CN118190431A - Engine flashback detection method and device, electronic equipment and storage medium - Google Patents
Engine flashback detection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses an engine backfire detection method, an engine backfire detection device, electronic equipment and a storage medium. Calculating confidence coefficients of occurrence of backfire of the exhaust system sensor according to measurement signals of the exhaust system sensor respectively; respectively calculating weighted values of confidence coefficients of backfire occurrence of an air inlet system sensor and an exhaust system sensor according to the working condition of the engine, the opening degree of an EGR valve, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor; and multiplying the confidence coefficients of the exhaust system sensor and the exhaust system sensor for backfire occurrence by the weighted values of the corresponding backfire occurrence confidence coefficients respectively to obtain respective weighted confidence coefficients, updating the time window queues of the respective weighted confidence coefficients, obtaining the final backfire occurrence confidence coefficients, and carrying out threshold comparison to obtain a final backfire detection result. The backfire detection method disclosed by the invention can not fail after the air inlet pressure and temperature sensor is damaged; and the cost and the system complexity can be reduced without additionally installing an additional sensor.
Description
Technical Field
The embodiment of the invention relates to the technical field of engine fault diagnosis, in particular to an engine backfire detection method, an engine backfire detection device, electronic equipment and a storage medium.
Background
With the increasing severity of carbon emission regulations, to take advantage of the greater potential for use of internal combustion engines, more and more manufacturers have introduced alternative-fuelled engines, with hydrogen fuelled engines being more widespread. Compared with the traditional internal combustion engine, the engine has the characteristics of zero carbon emission, low ignition energy, high thermal efficiency and the like. However, for a hydrogen engine adopting port fuel injection, the probability of occurrence of flashback phenomenon is high due to the inherent characteristics of the hydrogen engine, and the frequent flashback phenomenon can cause serious damage to the engine body, thereby affecting the stability and the service life of the hydrogen engine. Therefore, the occurrence of the backfire phenomenon can be timely detected, and corresponding measures are timely taken to avoid the occurrence of the backfire phenomenon again, so that the backfire phenomenon becomes a necessary means for controlling the hydrogen engine. Among them, how to accurately and timely detect the backfire phenomenon is a relatively challenging task.
The current engine backfire detection method mainly has the following problems: mainly based on air inlet pressure and temperature sensors, but the air inlet pressure and temperature sensors are easy to damage under severe tempering working conditions, and the method is basically invalid after the air inlet pressure and temperature sensors are damaged; the need to add additional sensors, such as vibration sensors, increases the cost and complexity of the system.
Disclosure of Invention
The invention provides an engine backfire detection method, an engine backfire detection device, electronic equipment and a storage medium, wherein after an air inlet pressure sensor and a temperature sensor are damaged, the backfire detection method cannot fail; no additional sensor is needed, which is beneficial to reducing the cost and the complexity of the system.
According to an aspect of the present invention, there is provided an engine flashback detection method including:
comprehensively judging whether the current backfire detecting function is enabled or not according to the engine speed, the engine temperature, the effective state of an air inlet system sensor and the effective state of an exhaust system sensor;
Calculating confidence coefficients of backfire occurrence of the air inlet system sensor according to measured values of the air inlet system sensor, wherein the air inlet system sensor comprises an air inlet flow sensor and an air inlet pressure temperature sensor;
Calculating confidence coefficients of occurrence of backfire of the exhaust system sensor according to measurement signals of the exhaust system sensor, wherein the exhaust system sensor comprises an exhaust temperature sensor, a Lambda sensor and a NOx sensor;
Respectively calculating weighted values of confidence coefficients of occurrence of backfire of the air inlet system sensor and the exhaust system sensor according to the working condition of the engine, the opening degree of the EGR valve, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor;
and multiplying the confidence coefficients of the exhaust system sensor and the exhaust system sensor for backfire occurrence by the weighted values of the confidence coefficients of the corresponding backfire occurrence to obtain respective weighted confidence coefficients, updating the time window queues of the respective weighted confidence coefficients, solving the final confidence coefficient of backfire occurrence, and carrying out threshold comparison to obtain a final backfire detection result.
Optionally, multiplying the confidence coefficients of the exhaust system sensor and the exhaust system sensor for backfire occurrence by weighted values of the confidence coefficients of the corresponding backfire occurrence to obtain respective weighted confidence coefficients, updating the queues of the respective weighted confidence coefficient time windows and obtaining final confidence coefficients of backfire occurrence, and performing threshold comparison to obtain final backfire detection results includes:
Multiplying the confidence coefficient of the exhaust system sensor backfire occurrence by the weighted value of the confidence coefficient of the exhaust system sensor backfire occurrence to obtain the weighted confidence coefficient of the exhaust system sensor, and multiplying the confidence coefficient of the exhaust system sensor backfire occurrence by the weighted value of the confidence coefficient of the exhaust system sensor backfire occurrence to obtain the weighted confidence coefficient of the exhaust system sensor;
Updating time window queues of each weighted confidence coefficient according to the weighted confidence coefficients of the exhaust system sensor and the exhaust system sensor, and solving the maximum weighted confidence coefficient in each queue and adding to obtain the confidence coefficient of final tempering;
Determining a current confidence coefficient threshold according to the current effective state of the air inlet system sensor and the effective state of the exhaust system sensor;
If the confidence coefficient of the final tempering is larger than the current confidence coefficient threshold value, tempering is performed; if not, tempering does not occur.
Optionally, the comprehensively determining whether the current flashback detection function is enabled according to the engine speed, the engine temperature, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor comprises:
When the engine speed is within an enabling speed range and the engine temperature is within an enabling temperature range and at least one sensor signal of the intake system sensor and the exhaust system sensor is valid, a flashback detection function is enabled; if not, the flashback detection function is not enabled.
