JPH04318246A - Air-fuel ratio study control device for internal combustion engine - Google Patents

Abstract

PURPOSE:To discriminate a concerned region on a study map by hysteresis characteristic in accordance with area of each region, in the case of an air-fuel ratio study classified by an operational region. CONSTITUTION:A concerned region (i) is discriminated based on a preset threshold data Tp[i] by a study map divided with a basic fuel injection amount Tp serving as a parameter. A correction value Tphis is calculated(S56) as a predetermined proportion of change width of the Tp in the concerned region (i). Further by discriminating(S57) a direction of change of the concerned region (i) to correct the original threshold data by the above-mentioned correction value Tphis, the threshold data such as delaying a response of discriminating the concerned region in the respective direction of change is set, and the threshold data after thus corrected is compared(S58, S61) with the actual Tp to specify the final concerned region I.

Description

[Detailed description of the invention]

0001

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an air-fuel ratio learning control device for an internal combustion engine, and more particularly, the present invention relates to an air-fuel ratio learning control device for an internal combustion engine. The present invention relates to an apparatus configured to learn correction values.

0002

[Prior Art] Conventionally, in an internal combustion engine equipped with an electronically controlled fuel injection device having an air-fuel ratio feedback correction control function, Japanese Patent Laid-Open No. 60-90944 and Japanese Patent Laid-Open No. 61-1
As disclosed in Japanese Patent No. 90142 and the like, there are some that employ learning control of the air-fuel ratio.

Air-fuel ratio feedback correction control uses an oxygen sensor installed in the engine exhaust system to determine whether the actual air-fuel ratio is rich or lean with respect to a target air-fuel ratio (for example, stoichiometric air-fuel ratio), and performs air-fuel ratio feedback correction based on the determination result. The coefficient LMD is set by proportional/integral control, etc., and the basic fuel injection amount Tp is calculated from engine operating state parameters (for example, intake air flow rate Q and engine rotational speed N) that are related to the amount of air taken into the engine. By correcting with the air-fuel ratio feedback correction coefficient LMD, the actual air-fuel ratio is feedback-controlled to the target air-fuel ratio.

[0004] Here, the deviation of the air-fuel ratio feedback correction coefficient LMD from the reference value (target convergence value) is learned for each of the plurality of operating regions and is determined as a learning correction coefficient KBLR.
C (air-fuel ratio learning correction value) is determined, and the basic fuel injection amount Tp is corrected by the learning correction coefficient KBLRC so that the base air-fuel ratio obtained without the correction coefficient LMD approximately matches the target air-fuel ratio, and the air-fuel ratio During feedback control, the fuel injection amount Ti is calculated by further correcting it using the correction coefficient LMD.

[0005] This makes it possible to perform fuel correction in response to air-fuel ratio correction requests that differ for each operating condition, stabilize the air-fuel ratio feedback correction coefficient LMD near the reference value, and improve air-fuel ratio controllability.

[0006]

[Problems to be Solved by the Invention] As mentioned above, when learning the learning correction coefficient KBLRC for each driving region, if the actual driving conditions fall near the boundaries of the divided driving regions, it is difficult to There is a problem in that the corresponding operating region changes due to changes in conditions, and as a result, the learning correction coefficient KBLRC for each region fluctuates, causing air-fuel ratio fluctuations.

[0007] In order to solve this problem, hysteresis may be provided in determining the relevant operating range to prevent hunting of the relevant operating range to be learned due to slight changes in operating conditions. That is, when operating regions are divided by a grid as shown in FIG. 21, and the actual operating conditions change in the direction of the arrows between the operating regions, when the actual operating conditions are located in the shaded area in the figure, the actual operating conditions change. Rather than the applicable area, the previous applicable area is continuously learned as the learning area, and due to the hysteresis setting, even if the operating conditions change near the boundary of the operating area, the learning will continue. It becomes possible to prevent hunting in the area.

[0008] In setting the hysteresis as described above, in a relatively narrow operating range, there are few chances that the operating condition remains in the relevant range, so when the operating condition moves from another range, it can be detected quickly. It is preferable to set the hysteresis relatively small so that it can be determined as a region, so that learning opportunities can be secured in a narrow driving region. In addition, when the operating region is narrow, the difference in the required correction level is small with respect to the adjacent region, so even if the hysteresis is set small and hunting is likely to occur when determining the relevant region, the learning correction will be applied accordingly. The coefficient does not fluctuate greatly.

