Method for self-learning of engine injection quantity and device for self-learning of engine injection quantity

Abstract

The application provides a self-learning method and device for engine injection quantity, which comprises the following steps: in the case where the engine has the risk of knock, a first initial coefficient is corrected by using a first difference and a first step length to obtain a first learning coefficient; a second step length and a second initial coefficient are obtained, and the second initial coefficient is corrected by using the first learning coefficient and the second step length to obtain a second learning coefficient; the injection quantity of the engine is obtained, and the injection quantity is corrected by using a target correction coefficient; the first correction step, the second correction step and the third correction step are repeated at least once, and in the repeated process, the first initial coefficient is updated to the first learning coefficient obtained in the last repeated process, and the second initial coefficient is updated to the second learning coefficient obtained in the last repeated process, until a preset time is reached. The method solves the problem of unstable measurement of excess air coefficient under the condition of knock.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of engines, and in particular, to a method for self-learning of engine injection quantity and a device for self-learning of engine injection quantity. BACKGROUND

[0002] A hydrogen engine is an engine that uses hydrogen gas as fuel, which mixes hydrogen gas with air and ignites to generate energy to drive the engine to run. The hydrogen engine can generate power by burning hydrogen gas, or can generate electricity by reacting hydrogen gas with oxygen, and then use an electric motor to drive the vehicle. The excess air coefficient (λ) refers to the ratio of actual air to theoretically required air, which is used to measure the degree of rich or lean combustion of the engine combustion process. At present, the knock of the hydrogen engine is highly sensitive to λ, but the measurement stability of the oxygen sensor is poor in the ultra-lean (λ value is 2-3) state, and small pump current fluctuations will bring large λ measurement fluctuations, thereby causing the problem that the hydrogen engine is difficult to use the oxygen sensor for closed-loop control.

[0003] Therefore, there is an urgent need for a method to solve the problem of unstable measurement of the excess air coefficient of the engine. SUMMARY

[0004] The main purpose of the present application is to provide a method for self-learning of engine injection quantity and a device for self-learning of engine injection quantity, to at least solve the problem of unstable measurement of the excess air coefficient of the engine in the prior art.

[0005] According to an aspect of the present application, a self-learning method for engine injection quantity is provided, comprising: a first correction step, determining whether the engine has a risk of knock, in the case that the engine has the risk of knock, obtaining a first step length and a first initial coefficient, and correcting the first initial coefficient by using a first difference value and the first step length to obtain a first learning coefficient, wherein the first difference value is a difference between a second difference value and a current excess air coefficient, the second difference value is a difference between a target excess air coefficient and an excess air coefficient threshold value, the current excess air coefficient is a measured excess air coefficient of exhaust gas discharged by the engine at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas discharged by the engine, the excess air coefficient threshold value is a limit value of the excess air coefficient, the first step length is a step length for first self-learning of the injection quantity of the engine, and the first initial coefficient is a coefficient for the first self-learning of the injection quantity of the engine; a second correction step, obtaining a second step length and a second initial coefficient, and correcting the second initial coefficient by using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length for second self-learning of the injection quantity of the engine, the second initial coefficient is a coefficient for the second self-learning of the injection quantity of the engine, a speed of the first self-learning is greater than a speed of the second self-learning, and the first step length is greater than the second step length; a third correction step, obtaining the injection quantity of the engine, correcting the injection quantity by using a target correction coefficient to obtain a target injection quantity, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient; repeating the first correction step, the second correction step and the third correction step at least once, and updating the first initial coefficient in the first correction step to the first learning coefficient in the first correction step obtained in the last repeated process, and updating the second initial coefficient in the second correction step to the second learning coefficient in the second correction step obtained in the last repeated process in the process of repeating, until a preset time is reached.

[0006] Optionally, determining whether the engine has a risk of knock comprises: obtaining the current excess air coefficient, the target excess air coefficient and the excess air coefficient threshold value; in the case that the current excess air coefficient is less than the second difference value, determining that the engine has the risk of knock; and in the case that the current excess air coefficient is greater than or equal to the second difference value, determining that the engine does not have the risk of knock.

[0007] Optionally, the obtaining the excess air coefficient threshold value comprises: obtaining a mapping relationship between a knock ignition retardation angle and an excess air coefficient limit value, and a current knock ignition retardation angle, the knock ignition retardation angle being a retardation amount of an ignition advance angle caused by knock; and determining the excess air coefficient threshold value according to the mapping relationship between the knock ignition retardation angle and the excess air coefficient limit value, and the current knock ignition retardation angle.

[0008] Optionally, the correcting the first initial coefficient by using the second difference value and the first step size to obtain a first learning coefficient comprises: calculating the first learning coefficient InjFacFast according to an equation InjFacFast = λ Error × FastStep + InjFacFast (z-1) , where λ Error is the second difference value, FastStep is the first step size, and InjFacFast (z-1) is the first initial coefficient.

[0009] Optionally, the correcting the second initial coefficient by using the first learning coefficient and the second step size to obtain a second learning coefficient comprises: calculating the second learning coefficient InjFacSlow according to an equation InjFacSlow = SlowStep × (InjFacFast - 1) + InjFacSlow (z-1) , where SlowStep is the second step size, InjFacFast is the first learning coefficient, and InjFacSlow (z-1) is the second initial coefficient.

[0010] Optionally, the correcting the injection amount by using the target correction coefficient to obtain a target injection amount comprises: calculating a product of the target correction coefficient and the injection amount to obtain the target injection amount.

[0011] Optionally, the method further comprises: in a case where the engine is not at risk of the knock, correcting the first initial coefficient by using the second step size to obtain the first learning coefficient; and correcting the second initial coefficient by using the second step size and the first initial coefficient to obtain the second learning coefficient.

[0012] Optionally, the correcting the first initial coefficient by using the second step size to obtain the first learning coefficient comprises: calculating the first learning coefficient InjFacFast according to an equation InjFacFast = InjFacFast (z-1) - SlowStep × InjFacFast (z-1) , where InjFacFast(z-1) SlowStep is the second step length.

[0013] Optionally, the second initial coefficient is modified by using the second step length to obtain the second learning coefficient, comprising: calculating the second learning coefficient InjFacSlow according to the formula InjFacSlow = SlowStep x InjFacFast (z-1) + InjFacSlow (z-1) , wherein SlowStep is the second step length, InjFacFast (z-1) is the first initial coefficient, and InjFacSlow (z-1) is the second initial coefficient.

[0014] According to another aspect of the present application, there is also provided an engine injection amount self-learning device, comprising: a first correction unit configured to determine whether an engine is at risk of knock in a first correction step, obtain a first step length and a first initial coefficient in the case that the engine is at risk of knock, and correct the first initial coefficient using a first difference value and the first step length to obtain a first learning coefficient, wherein the first difference value is a difference between a second difference value and a current excess air coefficient, the second difference value is a difference between a target excess air coefficient and an excess air coefficient threshold value, the current excess air coefficient is a measured excess air coefficient of exhaust gas discharged by the engine at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas discharged by the engine, the excess air coefficient threshold value is a limit value of the excess air coefficient, and the first step length is a step length for first self-learning of an injection amount of the engine, and the first initial coefficient is a coefficient for the first self-learning of the injection amount of the engine; a second correction unit configured to obtain a second step length and a second initial coefficient in a second correction step, and correct the second initial coefficient using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length for second self-learning of the injection amount of the engine, the second initial coefficient is a coefficient for the second self-learning of the injection amount of the engine, a speed of the first self-learning is greater than a speed of the second self-learning, and the first step length is less than the second step length; a third correction unit configured to obtain the injection amount of the engine in a third correction step, correct the injection amount using a target correction coefficient to obtain a target injection amount, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient; and a repeating unit configured to repeat the first correction step, the second correction step and the third correction step at least once, and update the first initial coefficient in the first correction step to the first learning coefficient in the first correction step obtained in a previous repetition process, and update the second initial coefficient in the second correction step to the second learning coefficient in the second correction step obtained in the previous repetition process in a repeating process until a preset time is reached.