Optionally, the calculating the confidence coefficient of occurrence of flashback of the air inlet system sensor according to the measured value of the air inlet system sensor includes:
corresponding mapping is carried out according to the air inlet flow, the engine speed and the air inlet flow change to obtain a backfire detection confidence coefficient based on the air inlet flow;
Corresponding mapping is carried out according to the air inlet pressure, the engine rotating speed and the change of the air inlet pressure to obtain a backfire detection confidence coefficient based on the air inlet pressure;
and corresponding mapping is carried out according to the change of the air inlet temperature, the air inlet flow and the air inlet temperature to obtain the backfire detection confidence coefficient based on the air inlet temperature.
Optionally, the calculating the confidence coefficient of occurrence of the flashback of the exhaust system sensor according to the measurement signals of the exhaust system sensor includes:
according to Lambda value, corresponding mapping is carried out on the engine speed and the air inlet flow to obtain a Lambda-based tempering detection confidence coefficient;
Corresponding mapping is carried out according to the exhaust temperature, the exhaust flow and the change of the exhaust temperature to obtain a tempering detection confidence coefficient based on the exhaust temperature;
And corresponding mapping is carried out according to the engine speed, the air inlet flow and the NOx concentration to obtain a flashback detection confidence coefficient based on the NOx concentration.
Optionally, the calculating the weighted values of the confidence coefficients of the occurrence of the backfire of the air inlet system sensor and the exhaust system sensor according to the working condition of the engine, the opening degree of the EGR valve, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor respectively includes:
When sensor signals in the air inlet system and the air outlet system are effective, mapping is carried out according to the engine speed and the air inlet flow respectively to obtain weighted values of a flashback detection confidence coefficient based on the air inlet flow, a flashback detection confidence coefficient based on Lambda, a flashback detection confidence coefficient based on the exhaust temperature and a flashback detection confidence coefficient based on the NOx concentration;
Respectively carrying out corresponding mapping according to the engine speed, the air inlet flow and the EGR valve opening to obtain a weighted value of a backfire detection confidence coefficient based on the air inlet pressure and a weighted value of a backfire detection confidence coefficient based on the air inlet temperature;
when the sensor signals in the air inlet system and the exhaust system are invalid, the weighting value of the corresponding flashback detection confidence coefficient is 0.
According to another aspect of the present invention, there is also provided an engine flashback detection apparatus including:
the backfire detection enabling module is used for comprehensively judging whether the current backfire detection function is enabled or not according to the engine rotating speed, the engine temperature, the effective state of an air inlet system sensor and the effective state of an exhaust system sensor;
The air inlet system backfire detection module is used for respectively calculating confidence coefficients of occurrence of backfire of the air inlet system sensor according to the measured value of the air inlet system sensor, and the air inlet system sensor comprises an air inlet flow sensor and an air inlet pressure temperature sensor;
the exhaust system backfire detection module is used for respectively calculating confidence coefficients of occurrence of backfire of the exhaust system sensor according to measurement signals of the exhaust system sensor, and the exhaust system sensor comprises an exhaust temperature sensor, a Lambda sensor and a NOx sensor;
The detection result weighting coefficient calculation module is used for calculating weighting values of confidence coefficients of occurrence of backfire of the air inlet system sensor and the exhaust system sensor respectively according to the working condition of the engine, the opening degree of the EGR valve, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor;
And the tempering detection result judging module is used for multiplying the confidence coefficients of the exhaust system sensor and the exhaust system sensor for tempering respectively by the weighted values of the confidence coefficients of the corresponding tempering to obtain respective weighted confidence coefficients, updating the time window queues of the respective weighted confidence coefficients, solving the final confidence coefficient of the tempering, and carrying out threshold comparison to obtain the final tempering detection result.
According to another aspect of the present invention, there is also provided an electronic apparatus including:
one or more processors;
A memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement an engine flashback detection method according to an embodiment of the invention.
According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the engine flashback detection method according to any one of the embodiments of the present invention.
According to the technical scheme provided by the embodiment of the invention, the engine backfire detection method is a fault detection method for backfire phenomenon caused by abnormal operation of the engine, and is suitable for an equivalent ratio combustion engine and a lean combustion engine. According to the invention, whether the current backfire detecting function is enabled is comprehensively judged, the confidence coefficients of backfire occurrence of the air inlet system sensor and the exhaust system sensor and the weighted value of the confidence coefficients of backfire occurrence are respectively calculated, and the final confidence coefficient of backfire occurrence is obtained; and determining a current confidence coefficient threshold according to the effective state of each current sensor, and judging a tempering detection result according to the final tempering occurrence confidence coefficient and the current confidence coefficient threshold. The invention is mainly based on the tempering detection of the air inlet system sensor and the exhaust system sensor, is not easy to damage under the severe tempering working condition, and the method can not fail after the damage; no additional sensor is required to be installed, the cost is not increased, and the complexity of the system is not increased. In summary, the invention solves the problems that the existing engine backfire detection method is easy to be damaged under severe backfire working conditions based on the air inlet pressure and temperature sensors, the method basically fails after the damage, and additional sensors are required to be added, so that the cost is increased and the complexity of the system is increased.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an engine flashback detection method provided in accordance with an embodiment of the invention;
FIG. 2 is a schematic diagram of a flashback detection enabled implementation provided in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a tempering detection result determination implementation manner according to an embodiment of the present invention;
FIG. 4 is a flow chart of yet another engine flashback detection method provided in accordance with an embodiment of the invention;
FIG. 5 is a schematic diagram of an intake system flashback detection implementation, according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an exhaust system flashback detection implementation, provided in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of a detection result weighting calculation implementation according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a four-cylinder hydrogen fuelled engine according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of an engine flashback detector according to an embodiment of the invention;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of an engine flashback detection method according to an embodiment of the present invention, and referring to fig. 1, an embodiment of the present invention provides an engine flashback detection method, which may be performed by an engine flashback detection device, which may be integrated into an electronic device, and which may be implemented by software and/or hardware. The engine backfire detection method comprises the following steps:
S110, comprehensively judging whether the current flashback detection function is enabled according to the engine speed, the engine temperature, the effective state of an air inlet system sensor and the effective state of an exhaust system sensor.