On the other hand, in a relatively wide operating range, if the hysteresis is set to a small value, the difference in correction request level with respect to adjacent operating ranges is large, so the fluctuation of the learning correction coefficient becomes large, which is undesirable. In a wide driving range, sufficient learning opportunities can be secured, so it is desirable to set the hysteresis relatively large. Therefore, if the size of each segmented driving area is different on the learning map, if the hysteresis is kept constant, learning opportunities in narrow driving areas will decrease, or the learning correction coefficient will become large in wide driving areas. In particular, if the overall driving area is divided into small sections and there are variations in the width of the driving area, the above-mentioned reduction in learning opportunities and hunting become more problematic. For this reason, it is desirable to preset and store hysteresis values for each operating region, and to determine the region to be learned based on the hysteresis characteristics that correspond to the size of the operating region. There is a problem in that having such data requires a large amount of storage capacity, especially when the number of operating region divisions is large.

The present invention has been made in view of the above-mentioned problems, and it is possible to specify learning areas using hysteresis characteristics suitable for each driving area on a learning map without requiring a large storage capacity. The purpose is to do so.

[0011]

[Means for Solving the Problems] Therefore, an air-fuel ratio learning control device for an internal combustion engine according to the present invention is constructed as shown in FIG. In FIG. 1, the engine operating condition detecting means detects the engine operating condition including at least an operating parameter related to the amount of air taken into the engine, and the basic fuel supply amount setting means detects the engine operating condition based on the detected engine operating condition. to set the basic fuel supply amount.

The air-fuel ratio feedback correction value setting means compares the air-fuel ratio of the engine intake air-fuel mixture detected by the air-fuel ratio detection means with a target air-fuel ratio, and sets the actual air-fuel ratio to be close to the target air-fuel ratio. An air-fuel ratio feedback correction value for correcting the basic fuel supply amount is set. Furthermore, the storage means rewritably stores an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each of a plurality of operating regions divided based on threshold data of preset engine operating condition parameters. There is.

[0013] Here, the correction value calculating means calculates a correction value for correcting the threshold data as a predetermined ratio of the deviation between the threshold data in the storage means, and the corresponding area discriminating means calculates a correction value for correcting the threshold data as a predetermined ratio of the deviation between the threshold data in the storage means. The relevant driving region among the plurality of driving regions in the storage means is determined based on the threshold value data corrected in a direction to suppress a change in the relevant driving region based on the calculated correction value and the calculated correction value.

The air-fuel ratio learning means learns the deviation of the air-fuel ratio feedback correction value from the target convergence value, and sets the air-fuel ratio learning correction value corresponding to the relevant operating region determined by the relevant region determining means to the deviation. Correct and rewrite in the direction of decreasing . The fuel supply amount setting means sets the final fuel supply amount based on the basic fuel supply amount, the air-fuel ratio feedback correction value, and the air-fuel ratio learning correction value for the corresponding operating region in the storage means, and the fuel supply control means , drive and control the fuel supply means based on the set fuel supply amount.

[0015]

[Operation] According to this air-fuel ratio learning control device, the relevant region determining means stores data based on threshold data for delimiting preset operating regions and threshold data corrected in the direction of suppressing changes in the relevant operating region. The device is configured to determine the relevant operating range from among a plurality of operating ranges in the means, and the relevant operating range is determined with so-called hysteresis. Here, the correction value used for correcting the threshold data is calculated as a predetermined ratio of the deviation between the preset threshold data, and since the deviation between the threshold data indicates the width of each driving region, the correction value used for correcting the threshold data is A correction value can be set according to the width of the region, and thus the region to be learned can be determined based on the hysteresis characteristic according to the width of each driving region.

[0016] Furthermore, the threshold data is the minimum necessary data for determining the relevant driving range, and in addition to the threshold data, data for determining a predetermined percentage of deviation between the threshold data is used to calculate the correction value. Since it is sufficient, a large amount of storage capacity is not required, and storage capacity can be saved.

