Intelligent advantage-seeking fire control method for complex fuel oil environment

Abstract

The invention provides an intelligent advantage-seeking ignition control method oriented to a complex fuel oil environment. The method comprises the steps that real-time operation parameters of an engine are obtained, and self-learning enabling conditions are judged; when the enabling condition is met, forward self-learning optimization is executed with the basic ignition angle or the conservative ignition angle under the EGR working condition as the starting point; monitoring a knock signal in real time in the optimizing process, and dynamically adjusting an ignition angle according to the knock intensity; independently learning areas, and updating the ignition angle compensation value of each area in real time; the ignition angle instruction is corrected through anti-oscillation and protection mechanisms such as partition delaying and signal filtering and is output to an actuator; meanwhile, the refueling working condition is recognized, and self-adaptive resetting of the self-learning value is achieved. Through closed-loop self-learning, detonation dynamic feedback, zoning fine control and EGR collaborative strategies, the method can adapt to fuel quality fluctuation and engine aging, and the dynamic property, fuel economy and emission compliance of the engine are remarkably improved, and particularly the method is suitable for scenes with large fuel quality fluctuation and complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of engine control technology, and in particular to an intelligent spot ignition control method for complex fuel environments. Background Technology

[0002] Engine ignition timing (ignition advance angle) is a core parameter affecting engine power, economy, and emissions. Traditional ignition control mainly relies on open-loop control strategies calibrated on a test bench (i.e., ignition MAP).

[0003] However, this traditional control method faces many challenges: First, the combustion environment is highly variable, and the actual combustion process is affected by various factors such as intake air temperature, pressure, fuel quality, and carbon deposits, making it difficult to maintain the theoretically optimal ignition angle; second, for overseas regions exporting inferior fuel, traditional ignition MAP typically adopts a conservative strategy to cover all extreme operating conditions, resulting in a loss of power and fuel economy during normal driving; third, with the application of EGR (exhaust gas recirculation) technology, the EGR rate model has uncontrollable biases, making it difficult for the ignition angle to adapt; in addition, increasingly stringent emission regulations and sensor drift caused by hardware aging further exacerbate the deviation of open-loop control.

[0004] Therefore, how to enable the ignition control system to adapt to different fuel qualities, environmental conditions and hardware aging states, and maintain optimal performance throughout its entire life cycle, is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] Therefore, it is necessary to provide an intelligent ignition control method for complex fuel environments to address the aforementioned technical problems.

[0006] A smart ignition control method for complex fuel environments includes the following steps: In one embodiment, the real-time operating parameters of the engine are obtained, and it is determined whether the current operating condition meets the self-learning enable condition. In response to the fulfillment of the enabling conditions, starting from the base ignition angle corresponding to the current operating condition, a positive self-learning optimization is performed to gradually increase the ignition advance angle; During the optimization process, the detonation signal is monitored in real time, and the ignition angle is dynamically adjusted according to the detonation intensity. The operating conditions are divided into multiple independent learning areas based on engine speed and load. Each area stores an independent ignition angle compensation value and updates it in real time during operation. The ignition angle is corrected based on the ignition angle compensation value by using an anti-vibration and protection mechanism to obtain the final ignition angle command; The final ignition angle command is output to the actuator.

[0007] In one embodiment, the self-learning enable condition includes at least: Knock control enable is on; The positive self-learning value under the current operating conditions is 0; The current water temperature, speed, and load are all within the preset normal operating range; It has not entered the exhaust gas recirculation control area or the exhaust gas recirculation control has been stabilized.

[0008] In one embodiment, before performing positive self-learning optimization and gradually increasing the ignition advance angle, the following steps are also included: Determine whether the exhaust gas recirculation control zone has been entered; When entering the exhaust gas recirculation control area and the exhaust gas recirculation rate exceeds the preset threshold, a conservative ignition angle is set as the starting point, and the forward self-learning is paused based on the knocking angle signal.

[0009] In one embodiment, performing forward self-learning optimization includes: In the absence of detonation, the ignition advance angle is increased by a small step every fixed time or several working cycles, continuously approaching the detonation boundary.