Specifically, fig. 2 is a schematic diagram of a implementation manner of flashback detection according to an embodiment of the present invention, referring to fig. 2, whether the flashback detection function enable flag 301 is set to 1, that is, enabled, is comprehensively determined according to an engine speed 201, an engine temperature 202 (where the engine temperature may be selected from an engine coolant temperature, an engine oil temperature, or a calculation combination of the two), an intake system related sensor valid state (including an intake air flow rate valid flag 203, an intake air pressure valid flag 204, and an intake air temperature valid flag 205), and an exhaust system related sensor valid state (including an exhaust air temperature valid flag 206, an NOx concentration valid flag 207, and a Lambda valid flag 208).
S120, calculating confidence coefficients of backfire occurrence of the air inlet system sensors according to measured values of the air inlet system sensors, wherein the air inlet system sensors comprise an air inlet flow sensor and an air inlet pressure temperature sensor.
Specifically, the intake pressure temperature sensor includes an intake pressure sensor and an intake temperature sensor, where the two sensors are combined into an intake TMAP sensor in this embodiment, and the confidence coefficients of the occurrence of the corresponding flashback are calculated respectively according to the measured values of the intake system sensors.
FIG. 3 is a schematic diagram of a implementation of determining a flashback detection result according to an embodiment of the present invention, and referring to FIG. 3, confidence coefficients of occurrence of flashback of an air intake system sensor include: a flashback detection confidence coefficient 302 based on the intake air flow rate, a flashback detection confidence coefficient 303 based on the intake air pressure, and a flashback detection confidence coefficient 304 based on the intake air temperature.
S130, calculating confidence coefficients of occurrence of backfire of the exhaust system sensors according to measurement signals of the exhaust system sensors, wherein the exhaust system sensors comprise an exhaust temperature sensor, a Lambda sensor and a NOx sensor.
Specifically, with continued reference to fig. 3, respective confidence coefficients for flashback occurrence are calculated from measurement signals of an exhaust gas temperature sensor, a NOx sensor, and a Lambda sensor, respectively. The confidence coefficients for exhaust system sensor flashback occurrence include: lambda-based flashback detection confidence coefficient 305, exhaust temperature-based flashback detection confidence coefficient 306, NOx concentration-based flashback detection confidence coefficient 307.
And S140, respectively calculating weighted values of confidence coefficients of occurrence of backfire of the air inlet system sensor and the exhaust system sensor according to the working condition of the engine, the opening degree of the EGR valve, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor.
Specifically, with continued reference to fig. 3, a weighted value of confidence coefficients for each flashback occurrence is calculated from engine operating conditions (engine speed, intake air flow), EGR valve opening, the effective state of each sensor, and the like. The weighted value of the confidence coefficient for each flashback occurrence includes: a flashback detection confidence coefficient weighting value 308 based on intake air flow, a flashback detection confidence coefficient weighting value 309 based on intake air pressure, a flashback detection confidence coefficient weighting value 310 based on intake air temperature, a flashback detection confidence coefficient weighting value 311 based on Lambda, a flashback detection confidence coefficient weighting value 312 based on exhaust air temperature, and a flashback detection confidence coefficient weighting value 313 based on NOx concentration.
And S150, multiplying the confidence coefficients of the exhaust system sensor and the exhaust system sensor for backfire occurrence by the weighted values of the corresponding backfire occurrence confidence coefficients respectively to obtain respective weighted confidence coefficients, updating the time window queues of the respective weighted confidence coefficients, obtaining the final backfire occurrence confidence coefficients, and carrying out threshold comparison to obtain a final backfire detection result.
Specifically, with continued reference to fig. 3, the tempering detection result determination process is as follows: first, the respective weighted confidence coefficients are obtained by multiplying the confidence coefficients 302 to 307 for each tempering occurrence by the corresponding weighting values 308 to 313, respectively: a flashback detection weighted confidence coefficient 314 based on intake air flow, a flashback detection weighted confidence coefficient 315 based on intake air pressure, a flashback detection weighted confidence coefficient 316 based on intake air temperature, a flashback detection weighted confidence coefficient 317 based on Lambda, a flashback detection weighted confidence coefficient 318 based on exhaust air temperature, a flashback detection weighted confidence coefficient 319 based on NOx concentration.
Secondly, updating the queue of each weighted confidence coefficient time window according to the weighted confidence coefficients, solving the maximum weighted confidence coefficient in each queue, and adding to obtain the final confidence coefficient 326 of tempering occurrence; again, a current flashback confidence coefficient threshold 327 is determined based on the current sensor status; finally, if the confidence coefficient 326 of the final flashback occurrence is greater than the current confidence coefficient threshold 327, flashback occurs with the flashback occurrence flag 328 set to 1; otherwise, no flashback occurs and the flashback occurrence flag 328 clears 0.
The length of the queue for each weighted confidence coefficient time window is determined based on how fast the flashback occurs that causes the sensor signal to change: the faster the sensor reacts, the longer the length of its corresponding time window queue. The principle of the time window queue update is first-in first-out (FIFO).
According to the technical scheme provided by the embodiment of the invention, the engine backfire detection method is a fault detection method for backfire phenomenon caused by abnormal operation of the engine, and is suitable for an equivalent ratio combustion engine and a lean combustion engine. According to the invention, whether the current backfire detecting function is enabled is comprehensively judged, the confidence coefficients of backfire occurrence of the air inlet system sensor and the exhaust system sensor and the weighted value of the confidence coefficients of backfire occurrence are respectively calculated, and the final confidence coefficient of backfire occurrence is obtained; and determining a current confidence coefficient threshold according to the effective state of each current sensor, and judging a tempering detection result according to the final tempering occurrence confidence coefficient and the current confidence coefficient threshold. The invention is mainly based on the tempering detection of the air inlet system sensor and the exhaust system sensor, is not easy to damage under the severe tempering working condition, and the method can not fail after the damage; no additional sensor is required to be installed, the cost is not increased, and the complexity of the system is not increased. In summary, the invention solves the problems that the existing engine backfire detection method is easy to be damaged under severe backfire working conditions based on the air inlet pressure and temperature sensors, the method basically fails after the damage, and additional sensors are required to be added, so that the cost is increased and the complexity of the system is increased.