[0017]

[Examples] Examples of the present invention will be described below. In FIG. 2 showing one embodiment, air is taken into an internal combustion engine 1 from an air cleaner 2 via an intake duct 3, a throttle valve 4, and an intake manifold 5. As shown in FIG. Each branch of the intake manifold 5 is provided with a fuel injection valve 6 as a fuel supply means for each cylinder. The fuel injection valve 6 is an electromagnetic fuel injection valve that opens when the solenoid is energized and closes when the energization is stopped, and opens when the solenoid is energized by a drive pulse signal from the control unit 12,
The engine 1 is injected with fuel that is pressure-fed from a fuel pump (not shown) and adjusted to a predetermined pressure by a pressure regulator.

Each combustion chamber of the engine 1 is provided with an ignition plug 7, which ignites a spark to ignite and burn the air-fuel mixture. Then, exhaust gas is discharged from the engine 1 via an exhaust manifold 8, an exhaust duct 9, a three-way catalyst 10, and a muffler 11. The control unit 12 includes a CPU,
Equipped with a microcomputer including ROM, RAM, A/D converter, and input/output interface, it receives input signals from various sensors, processes them as described later, and controls the operation of the fuel injection valve 6. Control.

The various sensors mentioned above include the intake duct 3
An air flow meter 13 is provided therein, and outputs a signal corresponding to the intake air flow rate Q of the engine 1. Further, a crank angle sensor 14 is provided, and in the case of the four-cylinder engine of this embodiment, outputs a reference signal REF for every 180 degrees of crank angle and a unit signal POS for every 1 degree or 2 degrees of crank angle. Here, the engine rotational speed N can be calculated by measuring the period of the reference signal REF or the number of occurrences of the unit signal POS within a predetermined time.

A water temperature sensor 15 is also provided to detect the temperature Tw of the cooling water in the water jacket of the engine 1. Here, the air flow meter 13, crank angle sensor 14, water temperature sensor 15, etc. correspond to the engine operating condition detection means in this embodiment, and the operating parameters related to the amount of air taken into the engine are These are the intake air flow rate Q and the engine rotation speed N.

Further, an oxygen sensor 16 as an air-fuel ratio detecting means is provided at the gathering part of the exhaust manifold 8, and detects the air-fuel ratio of the intake air-fuel mixture via the oxygen concentration in the exhaust gas. The oxygen sensor 16 detects that the oxygen concentration in the exhaust gas is at the stoichiometric air-fuel ratio (
This is a known method that detects whether the actual air-fuel ratio is rich or lean with respect to the stoichiometric air-fuel ratio by utilizing sudden changes after the target air-fuel ratio (in this example). A relatively high voltage signal is output when the air-fuel ratio is rich, and 0V when the air-fuel ratio is lean.
It shall output a nearby low voltage signal.

[0022] Here, the control unit 12
The CPU of the microcomputer built into the
Arithmetic processing is performed according to the programs on the ROM shown in the flowchart of FIG. 16, and the fuel injection amount Ti is set while executing the air-fuel ratio feedback correction control and the air-fuel ratio learning correction control for each operating region, and the fuel is supplied to the engine 1. control.

In this embodiment, basic fuel supply amount setting means, fuel supply amount setting means, fuel supply control means, air-fuel ratio feedback correction value setting means, air-fuel ratio learning means, correction value calculation means, and applicable area determination means As shown in the flowcharts of FIGS. 3 to 16, the function is provided by software, and the storage means is a RAM with a backup function of a microcomputer (not shown) built into the control unit 12. It shall be.

The program shown in the flowcharts of FIGS. 3 and 4 is a program for setting an air-fuel ratio feedback correction coefficient LMD (air-fuel ratio feedback correction value) to be multiplied by the basic fuel injection amount Tp by proportional/integral control. It is executed every revolution (1 rev) of the engine 1. First, in step 1 (indicated as S1 in the figure; the same applies hereinafter), a voltage signal output from the oxygen sensor (O2/S) 16 according to the oxygen concentration in the exhaust gas is read.

[0025] In the next step 2, step 1
The voltage signal from the oxygen sensor 16 read in and the slice level (for example, 50
0 mV). When it is determined that the voltage signal from the oxygen sensor 16 is greater than the slice level and the air-fuel ratio is richer than the stoichiometric air-fuel ratio, the process proceeds to step 3, where it is determined whether or not the current rich determination is the first.