[0010] In one embodiment, real-time monitoring of the detonation signal during the optimization process and dynamic adjustment of the ignition angle based on the detonation intensity include: In response to the absence of detected detonation, the positive self-learning optimization continues; In response to the detection of critical knock, the forward self-learning optimization is stopped and the current ignition angle is maintained; In response to the detection of a strong knock, the ignition advance angle is reduced according to a preset advance angle logic or a lookup table method until the knock signal disappears; Once the detonation disappears, the system enters an observation period and restarts positive self-learning optimization once the current operating conditions stabilize.

[0011] In one embodiment, after stopping the forward self-learning optimization and maintaining the current ignition angle in response to detecting critical knock, the method further includes: Determine the detonation retreat angle. If the detonation retreat angle remains unchanged, lock the current positive self-learning value. If the detonation retreat angle continues to increase, restore the positive self-learning value to 0.

[0012] In one embodiment, the operating conditions are divided into multiple independent learning regions based on engine speed and load. Each region stores an independent ignition angle compensation value, which is updated in real time during operation, including: Construct a two-dimensional array pulse spectrum with speed and load as coordinates, and store an independent ignition angle compensation value in each grid cell; The compensation value of the grid corresponding to the two-dimensional array pulse spectrum is updated based on the current rotational speed and load.

[0013] In one embodiment, it further includes: In response to the current speed or load rising above the first threshold above the partition boundary, switch to the adjacent grid; In response to the current speed or load dropping to the second threshold below the partition boundary, switch back to the original grid.

[0014] In one embodiment, after outputting the final ignition angle command to the actuator, the method further includes: The refueling condition is identified, and in response to the refueling condition meeting the refueling identification conditions, the positive self-learning value of the ignition angle is restored to the default value.

[0015] In one embodiment, the refueling identification conditions include: The system detected an increase in oil level and an engine shutdown duration exceeding the preset calibration value.

[0016] Compared to existing technologies, the advantages and beneficial effects of this invention are as follows: This invention introduces a closed-loop adaptive learning and cooperative control strategy, significantly improving the overall performance of the engine throughout its entire lifecycle. Through a dynamic optimization mechanism, the system can continuously approach the ignition limit without knocking, maximizing engine output torque, while simultaneously improving fuel economy through precise combustion phase control. Based on speed load-based zoned independent learning and switching hysteresis mechanisms, the system can adapt to different fuel qualities, environmental conditions, and hardware aging states, solving the adaptability problem of traditional open-loop control under complex operating conditions. A dynamic balance between optimization, maintenance, and protection is achieved through an intelligent state machine, combined with knock detection and rapid retraction protection, ensuring the engine's safe operation under extreme conditions. This invention, in conjunction with EGR control and refueling recognition and recovery mechanisms, further optimizes emission performance and reduces knock risk. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an intelligent ignition control method for complex fuel environments in one embodiment. Figure 2 This is a schematic diagram of the EGR collaborative decision-making process in one embodiment; Figure 3 This is a schematic diagram of the positive self-learning optimization process during the monitoring of detonation signals in one embodiment; Figure 4 This is a schematic diagram of the positive self-learning optimization process during the monitoring of the detonation angle in one embodiment; Figure 5 This is a schematic diagram of the partition forward self-learning optimization process in one embodiment. Detailed Implementation

[0018] Before describing the specific embodiments of the present invention, the overall concept of the present invention will be explained as follows: This invention is mainly developed based on the actual needs faced by engines throughout their entire life cycle, such as varying combustion environments, fuel quality differences, hardware aging, and increasingly stringent emission regulations. Currently, traditional ignition control mainly relies on open-loop MAP calibrated on a test bench, which has problems such as poor adaptability, conservative control, and loss of power and economy under complex operating conditions.