In addition to the above embodiments, the embodiment of the present invention further refines step S150, and the following details are given, but the present invention is not limited thereto.
Fig. 4 is a flowchart of still another engine backfire detecting method according to an embodiment of the present invention, referring to fig. 4, step S150 includes:
s151, multiplying the confidence coefficient of the backfire occurrence of the exhaust system sensor by the weighted confidence coefficient of the backfire occurrence of the exhaust system sensor, and multiplying the confidence coefficient of the backfire occurrence of the exhaust system sensor by the weighted confidence coefficient of the backfire occurrence of the exhaust system sensor.
S152, updating time window queues of each weighted confidence coefficient according to the weighted confidence coefficients of the exhaust system sensor and the exhaust system sensor, and obtaining the maximum weighted confidence coefficient in each queue and adding to obtain the confidence coefficient of final tempering.
S153, determining a current confidence coefficient threshold according to the current effective state of the air inlet system sensor and the effective state of the air outlet system sensor.
S154, if the confidence coefficient of the final tempering is larger than the current confidence coefficient threshold value, tempering is carried out; if not, tempering does not occur.
Specifically, with continued reference to fig. 3, first, the confidence coefficients 302-307 for each flashback occurrence are multiplied by the corresponding weighting values 308-313, respectively, to obtain the respective weighted confidence coefficients: a flashback detection weighted confidence coefficient 314 based on intake air flow, a flashback detection weighted confidence coefficient 315 based on intake air pressure, a flashback detection weighted confidence coefficient 316 based on intake air temperature, a flashback detection weighted confidence coefficient 317 based on Lambda, a flashback detection weighted confidence coefficient 318 based on exhaust air temperature, a flashback detection weighted confidence coefficient 319 based on NOx concentration. Secondly, updating the queue of each weighted confidence coefficient time window according to the weighted confidence coefficients, solving the maximum weighted confidence coefficient in each queue, and adding to obtain the final confidence coefficient 326 of tempering occurrence; again, a current flashback confidence coefficient threshold 327 is determined based on the current sensor status; finally, if the confidence coefficient 326 of the final flashback occurrence is greater than the current confidence coefficient threshold 327, flashback occurs with the flashback occurrence flag 328 set to 1; otherwise, no flashback occurs and the flashback occurrence flag 328 clears 0.
Optionally, comprehensively determining whether the current flashback detection function is enabled according to the engine speed, the engine temperature, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor comprises: when the engine speed is within the enabling speed range and the engine temperature is within the enabling temperature range and at least one sensor signal of the intake system sensor and the exhaust system sensor is valid, the flashback detection function is enabled; if not, the flashback detection function is not enabled.
Specifically, with continued reference to FIG. 2, when the following conditions are simultaneously met and hold the flashback detection enable delay time 405, the flashback detection function enable flag 301 is set to 1; otherwise, it is cleared. The following conditions need to be satisfied simultaneously: 1) The engine speed 201 is less than a flashback-detection-enabled speed upper threshold 401 and greater than a flashback-detection-enabled speed lower threshold 402; 2) The engine temperature 202 is less than a flashback-detection-enabled engine temperature upper threshold 403 and greater than a flashback-detection-enabled engine temperature lower threshold 404; 3) The following flag bits: at least one of the intake air flow rate effective flag 203, the intake air pressure effective flag 204, and the intake air temperature effective flag 205 is 1; 4) The following flag bits: at least one of the exhaust gas temperature valid flag 206, the NOx concentration valid flag 207, and the Lambda valid flag bit 208 is 1.
Optionally, calculating the confidence coefficient of occurrence of the flashback of the air intake system sensor based on the measured values of the air intake system sensor includes: corresponding mapping is carried out according to the air inlet flow, the engine speed and the air inlet flow change to obtain a backfire detection confidence coefficient based on the air inlet flow; corresponding mapping is carried out according to the air inlet pressure, the engine rotating speed and the change of the air inlet pressure to obtain a backfire detection confidence coefficient based on the air inlet pressure; and corresponding mapping is carried out according to the change of the air inlet temperature, the air inlet flow and the air inlet temperature to obtain the backfire detection confidence coefficient based on the air inlet temperature.
Specifically, fig. 5 is a schematic diagram of an implementation manner of flashback detection of an air intake system according to an embodiment of the present invention, referring to fig. 5, first, according to an engine speed 201, an air intake flow 210, and an air intake flow variation 211, a flashback detection confidence coefficient table 407 based on the air intake flow is searched for obtaining a flashback detection confidence coefficient 302 based on the air intake flow; secondly, looking up a flashback detection confidence coefficient table 408 based on the air inlet pressure according to the engine speed 201, the air inlet pressure 212 and the air inlet pressure variation 213 to obtain a flashback detection confidence coefficient 303 based on the air inlet pressure; again, the intake air temperature-based flashback detection confidence coefficient table 409 is searched according to the intake air flow rate 210, the intake air temperature 214, and the intake air temperature variation 215 to obtain the intake air temperature-based flashback detection confidence coefficient 304.
Optionally, calculating the confidence coefficient of occurrence of the flashback of the exhaust system sensor based on the measurement signals of the exhaust system sensor includes: according to Lambda value, corresponding mapping is carried out on the engine speed and the air inlet flow to obtain a Lambda-based tempering detection confidence coefficient; corresponding mapping is carried out according to the exhaust temperature, the exhaust flow and the change of the exhaust temperature to obtain a tempering detection confidence coefficient based on the exhaust temperature; and corresponding mapping is carried out according to the engine speed, the air inlet flow and the NOx concentration to obtain a flashback detection confidence coefficient based on the NOx concentration.
