CN116201647B - EGR rate optimization control method based on GPF active regeneration - Google Patents
EGR rate optimization control method based on GPF active regeneration Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/0025—Controlling engines characterised by use of non-liquid fuels, pluralities of fuels, or non-fuel substances added to the combustible mixtures
- F02D41/0047—Controlling exhaust gas recirculation [EGR]
- F02D41/005—Controlling exhaust gas recirculation [EGR] according to engine operating conditions
- F02D41/0055—Special engine operating conditions, e.g. for regeneration of exhaust gas treatment apparatus
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
The invention discloses an EGR rate optimization control method based on GPF active regeneration, which comprises the steps of checking whether an optimization control condition is met currently or not, and introducing an EGR rate correction factor during GPF active regeneration control if the optimization control condition is met; whether the condition of learning and updating the EGR rate self-learning correction factor is met or not is inspected under the GPF active regeneration working condition; determining whether the correction factor based on the self-learning of the rotation speed fluctuation is upward self-learning or downward self-learning according to the relation between the absolute value of the rotation speed difference of the engine and the preset value of the rotation speed difference of the engine or the absolute value of the rotation speed difference of the engine in different sampling periods; then, carrying out real-time EGR rate optimization control in the GPF active regeneration process; the method for optimizing and controlling the EGR rate after the GPF regeneration is finished is also included, and the method for controlling the GPF active regeneration is optimized on the basis of not changing the hardware cost, so that the GPF regeneration effect is improved, and meanwhile, the increase of NOx in emission is avoided.
Description
Technical Field
The invention relates to the field of engine control, in particular to a GPF regeneration optimization control method based on an EGR rate.
Background
Research shows that the EGR system has certain advantages in reducing NOx, reducing oil consumption and improving anti-knock capability. The EGR exhaust gas reduces the combustion temperature, avoids knocking, and suppresses the ignition advance retardation.
GPF regeneration comprises passive regeneration and active regeneration, wherein the passive regeneration means that the regeneration of the GPF does not perform active control, the GPF is regenerated by not actively regulating parameters, the control effect on a vehicle is small at the moment, but the regeneration effect is relatively poor, and the active regeneration can regulate parameters to actively perform the GPF regeneration, the dynamic performance, emission or drivability and the like of the vehicle are possibly influenced at the moment, but the regeneration effect is relatively good.
In the prior art, an EGR rate optimization control method for cooperative control of EGR and GPF active regeneration is not seen.
Disclosure of Invention
Aiming at the problems or defects existing in the prior art, the invention provides an EGR rate optimization control method for the cooperative control of the active regeneration of EGR and GPF.
The technical scheme adopted by the invention is as follows: an EGR rate optimization control method based on GPF active regeneration comprises an optimization control method in the GPF active regeneration process,
The optimization control method in the GPF active regeneration process comprises the following steps:
Step 1: checking whether the EGR rate optimization control condition based on GPF active regeneration is met or not under the current working condition, if yes, performing step 2, and if not, not performing the EGR rate optimization control based on GPF active regeneration;
Step 2: the EGR rate correction factor r EGRCompGPFPosReng,rEGRCompGPFPosReng when GPF active regeneration control is introduced is determined by the correction factors respectively determined by the engine speed and the air-fuel ratio r AFR, the ignition efficiency r SparkEff and the GPF temperature T GPFTemp and the correction factor r EGRAdpationRatio based on self-learning of speed fluctuation;
Step 3: checking whether the condition of learning and updating the EGR rate self-learning correction factor r EGRAdpationRatio is met under the GPF active regeneration working condition, and if so, performing step 4;
Step 4: determining whether the correction factor based on the self-learning of the rotation speed fluctuation is upward self-learning or downward self-learning according to the relation between an absolute value |n SpeedDiff | of an engine rotation speed difference n SpeedDiff and an engine rotation speed difference preset value or the absolute value of an engine rotation speed difference n SpeedDiff of different sampling periods;
step 5: and (3) carrying out real-time EGR rate optimization control in the GPF active regeneration process on the basis of the determined EGR rate correction factor r EGRCompGPFPosReng in the GPF active regeneration control in the step (4).
Further, the EGR rate-based GPF active regeneration control conditions in step 1 include: the passive regeneration times exceeds N times, and the instrument prompts the GPF to have too high accumulated carbon and turn on the fault lamp.