[0015] The technical scheme of the application is applied to first determine whether the engine has the risk of knock, correct the first initial coefficient by using the first difference and the first step length to obtain the first learning coefficient in the case that the engine has the risk of knock, then acquire the second step length and the second initial coefficient, correct the second initial coefficient by using the first learning coefficient and the second step length to obtain the second learning coefficient, then acquire the injection amount of the engine, correct the injection amount by using the target correction coefficient to obtain the target injection amount, and finally repeat the first correction step, the second correction step and the third correction step at least once, and update the first initial coefficient in the first correction step to the first learning coefficient in the first correction step obtained in the last repeated process, update the second initial coefficient in the second correction step to the second learning coefficient in the second correction step obtained in the last repeated process until the preset time is reached. By self-learning of the engine injection amount at two different speeds, when the excess air coefficient increases, the injection amount is corrected, the injection amount of fuel is increased to maintain the appropriate ratio of fuel and air, and conversely, when the excess air coefficient decreases, the injection amount is corrected, the injection amount of fuel is reduced to maintain the appropriate ratio of fuel and air, thereby improving the stability of the measurement of the excess air coefficient and solving the problem of unstable measurement of the excess air coefficient in the case of knock. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which form a part of the present description, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of the present application, and their

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for performing a self-learning method of engine injection amount according to an embodiment of the present application is shown;

[0018] Figure 2 A flowchart of a self-learning method of engine injection amount according to an embodiment of the present application is shown;

[0019] Figure 3 A diagram showing the variation of pump current with excess air coefficient is shown;

[0020] Figure 4 A flowchart of another self-learning method of engine injection amount according to an embodiment of the present application is shown;

[0021] Figure 5 A specific flowchart of a self-learning method of engine injection amount according to an embodiment of the present application is shown;

[0022] Figure 6A structural block diagram of an engine injection amount self-learning device according to an embodiment of the present application is shown.

[0023] In the above drawings, reference numerals:

[0024] 102, processor; 104, memory; 106, transmission device; 108, input / output device. DETAILED DESCRIPTION

[0025] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict. The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0026] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] For the convenience of description, the following explains some nouns or terms related to the embodiments of the present application:

[0029] Air-fuel ratio: refers to the mass ratio between air and fuel in the mixture during combustion. Generally, the number of grams of air consumed per gram of fuel burned is used to represent it. Under lean combustion conditions, the air-fuel ratio is greater than the theoretical air-fuel ratio, i.e. the fuel supply is insufficient. At this time, the combustion is incomplete, the combustion temperature is lower, and the carbon and hydrogen compounds in the fuel cannot be fully burned, producing a large amount of harmful substances such as unburned carbon and hydrogen compounds and carbon monoxide. At the same time, due to insufficient oxygen supply, the combustion reaction rate slows down and the combustion efficiency decreases. Under rich combustion conditions, the air-fuel ratio is less than the theoretical air-fuel ratio, i.e. the fuel supply is excessive. At this time, the combustion is excessive, the combustion temperature is high, and the carbon and hydrogen compounds in the fuel can be fully burned, but at the same time a large amount of nitrogen oxides (NOx Harmful substances such as oxygen are produced. Furthermore, an excess oxygen supply leads to a waste of energy and heat. Factors affecting the air-fuel ratio mainly include the type and quality of fuel, the air supply method, and the design of the combustion equipment. Different fuels have different theoretical air-fuel ratio ranges, while the actual air-fuel ratio is affected by factors such as the air supply system, burner design, and adjustments.

[0030] As described in the background section, the measurement of the excess air coefficient of an engine in the prior art is unstable. To solve the above problem, embodiments of this application provide a self-learning method and a self-learning device for engine injection quantity.

[0031] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0032] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a self-learning method for engine injection quantity according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0033] The memory 104 can be used to store computer programs, such as software programs of application software and modules, such as the computer program corresponding to the engine injection amount self-learning method of the embodiments of the present application. The processor 102 can execute various functional applications and data processing, i.e., implement the method described above, by running the computer program stored in the memory 104. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include memories disposed remotely with respect to the processor 102, which can be connected to the mobile terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is configured to receive or send data via a network. The specific examples of the network can include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is configured to communicate with the Internet in a wireless manner.

[0034] In the embodiments, a method for learning engine injection amount is provided, which is run on a mobile terminal, a computer terminal, or a similar computing device. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system, such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown herein.

[0035] Figure 2 is a flowchart of the method for learning engine injection amount according to the embodiments of the present application. As shown in Figure 2 , the method includes the following steps:

[0036] Step S201, a first correction step, determines whether the engine has a risk of knock, in the case where the engine has a risk of knock, a first step length and a first initial coefficient are obtained, and the first initial coefficient is corrected by using a first difference value and the first step length to obtain a first learning coefficient, wherein the first difference value is a difference between a second difference value and a current excess air coefficient, the second difference value is a difference between a target excess air coefficient and an excess air coefficient threshold value, the current excess air coefficient is an excess air coefficient of exhaust gas discharged by the engine measured at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas discharged by the engine, the excess air coefficient threshold value is a limit value of the excess air coefficient, and the first step length is a step length of first self-learning of injection quantity of the engine, and the first initial coefficient is a coefficient of the first self-learning of the injection quantity of the engine.

[0037] Specifically, engine knock refers to abnormal combustion of the mixture in the combustion chamber during the working process, which produces loud explosion sound and vibration. When the mixture in the combustion chamber is ignited by the spark plug or other ignition device, the burning speed is too fast or premature ignition occurs, which causes the combustion gas to produce abnormally high pressure and temperature in the cylinder, thereby causing explosion sound and vibration, which is called engine knock. The excess air coefficient is also called "excess air coefficient", "air excess coefficient", and is commonly known as "excess air coefficient". It refers to the ratio of the actual air supply for fuel combustion to the theoretical air supply. It is an important parameter reflecting the fuel and air matching ratio, commonly represented by the symbol "λ". Its value can be measured and calculated by a gas analyzer. In various furnaces or combustion chambers, the actual air supply must be greater than the theoretical air supply (the excess part is called "excess air") in order to make the fuel burn as completely as possible, that is, the excess air coefficient must be greater than 1. However, the combustion theory and operating experience show that λ is too large or too small (indicating that the air supply is too much or too little), which is not conducive to combustion, that is, different combustion equipment has its own optimal excess air coefficient value. It can be divided into two cases of lean oxygen and rich oxygen. Under lean oxygen conditions, the excess air coefficient is less than 1, that is, the air supply is insufficient, at which time the oxygen in the combustion reaction is insufficient and the fuel cannot be fully burned, resulting in low combustion efficiency and the generation of a large amount of carbon monoxide and unburned hydrocarbons and other harmful substances. Under rich oxygen conditions, the excess air coefficient is greater than 1, that is, the air supply is excessive. At this time, the oxygen is sufficient and the fuel can be fully burned, resulting in high combustion efficiency, but a large amount of nitrogen oxides and other harmful substances are also produced. The main factors affecting the excess air coefficient include the type and quality of fuel, the air supply method, the design of the combustion equipment, etc. By adjusting the air supply system, the design of the burner and adjusting other factors, the excess air coefficient can be controlled to balance the combustion efficiency and exhaust emission. Figure 3As shown, in the case where the value of λ (i.e. lambda value) is 2-3, the change of pump current is small, but it will bring larger λ measurement fluctuation, thus leading to inaccurate λ measurement.

