阅读说明：本技术 发动机egr阀自学习控制方法及装置 (Self-learning control method and device for engine EGR valve ) 是由 鹿文慧 江楠 王盼盼 张家林 于 2020-12-07 设计创作，主要内容包括：本发明公开了一种发动机EGR阀自学习控制方法及装置,方法包括：检测车辆类型；基于检测结果,确定采用自学习里程控制策略或自学习运行时间控制策略；基于不同的控制策略,对所述EGR阀进行控制；其中,所述自学习里程是指所述发动机自首次启动后的行驶里程总和,所述自学习运行时间是指所述发动机自首次启动后运行时间总和。本发明实施例提供的发动机EGR阀自学习控制方法,使得当EGR阀的老化,弹簧弹力降低,抖灰能力下降,避免了下电行驶里程太少时导致的不必要自学习。该种自学习方法可以保证EGR阀的正常控制且不损耗其使用寿命,因此本技术有着广阔的前景,能够满足市场需求。(The invention discloses a self-learning control method and a device for an EGR valve of an engine, wherein the method comprises the following steps: detecting a vehicle type; determining to adopt a self-learning mileage control strategy or a self-learning running time control strategy based on the detection result; controlling the EGR valve based on different control strategies; the self-learning mileage refers to the sum of the driving mileage of the engine after the engine is started for the first time, and the self-learning running time refers to the sum of the running time of the engine after the engine is started for the first time. According to the engine EGR valve self-learning control method provided by the embodiment of the invention, when the EGR valve is aged, the spring elasticity is reduced, the ash shaking capability is reduced, and unnecessary self-learning caused by too little driving mileage after power-off is avoided. The self-learning method can ensure the normal control of the EGR valve without losing the service life of the EGR valve, so the technology has wide prospect and can meet the market demand.)

1. A method of self-learning control of an EGR valve of an engine, comprising:

detecting a vehicle type;

determining to adopt a self-learning mileage control strategy or a self-learning running time control strategy based on the detection result;

controlling the EGR valve based on different control strategies;

the self-learning mileage refers to the sum of the driving mileage of the engine after the engine is started for the first time, and the self-learning running time refers to the sum of the running time of the engine after the engine is started for the first time.

2. The method of claim 1, wherein the vehicle types include road vehicles and off-road vehicles.

3. The method of claim 2, wherein the road vehicle employs a self-learning mileage control strategy.

4. The method of claim 2, wherein the off-road vehicle employs a self-learning run-time control strategy.

5. The method of claim 1, wherein the self-learning mileage controlling strategy comprises:

comparing the self-learning mileage with an average self-learning mileage threshold;

and if the self-learning mileage is larger than the average self-learning mileage threshold value, self-learning is carried out after the engine is powered off and stops.

6. The method of claim 1, wherein the self-learning runtime control strategy comprises:

comparing the self-learning runtime to an average self-learning runtime threshold;

and if the self-learning running time is greater than the average self-learning running time threshold, self-learning is carried out after the engine is powered off and stops.

7. The method of claim 5 or 6, further comprising:

and if the self-learning is unsuccessful, storing the self-learning mileage or the self-learning running time, and reporting the fault.

8. A self-learning control apparatus of an EGR valve of an engine, characterized by comprising:

a detection module for detecting a vehicle type;

the determining module is used for determining to adopt a self-learning mileage control strategy or a self-learning running time control strategy based on the detection result;

a control module to control the EGR valve based on different control strategies;

the self-learning mileage refers to the sum of the driving mileage of the engine after the engine is started for the first time, and the self-learning running time refers to the sum of the running time of the engine after the engine is started for the first time.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of any one of claims 1-7.

10. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor to implement the method according to any one of claims 1-7.

Technical Field

The invention relates to the technical field of engines, in particular to a self-learning control method and device for an EGR valve of an engine.