Further, the specific expression of the EGR rate correction factor r EGRCompGPFPosReng during the GPF active regeneration control in the step 2 is as follows:
[1-rEGRCompGPFPosReng1(rAFR,n)]×[1-rEGRCompGPFPosReng2(rSparkEff,n)]×[1-rEGRCompGPFPosReng3(TGPFTemp,n)]×[1+rEGRAdpationRatio]
wherein: r EGRCompGPFPosReng1(rAFR,n) is a correction factor determined by the air-fuel ratio r AFR and the engine speed, and the correction factor is obtained by looking up a calibration table obtained by a calibration test;
r EGRCompGPFPosReng2(rSparkEff, n) is a correction factor determined by the ignition efficiency r SparkEff and the engine speed, and the correction factor is obtained by looking up a calibration table obtained by a calibration test;
r EGRCompGPFPosReng3(TGPFTemp, n) is a correction factor determined by GPF temperature T GPFTemp and engine speed, and the correction factor is obtained by looking up a calibration table obtained by a calibration test;
r EGRAdpationRatio is a correction factor based on self-learning of rotation speed fluctuation, when the vehicle is off line, the correction factor r EGRAdpationRatio is 0, the correction factor is continuously self-learned in the whole life cycle of the engine, and the learning value is stored in an EEPROM of the controller after being electrified.
Further, in the step 3, if the condition for learning and updating the EGR rate self-learning correction factor r EGRAdpationRatio is not satisfied, the specific expression of the EGR rate correction factor r EGRCompGPFPosReng during the GPF active regeneration control is:
[1-r EGRCompGPFPosReng1(rAFR,n)]×[1-rEGRCompGPFPosReng2(rSparkEff,n)]×[1-rEGRCompGPFPosReng3(TGPFTemp, n) ] further, the condition for learning and updating the EGR rate self-learning correction factor r EGRAdpationRatio in the step 3 is as follows:
(1) The engine speed is activated in a closed-loop control manner;
(2) The change range of the flywheel electrical load does not exceed a preset value;
(3) The link state of the engine and the transmission system is unchanged;
(4) The water temperature of the engine is within a preset range;
(5) The gear is unchanged;
(6) The above conditions are satisfied for more than a preset time T1;
the above 6 conditions should be satisfied simultaneously.
Further, the correction factor based on the self-learning of the rotation speed fluctuation in the step 4 is divided into three cases:
First case: the absolute value of the engine speed difference n SpeedDiff (the speed difference between the target engine speed and the actual engine speed) n SpeedDiff is greater than the preset value n SpeedDiffMargin, and the absolute value of the engine speed difference n SpeedDiff n SpeedDiff is greater than the absolute value of the engine speed difference n SpeedDiff of the previous sampling period And absolute value of engine speed difference in last sampling periodAbsolute value of engine speed difference greater than last sampling period
Second case: the absolute value of the engine speed difference n SpeedDiff n SpeedDiff is smaller than or equal to a preset value n SpeedDiffMargin but larger than k1 times of a preset value n SpeedDiffMargin1 required by the design accuracy of engine speed fluctuation, k1 is a preset multiple, or the absolute value of the engine speed difference n SpeedDiff n SpeedDiff is not larger than the absolute value of the engine speed difference n SpeedDiff of the last sampling periodOr absolute value of engine speed difference in last sampling periodAbsolute value of engine speed difference greater than last sampling period
Third scenario: neither the first case nor the second case is satisfied;
The priorities of the above three conditions are lower and lower.
Further, the engine speed difference N SpeedDiff takes the maximum value and the minimum value of the speed difference before the last N sampling periods; the sampling times N are related to the engine rotation speed, and are obtained through a calibration table obtained through a calibration test, the lower the rotation speed is, the smaller the sampling times N are, the larger the rotation speed is, and the larger the N is, because the lower the rotation speed is, the rotation speed fluctuation can feel the vehicle stability more; the higher the rotation speed is, the too small value of N can cause the regulation and control of the EGR rate to be too frequent, so that the action advantage of the EGR is reduced.
Further, in the first case, it is indicated that the current working condition is easy to generate rotation speed fluctuation, and the rotation speed fluctuation is further increased, at this time, if it is detected that the absolute value |n SpeedDiff | of the engine rotation speed difference n SpeedDiff is greater than the preset value n SpeedDiffMargin for more than T1, which indicates that the EGR rate still needs to be further reduced at present, the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate downward learning state under the GPF active regeneration working condition, that is, the EGR rate self-learning correction factor r EGRAdpationRatio needs to be reduced under the GPF active regeneration working condition.