Specifically, fig. 6 is a schematic diagram of an implementation manner of detecting backfire of an exhaust system according to an embodiment of the present invention, referring to fig. 6, first, according to an engine speed 201, an intake air flow 210, and a Lambda value 216, a Lambda-based backfire detection confidence coefficient table 410 is searched to obtain a Lambda-based backfire detection confidence coefficient 305; secondly, looking up a flashback detection confidence coefficient table 411 based on the exhaust temperature according to the exhaust temperature 217, the exhaust temperature variation 218 and the exhaust flow 219 to obtain a flashback detection confidence coefficient 306 based on the exhaust temperature; again, the flashback detection confidence coefficient table 412 based on the NOx concentration is looked up based on the engine speed 201, the intake air flow rate 210, and the NOx concentration 220 to obtain the flashback detection confidence coefficient 307 based on the NOx concentration.
Optionally, calculating weighted values of confidence coefficients of occurrence of backfire of the air inlet system sensor and the exhaust system sensor according to the working condition of the engine, the opening degree of the EGR valve, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor respectively comprises: when sensor signals in the air inlet system and the air outlet system are effective, mapping is carried out according to the rotation speed of the engine and the air inlet flow to obtain weighted values of a flashback detection confidence coefficient based on the air inlet flow, a flashback detection confidence coefficient based on Lambda, a flashback detection confidence coefficient based on the air outlet temperature and a flashback detection confidence coefficient based on the NOx concentration; respectively carrying out corresponding mapping according to the engine speed, the air inlet flow and the EGR valve opening to obtain a weighted value of a backfire detection confidence coefficient based on the air inlet pressure and a weighted value of a backfire detection confidence coefficient based on the air inlet temperature; when the sensor signals in the air intake system and the exhaust system are invalid, the weighting value of the corresponding flashback detection confidence coefficient is 0.
Specifically, fig. 7 is a schematic diagram of a calculation implementation of a weighted value of a detection result according to an embodiment of the present invention, referring to fig. 7, when the related sensor signal is valid (the intake air flow valid flag 203, the Lambda valid flag 208, the exhaust gas temperature valid flag 206, and the NOx concentration valid flag 207 are 1), the weighted value table 413 of the flashback detection confidence coefficient based on the intake air flow, the weighted value table 416 of the flashback detection confidence coefficient based on the Lambda, the weighted value table 417 of the flashback detection confidence coefficient based on the exhaust gas temperature, the weighted value table 418 of the flashback detection confidence coefficient based on the NOx concentration are respectively searched according to the engine speed 201 and the intake air flow 210 to obtain the flashback detection confidence coefficient 308 based on the intake air flow, the flashback detection confidence coefficient 311 based on the Lambda, the flashback detection confidence coefficient 312 based on the exhaust gas temperature, and the weighted value 313 of the flashback detection confidence coefficient based on NOx; if the related sensor signal is invalid, the weighting value of the corresponding flashback detection confidence coefficient is 0; when the sensor signal is valid (the air inlet pressure valid flag 204 and the air inlet temperature valid flag 205 are 1), respectively looking up a flashback detection confidence coefficient weighted value table 414 based on air inlet pressure, a flashback detection confidence coefficient weighted value table 415 based on air inlet temperature and a flashback detection confidence coefficient weighted value 310 based on air inlet temperature according to the engine speed 201, the air inlet flow rate 210 and the EGR valve opening 209; if the relevant sensor signal is invalid, the weighting value of the corresponding flashback detection confidence coefficient is 0.
With continued reference to fig. 3, 5 and 6, assuming that flashback occurs, the steps from fast to slow causing the sensor signal to change are: the intake pressure 212, intake flow 210, intake temperature 214, lambda value 216, NOx concentration 220, exhaust temperature 217, and their corresponding weighted confidence coefficient window lengths are set to 8, 7, 6, 5, 4, 3. Firstly, multiplying a backfire detection confidence coefficient 302 based on air inlet flow by a backfire detection confidence coefficient weighting value 308 based on air inlet flow to obtain a backfire detection weighted confidence coefficient 314 based on air inlet flow, then sending the backfire detection confidence coefficient 314 to a FIFO queue with the length of 7, and taking the maximum value of all elements in the queue to obtain a backfire detection weighted confidence coefficient window maximum value 320 based on air inlet flow; multiplying the flashback detection confidence coefficient 303 based on the air inlet pressure by a flashback detection confidence coefficient weighting value 309 based on the air inlet pressure to obtain a flashback detection weighted confidence coefficient 315 based on the air inlet pressure, sending the flashback detection confidence coefficient 315 into a FIFO queue with the length of 8, and taking the maximum value of all elements in the queue to obtain a flashback detection weighted confidence coefficient window maximum value 321 based on the air inlet pressure; multiplying the backfire detection confidence coefficient 304 based on the air inlet temperature by the backfire detection confidence coefficient weighting value 310 based on the air inlet temperature to obtain backfire detection weighted confidence coefficient 316 based on the air inlet temperature, then sending the backfire detection weighted confidence coefficient 316 into a FIFO queue with the length of 6, and taking the maximum value of all elements in the queue to obtain backfire detection weighted confidence coefficient window maximum value 322 based on the air inlet temperature; after a Lambda-based tempering detection confidence coefficient 305 is multiplied by a Lambda-based tempering detection confidence coefficient weighting value 311 to obtain a Lambda-based tempering detection weighted confidence coefficient 317, the Lambda-based tempering detection confidence coefficient 317 is sent into a FIFO queue with the length of 5, and then the maximum value of elements in the queue is taken to obtain a Lambda-based tempering detection weighted confidence coefficient window maximum value 323; multiplying the tempering detection confidence coefficient 306 based on the exhaust temperature by the tempering detection confidence coefficient weighting value 312 based on the exhaust temperature to obtain a tempering detection weighted confidence coefficient 318 based on the exhaust temperature, then sending the tempering detection weighted confidence coefficient 318 into a FIFO queue with the length of 3, and then taking the maximum value of all elements in the queue to obtain a tempering detection confidence coefficient window maximum value 324 based on the exhaust temperature; multiplying the flashback detection confidence coefficient 307 based on the NOx concentration by the flashback detection confidence coefficient weighting value 313 based on the NOx concentration to obtain a flashback detection weighted confidence coefficient 319 based on the NOx concentration, sending the flashback detection weighted confidence coefficient 319 into a FIFO queue with the length of 4, and taking the maximum value of all elements in the queue to obtain a flashback detection weighted confidence coefficient window maximum value 325 based on the NOx concentration; adding the variables 320, 321, 322, 323, 324, 325 to obtain a confidence coefficient 326 of the final tempering occurrence; as shown in fig. 3: an unsigned 8-bit integer state variable (the highest two digits of which are 0) is formed according to the values of the air inlet flow effective mark 203, the air inlet pressure effective mark 204, the air inlet temperature effective mark 205, the air outlet temperature effective mark 206, the NOx concentration effective mark 207 and the Lambda effective mark bit 208: a signal validity status flag 328, and looking up a temper confidence coefficient threshold table 419 based on the signal validity status flag 328 to obtain a temper confidence coefficient threshold 327, if the confidence coefficient 326 for the occurrence of a final temper is greater than the temper confidence coefficient threshold 327, tempering occurs and the tempering occurrence flag 329 is set to 1; otherwise, no flashback occurs, and the flashback occurrence flag 329 is cleared.