Background

Gas engines have been widely accepted due to their economical and environmental protection properties, and are increasingly used in various power plants. When the gas engine works, combustible mixed gas is provided for a cylinder of the engine in a premixing air inlet mode, namely, the gas and the air are mixed in the mixer and then are sprayed into a combustion chamber of the engine through a mixed air passage to be combusted and do work. In order to optimize the emission of the engine, an exhaust gas recirculation EGR valve is arranged on an exhaust pipe of the engine, part of exhaust gas exhausted by the engine is returned to an intake manifold and enters the cylinder again together with fresh mixed gas, on one hand, part of exhaust gas is combusted again, so that emission pollution can be reduced, on the other hand, part of high-temperature exhaust gas enters the cylinder after being cooled by an intercooler, and the detonation risk can be reduced. Because the EGR valve is exposed in an exhaust gas environment for a long time, a layer of carbon deposit is formed on the surface of the EGR valve, so that the position sensor of the EGR valve is insensitive, and because the controller of the EGR valve cannot automatically correct the difference value between the position sensed by the sensor and the actual mechanical position, the correction is needed through a self-learning function. However, sensor offset is a problem due to long-term driving, and self-learning is not necessary every time, and frequent self-learning may affect the service life of the EGR valve.

Disclosure of Invention

The invention aims to provide a self-learning control method and device for an EGR valve of an engine. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

The existing strategy only considers the vehicle type provided with a vehicle speed sensor, but does not consider the non-road vehicle type without the vehicle speed sensor; the driving mileage limit value is fixed and is not flexible. Because the newly replaced EGR valve has less carbon deposition, the self-learning can be carried out only by setting a longer driving mileage, the spring force is reduced and the ash shaking capability is reduced along with the aging of the EGR valve, the self-learning frequency is improved, and the driving mileage is shorter than that before calibrated.

According to an aspect of an embodiment of the invention, a self-learning control method for an EGR valve of an engine is provided, which comprises the following steps:

detecting a vehicle type;

determining to adopt a self-learning mileage control strategy or a self-learning running time control strategy based on the detection result;

controlling the EGR valve based on different control strategies;

the self-learning mileage refers to the sum of the driving mileage of the engine after the engine is started for the first time, and the self-learning running time refers to the sum of the running time of the engine after the engine is started for the first time.

Further, the vehicle types include road vehicles and off-road vehicles.

Further, the road vehicle adopts a self-learning mileage control strategy.

Further, the off-road vehicle employs a self-learning runtime control strategy.

Further, the self-learning mileage control strategy comprises:

comparing the self-learning mileage with an average self-learning mileage threshold;

and if the self-learning mileage is larger than the average self-learning mileage threshold value, self-learning is carried out after the engine is powered off and stops.

Further, the self-learning runtime control strategy includes:

comparing the self-learning runtime to an average self-learning runtime threshold;

and if the self-learning running time is greater than the average self-learning running time threshold, self-learning is carried out after the engine is powered off and stops.

Further, the method further comprises:

and if the self-learning is unsuccessful, storing the self-learning mileage or the self-learning running time, and reporting the fault.

According to another aspect of an embodiment of the present invention, there is provided a self-learning control apparatus of an EGR valve of an engine, comprising:

a detection module for detecting a vehicle type;

the determining module is used for determining to adopt a self-learning mileage control strategy or a self-learning running time control strategy based on the detection result;

a control module to control the EGR valve based on different control strategies;

the self-learning mileage refers to the sum of the driving mileage of the engine after the engine is started for the first time, and the self-learning running time refers to the sum of the running time of the engine after the engine is started for the first time.

According to another aspect of the embodiments of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the engine EGR valve self-learning control method.

According to another aspect of an embodiment of the present invention, there is provided a non-transitory computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the engine EGR valve self-learning control method.