When the rich determination is made for the first time, the process proceeds to step 4, where the air-fuel ratio feedback correction coefficient LMD set up to the previous time is set to the maximum value a. In the next step 5, a predetermined proportionality constant P is subtracted from the previous correction coefficient LMD to control the correction coefficient LMD to decrease. Further, in step 6, FP, which is a flag indicating that proportional control has been executed, is set to 1.

On the other hand, if it is determined in step 3 that the rich determination is not the first time, the process proceeds to step 7, and the value obtained by multiplying the integral constant I by the latest fuel injection amount Ti is subtracted from the previous correction coefficient LMD. and updates the correction coefficient LMD. In addition, when it is determined in step 2 that the voltage signal from the oxygen sensor 16 is smaller than the slice level and that the air-fuel ratio is lean relative to the target, first, in step 8, the air-fuel ratio is determined to be lean relative to the target. It is determined whether or not the lean determination is the first time, and if it is the first time, the process advances to step 9 and the correction coefficient LMD up to the previous time is set to the minimum value b.

In the next step 10, the fuel injection amount Ti is increased by updating the previous correction coefficient LMD by adding the proportionality constant P, and in step 11, it is shown that the proportional control has been executed. The flag FP is set to 1. If it is determined in step 8 that the lean determination is not the first time, the process proceeds to step 12, where the value obtained by multiplying the integral constant I by the latest fuel injection amount Ti is added to the previous correction coefficient LMD, and the correction coefficient LMD is gradually increased. increase to

[0029] At the first time of rich/lean discrimination, the correction coefficient LM
When proportional control D is executed, various processes related to air-fuel ratio learning correction control, which will be described later, are further performed. In this embodiment, as shown in FIG. 17, the entire operating range is divided into 16 unit operating ranges, and the learning correction coefficient KBLRC1 is rewritably stored for each unit operating range.
It is configured with a region learning map and a 256 region learning map that divides the entire driving region into 256 unit driving regions and stores the learning correction coefficient KBLRC2 for each unit driving region in an updatable manner. In addition, as mentioned above, the learning correction coefficient K is applied under all operating conditions without dividing the operating range.
BLRC φ is also set to be learned, and the learning correction coefficients KBLRC1 and KBLRC2 of the corresponding driving range are searched from the two learning maps, and the basic fuel injection amount Tp is corrected by these and the learning correction coefficient KBLRC φ. It is now possible to do so.

When proportional control of the air-fuel ratio feedback correction coefficient LMD is performed, first, in step 13, 1
A count value cnt is determined to determine whether the vehicle is stably stopped in one driving region on the six-region learning map. In the programs shown in the flowcharts of FIGS. 5 to 8, which will be described later, when the corresponding operating region on the 16-region learning map changes at predetermined minute intervals, the count value cnt is set to a predetermined value (for example, 4). The count value cn is set in step 13.
If it is determined that t is not zero, the process proceeds to step 14, where the count value cnt is decreased by 1.

In step 15, as will be described later, a flag flag, which is set to 1 when most of the driving regions of the 16 region learning map have been learned, is determined. When the flag flag is 1, the process proceeds to step 16. In step 16, it is determined whether or not the operating conditions are substantially the same as last time, and only when the operating conditions are not substantially the same as last time, the process proceeds to step 17.

In step 17, the latest correction coefficient LMD
Target convergence value Target (=1
．． Based on the absolute value of the deviation with respect to 0), a map of Δstress indicating the degree of inappropriateness of the learned value is referred to, and Δstress is set to increase in accordance with an increase in the deviation of the correction coefficient LMD from the target convergence value Target. Then, the Δ stress obtained this time is added to the "stress" in which the integrated value of the Δ stress is set. As will be described later, when the stress exceeds a predetermined value, it is determined that the already learned air-fuel ratio learning correction coefficient KBLRC is inappropriate, and the learning is restarted from the beginning.

The programs shown in the flowcharts of FIGS. 5 to 8 are air-fuel ratio learning programs for each operating region, and are executed at predetermined minute intervals (for example, 10 ms). In step 21, the flag FP is determined, and FP is 1.
When the value is zero, the process proceeds to step 22, where the FP is reset to zero, and various processing by this program is performed, and when it is zero, the program is immediately terminated.