[0038] In step S202, a second correction step, a second step length and a second initial coefficient are obtained, and the second initial coefficient is corrected by using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length for second self-learning of the injection amount of the engine, the second initial coefficient is a coefficient for the second self-learning of the injection amount of the engine which is set in advance, the speed of the first self-learning is greater than the speed of the second self-learning, and the first step length is smaller than the second step length.

[0039] Specifically, the difference between the first self-learning and the second self-learning is the speed of self-learning, the first self-learning is actually a fast learning of a fast path, and the second self-learning is a slow learning of a slow path. By using the self-learning of the above two speeds, the stability of the measurement of the excess air coefficient can be further improved.

[0040] In step S203, a third correction step, the injection amount of the engine is obtained, and the injection amount is corrected by using a target correction coefficient to obtain a target injection amount, wherein the target correction coefficient is the product of the first learning coefficient and the second learning coefficient.

[0041] Specifically, the injection amount of the engine refers to the amount of fuel injected into the combustion chamber of the engine per unit time. In an internal combustion engine, fuel is injected into the combustion chamber by an injector or an injection pump, mixed with air in the combustion chamber, and then burned to generate energy to drive the engine. The size of the injection amount will affect the power output and fuel economy of the engine. Under normal circumstances, the injection amount of the automobile engine is controlled and adjusted by the electronic control unit according to the engine load, speed and other parameters, so as to achieve the best combustion effect and fuel economy. Larger injection amount can increase the power output of the engine, but at the same time, it will also increase the fuel consumption. Therefore, under different working conditions, the injection amount needs to be adjusted to balance the power performance and fuel economy. By the above self-learning method, the target correction coefficient can be obtained, and the injection amount can be corrected in time.

[0042] In step S204, the first correction step, the second correction step and the third correction step are repeated at least once, and in the repeating process, the first initial coefficient in the first correction step is updated to the first learning coefficient in the first correction step obtained in the last repeating process, and the second initial coefficient in the second correction step is updated to the second learning coefficient in the second correction step obtained in the last repeating process, until a preset time is reached.

[0043] Specifically, after completing one scheduling cycle, the above-mentioned first correction step, the above-mentioned second correction step and the above-mentioned third correction step are continued to adjust the engine injection amount in the next scheduling cycle.

[0044] By the embodiment, the self-learning method of the engine injection amount is provided. Firstly, it is determined whether the engine has the risk of knock. In the case that the engine has the risk of knock, the first initial coefficient is corrected by using the first difference and the first step to obtain the first learning coefficient. Then, the second step and the second initial coefficient are obtained, and the second initial coefficient is corrected by using the first learning coefficient and the second step to obtain the second learning coefficient. Then, the injection amount of the engine is obtained, and the injection amount is corrected by using the target correction coefficient to obtain the target injection amount. Finally, the first correction step, the second correction step and the third correction step are repeated at least once, and in the repeating process, the first initial coefficient in the first correction step is updated to the first learning coefficient in the first correction step obtained in the last repeating process, and the second initial coefficient in the second correction step is updated to the second learning coefficient in the second correction step obtained in the last repeating process, until the preset time is reached. By self-learning the engine injection amount at two different speeds, when the excess air coefficient increases, the injection amount is corrected, the injection amount of fuel is increased, and the appropriate fuel and air ratio is maintained. On the contrary, when the excess air coefficient decreases, the injection amount is corrected, the injection amount of fuel is reduced, and the appropriate fuel and air ratio is maintained, thereby improving the stability of the measurement of the excess air coefficient, and solving the problem of unstable measurement of the excess air coefficient in the knock condition.

[0045] In the specific implementation process, the step S201 can be implemented by the following steps: a step S2011 of obtaining the current excess air coefficient, the target excess air coefficient and the excess air coefficient threshold; a step S2012 of determining that the engine has the risk of knock in the case that the current excess air coefficient is less than the second difference; and a step S2013 of determining that the engine does not have the risk of knock in the case that the current excess air coefficient is greater than or equal to the second difference. The method can quickly determine whether the engine has the risk of knock according to the size relationship between the current excess air coefficient and the second difference.

[0046] Specifically, the actual theoretical significance of the second difference value is the combustion boundary value of the engine. The main reasons for engine knock are as follows: the gas pressure and temperature of the mixture are too high, exceeding the explosion limit of the mixture, causing premature ignition; the amount of combustible substances in the mixture is too much, exceeding the theoretical air-fuel ratio, leading to too fast combustion speed; poor fuel quality, prone to flammable impurities and precipitates, causing abnormal combustion; engine ignition system failure, such as ignition advance or ignition delay, etc. Engine intake system failure, such as intake valve not closed, intake port blocked, etc., leading to too rich or too lean mixture. In the case where the actual measured excess air coefficient is less than the above combustion boundary, it indicates that there is a risk of knock.

[0047] In order to further quickly determine the above excess air coefficient threshold value, in the case where the above predetermined processing is to calculate the standard deviation, the above step S2011 of the present application can be realized by the following steps: step S20111, obtaining the mapping relationship between the knock ignition delay angle and the excess air coefficient limit value and the current knock ignition delay angle, the knock ignition delay angle being the delay amount of the ignition advance angle caused by knock; step S20112, determining the excess air coefficient threshold value according to the mapping relationship between the knock ignition delay angle and the excess air coefficient limit value and the current knock ignition delay angle.

[0048] Specifically, the knock ignition angle delay is the angle delay of the engine ignition time relative to the piston reaching top dead center, which is an adjustment to maximize the pressure and temperature in the combustion chamber to improve combustion efficiency and driving force. The current knock ignition delay angle can be obtained using the engine management system or the ignition system adjustment tool. These tools can determine the optimal ignition time by reading the data of engine sensors, such as crankshaft position sensor, oxygen sensor, intake temperature sensor, etc. The electronic control unit or the ignition system adjustment tool can also adjust the ignition time to delay a certain amount relative to the angle of the piston reaching top dead center. For different engine types, fuel types and working conditions, the optimal knock ignition delay angle may be different. In practical application, a mapping table of knock ignition delay angle and excess air coefficient limit value can be pre-calibrated, and the corresponding excess air coefficient limit value can be obtained directly by looking up the current knock ignition delay angle. The excess air coefficient limit value and the knock ignition delay angle are negatively correlated, the larger the knock ignition delay angle, the smaller the excess air coefficient limit value.

[0049] In another embodiment, the above step S201 can also be realized by the following steps: step S2014, calculating the first learning coefficient InjFacFast according to the formula InjFacFast = λ Error × FastStep + InjFacFast (z-1) , where λError FastStep is the first step length, and InjFacFast is the first learning coefficient. (z-1) The first initial coefficient is calculated. The first learning coefficient can be calculated according to the first difference, the first step length, and the first initial coefficient.

[0050] Specifically, in practical applications, the first step length can be set to 0.8, and the first initial coefficient can be set to 1. In the process of the loop, the first initial coefficient is the first learning coefficient obtained in the last loop.