Further, in the second case, the current working condition rotation speed fluctuation is weakened, and after the T2 time related to the actual engine rotation speed n is passed, the EGR rate is increased at the set rate R1; however, if once the absolute value |n SpeedDiff | of the engine speed difference n SpeedDiff is detected to be larger than the preset value n SpeedDiffMargin, it is indicated that the EGR rate still needs to be further reduced currently, the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate downward learning state under the GPF active regeneration working condition, that is, the EGR rate self-learning correction factor r EGRAdpationRatio needs to be reduced under the GPF active regeneration working condition;
If the absolute value |n SpeedDiff | of the engine speed difference n SpeedDiff is not detected to be larger than the preset value n SpeedDiffMargin for more than T3 in the process, the EGR rate is further increased, and the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate upward learning state under the GPF active regeneration working condition, namely the EGR rate self-learning correction factor r EGRAdpationRatio under the GPF active regeneration working condition needs to be increased.
Further, in the third situation, if the EGR rate self-learning state under the GPF active regeneration working condition in the last sampling period is detected to be the EGR rate upward learning state under the GPF active regeneration working condition, the EGR rate self-learning correction factor r EGRAdpationRatio under the GPF active regeneration working condition is increased at the set rate k 2; if the EGR rate self-learning state under the GPF active regeneration working condition in the last sampling period is detected to be the EGR rate downward learning state under the GPF active regeneration working condition, the EGR rate self-learning correction factor r EGRAdpationRatio under the GPF active regeneration working condition is increased at a set rate k 3; the downward learning rate is higher than the upward learning rate, so that the occurrence of rotation speed fluctuation under the GPF active regeneration working condition is avoided.
Further, after the real-time EGR rate optimizing control in the GPF active regeneration process in step 5 is finished, the EGR rate optimizing control after the GPF active regeneration is finished and the air-fuel ratio optimizing control after the GPF active regeneration is finished are performed.
Further, the EGR rate is increased after the active GPF regeneration is finished, wherein the target EGR rate correction factor r DsrdEGRCompAftGPFPosReng is determined by the engine speed and the oxygen-containing capacity coefficient of the catalyst, and the oxygen-containing capacity coefficient of the catalyst is equal to the ratio of the actual oxygen content of the catalyst to the allowable maximum oxygen content of the catalyst;
EGR rate correction factor r finally used after GPF regeneration is finished EGRCompAftGPFPosRengFinal
Multiplying r EGRCompAftGPFPosRengFinal by the EGR rate after the EGR rate optimization control in the GPF active regeneration process to obtain a final target EGR rate r DsrdEGRFinal;
Wherein, For the target EGR rate of the last sampling period (sampling period time interval is 10ms in this example), r ActEGR is real-time real EGR rate, r DsrdEGRFinal-rActEGR is EGR rate deviation, f [ (r DsrdEGRFinal-rActEGR), n ] is a function of EGR rate deviation and engine speed, and the determining method is that the EGR rate is as large as possible under the premise of ensuring that NOx is minimum and no guarantee occurs, and f [ (r DsrdEGRFinal-rActEGR), n ] is obtained by looking up a calibration table obtained by calibration test.
Further, the air-fuel ratio optimization control method after the end of the active GPF regeneration comprises the following steps: the air-fuel ratio correction factor r AFRComp depends on the actual EGR rate r ActEGR and the engine speed n. ; the air-fuel ratio correction factor r AFRComp is obtained through test calibration, and the determination method is that the EGR rate is as large as possible on the premise of ensuring that the NOx is minimum and no guarantee occurs; the greater the EGR rate, the less the air-fuel ratio correction factor, the more NOx can be improved;
Finally, r AFRComp is multiplied by the target air-fuel ratio r AFRRaw before the EGR rate correction after the end of the GPF regeneration to obtain the final target air-fuel ratio r AFRFinal.
The invention has the beneficial effects and characteristics that:
1. according to the EGR rate optimization control method based on GPF active regeneration, the GPF active regeneration control method is optimized on the basis of not changing hardware cost, so that the GPF regeneration effect is improved, and meanwhile, the increase of NOx in emission is avoided.