After the FP is reset to zero in step 22, in the next step 23, the learning correction coefficient KBLRC φ (initial value 1
．． A flag Fφ indicating whether or not 0) has been learned is determined. Here, if the flag Fφ is zero and learning of the learning correction coefficient KBLRCφ has not been completed, the process proceeds to step 24, and the maximum and minimum values a of the correction coefficient LMD are
, b is approximately 1.0.

When (a+b)/2 is not approximately 1.0, the process proceeds to step 26, and the target convergence value Target (in this embodiment, 1.0) of the correction coefficient LMD is determined from (a+b)/2.
) is multiplied by a predetermined coefficient X to the previous learning correction coefficient KBLRC φ, and the addition result is set as a new learning correction coefficient KBLRC φ. KBLRC φ←KBLRC φ+X{(a+b)/2
-Target} Also, in step 26, the learning correction coefficient KBLRC1 and the learning correction coefficient KBLRC2 stored in the respective driving regions of the 16-area learning map and the 256-area learning map are reset to the initial value of 1.0.

In step 24, (a+b)/2 is approximately 1.
If it is determined that the flag Fφ is
is set to 1 so that it is determined that learning of the learning correction coefficient KBLRCφ corresponding to all driving ranges has been completed. On the other hand, if it is determined in step 23 that the flag Fφ is 1, then the operating range is changed to the basic fuel injection amount Tp.
Air-fuel ratio learning is performed for each operating region divided into a plurality of regions based on the engine speed and the engine rotational speed N.

First, in step 27, on the 256 area learning map, the area [K, I
]. Here, as shown in FIG. 17, K indicates the corresponding position on the grid divided using the engine rotational speed N as a parameter, and I indicates the corresponding position on the grid divided using the basic fuel injection amount Tp as a parameter. . Furthermore, Figure 17
Although each area on the 256-area learning map is described as being set to the same width, in reality, the width of each operating area is changed as appropriate due to requirements for air-fuel ratio correction accuracy. Assume that there is.

The determination of the relevant area in step 27 is performed with hysteresis, as shown in detail in the flowcharts of FIGS. 15 and 16. In the program shown in the flowcharts of FIGS. 15 and 16, first, in step 51, the current basic fuel injection amount Tp is set to 256.
A counter i for determining the corresponding area position on the area learning map is reset to zero, and in the next step 52, the counter i is set to the maximum address 15 (I is 0 to 15).
(set within the range) is determined.

When the counter i is less than or equal to the maximum value 15, the process proceeds to step 53, where the maximum basic fuel injection amount Tp[i] in the area indicated by the counter i is compared with the actual basic fuel injection amount Tp. . The maximum basic fuel injection amount Tp [i] corresponds to threshold data when dividing the operating region based on the basic fuel injection amount Tp, and for example, the basic fuel injection amount T included in the region corresponding to counter i=2.
p falls within the range of Tp[1]≦Tp<Tp[2] (see FIG. 18).

If it is determined in step 53 that Tp[i]≦Tp, since the actual Tp is larger than the Tp area indicated by the counter i, it is determined whether or not it is included in the next larger Tp area. Next, in order to make a determination, step 54
After incrementing the counter i by 1, the process returns to step 52. Then, the area indicated by the counter i when it is determined for the first time in step 53 that Tp[i]≦Tp is determined as the corresponding area, and the process proceeds to step 56. On the other hand, threshold value data Tp of the basic fuel injection amount Tp set in advance
If the actual basic fuel injection amount Tp is larger than the maximum value in [i], the counter i will be counted up to 16, but in this case, the process proceeds from step 52 to step 55. After setting the counter i to 15 again, the process proceeds to step 56.

