the average knock retraction angle value falls into each retraction angle interval within a statistical period is calculated, wherein the retraction angle interval is obtained according to multiple preset retraction angle thresholds; The octane rating parameter of the fuel gas is calculated based on the effective number of cycles.

[0041] Furthermore, in one embodiment, the natural gas quality self-learning system further includes a judgment module, used for: Determine whether the engine operating condition meets the preset learning operating condition conditions, which include the engine speed being within a preset calibration range, the knock sensor being fault-free, and the ignition angle having a preset allowable margin. If satisfied, then proceed with the step of analyzing the knock detection signals output by each cylinder of the engine to obtain the knock retraction angle value corresponding to each cylinder in a single working cycle. If the condition is not met, the preset minimum limit value will be used directly as the octane rating parameter for the fuel gas.

[0042] Furthermore, in one embodiment, the determining module is used to: Based on the effective number of cycles and the preset correction range corresponding to each setback interval, the actual correction range of each setback interval is determined; At the end of a statistical period, the total correction magnitude is obtained by summing the actual correction magnitudes of all back angle intervals. The octane rating coefficients in the octane rating coefficient table are updated using the total correction magnitude, wherein the octane rating coefficient table is indexed by engine speed and load; Based on the current engine speed and load, the target octane rating is matched from the updated octane rating table, and the target octane rating is constrained by upper and lower limits to obtain the fuel octane rating parameter.

[0043] Furthermore, in one embodiment, the natural gas quality self-learning system further includes a computing module for: Based on the mapping relationship between the gas calorific value parameter, the gas octane number parameter, and the preset control parameter, the fuel injection correction coefficient, the gas partial pressure correction coefficient, and the exhaust gas recirculation rate correction coefficient are obtained. Combined with the engine's basic control parameters, the basic fuel injection quantity, the basic gas partial pressure, and the basic exhaust gas recirculation rate are determined. The target fuel injection quantity is calculated based on the fuel injection correction coefficient and the base fuel injection quantity. The target gas partial pressure is calculated based on the gas partial pressure correction coefficient and the base gas partial pressure. The target exhaust gas recirculation rate is calculated based on the exhaust gas recirculation rate correction coefficient and the basic exhaust gas recirculation rate. The target fuel injection quantity, target gas partial pressure, and target exhaust gas recirculation rate are used as the control parameters of the modified engine.

[0044] Furthermore, in one embodiment, the determining module is used to: The amount of fuel added last time and the total amount of fuel remaining are determined based on the fuel tank level signal and historical level data. The fuel replenishment ratio is calculated based on the amount of fuel added in the last refueling and the total amount of remaining fuel.

[0045] The functions of each module in the above-mentioned natural gas quality self-learning system correspond to the steps in the above-mentioned natural gas quality self-learning method embodiment, and their functions and implementation processes will not be described in detail here.

[0046] Thirdly, embodiments of this application provide a natural gas quality self-learning device, which may be a vehicle controller, engine controller, or other similar devices.

[0047] Reference Figure 5 , Figure 5 This is a schematic diagram of the hardware structure of the natural gas quality self-learning device involved in the embodiments of this application. In the embodiments of this application, the natural gas quality self-learning device may include a processor, a memory, a communication interface, and a communication bus.

[0048] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0049] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the natural gas quality self-learning device, as well as interfaces used for interconnecting the natural gas quality self-learning device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0050] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0051] The processor can be a general-purpose processor, which can call the natural gas quality self-learning program stored in the memory and execute the natural gas quality self-learning method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the natural gas quality self-learning program is called can be referred to in the various embodiments of the natural gas quality self-learning method of this application, and will not be repeated here.

[0052] Those skilled in the art will understand that Figure 5 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0053] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0054] The present application provides a computer-readable storage medium storing a natural gas quality self-learning program, wherein when the natural gas quality self-learning program is executed by a processor, it implements the steps of the natural gas quality self-learning method described above.

[0055] The method implemented when the natural gas quality self-learning program is executed can be referred to in various embodiments of the natural gas quality self-learning method of this application, and will not be repeated here.

[0056] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0057] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0058] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0059] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0060] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0061] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0062] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A natural gas quality self-learning method, characterized in that, The natural gas quality self-learning method includes: The current natural gas status is determined based on the fuel replenishment ratio to determine whether it meets the gas quality self-learning trigger condition. If it does, a gas quality self-learning activation command is generated and the component aging self-learning process is frozen. The fuel replenishment ratio is the ratio of the last fuel added to the total remaining fuel. In response to the gas quality self-learning activation command, if the engine operating conditions meet the preset learning conditions, the gas calorific value parameter is determined based on the engine air-fuel ratio feedback signal, the preset target reference value, and the effective duration of the current gas quality self-learning, and the gas octane number parameter is determined based on the distribution characteristics of the knock detection signal in multiple working cycles.