2. The EGR rate optimization control method based on GPF active regeneration not only comprises the optimization control method in the GPF active regeneration process, but also comprises the EGR rate optimization control method after GPF regeneration is finished, and the GPF regeneration effect is improved in the whole flow.
Drawings
FIG. 1 is a schematic diagram of logic determination according to a preferred embodiment of the present invention;
FIG. 2 is an overall flow chart of a preferred embodiment of the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
Referring to FIG. 1, the invention relates to an EGR rate optimization control method based on GPF active regeneration, which comprises an optimization control method in the GPF active regeneration process, an EGR rate optimization control method after GPF regeneration is finished and an air-fuel ratio optimization control method after GPF regeneration is finished
The optimization control method in the GPF active regeneration process comprises the following steps:
Step 1: under the current working condition, whether the EGR rate optimization control condition based on GPF active regeneration is met or not is inspected, wherein the conditions comprise: the passive regeneration times exceeds N times, and the instrument prompts the GPF to have too high accumulated carbon and turn on the fault lamp. If yes, performing step 2, and if not, not performing EGR rate optimization control based on GPF active regeneration;
Step 2: the EGR rate correction factor r EGRCompGPFPosReng,rEGRCompGPFPosReng when GPF active regeneration control is introduced is determined by the correction factors respectively determined by the engine speed and the air-fuel ratio r AFR, the ignition efficiency r SparkEff and the GPF temperature T GPFTemp and is determined based on the self-learning correction factors of speed fluctuation;
The specific expression of r EGRCompGPFPosReng is:
[1-rEGRCompGPFPosReng1(rAFR,n)]×[1-rEGRCompGPFPosReng2(rSparkEff,n)]×[1-rEGRCompGPFPosReng3(TGPFTemp,n)]×[1+rEGRAdpationRatio]
wherein: r EGRCompGPFPosReng1(rAFR,n) is a correction factor determined by the air-fuel ratio r AFR and the engine speed, and the correction factor is obtained by looking up a calibration table obtained by a calibration test;
in this example, r EGRCompGPFPosReng1(rAFR, n) the calibration table obtained by the calibration test is as follows:
TABLE 1
R EGRCompGPFPosReng2(rSparkEff, n) is a correction factor determined by the ignition efficiency r SparkEff and the engine speed, and the correction factor is obtained by looking up a calibration table obtained by a calibration test;
In this example, r EGRCompGPFPosReng2(rSparkEff, n) the calibration table obtained by the calibration test is as follows:
TABLE 2
R EGRCompGPFPosReng3(TGPFTemp, n) is a correction factor determined by GPF temperature T GPFTemp and engine speed, and the correction factor is obtained by looking up a calibration table obtained by a calibration test;
In this example, r EGRCompGPFPosReng3(TGPFTemp, n) the calibration table obtained by the calibration test is as follows:
TABLE 3 Table 3
R EGRAdpationRatio is a correction factor based on self-learning of rotation speed fluctuation, when the vehicle is off line, the correction factor r EGRAdpationRatio is 0, the correction factor is continuously self-learned in the whole life cycle of the engine, and the learning value is stored in an EEPROM of the controller after being electrified.
Step 3: whether the condition of learning and updating the EGR rate self-learning correction factor r EGRAdpationRatio is met or not is examined under the GPF active regeneration working condition:
If the condition for learning and updating the EGR rate self-learning correction factor r EGRAdpationRatio is not satisfied, the specific expression of the EGR rate correction factor rEGRCompGPFPosReng during GPF active regeneration control is as follows:
[1-r EGRCompGPFPosReng1(rAFR,n)]×[1-rEGRCompGPFPosReng2(rSparkEff,n)]×[1-rEGRCompGPFPosReng3(TGPFTemp, n) ] i.e., at this time, r EGRCompGPFPosReng is determined only by the correction factors determined by the engine speed and air-fuel ratio r AFR, the ignition efficiency r SparkEff, and the GPF temperature T GPFTemp.