[0051] In some embodiments, step S202 can be implemented by the following steps: step S2021, calculating the second learning coefficient InjFacSlow according to the formula InjFacSlow = SlowStep × (InjFacFast - 1) + InjFacSlow (z-1) , wherein SlowStep is the second step length, InjFacFast is the first learning coefficient, and InjFacSlow (z-1) is the second initial coefficient. The second learning coefficient can be calculated according to the second step length, the first learning coefficient, and the second initial coefficient.

[0052] Specifically, in practical applications, the first step length can be set to 0.01, and the second initial coefficient can be set to 1. In the process of the loop, the second initial coefficient is the second learning coefficient obtained in the last loop.

[0053] In some embodiments, step S203 can be implemented by the following steps: step S2031, calculating the product of the target correction coefficient and the injection amount to obtain the target injection amount. The target injection amount can be quickly calculated according to the target correction coefficient and the injection amount.

[0054] Specifically, the target correction coefficient is actually a correction coefficient for the injection amount of the engine. After obtaining the target correction coefficient, the target injection amount can be obtained by directly multiplying the target correction coefficient and the injection amount. In practical applications, it can also be determined whether the first self-learning condition is met. The first self-learning condition includes at least one of the following: the oxygen sensor heating is completed, the oxygen sensor has no fault, and the engine is not in the initial stage of recovery of oil supply after the oil is cut off. When the first self-learning condition is not met, the target correction coefficient is equal to the second learning coefficient. When the first self-learning condition is met, the target correction coefficient is the product of the first learning coefficient and the second learning coefficient.

[0055] In order to further determine the first learning coefficient and the second learning coefficient in the case where the engine is not at risk of the above-mentioned knocking, Figure 4 is a flow chart of the self-learning method of the engine injection amount according to the embodiment of the present application. As shown in the figure, Figure 4 the method comprises the following steps: step S205, in the case where the engine is not at risk of the above-mentioned knocking, the first initial coefficient is corrected by the second step length to obtain the first learning coefficient; step S206, the second initial coefficient is corrected by the second step length and the first initial coefficient to obtain the second learning coefficient.

[0056] Specifically, in the case where the engine is not at risk of the knocking, the first initial coefficient and the second initial coefficient can also be updated.

[0057] The step S205 comprises the following steps: according to the formula InjFacFast = InjFacFast (z-1) SlowStep × InjFacFast (z-1) , the first learning coefficient InjFacFast is calculated, wherein InjFacFast (z-1) is the first initial coefficient, and SlowStep is the second step length. The method uses the first initial coefficient and the second step length, and can further quickly calculate the first learning coefficient in the case where the engine is not at risk of the above-mentioned knocking.

[0058] Specifically, in actual application, the second step length can be set to 0.01, and the first initial coefficient can be set to 1. In the process of the cycle, the first initial coefficient is the first learning coefficient obtained in the last cycle.

[0059] In the specific implementation process, the step S206 comprises the following steps: according to the formula InjFacSlow = SlowStep × InjFacFast (z-1) + InjFacSlow (z-1) , the second learning coefficient InjFacSlow is calculated, wherein SlowStep is the second step length, InjFacFast (z-1) is the first initial coefficient, and InjFacSlow (z-1) is the second initial coefficient. The method uses the first initial coefficient and the second initial coefficient, and can further quickly calculate the second learning coefficient in the case where the engine is not at risk of the above-mentioned knocking.

[0060] Specifically, in actual application, the first initial coefficient and the second initial coefficient can be set as 1. In the process of the loop, the first initial coefficient is the first learning coefficient obtained in the last loop, and the second initial coefficient is the second learning coefficient obtained in the last loop.

[0061] In order for those skilled in the art to more clearly understand the technical solutions of the present application, the implementation process of the engine injection amount self-learning method of the present application will be described in detail below in combination with specific embodiments.

[0062] The present embodiment relates to a specific engine injection amount self-learning method, as shown in the flow chart of FIG. 1, comprising the following steps: Figure 5

[0063] Step S1: the engine enters the oxygen closed loop limiting program, at this time, the fast learning coefficient injFacFast is initialized as 1, the slow learning coefficient injFacSlow is initialized as 1, and the final learning coefficient injFacFin is initialized as 1;

[0064] Step S2: the target λ of the current working condition and the λ deviation limit delta_λ of the current working condition are obtained, and the delta_λ can be calculated according to the current knock ignition angle retardation, the greater the retardation, the smaller the delta_λ;

[0065] Step S3: determine whether the fast path learning condition is met, if not, calculate the final correction coefficient as injFacFin = InjFacSlow;

[0066] Step S4: if the fast path learning condition is met, determine whether the measured λ is less than (target λ-delta_λ);

[0067] Step S5: if the measured λ is less than (target λ-delta_λ), calculate the target λ-delta_λ-measured λ to obtain λ Error , InjFacSlow = SlowStep × (InjFacFast-1) + InjFacSlow (z-1) , InjFacFast = λ Error × FastStep + InjFacFast (z-1) , wherein InjFacSlow is the slow learning amount, SlowStep is the slow learning step, InjFacFast is the fast learning amount, InjFacSlow (z-1) is the initial slow learning amount, FastStep is the fast learning step, and InjFacFast (z-1) is the initial fast learning amount;

[0068] ​Step S6: if the measured lambda is greater than or equal to (target lambda-delta_lambda), it is considered that there is no risk of knock at this time, and the fast and slow learning quantities are updated, InjFacSlow = SlowStep x InjFacFast (z-1) + InjFacSlow (z-1) , InjFacFast = InjFacFast (z-1) - SlowStep x InjFacFast (z-1) , wherein InjFacSlow is the slow learning quantity, SlowStep is the slow path step length, InjFacFast is the initial fast learning quantity, InjFacSlow is the initial slow learning quantity, and InjFacFast is the fast learning quantity. (z-1) (z-1)

[0069] Step S7: the final correction coefficient is calculated as injFacFin = InjFacSlow x InjFacFast.

[0070] Step S8: the next scheduling period is waited for, and the above steps are repeated.

[0071] The engine injection quantity self-learning device provided in the embodiments of the present application can be used to execute the engine injection quantity self-learning method provided in the embodiments of the present application. The device is used to implement the above embodiments and preferred embodiments, and has been described above. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, implementation of hardware, or a combination of software and hardware, is also possible and is contemplated.

[0072] The engine injection quantity self-learning device provided in the embodiments of the present application is described below.

[0073] Figure 6 is a schematic diagram of an engine injection quantity self-learning device according to the embodiments of the present application. As shown in Figure 6 , the device includes:

[0074] ​​The first correction unit 10 is configured to determine whether the engine has a risk of knock in the first correction step, obtain a first step length and a first initial coefficient in the case where the engine has the risk of knock, and correct the first initial coefficient by using a first difference value and the first step length to obtain a first learning coefficient, wherein the first difference value is a difference between a second difference value and a current excess air coefficient, the second difference value is a difference between a target excess air coefficient and an excess air coefficient threshold value, the current excess air coefficient is a measured excess air coefficient of exhaust gas discharged by the engine at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas discharged by the engine, the excess air coefficient threshold value is a limit value of the excess air coefficient, and the first step length is a step length of first self-learning of injection quantity of the engine, and the first initial coefficient is a coefficient of the first self-learning of the injection quantity of the engine.