In step 56, the basic fuel injection amount Tp is set in order to set the hysteresis characteristic in determining the relevant region.
A correction value (hysteresis value) Tphis for correcting the threshold value data Tp[i] is calculated according to the following formula. Tphis ←(Tp[i]-Tp[i-1])/Ym
That is, the width of the basic fuel injection amount Tp at the region position i that is determined to be applicable this time is set to the threshold data Tp[i], Tp
By determining the deviation between [i-1] and dividing this by a preset fixed value Ym (for example, Ym = 2, 4, 8), the predetermined range of change in the basic fuel injection amount Tp at area position i can be determined. A correction value Tphis is set as a ratio. As a result, it is possible to set the correction value Tphi according to the width of each area, and as described later, the correction value Tphi
The larger s is, the slower the response for determining the relevant driving region will be, so in a wide region, hunting in determining the relevant driving region can be prevented, while in a narrow region, the response will be unnecessarily delayed and learning opportunities will be lost. This can be suppressed.

After setting the correction value Tphis in step 56, in the next step 57, the region position I finally determined last time is compared with the region position i determined this time without hysteresis, and changes in the corresponding region are compared. Determine direction. Here, when it is determined that I<i, it means that the address of the corresponding area has increased due to an increase in the basic fuel injection amount Tp, and in this case, the process proceeds to step 58, and the minimum basic fuel injection amount at the area position i The value obtained by adding the correction value Tphis to Tp[i-1] (Tp[i-1]+
It is determined whether the actual Tp is larger than Tphis). In other words, it is determined whether or not the corresponding area has changed due to a change in the basic fuel injection amount Tp that slightly exceeds the boundary (threshold value data), although there has been a movement of the corresponding area (Fig. (see 19).

When Tp[i-1]+Tphis≧Tp, the process proceeds to step 59, sets i-1 to the final area position I, and sets the current threshold value data Tp[i-1].
Although it is determined that the engine has crossed the boundary and entered area position i, it has only slightly crossed the boundary, so the engine moves to an area one level lower than that (an area where the basic fuel injection amount Tp level is one level lower than area position i). In other words, the response for determining the relevant area is delayed and the relevant area is finally determined.

On the other hand, Tp[i-1]+Tphis<T
When it is p, it is determined that the threshold value data Tp[i-1] has been greatly exceeded and the area position i has entered the range,
In this case, the process advances to step 60 and the current determination result i
is set to I as the final judgment. Further, when it is determined in step 57 that I>i, it is a situation where a change in the region position has occurred due to a decrease in the basic fuel injection amount Tp, and in this case, the process advances to step 61 and Tp[i] - Compare Tphis and actual Tp. This is a case where the relevant region i is determined because the basic fuel injection amount Tp, which was larger than Tp[i], has become lower than Tp[i], so the boundary Tp[i] In this case, only when the actual Tp is lower than Tp[i]-Tphis, the process proceeds to step 62, and the area position i is determined as the final determination result ( I←i), Tp[
i]-Tphis≦Tp<Tp[i], and when the boundary is only slightly exceeded, proceed to step 63,
Assuming that the region remains in the region one step higher, the region is finally determined (I←i+1).

Furthermore, when it is determined in step 57 that I=i, since there is no change in the corresponding area, steps 58 to 63 are skipped without updating the final area position I, and the next The process proceeds to determination of the corresponding region position K of the engine rotational speed N. As described above, by correcting the threshold value data Tp[i] using the correction value Tphis in a direction that delays the discrimination response of the relevant region, hunting in the relevant region due to changes in Tp near the threshold can be prevented, and This can prevent hunting in learning for each driving region, which will be described later. Further, as described above, since the relevant area can be determined based on the hysteresis characteristic depending on the size of each area, it is possible to prevent hunting in learning from occurring and to prevent a decrease in learning opportunities at the same time. Furthermore, when setting the hysteresis characteristic according to the width of the operating region, the correction value Tphis that determines the hysteresis characteristic is calculated each time as a predetermined ratio of the deviation between the threshold data Tp[i], so the correction value can be adjusted in advance for each region. There is no need to store the value Tphis, and storage capacity can be saved, especially in a learning map with a large number of area divisions as in this embodiment.

After determining the corresponding position I on the area learning map where the current basic fuel injection amount Tp is 256 with hysteresis as described above, next, the corresponding position K of the engine rotational speed N is determined. To determine the position K,
Since it is performed in exactly the same manner as the determination of the area position I described above, steps 64 to 7 in the flowchart of FIG.
A description of various calculation processes related to the determination of the area position K shown in 6 will be omitted.