Fig. 8 is a schematic structural diagram of a four-cylinder hydrogen fuel engine according to an embodiment of the present invention, and referring to fig. 8, the embodiment of the present invention further provides a four-cylinder hydrogen fuel engine, which includes: an intake flow sensor 120, a supercharger 10, an intercooler device 20, an exhaust intercooler 70, an exhaust gas recirculation valve 80, a hydrogen fuel nozzle 90, an intake TMAP sensor 100, an intake valve 30, an ignition device 40, an exhaust valve 50, an exhaust temperature sensor 60, a Lambda sensor 110, and a NOx sensor 130; the intake valve 30, the ignition device 40 and the exhaust valve 50 are provided on the engine cylinder, the intake pipe is connected with one end of the engine cylinder, the exhaust pipe is connected with the other end of the engine cylinder, the EGR pipe is connected between the intake pipe and the exhaust pipe, the supercharger 10 is connected between the intake pipe and the exhaust pipe, the intake flow sensor 120, the intercooler device 20, the intake TMAP sensor 100 and the hydrogen fuel nozzle 90 are provided in this order on the intake pipe, the exhaust gas intercooler 70 and the exhaust gas recirculation valve 80 are provided in this order on the EGR pipe, and the exhaust gas temperature sensor 60, the Lambda sensor 110 and the NOx sensor 130 are provided in this order on the exhaust pipe.
In particular, the present invention is applicable to various types of engines, including both spark-ignition and compression-ignition engines, equivalence-ratio combustion and lean-burn engines, single-fuel and multi-fuel engines, and the like. After passing through the intake flow sensor 120, the air in the environment is cooled by the intercooler device 20 after passing through the supercharger 10, mixed with the cooled exhaust gas discharged from the engine and passing through the exhaust gas intercooler 70 and the exhaust gas recirculation valve 80 and the hydrogen gas injected by the hydrogen fuel nozzle 90, and then enters the engine cylinder through the intake valve 30 in the intake stroke after passing through the intake TMAP sensor 100; at the same time, the ignition device 40 ignites before and after the compression stroke piston of the engine runs to the top dead center, and ignites the mixed gas in the cylinder to do work, so that the piston is pushed to drive the crankshaft to output torque; during the exhaust stroke, gas in the cylinder enters the exhaust pipe through the exhaust valve 50, the temperature of which can be measured by the exhaust temperature sensor 60. The gas in the exhaust pipe continues to move forward, part of the gas enters the EGR pipeline, and part of the gas pushes the turbine forward, and the gas can be measured by the Lambda sensor 110 and the NOx sensor 130 to obtain corresponding sensor values. Note that, only one of the Lambda sensor 110 and the NOx sensor 130 is installed in most application scenarios, and it is assumed that both are installed here for better explanation. For the above-described port-injected hydrogen fuel engine, the intake valve 30 ignites the mixture in the intake manifold before closing due to the high heat of the residual gas in the cylinder, etc., resulting in the occurrence of flashback.
Fig. 9 is a schematic structural diagram of an engine flashback detecting device according to an embodiment of the present invention, and referring to fig. 9, an embodiment of the present invention further provides an engine flashback detecting device, including:
The flashback detection enabling module 901 is configured to comprehensively determine whether the current flashback detection function is enabled according to the engine speed, the engine temperature, the effective state of the air intake system sensor, and the effective state of the exhaust system sensor;
The air intake system backfire detection module 902 is configured to respectively calculate confidence coefficients of occurrence of backfire of an air intake system sensor according to measurement values of the air intake system sensor, where the air intake system sensor includes an air intake flow sensor and an air intake pressure temperature sensor;
The exhaust system backfire detection module 903 is configured to respectively calculate confidence coefficients of occurrence of backfire of an exhaust system sensor according to measurement signals of the exhaust system sensor, where the exhaust system sensor includes an exhaust temperature sensor, a Lambda sensor, and a NOx sensor;
The detection result weighting coefficient calculation module 904 is configured to calculate weighting values of confidence coefficients of occurrence of flashback of the air intake system sensor and the exhaust system sensor according to an engine working condition, an EGR valve opening, an air intake system sensor effective state, and an exhaust system sensor effective state, respectively;
The flashback detection result determination module 905 is configured to multiply the confidence coefficients of the exhaust system sensor and the exhaust system sensor for flashback occurrence by the weighted values of the confidence coefficients of corresponding flashback occurrence to obtain respective weighted confidence coefficients, update the time window queues of the respective weighted confidence coefficients, determine the final confidence coefficient of flashback occurrence, and perform threshold comparison to obtain the final flashback detection result.