[0019] The inventors discovered through analysis that the main reason for the aforementioned problems is that open-loop control cannot perceive changes in combustion state in real time, making it difficult to dynamically approximate the theoretically optimal ignition angle. Furthermore, it lacks adaptive capabilities for special operating conditions such as EGR activation, knock critical states, and fuel switching, leading to control deviations and performance losses. By introducing an intelligent state machine to achieve a dynamic balance between optimization, maintenance, and protection, combined with strategies such as independent learning by speed and load zones, switching hysteresis mechanisms, EGR collaborative control, and refueling recognition recovery, the aforementioned problems can be avoided. Therefore, this invention proposes an intelligent ignition control method for complex fuel environments, achieving real-time optimization and adaptive learning of the ignition angle across the entire operating range, significantly improving engine power, fuel economy, emission compliance, and environmental adaptability.

[0020] After introducing the overall concept of the present invention, in order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of this specification should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word covers the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] For ease of understanding, the terms used in the embodiments of this invention are explained below: Ignition advance angle: refers to the crankshaft angle between the piston and top dead center when the spark plug ignites. An excessively large ignition advance angle can easily lead to knocking, while an excessively small ignition advance angle will reduce power and fuel economy. Knock: During engine combustion, the air-fuel mixture spontaneously combusts before the flame front arrives, generating a strong shock wave that causes engine vibration and increased noise, and in severe cases, damages engine parts. EGR (Exhaust Gas Recirculation): Introduces some of the exhaust gas from the engine back into the intake system, mixes it with fresh air, and then enters the cylinder for combustion. This reduces the combustion temperature in the cylinder and reduces NOx emissions. Positive self-learning optimization: Starting from the basic ignition angle, gradually increase the ignition advance angle to approach the knocking boundary and find the best ignition angle under the current working conditions; Two-dimensional array pulse spectrum diagram (MAP diagram): The operating conditions are divided into multiple grids with speed and load as coordinate axes. Each grid stores the control parameters (such as ignition angle compensation value) of the corresponding operating conditions for quick query and update. Zone switching hysteresis: A buffer zone is set for switching between operating zones to avoid frequent switching caused by small fluctuations in operating conditions and to ensure control stability; Positive self-learning value: During the self-learning optimization process, the ignition advance angle compensation value is added on the basis of the basic ignition angle to adjust the ignition angle to the optimal state; ECU (Engine Control Unit) is the engine control unit.

[0023] In one embodiment, such as Figure 1 As shown, an intelligent ignition control method for complex fuel environments is provided, including the following steps: Step S101: Obtain the real-time operating parameters of the engine and determine whether the current operating condition meets the self-learning enable condition.

[0024] Specifically, the engine ECU reads and filters all relevant sensor signals in real time to obtain the engine's real-time operating parameters, including but not limited to: engine speed, load, water temperature, knock signal, EGR valve opening, oil level, and engine downtime.

[0025] The ECU determines whether the current operating conditions meet the self-learning enable conditions based on the collected real-time operating parameters.

[0026] Based on this, the conditions enabling self-learning should include at least the following: Knock control enable is on; The positive self-learning value under the current operating conditions is 0; The current water temperature, speed, and load are all within the preset normal operating range; It has not entered the exhaust gas recirculation control area or the exhaust gas recirculation control has been stabilized.

[0027] Specifically, the conditions enabling self-learning include at least: (1) Knock control is enabled. Knock control is automatically controlled by the engine ECU according to the engine operating status. When the engine is in the starting stage, the idling speed is unstable stage or the fault state, knock control is turned off and self-learning is not started. (2) The positive self-learning value under the current working condition is 0, ensuring that each self-learning starts from the initial state, avoiding interference from the original learning value to this optimization, and ensuring the optimization accuracy; (3) The current water temperature, speed, and load are within the preset normal operating range. The normal range of water temperature is usually 50℃-100℃, the normal range of speed is determined according to the engine model, and the normal range of load is 10%-90%. Avoid starting self-learning under conditions of cold start, overheating, low idle speed, or full load limit to prevent malfunctions during optimization. (4) Not entering the EGR control area or EGR control is stable. When not entering the EGR control area, the optimization can be started directly from the basic ignition angle; when entering the EGR control area but the EGR control is not stable, self-learning should not be started temporarily to avoid combustion instability caused by EGR rate fluctuations, which would affect the optimization effect.