Here, the explanation will be continued by returning to the flowchart of FIG. 6 again. When the corresponding area [K, I] on the 256 area learning map is detected in step 27, in the next step 28, the 16 area including the corresponding area [K, I] is detected.
The corresponding area [B, A] on the 16 area learning map is detected as the area on the area learning map. Then, in the next step 29, it is determined whether the corresponding area on the 16-area learning map is the same as the previous one. When the corresponding driving region changes on the 16-region learning map, the process proceeds to step 30, where the count value cnt, which is decremented by 1 in step 14, is set to a predetermined value (for example, 4).

In step 31, flag F[B, A] indicating whether or not learning in area [B, A] has been completed in the 16 area learning map is determined, and this flag F[
B, A] is zero and learning in the area [B, A] is not completed, the process advances to step 32. Step 3
In step 2, it is determined whether or not the count value cnt is zero, and if the count value cnt is not zero and there is a change in the corresponding area in the 16-area learning map, the program is immediately terminated and the count value cnt is determined to be zero. The process proceeds to step 33 only when the corresponding driving area is stable on the 16-area learning map.

In step 33, the maximum and minimum values a of the air-fuel ratio feedback correction coefficient LMD sampled by the program shown in the flowcharts of FIGS. 3 and 4 are determined.
, b, that is, the center value of the correction coefficient LMD is the initial value (=1
．． 0) Determine the progress of learning based on whether or not it is in the vicinity. Here, if it is not recognized that the average value of the correction coefficient LMD is approximately 1.0 and learning has not been completed, the process directly proceeds to step 35, and the average value of the correction coefficient LMD is approximately 1.0.
If it is determined that the learning has been completed, the flag F[B, A] is set to 1 in step 34, and the process proceeds to step 35.

In step 35, a target convergence value Target is calculated from the average value of the maximum and minimum values a and b for the learning correction coefficient KBLRC1 stored corresponding to the area [B, A] on the 16-area learning map. (1.0 in this example) is multiplied by a predetermined coefficient X1, and the result is added to the current driving area [B, A] on the 16-area learning map.
A new learning correction coefficient KBLRC1 is set corresponding to , and the map data is updated.

[0051] KBLRC1[B,A]←KBLRC1[B,
A] +
A] Learning correction coefficient KBLRC of 16 areas included in area
2, all of them are set to the initial value 1.0 in step 36.
Reset to . On the other hand, in step 31, the flag F [
B, A] is 1, step 3
Proceed to step 7, driving area [B, A] on the 16 area learning map
256 area learning map is further subdivided into 16 areas.

In step 37, it is determined whether (a+b)/2, which is the average value of the correction coefficient LMD, substantially matches the target convergence value Target of 1.0, and (a+b)/2 is determined.
b) If /2 is not approximately 1.0 and is in an unlearned state requiring correction by the air-fuel ratio feedback correction coefficient LMD, the process proceeds to step 38. In step 38, (
The value obtained by subtracting the target convergence value Target (1.0 in this example) from a+b)/2 and multiplying by a predetermined coefficient X2 is calculated as the driving region [K, I ] is added to the learning correction coefficient KBLRC2 [K, I] stored corresponding to
Set as RC2 [K, I] and update the map data.

[0053] KBLRC2[K, I]←KBLRC2[K,
I]+X2 {(a+b)/2-Target} On the other hand,
In step 37, the average value of the correction coefficient LMD (a+
b) When it is determined that /2 substantially matches the target convergence value Target of 1.0, the process proceeds to step 39;
256 Set the flag FF [K, I] to 1 so that it is determined that the learning of the operating region [K, I] that includes the current operating conditions of the region learning map is completed.

[0054] From step 40 onwards, for the predetermined driving area [K, I] on the 256 area learning map for which it is determined that learning has been completed this time, multiple driving areas with similar driving conditions are adjacent on the same map. (See Figure 20) For each, the learning correction coefficient K in the corresponding area [K, I]
Appropriate learning correction coefficient KB estimated based on BLRC2
Performs control to set LRC2.