[0075] Specifically, engine knock refers to abnormal combustion of the mixture in the combustion chamber during operation, which produces loud explosion sound and vibration. When the mixture in the combustion chamber is ignited by the spark plug or other ignition device, the burning speed is too fast or premature ignition occurs, resulting in abnormally high pressure and temperature of the combustion gas in the cylinder, thereby causing explosion sound and vibration, which is called engine knock. The excess air coefficient, also known as the excess air coefficient or air excess coefficient, is commonly known as the excess air coefficient. It refers to the ratio of the actual air quantity supplied for fuel combustion to the theoretical air quantity. It is an important parameter reflecting the fuel and air matching ratio, commonly represented by the symbol "λ". Its value can be measured and calculated by a gas analyzer. In various furnaces or combustion chambers, the actual air quantity supplied must be greater than the theoretical air quantity (the excess part is called "excess air quantity"), i.e. the excess air coefficient must be greater than 1. However, the combustion theory and operating experience show that λ is too large or too small (indicating that the air supply is too much or too little), which is not conducive to combustion, i.e. different combustion equipment has its own optimal excess air coefficient value. It can be divided into two cases of lean oxygen and rich oxygen. Under lean oxygen conditions, the excess air coefficient is less than 1, i.e. the air supply is insufficient. At this time, the oxygen in the combustion reaction is insufficient, the fuel cannot be fully burned, the combustion efficiency is low, and a large amount of harmful substances such as carbon monoxide and unburned hydrocarbons are produced. Under rich oxygen conditions, the excess air coefficient is greater than 1, i.e. the air supply is excessive. At this time, the oxygen is sufficient, the fuel can be fully burned, and the combustion efficiency is high, but a large amount of harmful substances such as nitrogen oxides are also produced. The main factors affecting the excess air coefficient include the type and quality of fuel, the air supply method, the design of the combustion equipment, etc. By adjusting the air supply system, the design of the burner, and other factors, the excess air coefficient can be controlled to balance the combustion efficiency and exhaust emission. Figure 3As shown, in the case where the value of lambda (i.e. lambda value) is 2-3, the pump current changes less, which will bring greater lambda measurement fluctuation, thus leading to inaccurate lambda measurement.

[0076] The second correction unit 20 is configured to perform a second correction step, obtain a second step length and a second initial coefficient, and correct the second initial coefficient by using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length for second self-learning of the injection amount of the engine, the second initial coefficient is a coefficient for the second self-learning of the injection amount of the engine, the speed of the first self-learning is greater than the speed of the second self-learning, and the first step length is less than the second step length.

[0077] Specifically, the difference between the first self-learning and the second self-learning is the speed of self-learning, the first self-learning is actually a fast learning of a fast path, and the second self-learning is a slow learning of a slow path. The self-learning with the two speeds can further improve the stability of the measurement of the excess air coefficient.

[0078] The third correction unit 30 is configured to perform a third correction step, obtain the injection amount of the engine, and correct the injection amount by using a target correction coefficient to obtain a target injection amount, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient.

[0079] Specifically, the injection amount of the engine refers to the amount of fuel injected into the combustion chamber of the engine per unit time. In an internal combustion engine, fuel is injected into the combustion chamber by an injector or an injection pump, mixed with air in the combustion chamber, and then burned to generate energy to drive the engine. The size of the injection amount will affect the power output and fuel economy of the engine. Generally, the injection amount of a car engine is controlled and adjusted by an electronic control unit according to engine load, speed and other parameters to achieve the best combustion effect and fuel economy. Larger injection amount can increase the power output of the engine, but at the same time, it will also increase fuel consumption. Therefore, under different working conditions, the injection amount needs to be adjusted to balance the power performance and fuel economy. The target correction coefficient can be obtained through the above self-learning method, and the injection amount can be corrected in time.

[0080] The repeating unit 40 is configured to repeat the first correction step, the second correction step and the third correction step at least once, and update the first initial coefficient in the first correction step to the first learning coefficient in the first correction step obtained in the last repetition process, update the second initial coefficient in the second correction step to the second learning coefficient in the second correction step obtained in the last repetition process, until a preset time is reached.

[0081] Specifically, after completing one scheduling cycle, the above-mentioned first correction step, the above-mentioned second correction step and the above-mentioned third correction step are continued to adjust the engine injection amount in the next scheduling cycle.

[0082] By the embodiment, the self-learning device for the engine injection amount is provided, the first correction unit determines whether the engine has the risk of knock, in the case that the engine has the risk of knock, the first initial coefficient is corrected by the first difference and the first step length to obtain the first learning coefficient; the second correction unit obtains the second step length and the second initial coefficient, and corrects the second initial coefficient by the first learning coefficient and the second step length to obtain the second learning coefficient; the third correction unit obtains the injection amount of the engine, and corrects the injection amount by the target correction coefficient to obtain the target injection amount; the repeating unit repeats the first correction step, the second correction step and the third correction step at least once, and updates the first initial coefficient in the first correction step to the first learning coefficient in the first correction step obtained in the last repeating process, updates the second initial coefficient in the second correction step to the second learning coefficient in the second correction step obtained in the last repeating process in the repeating process until the preset time is reached. By self-learning the engine injection amount at two different speeds, when the excess air coefficient increases, the injection amount is corrected, the injection amount of fuel is increased to maintain the appropriate ratio of fuel and air; on the contrary, when the excess air coefficient decreases, the injection amount is corrected, the injection amount of fuel is reduced to maintain the appropriate ratio of fuel and air, thereby improving the stability of the measurement of the excess air coefficient, and solving the problem of unstable measurement of the excess air coefficient in the knock condition.

[0083] An optional solution, the first correction unit comprises an obtaining module, a first determination module and a second determination module, wherein the obtaining module is configured to obtain the current excess air coefficient, the target excess air coefficient and the excess air coefficient threshold value; the first determination module is configured to determine that the engine has the risk of knock in the case that the current excess air coefficient is less than the second difference; and the second determination module is configured to determine that the engine does not have the risk of knock in the case that the current excess air coefficient is greater than or equal to the second difference. The device can quickly determine whether the engine has the risk of knock according to the size relationship between the current excess air coefficient and the second difference.

[0084] Specifically, the actual theoretical significance of the second difference value is the combustion boundary value of the engine. The main reasons for engine knock are as follows: the gas pressure and temperature of the mixture are too high, exceeding the explosion limit of the mixture, causing premature ignition; the amount of combustible substances in the mixture is too much, exceeding the theoretical air-fuel ratio, leading to too fast combustion speed; poor fuel quality, prone to flammable impurities and precipitates, causing abnormal combustion; engine ignition system failure, such as ignition advance or ignition delay, etc. Engine intake system failure, such as intake valve not closed, intake port blocked, etc., leading to too rich or too lean mixture. In the case where the actual measured excess air coefficient is less than the above combustion boundary, it indicates that there is a risk of knock.

[0085] In order to further quickly determine the above excess air coefficient threshold value, in the case where the above predetermined processing is to calculate the standard deviation, the above obtaining module of the present application includes an obtaining submodule and a determining submodule, wherein the obtaining submodule is configured to obtain a mapping relationship between a knock ignition retardation angle and an excess air coefficient limit value and a current knock ignition retardation angle, the knock ignition retardation angle being the amount of advance angle delay caused by knock; the determining submodule is configured to determine the above excess air coefficient threshold value according to the above mapping relationship between the above knock ignition retardation angle and the above excess air coefficient limit value and the above current knock ignition retardation angle.