If the condition of learning and updating the EGR rate self-learning correction factor r EGRAdpationRatio is met, performing step 4;
Specifically, the condition for learning and updating the EGR rate self-learning correction factor r EGRAdpationRatio is as follows:
(1) The engine speed is activated in a closed-loop control manner;
(2) The change range of the flywheel electric load (the torque consumed by electric equipment connected with the flywheel, such as an air conditioner, a generator and the like) does not exceed a preset value, and the change range is + -3 Nm in the example;
(3) The state of the linkage between the engine and the transmission system is unchanged (disconnected or linked);
(4) The water temperature of the engine is within a preset range, and the temperature of the engine is 30-90 ℃ in the embodiment;
(5) The gear is unchanged (P gear, N gear, R gear or forward gear)
(6) The above conditions are satisfied for more than a preset time T1;
the above 6 conditions should be satisfied simultaneously.
Step 4: the correction factor based on the self-learning of the rotational speed fluctuation is determined to be the self-learning up or the self-learning down according to the relation between the absolute value |n SpeedDiff | of the engine rotational speed difference n SpeedDiff and the preset value of the engine rotational speed difference or the magnitude between the absolute values of the engine rotational speed difference n SpeedDiff of different sampling periods.
Specifically, the method for determining whether the correction factor based on the rotational speed fluctuation self-learning is the upward self-learning or the downward self-learning is divided into three cases:
First case: the absolute value of the engine speed difference n SpeedDiff (the speed difference between the target engine speed and the actual engine speed) n SpeedDiff is greater than the preset value n SpeedDiffMargin (100 rpm is taken in this example, the speed fluctuation is excessive), and the absolute value of the engine speed difference n SpeedDiff n SpeedDiff is greater than the absolute value of the engine speed difference n SpeedDiff in the previous sampling period And absolute value of engine speed difference in last sampling periodAbsolute value of engine speed difference greater than last sampling period
Second case: the absolute value of the engine speed difference n SpeedDiff |n SpeedDiff | is smaller than or equal to a preset value n SpeedDiffMargin but is larger than a k1 multiple of a preset value n SpeedDiffMargin1 required by the design accuracy of engine speed fluctuation (45 rpm is taken in this example for the design accuracy of the speed fluctuation of GPF regeneration), k1 is a preset multiple, (i.e., n SpeedDiffPrecision ×k1, 1.2 is taken in this example k1, 54rpm is taken in this example n SpeedDiffMargin1, i.e., the speed fluctuation error of the speed fluctuation below the design accuracy of the engine speed fluctuation k1 times is regulated by the speed control), or the absolute value of the engine speed difference n SpeedDiff |n SpeedDiff | is not larger than the absolute value of the engine speed difference n SpeedDiff in the previous sampling periodOr absolute value of engine speed difference in last sampling periodAbsolute value of engine speed difference greater than last sampling period
Third scenario: neither the first case nor the second case is satisfied;
The priorities of the above three conditions are lower and lower.
The engine speed difference N SpeedDiff takes the maximum value and the minimum value of the speed difference before the last N sampling periods (the single sampling period is 10 ms); the sampling times N are related to the engine speed and are obtained through a calibration table obtained through a calibration test, the lower the sampling times N are related to the engine speed, the smaller the value of the sampling times N is, the larger the speed is, the larger the value of the N is, and the lower the speed is, the speed fluctuation is more capable of sensing the vehicle stability; the higher the rotation speed is, the too small value of N can cause the regulation and control of the EGR rate to be too frequent, so that the action advantage of the EGR is reduced. The number of sampling N is related to the engine speed, and a calibration table obtained through a calibration test is as follows:
TABLE 4 Table 4
In the first case, it is indicated that the current working condition is easy to generate rotation speed fluctuation, and the rotation speed fluctuation is further increased, at this time, if it is detected that the absolute value |n SpeedDiff | of the engine rotation speed difference n SpeedDiff is greater than the preset value n SpeedDiffMargin for more than T1 (0.4-0.6 s is taken in this example), which indicates that the EGR rate still needs to be further reduced currently, the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate downward learning state under the GPF active regeneration working condition, that is, the EGR rate self-learning correction factor r EGRAdpationRatio needs to be reduced under the GPF active regeneration working condition.