[0028] In step S102, in response to the fulfillment of the enable condition, starting from the base ignition angle corresponding to the current operating condition, a forward self-learning optimization is performed to gradually increase the ignition advance angle.

[0029] Specifically, when the enabling conditions are met, the starting point for optimization is determined and positive self-learning optimization is initiated.

[0030] Based on this, the process of implementing positive self-learning optimization and gradually increasing the ignition advance angle also includes: Determine whether the exhaust gas recirculation control zone has been entered; When entering the exhaust gas recirculation control area and the exhaust gas recirculation rate exceeds the preset threshold, a conservative ignition angle is set as the starting point, and the forward self-learning is paused based on the knocking angle signal.

[0031] Specifically, it determines whether to enter the EGR control area. When entering the exhaust gas recirculation control area and the exhaust gas recirculation rate exceeds the preset threshold, a conservative ignition angle is set as the starting point, and the positive self-learning is paused based on the knocking angle signal.

[0032] The EGR control zone is determined by whether the EGR valve opening exceeds the preset calibration value and the duration reaches the preset duration. The preset threshold for the exhaust gas recirculation rate is determined based on engine bench testing. Different thresholds can be set under different operating conditions to ensure that when EGR is on and the recirculation rate is high, the risk of knocking is avoided through a conservative ignition angle. The conservative ignition angle is set based on the baseline ignition angle under the current operating conditions, appropriately delaying ignition (reducing the ignition advance angle) to reserve sufficient knock safety margin. The specific value is calibrated through bench testing combined with the range of fuel quality fluctuations.

[0033] If the EGR control area is not entered, the basic ignition angle corresponding to the current operating condition is used as the starting point for optimization. The basic ignition angle is obtained by querying the basic ignition angle MAP diagram calibrated on the test bench. This MAP diagram uses speed and load as coordinates and stores the initial ignition angle parameters under different operating conditions in advance.

[0034] If the system enters the EGR control zone, it further determines whether the exhaust gas recirculation rate exceeds a preset threshold. The exhaust gas recirculation rate is calculated based on parameters such as the EGR valve opening and intake volume. If the exhaust gas recirculation rate is less than or equal to the preset threshold, the basic ignition angle is still used as the optimization starting point. If the exhaust gas recirculation rate is greater than the preset threshold, a conservative ignition angle is set as the optimization starting point, while the knock angle signal is monitored in real time. If the knock angle is greater than or equal to 1°CA, forward self-learning is paused, and the conservative ignition angle is maintained. If the knock angle is less than 1°CA, forward self-learning optimization is initiated.

[0035] Based on this, the implementation of positive self-learning optimization includes: In the absence of detonation, the ignition advance angle is increased by a small step every fixed time or several working cycles, continuously approaching the detonation boundary.

[0036] Specifically, under non-knock conditions, the ignition advance angle is increased by a small step at fixed intervals or after several working cycles, continuously approaching the knock boundary. The fixed interval can be set to 1-5 seconds, and the working cycle can be set to 2-5 cylinder working cycles. The small step size is calibrated based on fuel quality fluctuations and operating conditions; too large a step size increases the risk of knock, while too small a step size reduces optimization efficiency. By gradually increasing the ignition advance angle, the optimal ignition angle for the current fuel quality and operating conditions can be quickly found while ensuring safety, achieving an optimal balance between power and economy.

[0037] Step S103: During the optimization process, the detonation signal is monitored in real time, and the ignition angle is dynamically adjusted according to the detonation intensity.

[0038] Specifically, during the optimization process, the detonation signal is monitored in real time, and the ignition angle is dynamically adjusted according to the detonation intensity.

[0039] Based on this, during the optimization process, the detonation signal is monitored in real time, and the ignition angle is dynamically adjusted according to the detonation intensity, including: In response to the absence of detected detonation, the positive self-learning optimization continues; In response to the detection of critical knock, the forward self-learning optimization is stopped and the current ignition angle is maintained; In response to the detection of a strong knock, the ignition advance angle is reduced according to a preset advance angle logic or a lookup table method until the knock signal disappears; Once the detonation disappears, the system enters an observation period and restarts positive self-learning optimization once the current operating conditions stabilize.