[0086] Specifically, the knock ignition angle delay is the amount of delay of the engine ignition time relative to the angle of the piston reaching top dead center, which is an adjustment to maximize the pressure and temperature in the combustion chamber to improve combustion efficiency and driving force. The current knock ignition delay angle can be obtained using the engine management system or the ignition system adjustment tool. These tools can determine the optimal ignition time by reading the data of engine sensors, such as crankshaft position sensor, oxygen sensor, intake temperature sensor, etc. The electronic control unit or the ignition system adjustment tool can also adjust the ignition time to delay a certain amount relative to the angle of the piston reaching top dead center. For different engine types, fuel types and working conditions, the optimal knock ignition delay angle may be different. In practical application, a mapping table of knock ignition delay angle and excess air coefficient limit value can be calibrated in advance, and the corresponding excess air coefficient limit value can be obtained by looking up the table with the current knock ignition delay angle. The excess air coefficient limit value is negatively correlated with the knock ignition delay angle, the larger the knock ignition delay angle, the smaller the excess air coefficient limit value.

[0087] In another embodiment, the first correction unit is also configured to calculate the first learning coefficient InjFacFast according to the formula Error ×FastStep+InjFacFast (z-1) , wherein λ ErrorInjFacFast = FastStep x (FirstDiff - 1) + InjFacFast (z-1) InjFacSlow = SlowStep x (InjFacFast - 1) + InjFacSlow

[0088] Specifically, in practical applications, the first step length can be set to 0.8, and the first initial coefficient can be set to 1. In the process of the loop, the first initial coefficient is the first learning coefficient obtained in the last loop.

[0089] In some embodiments, the second correction unit is further configured to calculate the second learning coefficient InjFacSlow according to the formula InjFacSlow = SlowStep x (InjFacFast - 1) + InjFacSlow (z-1) , wherein SlowStep is the second step length, InjFacFast is the first learning coefficient, and InjFacSlow (z-1) is the second initial coefficient. The device can further calculate the accurate second learning coefficient according to the second step length, the first learning coefficient, and the second initial coefficient.

[0090] Specifically, in practical applications, the first step length can be set to 0.01, and the second initial coefficient can be set to 1. In the process of the loop, the second initial coefficient is the second learning coefficient obtained in the last loop.

[0091] In some embodiments, the third correction unit is further configured to calculate the product of the target correction coefficient and the injection amount to obtain the target injection amount. The device can quickly calculate the target injection amount according to the target correction coefficient and the injection amount.

[0092] Specifically, the target correction coefficient is actually a correction coefficient for the injection amount of the engine. After obtaining the target correction coefficient, it is directly multiplied by the injection amount to obtain the target injection amount. In practical applications, it can also be determined whether the first self-learning condition is met. The first self-learning condition includes at least one of the following: the oxygen sensor heating is completed, the oxygen sensor has no fault, and the engine is not in the initial stage of recovery of oil supply after the oil is broken by reverse drag. When the first self-learning condition is not met, the target correction coefficient is equal to the second learning coefficient. When the first self-learning condition is met, the target correction coefficient is the product of the first learning coefficient and the second learning coefficient.

[0093] To further determine the first learning coefficient and the second learning coefficient in the case that the engine is not at risk of the knock, the device further comprises a fourth correction unit and a fifth correction unit, wherein the fourth correction unit is configured to correct the first initial coefficient by the second step length to obtain the first learning coefficient in the case that the engine is not at risk of the knock; and the fifth correction unit is configured to correct the second initial coefficient by the second step length and the first initial coefficient to obtain the second learning coefficient.

[0094] Specifically, the first initial coefficient and the second initial coefficient can also be updated in the case that the engine is not at risk of the knock.

[0095] The fourth correction unit is further configured to calculate the first learning coefficient InjFacFast according to the formula InjFacFast = InjFacFast (z-1) SlowStep x InjFacFast (z-1) , wherein InjFacFast (z-1) is the first initial coefficient, and SlowStep is the second step length. The device can further quickly calculate the first learning coefficient in the case that the engine is not at risk of the knock by using the first initial coefficient and the second step length.

[0096] Specifically, in actual application, the second step length can be set to 0.01, and the first initial coefficient can be set to 1. In the process of the loop, the first initial coefficient is the first learning coefficient obtained in the last loop.

[0097] In the specific implementation process, the fifth correction unit is further configured to calculate the second learning coefficient InjFacSlow according to the formula InjFacSlow = SlowStep x InjFacFast (z-1) + InjFacSlow (z-1) , wherein SlowStep is the second step length, InjFacFast (z-1) is the first initial coefficient, and InjFacSlow (z-1) is the second initial coefficient. The device can further quickly calculate the second learning coefficient in the case that the engine is not at risk of the knock by using the first initial coefficient and the second initial coefficient.

[0098] Specifically, in actual application, the first initial coefficient and the second initial coefficient can be set to 1. In the process of the loop, the first initial coefficient is the first learning coefficient obtained in the last loop, and the second initial coefficient is the second learning coefficient obtained in the last loop.

[0099] The engine injection amount self-learning device includes a processor and a memory, the first correction unit, the second correction unit, the third correction unit, and the repeating unit are stored in the memory as program units, and the corresponding functions are realized by the processor executing the program units stored in the memory. The modules are located in the same processor, or the modules are located in different processors in any combination.

[0100] The processor includes a core, and the core calls the corresponding program units in the memory. The core can be one or more, and the engine injection amount self-learning is realized by adjusting the core parameters.

[0101] The memory can include a non-permanent memory in a computer readable medium, a random access memory (RAM), and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0102] The embodiment of the application provides a computer readable storage medium, and the computer readable storage medium includes a stored program, wherein the program controls a device where the computer readable storage medium is located to execute the engine injection amount self-learning method when the program is running.

[0103] Specifically, the engine injection amount self-learning method includes:

[0104] In step S201, a first correction step, it is determined whether the engine has a risk of knock, in the case where the engine has the risk of knock, a first step length and a first initial coefficient are obtained, and the first initial coefficient is corrected by using a first difference value and the first step length to obtain a first learning coefficient, wherein the first difference value is the difference between a second difference value and a current excess air coefficient, the second difference value is the difference between a target excess air coefficient and an excess air coefficient threshold, the current excess air coefficient is the excess air coefficient of the exhaust gas discharged by the engine measured at the current time, the target excess air coefficient is the target value of the excess air coefficient of the exhaust gas discharged by the engine, the excess air coefficient threshold is the limit value of the excess air coefficient, the first step length is the step length of the first self-learning of the injection amount of the engine, and the first initial coefficient is the coefficient of the first self-learning of the injection amount of the engine.

[0105] In step S202, a second correction step, a second step length and a second initial coefficient are obtained, and the second initial coefficient is corrected by using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length of second self-learning of the injection amount of the engine, the second initial coefficient is a coefficient of the second self-learning of the injection amount of the engine, the speed of the first self-learning is greater than the speed of the second self-learning, and the first step length is less than the second step length.

[0106] In step S203, a third correction step, the injection amount of the engine is obtained, and the injection amount is corrected by using a target correction coefficient to obtain a target injection amount, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient.

[0107] In step S204, the first correction step, the second correction step and the third correction step are repeated at least once, and in the repeated process, the first initial coefficient in the first correction step is updated to the first learning coefficient in the first correction step obtained in the last repeated process, and the second initial coefficient in the second correction step is updated to the second learning coefficient in the second correction step obtained in the last repeated process, until a preset time is reached.