In the second case, the current working condition rotation speed fluctuation is weakened, and after the T2 time related to the actual rotation speed n of the engine is passed, the EGR rate is increased at a set rate R1 (0.02/10 ms in the example); however, if once the absolute value |n SpeedDiff | of the engine speed difference n SpeedDiff is detected to be larger than the preset value n SpeedDiffMargin, it is indicated that the EGR rate still needs to be further reduced currently, the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate downward learning state under the GPF active regeneration working condition, that is, the EGR rate self-learning correction factor r EGRAdpationRatio needs to be reduced under the GPF active regeneration working condition;
Wherein T2 is determined by calibration table 5:
TABLE 5
If the absolute value |n SpeedDiff | of the engine speed difference n SpeedDiff is not detected to be larger than the preset value n SpeedDiffMargin for more than T3 (0.7-0.9 s is taken in the example), the EGR rate is still required to be further reduced currently, the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate upward learning state under the GPF active regeneration working condition, namely the EGR rate self-learning correction factor r EGRAdpationRatio under the GPF active regeneration working condition needs to be increased.
In the third situation, if the EGR rate self-learning state under the GPF active regeneration working condition in the last sampling period is detected to be the EGR rate upward learning state under the GPF active regeneration working condition, the EGR rate self-learning correction factor r EGRAdpationRatio under the GPF active regeneration working condition is increased at a set rate K2 (for example, k2=0.002/10 ms); if the EGR rate self-learning state under the GPF active regeneration working condition in the last sampling period is detected to be the EGR rate downward learning state under the GPF active regeneration working condition, the EGR rate self-learning correction factor r EGRAdpationRatio under the GPF active regeneration working condition is set at a rate k3 (for example, k 3= -0.005/10ms is preferable); the downward learning rate is higher than the upward learning rate, so that the occurrence of rotation speed fluctuation under the GPF active regeneration working condition is avoided.
Step 5: and (3) carrying out real-time EGR rate optimization control in the GPF active regeneration process on the basis of the determined EGR rate correction factor r EGRCompGPFPosReng in the GPF active regeneration control in the step (4).
Example 2:
Referring to fig. 1, as another aspect of the present invention, after the GPF active regeneration is completed (the active regeneration condition is not satisfied, the GPF may be completed), an EGR rate optimization control method after the GPF active regeneration is completed and an air-fuel ratio optimization control method after the GPF active regeneration is completed may be performed.
Wherein, the EGR rate optimization control method after GPF active regeneration is to increase the EGR rate, wherein, the target EGR rate correction factor r DsrdEGRCompAftGPFPosReng is dependent on the engine speed and the oxygen-containing capacity coefficient of the catalyst (the oxygen-containing capacity coefficient of the catalyst is equal to the ratio of the actual oxygen content of the catalyst to the maximum allowable oxygen content thereof),
EGR rate correction factor r finally used after GPF regeneration is finished EGRCompAftGPFPosRengFinal
Multiplying r EGRCompAftGPFPosRengFinal by the target EGR rate r DsrdEGRRaw before the EGR rate correction after the GPF regeneration is completed to obtain the final target EGR rate r DsrdEGRFinal
Wherein,For the target EGR rate of the last sampling period (sampling period time interval is 10ms in this example), r ActEGR is the real-time real EGR rate, r DsrdEGRFinal-rActEGR is the EGR rate deviation, f [ (r DsrdEGRFinal-rActEGR), n ] is a function of the EGR rate deviation and the engine speed, the determining method is that the EGR rate is as large as possible under the premise of ensuring that the NOx is minimum and no guarantee occurs, and the value of f [ (r DsrdEGRFinal-rActEGR), n ] in this example is obtained by looking up a calibration table obtained by a calibration test:
TABLE 6
The air-fuel ratio optimization control method after GPF active regeneration is finished comprises the following steps: the air-fuel ratio correction factor r AFRComp depends on the actual EGR rate r ActEGR and the engine speed n. ; the air-fuel ratio correction factor r AFRComp is obtained through test calibration (see table 7 below), and is determined by the method that the EGR rate is as large as possible under the premise of ensuring that the NOx is minimum and no guarantee occurs; the greater the EGR rate, the less the air-fuel ratio correction factor, the more NOx can be improved;
TABLE 7
Finally, r AFRComp is multiplied by the target air-fuel ratio r AFRRaw before the EGR rate correction after the end of the GPF regeneration to obtain the final target air-fuel ratio r AFRFinal.
When the GPF regeneration request is made, if the EGR rate is too large, the temperature of an exhaust system is too low, and the GPF regeneration effect is poor; also after the GPF regeneration request is over, the EGR rate can be increased appropriately to lower the combustion temperature, avoiding the increase in exhaust oxygen-rich NOx emissions that may result from regeneration.