[0040] Specifically, during the optimization process, real-time monitoring of the detonation signal and dynamic adjustment of the ignition angle based on the detonation intensity include: (1) If no knock is detected, continue the positive self-learning optimization; this means that the current ignition angle has not yet reached the knock boundary, and the ignition advance angle can be increased to approach the optimal ignition angle. (2) When critical knock is detected, stop the forward self-learning optimization and maintain the current ignition angle; critical knock refers to the detection of an extremely weak knock signal (such as the vibration amplitude detected by the knock sensor being within the preset critical value range). At this time, it indicates that the current ignition angle is close to the optimal ignition angle. Maintaining the current ignition angle can achieve the best combustion effect and at the same time avoid the detonation from intensifying. (3) When strong knock is detected, the ignition advance angle is reduced according to the preset back angle logic or lookup table method until the knock signal disappears; strong knock refers to the vibration amplitude detected by the knock sensor exceeding the preset threshold. At this time, the ignition needs to be delayed quickly to suppress knock. The back angle amplitude is calibrated according to the knock intensity and can be determined by the preset back angle logic (such as linear back angle, stepped back angle) or lookup table method to ensure that knock is eliminated quickly. (4) When the knocking disappears, enter the observation period. After the current working condition stabilizes, restart the positive self-learning optimization. The duration of the observation period is set according to the stability of the working condition to ensure the stability of the engine working condition and avoid knocking misjudgment caused by working condition fluctuation. When restarting the optimization, a smaller step size can be used to further improve the optimization accuracy.

[0041] Based on this, in response to the detection of critical knock, after stopping the forward self-learning optimization and maintaining the current ignition angle, the following further steps are included: Determine the detonation retreat angle. If the detonation retreat angle remains unchanged, lock the current positive self-learning value. If the detonation retreat angle continues to increase, restore the positive self-learning value to 0.

[0042] Specifically, when knocking occurs, the knock retraction angle is determined. If the knock retraction angle remains unchanged, the current positive self-learning value is locked. If the knock retraction angle continues to increase, the positive self-learning value is restored to 0. The knock retraction angle refers to the reduction in ignition advance angle to suppress knocking. By monitoring changes in the knock retraction angle in real time, the stability of the current ignition angle can be determined: if the knock retraction angle remains unchanged, it indicates that the current ignition angle is in a stable optimal state, and locking the learning value can avoid interference in subsequent optimization; if the knock retraction angle continues to increase, it indicates that the current fuel quality or operating conditions have changed, and the original learning value is no longer applicable. After restoring it to 0, optimization is restarted to ensure control accuracy.

[0043] Step S104: Divide the operating conditions into multiple independent learning areas according to the engine speed and load. Each area stores an independent ignition angle compensation value and updates it in real time during operation.

[0044] Based on this, the operating conditions are divided into multiple independent learning regions according to engine speed and load. Each region stores independent ignition angle compensation values, which are updated in real time during operation, including: Construct a two-dimensional array pulse spectrum with speed and load as coordinates, and store an independent ignition angle compensation value in each grid cell; The compensation value of the grid corresponding to the two-dimensional array pulse spectrum is updated based on the current rotational speed and load.

[0045] Specifically, a two-dimensional array pulse map (MAP) is constructed with engine speed and load as coordinates. Each grid cell (Bin) stores an independent ignition angle compensation value. The engine speed coordinate is divided according to the engine speed range, such as every 100 rpm as an interval. The load coordinate is divided according to the intake manifold pressure or fuel injection quantity, such as every 5% load as an interval. The specific interval is calibrated according to the engine model and control accuracy requirements. It is preferred to divide it into 80 independent regions to achieve fine coverage of the entire operating condition. Based on the current speed and load, the compensation value of the two-dimensional array pulse spectrum corresponding to the grid is updated. The ECU reads the signals from the speed sensor and load sensor (such as the intake manifold pressure sensor) in real time, and quickly locates the grid corresponding to the current operating condition by looking up a table or interpolation calculation. After each self-learning optimization is completed, only the ignition angle compensation value of the current grid is updated, while the compensation values ​​of other grids remain unchanged, realizing independent optimization of each operating point without interference, and ensuring accurate ignition angle control across the entire operating range.