[0108] The embodiment of the present application provides a processor for running a program, wherein the self-learning method of the injection amount of the engine is executed when the program is running.

[0109] Specifically, the self-learning method of the injection amount of the engine comprises:

[0110] In step S201, a first correction step, it is determined whether the engine has a risk of knock, in the case that the engine has the risk of knock, a first step length and a first initial coefficient are obtained, and the first initial coefficient is corrected by using a first difference and the first step length to obtain a first learning coefficient, wherein the first difference is a difference between a second difference and a current excess air coefficient, the second difference is a difference between a target excess air coefficient and an excess air coefficient threshold, the current excess air coefficient is a measured excess air coefficient of exhaust gas of the engine at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas of the engine, the excess air coefficient threshold is a limit value of the excess air coefficient, the first step length is a step length of first self-learning of the injection amount of the engine, and the first initial coefficient is a coefficient of the first self-learning of the injection amount of the engine.

[0111] In step S202, a second correction step, a second step length and a second initial coefficient are obtained, and the second initial coefficient is corrected by using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length of a second self-learning of the injection amount of the engine, the second initial coefficient is a coefficient of the second self-learning of the injection amount of the engine which is set in advance, the speed of the first self-learning is greater than the speed of the second self-learning, and the first step length is less than the second step length.

[0112] In step S203, a third correction step, the injection amount of the engine is obtained, and the injection amount is corrected by using a target correction coefficient to obtain a target injection amount, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient.

[0113] In step S204, the first correction step, the second correction step and the third correction step are repeated at least once, and in the repeating process, the first initial coefficient in the first correction step is updated to the first learning coefficient in the first correction step obtained in the last repeating process, and the second initial coefficient in the second correction step is updated to the second learning coefficient in the second correction step obtained in the last repeating process, until a preset time is reached.

[0114] An embodiment of the present application provides a device, which comprises a processor, a memory, and a program stored on the memory and executable on the processor, and the processor implements at least the following steps when executing the program:

[0115] In step S201, a first correction step, it is determined whether the engine has a risk of knock, and in the case that the engine has the risk of knock, a first step length and a first initial coefficient are obtained, and the first initial coefficient is corrected by using a first difference value and the first step length to obtain a first learning coefficient, wherein the first difference value is a difference value between a second difference value and a current excess air coefficient, the second difference value is a difference value between a target excess air coefficient and an excess air coefficient threshold, the current excess air coefficient is a measured excess air coefficient of exhaust gas of the engine at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas of the engine which is set in advance, the excess air coefficient threshold is a limit value of the excess air coefficient, the first step length is a step length of a first self-learning of the injection amount of the engine, and the first initial coefficient is a coefficient of the first self-learning of the injection amount of the engine which is set in advance.

[0116] In step S202, a second correction step, a second step length and a second initial coefficient are obtained, and the second initial coefficient is corrected by using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length for second self-learning of the injection amount of the engine, the second initial coefficient is a coefficient for the second self-learning of the injection amount of the engine, the speed of the first self-learning is greater than the speed of the second self-learning, and the first step length is less than the second step length.

[0117] In step S203, a third correction step, the injection amount of the engine is obtained, and the injection amount is corrected by using a target correction coefficient to obtain a target injection amount, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient.

[0118] In step S204, the first correction step, the second correction step and the third correction step are repeated at least once, and in the repeated process, the first initial coefficient in the first correction step is updated to the first learning coefficient in the first correction step obtained in the last repeated process, and the second initial coefficient in the second correction step is updated to the second learning coefficient in the second correction step obtained in the last repeated process, until a preset time is reached.

[0119] The application also provides a computer program product adapted to execute the program of at least the following method steps when executed on a data processing device:

[0120] In step S201, a first correction step, it is determined whether the engine has a risk of knock, and in the case that the engine has the risk of knock, a first step length and a first initial coefficient are obtained, and the first initial coefficient is corrected by using a first difference and the first step length to obtain a first learning coefficient, wherein the first difference is a difference between a second difference and a current excess air coefficient, the second difference is a difference between a target excess air coefficient and an excess air coefficient threshold, the current excess air coefficient is a measured excess air coefficient of exhaust gas of the engine at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas of the engine, the excess air coefficient threshold is a limit value of the excess air coefficient, the first step length is a step length for first self-learning of the injection amount of the engine, and the first initial coefficient is a coefficient for the first self-learning of the injection amount of the engine.

[0121] In step S202, a second correction step, a second step size and a second initial coefficient are acquired, and the second initial coefficient is corrected using the first learning coefficient and the second step size to obtain a second learning coefficient, wherein the second step size is a step size for second self-learning of the injection amount of the engine, the second initial coefficient is a coefficient for the second self-learning of the injection amount of the engine which is set in advance, the speed of the first self-learning is greater than the speed of the second self-learning, and the first step size is smaller than the second step size.

[0122] In step S203, a third correction step, the injection amount of the engine is acquired, and the injection amount is corrected using a target correction coefficient to obtain a target injection amount, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient.

[0123] In step S204, the first correction step, the second correction step, and the third correction step are repeated at least once, and in the repeating process, the first initial coefficient in the first correction step is updated to the first learning coefficient in the first correction step obtained in the last repeating process, and the second initial coefficient in the second correction step is updated to the second learning coefficient in the second correction step obtained in the last repeating process, until a preset time is reached.

[0124] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be realized by general computing devices, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and can be realized by program codes executable by the computing devices, so that they can be stored in storage devices and executed by the computing devices, and in some cases, the steps shown or described can be executed in different order, or they can be respectively manufactured into individual integrated circuit modules, or multiple modules or steps thereof can be manufactured into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.

[0125] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0126] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0127] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0128] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0129] In one typical configuration, the computing device includes one or more processors (CPU's), input / output interfaces, network interfaces, and memory.

[0130] The memory can include non-persistent memory and / or persistent memory, such as flash memory, read-only memory (ROM), and / or volatile or non-volatile random access memory (RAM), among others. The memory is an example of computer-readable media.

[0131] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0132] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article or apparatus including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such a process, method, article or apparatus. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or apparatus including the element.

[0133] From the above description, it can be seen that the above-mentioned embodiments of the present application achieve the following technical effects:

[0134] 1) The engine injection amount self-learning method of the present application first determines whether the engine has the risk of knock, in the case that the engine has the risk of knock, the first initial coefficient is corrected by the first difference value and the first step length to obtain the first learning coefficient; then the second step length and the second initial coefficient are obtained, and the second initial coefficient is corrected by the first learning coefficient and the second step length to obtain the second learning coefficient; then the injection amount of the engine is obtained, and the injection amount is corrected by the target correction coefficient to obtain the target injection amount; finally, the first correction step, the second correction step and the third correction step are repeated at least once, and in the repeating process, the first initial coefficient in the first correction step is updated to the first learning coefficient in the first correction step obtained in the last repeating process, the second initial coefficient in the second correction step is updated to the second learning coefficient in the second correction step obtained in the last repeating process, until the preset time is reached. Through two different speed self-learning of the engine injection amount, when the excess air coefficient increases, the injection amount is corrected, the injection amount of fuel is increased to maintain the appropriate fuel and air ratio; on the contrary, when the excess air coefficient decreases, the injection amount is corrected, the injection amount of fuel is reduced to maintain the appropriate fuel and air ratio, thereby improving the stability of the measurement of the excess air coefficient, solving the problem of unstable measurement of the excess air coefficient under the condition of knock.