2. The natural gas quality self-learning method as described in claim 1, characterized in that, The determination of the gas calorific value parameters based on the engine's air-fuel ratio feedback signal, the preset target reference value, and the effective duration of the current gas quality self-learning includes: Based on the engine's air-fuel ratio feedback signal, determine the closed-loop correction coefficient of the engine's front oxygen sensor; The closed-loop correction deviation value is obtained by calculating the absolute value of the difference between the closed-loop correction coefficient and the preset target benchmark value. Based on the cumulative duration of the engine operating conditions meeting the preset learning conditions, the effective duration of the current quality self-learning is obtained, and an integral accumulation calculation is performed based on the closed-loop correction deviation value and the effective duration to obtain the integral result. The integral result is subjected to upper and lower limit processing and combined with the preset target benchmark value to obtain the gas calorific value parameter.

3. The natural gas quality self-learning method as described in claim 1, characterized in that, The determination of the fuel gas octane number parameter based on the distribution characteristics of the knock detection signal across multiple working cycles includes: Based on the knock detection signals output by each cylinder of the engine, the knock retraction angle value corresponding to each cylinder in a single working cycle is obtained by analysis. The average knock angle value of all cylinders is taken to obtain the average knock angle value for a single working cycle; Using N consecutive working cycles of the engine as a statistical period, the effective number of cycles in which the average knock retraction angle value falls into each retraction angle interval within a statistical period is calculated, wherein the retraction angle interval is obtained according to multiple preset retraction angle thresholds; The octane rating parameter of the fuel gas is calculated based on the effective number of cycles.

4. The natural gas quality self-learning method according to claim 3, characterized in that, Before obtaining the knock retraction angle value corresponding to a single working cycle of each cylinder based on the knock detection signals output by each cylinder of the engine, the following steps are also included: Determine whether the engine operating condition meets the preset learning operating condition conditions, which include the engine speed being within a preset calibration range, the knock sensor being fault-free, and the ignition angle having a preset allowable margin. If satisfied, then proceed with the step of analyzing the knock detection signals output by each cylinder of the engine to obtain the knock retraction angle value corresponding to each cylinder in a single working cycle. If the condition is not met, the preset minimum limit value will be used directly as the octane rating parameter for the fuel gas.

5. The natural gas quality self-learning method according to claim 3, characterized in that, The calculation of the fuel octane rating parameter based on the effective cycle count includes: Based on the effective number of cycles and the preset correction range corresponding to each setback interval, the actual correction range of each setback interval is determined; At the end of a statistical period, the total correction magnitude is obtained by summing the actual correction magnitudes of all back angle intervals. The octane rating coefficients in the octane rating coefficient table are updated using the total correction magnitude, wherein the octane rating coefficient table is indexed by engine speed and load; Based on the current engine speed and load, the target octane rating is matched from the updated octane rating table, and the target octane rating is constrained by upper and lower limits to obtain the fuel octane rating parameter.

6. The natural gas quality self-learning method as described in claim 1, characterized in that, After determining the octane rating parameters of the fuel gas based on the distribution characteristics of the knock detection signal across multiple operating cycles, the method further includes: Based on the mapping relationship between the gas calorific value parameter, the gas octane number parameter, and the preset control parameter, the fuel injection correction coefficient, the gas partial pressure correction coefficient, and the exhaust gas recirculation rate correction coefficient are obtained. Combined with the engine's basic control parameters, the basic fuel injection quantity, the basic gas partial pressure, and the basic exhaust gas recirculation rate are determined. The target fuel injection quantity is calculated based on the fuel injection correction coefficient and the base fuel injection quantity. The target gas partial pressure is calculated based on the gas partial pressure correction coefficient and the base gas partial pressure. The target exhaust gas recirculation rate is calculated based on the exhaust gas recirculation rate correction coefficient and the basic exhaust gas recirculation rate. The target fuel injection quantity, target gas partial pressure, and target exhaust gas recirculation rate are used as the control parameters of the modified engine.

7. The natural gas quality self-learning method as described in claim 1, characterized in that, Before determining whether the current natural gas state meets the gas quality self-learning trigger condition based on the fuel replenishment ratio, the following steps are also included: The amount of fuel added last time and the total amount of fuel remaining are determined based on the fuel tank level signal and historical level data. The fuel replenishment ratio is calculated based on the amount of fuel added in the last refueling and the total amount of remaining fuel.

8. A natural gas quality self-learning system, characterized in that, The natural gas quality self-learning system includes: The generation module is used to determine whether the current natural gas status meets the gas quality self-learning trigger condition based on the fuel replenishment ratio. If it does, the gas quality self-learning activation command is generated and the component aging self-learning process is frozen. The fuel replenishment ratio is the ratio of the amount of fuel added last time to the total amount of fuel remaining. The determination module is used to respond to the gas quality self-learning activation command. If the engine operating conditions meet the preset learning operating conditions, it determines the gas calorific value parameter based on the engine air-fuel ratio feedback signal, the preset target reference value, and the effective duration of the current gas quality self-learning. It also determines the gas octane number parameter based on the distribution characteristics of the knock detection signal in multiple working cycles.

9. A natural gas quality self-learning device, characterized in that, The natural gas quality self-learning device includes a processor, a memory, and a natural gas quality self-learning program stored in the memory and executable by the processor, wherein when the natural gas quality self-learning program is executed by the processor, it implements the steps of the natural gas quality self-learning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a natural gas quality self-learning program, wherein when the natural gas quality self-learning program is executed by a processor, it implements the steps of the natural gas quality self-learning method as described in any one of claims 1 to 7.

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