The foregoing has shown and described the basic principles and main features and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the foregoing embodiments, but rather, the foregoing embodiments and description illustrate the structural relationships and principles of the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (13)
1. An EGR rate optimization control method based on GPF active regeneration is characterized by comprising an optimization control method in the GPF active regeneration process,
The optimization control method in the GPF active regeneration process comprises the following steps:
Step 1: checking whether the EGR rate optimization control condition based on GPF active regeneration is met or not under the current working condition, if yes, performing step 2, and if not, not performing the EGR rate optimization control based on GPF active regeneration;
step 2: EGR rate correction factor when GPF active regeneration control is introduced ，From engine speed and air-fuel ratioEfficiency of ignitionGPF temperatureCorrection factors determined respectively and correction factors based on rotational speed fluctuation self-learningJointly determining;
step 3: whether the EGR rate self-learning correction factor is met or not under the GPF active regeneration working condition is inspected Learning updated conditions, and if the updated conditions are met, performing step 4;
Step 4: according to the engine speed difference Absolute value of (2)And the relation between the preset value of the engine speed difference or the engine speed difference of different sampling periodsDetermining whether a correction factor based on the rotational speed fluctuation self-learning is upward self-learning or downward self-learning;
Step 5: EGR rate correction factor during GPF active regeneration control determined in step 2 And (3) carrying out real-time EGR rate optimization control in the GPF active regeneration process on the basis of the control method.
2. The EGR rate optimization control method based on GPF active regeneration according to claim 1, characterized in that: the GPF active regeneration control condition based on the EGR rate in the step 1 comprises the following steps: the passive regeneration times exceeds N times, and the instrument prompts the GPF to have too high accumulated carbon and turn on the fault lamp.
3. The EGR rate optimization control method based on GPF active regeneration according to claim 1, characterized in that: the EGR rate correction factor in GPF active regeneration control in the step 2The specific expression of (2) is:
Wherein: Is the air-fuel ratio The correction factor is determined by the engine speed and is obtained by looking up a calibration table obtained by a calibration test;
Is the ignition efficiency And a correction factor for determining the engine speed, wherein the correction factor is obtained by looking up a calibration table obtained by a calibration test;
is GPF temperature And a correction factor for determining the engine speed, wherein the correction factor is obtained by looking up a calibration table obtained by a calibration test;
is a correction factor based on self-learning of rotation speed fluctuation, and is used when a vehicle is off line Is 0 and is continuously self-learned throughout the life of the engine, and the learned value is stored in the EEPROM of the controller after being powered down.
4. The EGR rate optimization control method based on GPF active regeneration according to claim 3, characterized in that: in the step 3, if the EGR rate self-learning correction factor is not satisfiedLearning the updated condition, the EGR rate correction factor during the GPF active regeneration controlThe specific expression of (2) is:
。
5. the EGR rate optimization control method based on GPF active regeneration according to claim 1, characterized in that: the EGR rate self-learning correction factor in the step 3 The conditions for learning and updating are as follows:
(1) The engine speed is activated in a closed-loop control manner;
(2) The change range of the flywheel electrical load does not exceed a preset value;
(3) The link state of the engine and the transmission system is unchanged;
(4) The water temperature of the engine is within a preset range;
(5) The gear is unchanged;
(6) The above conditions are satisfied for more than a preset time T1;
the above 6 conditions should be satisfied simultaneously.
6. The EGR rate optimization control method based on GPF active regeneration according to claim 1, characterized in that: the correction factor based on the self-learning of the rotation speed fluctuation in the step 4 is divided into three cases by the self-learning method in the upward direction or the self-learning method in the downward direction:
First case: engine speed difference Absolute value ofIs greater than a preset valueAnd engine speed differenceAbsolute value ofEngine speed difference greater than last sampling periodAbsolute value ofAbsolute value of engine speed difference in last sampling periodAbsolute value of engine speed difference greater than last sampling period；
Second case: engine speed differenceAbsolute value ofLess than or equal to a preset valueBut is larger than the preset value required by the design precision of the fluctuation of the engine rotation speedK1 times, k1 is a preset multiple, or engine speed differenceAbsolute value ofEngine speed difference no greater than last sampling periodAbsolute value ofOr absolute value of engine speed difference in last sampling periodAbsolute value of engine speed difference greater than last sampling period；
Third scenario: neither the first case nor the second case is satisfied;
The priorities of the above three conditions are lower and lower.