[0135] 2) The engine injection amount self-learning device of the present application, the first correction unit determines whether the engine has the risk of knock, in the case that the engine has the risk of knock, the first initial coefficient is corrected by the first difference value and the first step length to obtain the first learning coefficient; the second correction unit obtains the second step length and the second initial coefficient, and corrects the second initial coefficient by the first learning coefficient and the second step length to obtain the second learning coefficient; the third correction unit obtains the injection amount of the engine, and corrects the injection amount by the target correction coefficient to obtain the target injection amount; the repeating unit repeats the first correction step, the second correction step and the third correction step at least once, and in the repeating process, the first initial coefficient in the first correction step is updated to the first learning coefficient in the first correction step obtained in the last repeating process, the second initial coefficient in the second correction step is updated to the second learning coefficient in the second correction step obtained in the last repeating process, until the preset time is reached. Through two different speed self-learning of the engine injection amount, when the excess air coefficient increases, the injection amount is corrected, the injection amount of fuel is increased to maintain the appropriate fuel and air ratio; on the contrary, when the excess air coefficient decreases, the injection amount is corrected, the injection amount of fuel is reduced to maintain the appropriate fuel and air ratio, thereby improving the stability of the measurement of the excess air coefficient, solving the problem of unstable measurement of the excess air coefficient under the condition of knock.

[0136] The above descriptions are only the preferred embodiments of the present application, and are not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of self-learning of an engine injection amount, characterized by, The method comprises: a first correction step of determining whether the engine has a risk of knock, obtaining a first step length and a first initial coefficient in the case where the engine has the risk of knock, and correcting the first initial coefficient by using a first difference value and the first step length to obtain a first learning coefficient, wherein the first difference value is a difference between a second difference value and a current excess air coefficient, the second difference value is a difference between a target excess air coefficient and an excess air coefficient threshold value, the current excess air coefficient is a measured excess air coefficient of exhaust gas of the engine at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas of the engine, the excess air coefficient threshold value is a limit value of the excess air coefficient, the first step length is a step length of first self-learning of injection quantity of the engine, and the first initial coefficient is a coefficient of the first self-learning of the injection quantity of the engine; a second correction step of obtaining a second step length and a second initial coefficient, and correcting the second initial coefficient by using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length of second self-learning of injection quantity of the engine, the second initial coefficient is a coefficient of the second self-learning of the injection quantity of the engine, a speed of the first self-learning is greater than a speed of the second self-learning, and the first step length is greater than the second step length; a third correction step of obtaining injection quantity of the engine, correcting the injection quantity by using a target correction coefficient to obtain target injection quantity, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient; repeating the first correction step, the second correction step and the third correction step at least once, and updating the first initial coefficient in the first correction step to the first learning coefficient in the first correction step obtained in the last repetition process, and updating the second initial coefficient in the second correction step to the second learning coefficient in the second correction step obtained in the last repetition process until a preset time is reached.

2. The method of claim 1, wherein, The method comprises: obtaining the current excess air coefficient, the target excess air coefficient and the excess air coefficient threshold value; in the case where the current excess air coefficient is less than the second difference value, determining that the engine has the risk of knock; in the case where the current excess air coefficient is greater than or equal to the second difference value, determining that the engine does not have the risk of knock.

3. The method of claim 2, wherein, The method comprises: obtaining a mapping relationship between a knock ignition retardation angle and an excess air coefficient limit value and a current knock ignition retardation angle, wherein the knock ignition retardation angle is a retardation amount of an ignition advance angle caused by knock; determining the excess air coefficient threshold value according to the mapping relationship between the knock ignition retardation angle and the excess air coefficient limit value and the current knock ignition retardation angle.

4. The method of claim 1, wherein, The first initial coefficient is corrected by using the first difference value and the first step length to obtain a first learning coefficient, including: According to the formula InjFacFast = λ Error × FastStep + InjFacFast (z-1) , the first learning factor InjFacFast is calculated, wherein λ Error is the first difference, FastStep is the first step length, and InjFacFast (z-1) is the first initial factor.

5. The method of claim 1, wherein, The second initial coefficient is corrected by using the first learning coefficient and the second step length to obtain a second learning coefficient, including: According to the formula InjFacSlow = SlowStep x (InjFacFast - 1) + InjFacSlow (z-1) , the second learning coefficient InjFacSlow is calculated, wherein SlowStep is the second step length, InjFacFast is the first learning coefficient, and InjFacSlow (z-1) is the second initial coefficient.

6. The method of claim 1, wherein, The injection quantity is corrected by using a target correction coefficient to obtain a target injection quantity, including: The target injection quantity is calculated by multiplying the target correction coefficient and the injection quantity.

7. The method of claim 1, wherein, The method further includes: The first initial coefficient is corrected by using the second step length to obtain the first learning coefficient in the case that the engine has no risk of knocking; The second initial coefficient is corrected by using the second step length and the first initial coefficient to obtain the second learning coefficient.

8. The method of claim 7, wherein, The first initial coefficient is corrected by using the second step length to obtain the first learning coefficient, including: According to the formula InjFacFast = InjFacFast (z-1) - SlowStep x InjFacFast (z-1) , the first learning coefficient InjFacFast is calculated, wherein InjFacFast (z-1) is the first initial coefficient and SlowStep is the second step length.

9. The method of claim 7, wherein, The second initial coefficient is corrected by using the second step length to obtain the second learning coefficient, including: According to the formula InjFacSlow = SlowStep x InjFacFast (z-1) + InjFacSlow (z-1) , the second learning factor InjFacSlow is calculated, wherein SlowStep is the second step length, InjFacFast (z-1) is the first initial factor, and InjFacSlow (z-1) is the second initial factor.

10. An engine injection amount self-learning device characterized by comprising: The method further includes: The first correction unit is configured to determine whether the engine has a risk of knocking in the first correction step, obtain a first step length and a first initial coefficient in the case that the engine has the risk of knocking, and correct the first initial coefficient by using a first difference value and the first step length to obtain a first learning coefficient, wherein the first difference value is a difference between a second difference value and a current excess air coefficient, the second difference value is a difference between a target excess air coefficient and an excess air coefficient threshold value, the current excess air coefficient is an excess air coefficient of exhaust gas discharged by the engine measured at a current time, the target excess air coefficient is a target value of the excess air coefficient of the exhaust gas discharged by the engine, the excess air coefficient threshold value is a limit value of the excess air coefficient, the first step length is a step length for first self-learning of an injection quantity of the engine, and the first initial coefficient is a coefficient for the first self-learning of the injection quantity of the engine; The second correction unit is configured to obtain a second step length and a second initial coefficient in the second correction step, and correct the second initial coefficient by using the first learning coefficient and the second step length to obtain a second learning coefficient, wherein the second step length is a step length for second self-learning of the injection quantity of the engine, the second initial coefficient is a coefficient for the second self-learning of the injection quantity of the engine, a speed of the first self-learning is greater than a speed of the second self-learning, and the first step length is greater than the second step length; The third correction unit is configured to obtain the injection quantity of the engine in the third correction step, and correct the injection quantity by using a target correction coefficient to obtain a target injection quantity, wherein the target correction coefficient is a product of the first learning coefficient and the second learning coefficient. a repeating unit for repeating the first correction step, the second correction step and the third correction step at least once, and updating the first initial coefficient in the first correction step to the first learning coefficient in the first correction step obtained in the last repetition, updating the second initial coefficient in the second correction step to the second learning coefficient in the second correction step obtained in the last repetition, until a preset time is reached.

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