The technical scheme provided by the embodiment of the invention has the following beneficial effects:

according to the engine EGR valve self-learning control method provided by the embodiment of the invention, when the EGR valve is aged, the spring elasticity is reduced, the ash shaking capability is reduced, a variable threshold value is selected to adapt to the self-learning requirement more flexibly, whether self-learning is carried out or not is judged according to the driving mileage or the running time, the self-learning function is completed when the power is off, and unnecessary self-learning caused by too little driving mileage when the power is off is avoided. Through the method of the variable threshold value, the frequency of self-learning can be flexibly selected, because the carbon deposition of the EGR valve is formed for a long time, the position of the sensor can be corrected by the self-learning at intervals, and if the carbon deposition needs to be learned once in each driving cycle, the service life of the EGR valve is not necessary and can be influenced. The self-learning method can ensure the normal control of the EGR valve and does not lose the service life of the EGR valve.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a flow chart of a method of self-learning control of an EGR valve of an engine consistent with certain embodiments of the present invention;

FIG. 2 is a schematic diagram of a self-learning mileage calculation method consistent with certain embodiments of the present invention;

FIG. 3 is a schematic diagram of a self-learning runtime calculation method consistent with certain embodiments of the present invention;

FIG. 4 is a schematic diagram of a method of calculating a self-learned mileage threshold and a runtime threshold consistent with certain embodiments of the present invention;

FIG. 5 is a schematic diagram of self-learning control logic consistent with certain embodiments of the present invention;

fig. 6 is a block diagram of an engine EGR valve self-learning control apparatus consistent with an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

A gas engine: an engine using natural gas as raw material features that air and gas are mixed and then fed into cylinder to burn and do work.

An EGR valve: and (4) recycling the exhaust gas, reintroducing part of the exhaust gas discharged by the engine into the intake manifold, and re-entering the cylinder for combustion together with the fresh mixed gas.

EWMA (exponentially weighted moving average): and introducing a weight coefficient, and smoothly modifying the current value through the current actual value and the average value of the previous period to generate a stable trend curve to obtain the average value of the current moment.

Example one

As shown in FIG. 1, the present embodiment provides a self-learning control method for an EGR valve of an engine, including:

detecting a vehicle type;

determining to adopt a self-learning mileage control strategy or a self-learning running time control strategy based on the detection result;

controlling the EGR valve based on different control strategies;

the self-learning mileage refers to the sum of the driving mileage of the engine after the engine is started for the first time, and the self-learning running time refers to the sum of the running time of the engine after the engine is started for the first time.

Further, the vehicle types include road vehicles and off-road vehicles.

Further, the road vehicle adopts a self-learning mileage control strategy.

Further, the off-road vehicle employs a self-learning runtime control strategy.

Further, the self-learning mileage control strategy comprises:

comparing the self-learning mileage with an average self-learning mileage threshold;

and if the self-learning mileage is larger than the average self-learning mileage threshold value, self-learning is carried out after the engine is powered off and stops.

Further, the self-learning runtime control strategy includes:

comparing the self-learning runtime to an average self-learning runtime threshold;

and if the self-learning running time is greater than the average self-learning running time threshold, self-learning is carried out after the engine is powered off and stops.

Further, the method further comprises:

and if the self-learning is unsuccessful, storing the self-learning mileage or the self-learning running time, and reporting the fault.

Example II,

The driving information of the engine comprises information such as driving mileage, engine running time and the like, and when the power is off, current values are stored in an EEPROM area in the ECU controller. After power up again, variable information is read from the EEPROM for engine control use. And newly adding the self-learning mileage and the self-learning running time of the EGR valve in the EEPROM, and judging whether the EGR valve is required to be self-learned or not according to the self-learning mileage or the self-learning running time.

The specific logic comprises:

1) as shown in fig. 2, the self-learning mileage calculation is calculated based on the vehicle speed signal, and when the vehicle speed is not 0, the timer starts counting time, and when the vehicle speed is 0, the calculation is stopped, and the accumulated mileage value is stored in the EEPROM (unit: m);

2) as shown in fig. 3, the self-learning operation time calculation is calculated based on the engine speed, and when the engine speed is not 0, the timer starts counting, and when the engine speed is 0, the calculation is stopped, and the accumulated time value is stored in the EEPROM (unit: h) (ii) a

3) As shown in fig. 4, the current self-learned mileage is greater than an average self-learned mileage threshold calculated using the EWMA algorithm, or the self-learned running time is greater than an average self-learned time threshold also calculated using the EWMA algorithm.