7. The EGR rate optimization control method based on GPF active regeneration according to claim 6, characterized in that: the engine speed differenceTaking the maximum value and the minimum value of the rotating speed difference before the last N sampling periods; the sampling times N are related to the engine rotation speed, and are obtained through a calibration table obtained through a calibration test, and the lower the rotation speed is, the smaller the sampling times N are, the larger the rotation speed is, and the larger the N is.
8. The EGR rate optimization control method based on GPF active regeneration according to claim 6, characterized in that: in the first case, the current working condition is easy to generate rotation speed fluctuation, and the rotation speed fluctuation is further increased, if the engine rotation speed difference is detectedAbsolute value ofIs greater than a preset valueWhen the time exceeds T1, the EGR rate still needs to be further reduced, and the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate downward learning state under the GPF active regeneration working condition, namely the EGR rate self-learning correction factor under the GPF active regeneration working conditionThe need for reduction.
9. The EGR rate optimization control method based on GPF active regeneration according to claim 6, characterized in that: in the second case, the fluctuation of the current working condition rotating speed is weakened, and after the T2 time related to the actual rotating speed n of the engine is passed, the EGR rate is increased at a set rate; but if once the engine speed difference is detectedAbsolute value ofIs greater than a preset valueThe description that the EGR rate still needs to be further reduced still at present, the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate downward learning state under the GPF active regeneration working condition, namely the EGR rate self-learning correction factor under the GPF active regeneration working conditionThe need for reduction;
if no engine speed difference is detected in the process Absolute value ofIs greater than a preset valueIf the time exceeds the set time T3, the EGR rate is further increased, and the EGR rate self-learning state under the GPF active regeneration working condition is the EGR rate upward learning state under the GPF active regeneration working condition, namely the EGR rate self-learning correction factor under the GPF active regeneration working conditionAn increase is required.
10. The EGR rate optimization control method based on GPF active regeneration according to claim 6, characterized in that: in the third situation, if the EGR rate self-learning state under the GPF active regeneration working condition in the last sampling period is detected to be the EGR rate upward learning state under the GPF active regeneration working condition, the EGR rate self-learning correction factor under the GPF active regeneration working conditionIf the EGR rate self-learning state under the GPF active regeneration working condition in the last sampling period is detected to be the EGR rate downward learning state under the GPF active regeneration working condition, the EGR rate self-learning correction factor under the GPF active regeneration working condition is detected to be increased at the set rate k2Increasing at a set rate k 3; the downward learning rate is higher than the upward learning rate.
11. The EGR rate optimization control method based on GPF active regeneration according to claim 1, characterized in that: and (3) after the real-time EGR rate optimization control in the GPF active regeneration process in the step (5) is finished, performing the EGR rate optimization control after the GPF active regeneration process is finished and the air-fuel ratio optimization control after the GPF active regeneration process is finished.
12. The EGR rate optimization control method based on GPF active regeneration according to claim 11, characterized in that: the EGR rate optimization control method after GPF active regeneration is finished comprises the steps of increasing the EGR rate, wherein a target EGR rate correction factor is used for optimizing the EGR rateDepending on the engine speed and the catalyst oxygen capacity coefficient;
EGR rate correction factor finally used after GPF regeneration is finished ,
；
Will beMultiplying the EGR rate after the EGR rate optimization control in the GPF active regeneration process to obtain the final target EGR rate；
Wherein,For the target EGR rate for the last sampling period,For a real-time true EGR rate,In order to provide for an EGR rate deviation,The method is to ensure that the EGR rate is as large as possible under the premise of ensuring that NOx is minimized and knocking does not occur,The value is obtained by looking up a calibration table obtained by a calibration test.
13. The EGR rate optimization control method based on GPF active regeneration according to claim 11, characterized in that: the air-fuel ratio optimization control method after GPF active regeneration is finished comprises the following steps: air-fuel ratio correction factorDepending on the actual EGR rateAnd engine speed n o; air-fuel ratio correction factorThe method is obtained through experimental calibration, and the determination method is that the EGR rate is as large as possible on the premise of ensuring that the NOx is minimum and knocking does not occur; the greater the EGR rate, the less the air-fuel ratio correction factor, the more NOx can be improved;
Will eventually Multiplying by target air-fuel ratio before EGR rate correction after GPF regeneration is completedObtaining the final target air-fuel ratio。
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