The engine flashback detection device can execute the engine flashback detection method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the engine flashback detection method.
With continued reference to fig. 9, optionally, the flashback detection enabling module 901 is further configured to comprehensively determine whether the current flashback detection function is enabled according to the engine speed, the engine temperature, the valid state of the air intake system sensor, and the valid state of the exhaust system sensor, including: when the engine speed is within the enabling speed range and the engine temperature is within the enabling temperature range and at least one sensor signal of the intake system sensor and the exhaust system sensor is valid, the flashback detection function is enabled; if not, the flashback detection function is not enabled.
With continued reference to FIG. 9, the intake system flashback detection module 902 is also operable to calculate confidence coefficients for intake system sensor flashback occurrence based on intake system sensor measurements, respectively, including: corresponding mapping is carried out according to the air inlet flow, the engine speed and the air inlet flow change to obtain a backfire detection confidence coefficient based on the air inlet flow; corresponding mapping is carried out according to the air inlet pressure, the engine rotating speed and the change of the air inlet pressure to obtain a backfire detection confidence coefficient based on the air inlet pressure; and corresponding mapping is carried out according to the change of the air inlet temperature, the air inlet flow and the air inlet temperature to obtain the backfire detection confidence coefficient based on the air inlet temperature.
With continued reference to FIG. 9, the exhaust system flashback detection module 903 is also optionally configured to calculate confidence coefficients for the occurrence of an exhaust system sensor flashback based on measurement signals of the exhaust system sensor, respectively, including: according to Lambda value, corresponding mapping is carried out on the engine speed and the air inlet flow to obtain a Lambda-based tempering detection confidence coefficient; corresponding mapping is carried out according to the exhaust temperature, the exhaust flow and the change of the exhaust temperature to obtain a tempering detection confidence coefficient based on the exhaust temperature; and corresponding mapping is carried out according to the engine speed, the air inlet flow and the NOx concentration to obtain a flashback detection confidence coefficient based on the NOx concentration.
With continued reference to fig. 9, optionally, the detection result weighting coefficient calculating module 904 is further configured to calculate weighted values of confidence coefficients of occurrence of flashback of the intake system sensor and the exhaust system sensor according to the engine operating condition, the EGR valve opening, the active state of the intake system sensor, and the active state of the exhaust system sensor, respectively, including: when sensor signals in the air inlet system and the air outlet system are effective, mapping is carried out according to the rotation speed of the engine and the air inlet flow to obtain weighted values of a flashback detection confidence coefficient based on the air inlet flow, a flashback detection confidence coefficient based on Lambda, a flashback detection confidence coefficient based on the air outlet temperature and a flashback detection confidence coefficient based on the NOx concentration; respectively carrying out corresponding mapping according to the engine speed, the air inlet flow and the EGR valve opening to obtain a weighted value of a backfire detection confidence coefficient based on the air inlet pressure and a weighted value of a backfire detection confidence coefficient based on the air inlet temperature; when the sensor signals in the air intake system and the exhaust system are invalid, the weighting value of the corresponding flashback detection confidence coefficient is 0.
With continued reference to fig. 9, optionally, the flashback detection result determination module 905 is further configured to multiply the confidence coefficient of the occurrence of flashback of the exhaust system sensor by the weighted confidence coefficient of the occurrence of flashback of the exhaust system sensor, and multiply the confidence coefficient of occurrence of flashback of the exhaust system sensor by the weighted confidence coefficient of the occurrence of flashback of the exhaust system sensor; updating time window queues of each weighted confidence coefficient according to the weighted confidence coefficients of the exhaust system sensor and the exhaust system sensor, solving the maximum weighted confidence coefficient in each queue, and adding to obtain the confidence coefficient of final tempering; determining a current confidence coefficient threshold according to the current effective state of the air inlet system sensor and the effective state of the exhaust system sensor; if the confidence coefficient of the final tempering is larger than the current confidence coefficient threshold value, tempering is performed; if not, tempering does not occur.
Fig. 10 shows a schematic diagram of the structure of an electronic device 1 that can be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 10, the electronic device 1 includes at least one processor 11, and a memory such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc. communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic apparatus 1 can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A plurality of components in the electronic device 1 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 1 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as an engine flashback detection method.
In some embodiments, the engine flashback detection method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 1 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the engine flashback detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the engine flashback detection method in any other suitable manner (e.g., via firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. An engine flashback detection method, characterized by comprising:
comprehensively judging whether the current backfire detecting function is enabled or not according to the engine speed, the engine temperature, the effective state of an air inlet system sensor and the effective state of an exhaust system sensor;
Calculating confidence coefficients of backfire occurrence of the air inlet system sensor according to measured values of the air inlet system sensor, wherein the air inlet system sensor comprises an air inlet flow sensor and an air inlet pressure temperature sensor;
Calculating confidence coefficients of occurrence of backfire of the exhaust system sensor according to measurement signals of the exhaust system sensor, wherein the exhaust system sensor comprises an exhaust temperature sensor, a Lambda sensor and a NOx sensor;
Respectively calculating weighted values of confidence coefficients of occurrence of backfire of the air inlet system sensor and the exhaust system sensor according to the working condition of the engine, the opening degree of the EGR valve, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor;
and multiplying the confidence coefficients of the exhaust system sensor and the exhaust system sensor for backfire occurrence by the weighted values of the confidence coefficients of the corresponding backfire occurrence to obtain respective weighted confidence coefficients, updating the time window queues of the respective weighted confidence coefficients, solving the final confidence coefficient of backfire occurrence, and carrying out threshold comparison to obtain a final backfire detection result.