[0046] In addition to this, it also includes: In response to the current speed or load rising above the first threshold above the partition boundary, switch to the adjacent grid; In response to the current speed or load dropping to the second threshold below the partition boundary, switch back to the original grid.

[0047] Specifically, when the current speed or load rises above a first threshold above the zone boundary, the system switches to the adjacent grid; when the current speed or load falls below a second threshold below the zone boundary, it switches back to the original grid. The first and second thresholds form a buffer zone for zone switching. For example, if the zone boundary is set to 2000 rpm, the first threshold can be set to 2050 rpm, and the second threshold to 1950 rpm. When the speed rises above 2050 rpm, the system switches to the adjacent high-speed grid; when the speed falls below 1950 rpm, it switches back to the original low-speed grid. This hysteresis mechanism effectively avoids frequent grid switching by the ECU due to small fluctuations in operating conditions at the zone boundary (such as slight changes in speed during driving), preventing the ignition angle learning value from jumping back and forth and causing torque fluctuations, thus ensuring smooth driving.

[0048] Step S105: The ignition angle is corrected based on the ignition angle compensation value through the anti-vibration and protection mechanism to obtain the final ignition angle command.

[0049] Specifically, the anti-vibration and protection mechanisms include sub-mechanisms such as zoned hysteresis, signal filtering, and operating condition shielding, as detailed below: (1) Partition hysteresis: that is, the partition switching hysteresis mechanism in the above steps, to avoid ignition angle oscillation caused by frequent grid switching; (2) Signal filtering: Time filtering is performed on knock signals, speed signals, load signals, oil level signals, etc. to filter out instantaneous interference signals (such as sensor instantaneous errors and external vibration interference) to ensure the accuracy of signal acquisition and avoid incorrect ignition angle adjustment due to signal interference; (3) Operating condition shielding: Under operating conditions such as engine start-up, shutdown, rapid acceleration, and rapid deceleration, the self-learning optimization function is shielded to maintain the current ignition angle or adopt the preset transition ignition angle to avoid control disorder caused by optimization when the operating conditions are unstable. Self-learning is restored after the operating conditions stabilize.

[0050] Step S106: Output the final ignition angle command to the actuator.

[0051] Specifically, the final ignition angle command, after all logical judgments and corrections, is sent to the executor.

[0052] like Figure 2 The diagram shown is a schematic of the EGR collaborative decision-making process in one embodiment, which determines whether to enter the EGR control area and outputs a positive compensation value of 1.

[0053] like Figure 3 The diagram illustrates a forward self-learning optimization process during the monitoring of detonation signals in one embodiment. When detonation is present, time filter 1 is selected; when detonation is absent, time filter 2 is selected. Different filters are selected, and a positive compensation value 2 is output.

[0054] like Figure 4 As shown, this is a schematic diagram of the positive self-learning optimization process during the monitoring of the detonation angle in one embodiment. The positive compensation 3 is calculated based on the positive compensation value 2 and the correction coefficient of MAP.

[0055] like Figure 5 The diagram shown is a schematic of the partition forward self-learning optimization process in one embodiment, where the final compensation 4 is calculated based on the forward compensation value 3.

[0056] The final ignition angle output value is the sum of the base ignition angle value, other compensation values, and the final positive compensation value. The other compensation values ​​refer to all dynamic corrections used to adjust the ignition angle, excluding the base ignition angle value and the positive self-learning compensation value (the core knock optimization learning value of this invention). These are typically open-loop correction terms derived from table lookups or calculations based on the engine's current real-time status, used to cope with various environmental changes and operating condition fluctuations.

[0057] Based on this, after outputting the final ignition angle command to the actuator, the following is also included: The refueling condition is identified, and in response to the refueling condition meeting the refueling identification conditions, the positive self-learning value of the ignition angle is restored to the default value.

[0058] The refueling identification criteria include: The system detected an increase in oil level and an engine shutdown duration exceeding the preset calibration value.