The specific processing mode of the EWMA is that the average self-learning mileage threshold values of the last times and the average self-learning running time threshold values of the last times are respectively stored into an EEPROM and can be known according to an exponential weighted moving average formula:

the calculation formula of the average self-learning mileage threshold value is as follows:

S_i(0)＝0；

S_i(1)＝β*S_i(0)+(1-β)*S₁；

S_i(2)＝β*S_i(1)+(1-β)*S₂；

S_i(3)＝β*S_i(2)+(1-β)*S₃；

…

S_i(t-1)＝β*S_i(t-2)+(1-β)*S_t-1；

S_i(t)＝β*S_i(t-1)+(1-β)*S₁

the above formula is simplified to obtain:

S_i(t)＝(1-β)*S_t+β*(1-β)*S_t-1+β²*(1-β)*S_t-2+…+β^t-2*(1-β)*S₂+β^t-1*(1-β)*S₁ (1)

wherein:

s (t) is an average self-learning mileage threshold value at the time t;

S_i(t-1) is the average self-learning mileage threshold at time t-1;

S_tsetting a self-learning mileage threshold value at the time t;

beta is the weight occupied by the average self-learning mileage threshold value at the previous moment;

the calculation result of the average self-learning time threshold obtained by the same method is as follows:

T_i(0)＝0；

T_i(1)＝β*T_i(0)+(1-β)*T₁；

T_i(2)＝β*T_i(1)+(1-β)*T₂；

T_i(3)＝β*T_i(2)+(1-β)*T₃；

…

T_i(t-1)＝β*T_i(t-2)+(1-β)*T_t-1；

T_i(t)＝β*T_i(t-1)+(1-β)*T₁

the above formula is simplified to obtain:

T_i(t)＝(1-β)*T_t+β*(1-β)*T_t-1+β²*(1-β)*T_t-2+…+β^t-2*(1-β)*T₂+β^t-1*(1-β)*T₁ (2)

wherein:

t (t) is the average self-learning mileage threshold at time t;

T_i(t-1) is the average self-learning mileage threshold at time t-1;

T_tsetting a self-learning mileage threshold value at the time t;

beta is the weight occupied by the average self-learning mileage threshold value at the previous moment;

as can be seen from equations (1) and (2), the self-learned mileage and the self-learned time decay with the weight β as time progresses backward. When the beta is 0, the average learning mileage and the self-learning time information of the previous times can be ignored, and the weight of the current set threshold value is continuously reduced along with the continuous increase of the beta from 0 to 1. After the processing, the threshold value is variable, the operation is more flexible, and the calibration workload is reduced.

4) Powering down the engine;

the conditions of 3) and 4) above are met, as shown in fig. 5, the current driving cycle is subjected to power-off self-learning, when the self-learning success of the EGR valve is detected, the self-learning mileage and the self-learning running time are cleared, and the calculation of the next driving cycle is restarted.

For the road engine, the self-learning mileage calculated by the vehicle speed is used as a self-learning judgment condition more reasonably, and for the non-road engine such as a mixer truck and a crane, long-term in-situ operation is carried out, the vehicle speed sensor is not installed, and the self-learning running time calculated by the rotating speed is used as a self-learning judgment condition more reasonably, so that two modes of self calibration are carried out according to different purposes. And after a large number of tests are verified, selecting a reasonable beta weight value, and obtaining the subsequent threshold value by EWMA self-learning without calibration. And meanwhile, if the self-learning is not successful, a fault is reported to remind a driver of the reason of the self-learning failure.

Example III,

As shown in fig. 6, the present embodiment also provides a self-learning control apparatus of an EGR valve of an engine, characterized by comprising:

a detection module for detecting a vehicle type;

the determining module is used for determining to adopt a self-learning mileage control strategy or a self-learning running time control strategy based on the detection result;

a control module to control the EGR valve based on different control strategies;

the self-learning mileage refers to the sum of the driving mileage of the engine after the engine is started for the first time, and the self-learning running time refers to the sum of the running time of the engine after the engine is started for the first time.

The embodiment also provides electronic equipment comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the engine EGR valve self-learning control method.

The present embodiment also provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the engine EGR valve self-learning control method described above.

It should be noted that, in the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the system or the device disclosed by the embodiment, the description is simple because the system or the device corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.

It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.