2. The method of claim 1, wherein multiplying the exhaust system sensor and the exhaust system sensor confidence coefficient for flashback occurrence by weighted values corresponding to the flashback occurrence confidence coefficient to obtain respective weighted confidence coefficients, updating the queue of the respective weighted confidence coefficient time window and finding a final flashback occurrence confidence coefficient, and performing a threshold comparison to obtain a final flashback detection result comprises:
Multiplying the confidence coefficient of the exhaust system sensor backfire occurrence by the weighted value of the confidence coefficient of the exhaust system sensor backfire occurrence to obtain the weighted confidence coefficient of the exhaust system sensor, and multiplying the confidence coefficient of the exhaust system sensor backfire occurrence by the weighted value of the confidence coefficient of the exhaust system sensor backfire occurrence to obtain the weighted confidence coefficient of the exhaust system sensor;
Updating time window queues of each weighted confidence coefficient according to the weighted confidence coefficients of the exhaust system sensor and the exhaust system sensor, and solving the maximum weighted confidence coefficient in each queue and adding to obtain the confidence coefficient of final tempering;
Determining a current confidence coefficient threshold according to the current effective state of the air inlet system sensor and the effective state of the exhaust system sensor;
If the confidence coefficient of the final tempering is larger than the current confidence coefficient threshold value, tempering is performed; if not, tempering does not occur.
3. The method of claim 1, wherein comprehensively determining whether a current flashback detection function is enabled based on an engine speed, an engine temperature, an intake system sensor active state, an exhaust system sensor active state comprises:
When the engine speed is within an enabling speed range and the engine temperature is within an enabling temperature range and at least one sensor signal of the intake system sensor and the exhaust system sensor is valid, a flashback detection function is enabled; if not, the flashback detection function is not enabled.
4. The method of claim 1, wherein the separately calculating a confidence coefficient for the occurrence of flashback of the air intake system sensor based on the measured values of the air intake system sensor comprises:
corresponding mapping is carried out according to the air inlet flow, the engine speed and the air inlet flow change to obtain a backfire detection confidence coefficient based on the air inlet flow;
Corresponding mapping is carried out according to the air inlet pressure, the engine rotating speed and the change of the air inlet pressure to obtain a backfire detection confidence coefficient based on the air inlet pressure;
and corresponding mapping is carried out according to the change of the air inlet temperature, the air inlet flow and the air inlet temperature to obtain the backfire detection confidence coefficient based on the air inlet temperature.
5. The method of claim 1, wherein the separately calculating a confidence coefficient for the occurrence of flashback of the exhaust system sensor based on the measurement signals of the exhaust system sensor comprises:
according to Lambda value, corresponding mapping is carried out on the engine speed and the air inlet flow to obtain a Lambda-based tempering detection confidence coefficient;
Corresponding mapping is carried out according to the exhaust temperature, the exhaust flow and the change of the exhaust temperature to obtain a tempering detection confidence coefficient based on the exhaust temperature;
And corresponding mapping is carried out according to the engine speed, the air inlet flow and the NOx concentration to obtain a flashback detection confidence coefficient based on the NOx concentration.
6. The method of claim 1, wherein calculating the weighted values of the confidence coefficients of the intake system sensor and the exhaust system sensor flashback occurrence based on the engine operating conditions, the EGR valve opening, the intake system sensor active state, and the exhaust system sensor active state, respectively, comprises:
When sensor signals in the air inlet system and the air outlet system are effective, mapping is carried out according to the engine speed and the air inlet flow respectively to obtain weighted values of a flashback detection confidence coefficient based on the air inlet flow, a flashback detection confidence coefficient based on Lambda, a flashback detection confidence coefficient based on the exhaust temperature and a flashback detection confidence coefficient based on the NOx concentration;
Respectively carrying out corresponding mapping according to the engine speed, the air inlet flow and the EGR valve opening to obtain a weighted value of a backfire detection confidence coefficient based on the air inlet pressure and a weighted value of a backfire detection confidence coefficient based on the air inlet temperature;
when the sensor signals in the air inlet system and the exhaust system are invalid, the weighting value of the corresponding flashback detection confidence coefficient is 0.
7. An engine flashback detection device, characterized by comprising:
the backfire detection enabling module is used for comprehensively judging whether the current backfire detection function is enabled or not according to the engine rotating speed, the engine temperature, the effective state of an air inlet system sensor and the effective state of an exhaust system sensor;
The air inlet system backfire detection module is used for respectively calculating confidence coefficients of occurrence of backfire of the air inlet system sensor according to the measured value of the air inlet system sensor, and the air inlet system sensor comprises an air inlet flow sensor and an air inlet pressure temperature sensor;
the exhaust system backfire detection module is used for respectively calculating confidence coefficients of occurrence of backfire of the exhaust system sensor according to measurement signals of the exhaust system sensor, and the exhaust system sensor comprises an exhaust temperature sensor, a Lambda sensor and a NOx sensor;
The detection result weighting coefficient calculation module is used for calculating weighting values of confidence coefficients of occurrence of backfire of the air inlet system sensor and the exhaust system sensor respectively according to the working condition of the engine, the opening degree of the EGR valve, the effective state of the air inlet system sensor and the effective state of the exhaust system sensor;
And the tempering detection result judging module is used for multiplying the confidence coefficients of the exhaust system sensor and the exhaust system sensor for tempering respectively by the weighted values of the confidence coefficients of the corresponding tempering to obtain respective weighted confidence coefficients, updating the time window queues of the respective weighted confidence coefficients, solving the final confidence coefficient of the tempering, and carrying out threshold comparison to obtain the final tempering detection result.
8. An electronic device, comprising:
one or more processors;
A memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the engine flashback detection method of any of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the engine flashback detection method according to any one of claims 1-6.
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| CN202410352917.1A CN118190431A (en) | 2024-03-26 | 2024-03-26 | Engine flashback detection method and device, electronic equipment and storage medium |
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