[0059] Specifically, once the refueling conditions are detected and the refueling identification criteria are met, the positive self-learning value is reset to 0 for safety reasons. These refueling identification criteria include: detecting an increase in fuel level and an engine shutdown duration exceeding a preset calibration value.

[0060] The detection of increased fuel level is achieved by comparing real-time signals from the fuel level sensor with historical signals. When the increase in fuel level exceeds a preset value, it is determined to be an increase in fuel level. The preset calibration value for engine shutdown time is set according to actual usage scenarios, typically ranging from 30 minutes to 2 hours, to avoid misjudgments caused by short shutdowns. Restoring the positive self-learning value to the default value is to cope with sudden changes in fuel quality (such as in overseas regions where the previous tank contained high-octane gasoline and the next tank contains low-octane gasoline), ensuring that the engine will not experience knocking after restarting due to the original learning value adapting to the old fuel quality, thus ensuring operational safety.

[0061] The present invention provides an intelligent ignition control method for complex fuel environments, the advantages and beneficial effects of which are as follows: This invention employs a closed-loop positive self-learning optimization strategy, enabling it to adapt to fluctuations in fuel quality in real time. Without manual intervention, it can automatically adjust the ignition angle to the optimal state, solving the problem that traditional open-loop control cannot adapt to complex fuel environments. It is particularly suitable for scenarios where there are significant differences in fuel quality, such as in overseas regions with inferior fuel or in China.

[0062] By using a two-dimensional partitioned learning mechanism based on engine speed and load, the engine operating conditions are divided into multiple independent regions. Each region independently stores and updates the ignition angle compensation value, avoiding the defect that a unified learning value cannot meet the requirements of all operating conditions. This achieves precise ignition control under different operating conditions such as low speed and high load, and high speed and low load, and can achieve the optimal balance of power and economy across the entire operating range.

[0063] By introducing EGR condition judgment and conservative ignition angle strategy, when entering the EGR control area and the cycle rate exceeds the threshold, the starting point of the ignition angle is automatically adjusted and it is determined whether to pause optimization. This effectively solves the combustion instability problem caused by EGR rate fluctuations, and the coordinated EGR control significantly reduces NOx emissions, meeting emission regulations.

[0064] A comprehensive dynamic adjustment mechanism for knock and an anti-vibration protection mechanism are set up. Through measures such as critical knock maintenance, rapid ignition angle reduction during strong knock, post-knock observation and recovery, zone switching delay, and signal filtering, frequent fluctuations in ignition angle and the risk of knock are avoided, ensuring the smoothness of engine operation and extending the service life of the ignition system and engine.

[0065] While ensuring no knocking, the system continuously approaches the knocking boundary through positive optimization, resulting in more complete combustion of the in-cylinder mixture and more precise combustion phase. This maximizes engine output torque and improves power response on the one hand, and reduces fuel consumption on the other. Bench tests have verified that this reduces fuel consumption and increases power output.

[0066] The self-learning mechanism of this invention can adapt to the performance degradation caused by engine hardware aging (such as sensor drift and component wear). By updating the ignition angle compensation value in real time, it can maintain the performance stability of the engine throughout its entire life cycle, without the need for frequent bench calibration and parameter adjustment, thus reducing maintenance costs.

[0067] A refueling condition recognition mechanism is set up. When refueling is detected, the self-learned value is automatically restored to the default value to avoid the risk of knocking caused by sudden changes in fuel quality. At the same time, the setting of self-learning enable conditions avoids starting optimization under abnormal engine conditions, further improving the safety and reliability of the system.

[0068] It should be noted that the method of this embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this embodiment, and the multiple devices will interact with each other to complete the method described.

[0069] It should be noted that the above description describes some embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of the invention as described above, which are not provided in the details for the sake of brevity.

[0071] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0072] While specific details have been set forth to describe exemplary embodiments of the invention, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive. Although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description.

[0073] The embodiments of this invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this invention should be included within the protection scope of this invention.

Claims

1. A smart ignition control method for complex fuel environments, characterized in that, include: Obtain the engine's real-time operating parameters and determine whether the current operating conditions meet the self-learning enable conditions; In response to the fulfillment of the enabling conditions, starting from the base ignition angle corresponding to the current operating condition, a positive self-learning optimization is performed to gradually increase the ignition advance angle; During the optimization process, the detonation signal is monitored in real time, and the ignition angle is dynamically adjusted according to the detonation intensity. The operating conditions are divided into multiple independent learning areas based on engine speed and load. Each area stores an independent ignition angle compensation value and updates it in real time during operation. The ignition angle is corrected based on the ignition angle compensation value by using an anti-vibration and protection mechanism to obtain the final ignition angle command; The final ignition angle command is output to the actuator.

2. The intelligent ignition control method for complex fuel environments according to claim 1, characterized in that, The self-learning enabling conditions include at least the following: Knock control enable is on; The positive self-learning value under the current operating conditions is 0; The current water temperature, speed, and load are all within the preset normal operating range; It has not entered the exhaust gas recirculation control area or the exhaust gas recirculation control has been stabilized.

3. The intelligent spot-finding ignition control method for complex fuel environments according to claim 1, characterized in that, Before performing positive self-learning optimization and gradually increasing the ignition advance angle, the process also includes: Determine whether the exhaust gas recirculation control area has been entered; When entering the exhaust gas recirculation control area and the exhaust gas recirculation rate exceeds the preset threshold, a conservative ignition angle is set as the starting point, and the forward self-learning is paused based on the knocking angle signal.

4. The intelligent spot-finding ignition control method for complex fuel environments according to claim 1, characterized in that, The aforementioned positive self-learning optimization includes: In the absence of detonation, the ignition advance angle is increased by a small step every fixed time or several working cycles, continuously approaching the detonation boundary.

5. The intelligent spot-finding ignition control method for complex fuel environments according to claim 1, characterized in that, The method of real-time monitoring of detonation signals during the optimization process and dynamically adjusting the ignition angle based on the detonation intensity includes: In response to the absence of detected detonation, the positive self-learning optimization continues; In response to the detection of critical knock, the forward self-learning optimization is stopped and the current ignition angle is maintained; In response to the detection of a strong knock, the ignition advance angle is reduced according to a preset advance angle logic or a lookup table method until the knock signal disappears; Once the detonation disappears, the system enters an observation period and restarts positive self-learning optimization once the current operating conditions stabilize.

6. The intelligent spot-finding ignition control method for complex fuel environments according to claim 5, characterized in that, The response to detecting critical knock, stopping the forward self-learning optimization and maintaining the current ignition angle, further includes: Determine the detonation retreat angle. If the detonation retreat angle remains unchanged, lock the current positive self-learning value. If the detonation retreat angle continues to increase, restore the positive self-learning value to 0.

7. The intelligent spot-finding ignition control method for complex fuel environments according to claim 1, characterized in that, The process of dividing the operating conditions into multiple independent learning regions based on engine speed and load, with each region storing an independent ignition angle compensation value and updating it in real time during operation, includes: Construct a two-dimensional array pulse spectrum with speed and load as coordinates, and store an independent ignition angle compensation value in each grid cell; The compensation value of the grid corresponding to the two-dimensional array pulse spectrum is updated based on the current rotational speed and load.

8. The intelligent spot-finding ignition control method for complex fuel environments according to claim 7, characterized in that, Also includes: In response to the current speed or load rising above the first threshold above the partition boundary, switch to the adjacent grid; In response to the current speed or load dropping to the second threshold below the partition boundary, switch back to the original grid.

9. The intelligent spot-finding ignition control method for complex fuel environments according to claim 1, characterized in that, After outputting the final ignition angle command to the actuator, the process also includes: The refueling condition is identified, and in response to the refueling condition meeting the refueling identification conditions, the positive self-learning value of the ignition angle is restored to the default value.

10. The intelligent spot-finding ignition control method for complex fuel environments according to claim 9, characterized in that, The refueling identification conditions include: The system detected an increase in oil level and an engine shutdown duration exceeding the preset calibration value.

Images