In step 40, K indicating the position of the area including the current driving condition in the 256 area learning map is selected.
,I by subtracting 1 from each of them and setting them to m and n,
In the next step 41, it is determined whether m=K+2. When the process advances from step 40 to step 41, a determination of NO is made in step 41, so step 42
256 The flag F indicates whether the learning of the driving area on the area learning map has been completed or not.
The determination is made depending on whether F[m,n] is 1 or zero.

Here, if the flag FF [m, n] is zero and learning has not been completed, the process advances to step 43. In this step 43, the area position [m, n] on the 256 area learning map is converted to the area position [m/4, n/4] on the 16 area learning map.
m, n] on the 16-area learning map. Then, the area [m, n] adjacent to the corresponding area [K, I] on the 256 area learning map is included in the same area [B, A] as [K, I] on the 16 area learning map. Determine whether or not it is possible.

In other words, [K, I] is an area included in [B, A], but the adjacent area of [K, I] is another area adjacent to [B, A] on the 16 area learning map. This is because it may be included in the area, and the corresponding area [K, I] and the adjacent area [
m, n] are included in the same [B, A], then ([m
/4,n/4]=[B,A]), proceed to step 44,
The learning correction coefficient KBLRC2 corresponding to the [K, I] region that was determined to have been learned this time is directly applied to the adjacent region [m,
n] is stored as a learned value.

On the other hand, in step 43, the corresponding area [K, I]
When the adjacent area [m, n] is determined to be included in a different area on the 16 area learning map, ([m/4, n
/4]≠[B,A]), the process proceeds to step 45, and the learning correction coefficient KBL calculated by the following formula is added to the adjacent region [m, n].
Store RC2. KBLRC2 [m, n]←KBLRC1 [B, A]
+KBLRC2[K,I]-KBLRC1[m/4,n
/4] When KBLRC2 [m, n] is updated and set as described above, in step 46, the m is increased by 1 and the process returns to step 41, where the learned and learned information is updated for each driving region until m=K+2. Determine unlearned.

Then, as a result of the 1-up processing of m in step 46, if it is determined in step 41 that m=K+2, then the process proceeds to step 47, where it is determined whether or not n=I+2, and n≠I+2. , step 4
In step 8, m is set to K-1 again, and in the next step 49, n is incremented by 1, and then the process proceeds to step 42. When it is determined in step 47 that n=I+2, [K,
This means that the determination processing for all eight operating regions surrounding [I] has been completed, so at this time, proceed to step 38,
The learning correction coefficient KBLRC2, which is determined to have already been learned in the current area [K, I], is updated.

As described above, in this embodiment, first, the learning correction coefficient KBLRC φ corresponding to all driving regions is learned, and then learning is performed for each driving region on the 16 region learning map. For areas where the 16-area learning map has already been learned, the area is further divided into 16 areas for learning, so the learning progresses from the large driving area to the small driving area. Convergence of the air-fuel ratio is ensured by learning over a large operating range, and as learning progresses, learning is performed for each operating range in detail, making it possible to respond accurately to differences in required correction values due to differences in operating conditions. .

Three learning correction coefficients KBLRC φ, KBLRC1, KBLRC2 learned as described above
The setting of the final air-fuel ratio learning correction coefficient KBLRC based on is performed according to the program shown in the flowchart of FIG. The program shown in the flowchart of FIG.
This is processed as a background job (BGJ). First, in step 71, the learning correction coefficient KBLRC1 stored in the corresponding area [B, A] on the 16 area learning map is read out, and in the next step 72, ,2
56 Read the learning correction coefficient KBLRC2 stored in the corresponding area [K, I] on the area learning map. In addition, B×4+A and K×16+I shown in the flowchart are as follows.
This converts each area position into an address on memory.

In step 73, KBLRC φ+KB
The final learning correction coefficient KBLRC is set as LRC1+KBLRC2-2.0 →KBLRC. The learning correction coefficient KBLRC finally set in the program shown in the flowchart of FIG. 9 is used in the fuel injection amount setting program shown in the flowchart of FIG.

The fuel injection amount setting program shown in the flowchart of FIG.
First, in step 81,
The engine rotation speed N calculated based on the intake air flow rate Q detected by the air flow meter 13 and the detection signal from the crank angle sensor 14 is input. Then, the next step 82
Now, based on the intake air flow rate Q and the engine rotational speed N input in step 81, the intake air flow rate Q per unit rotation is determined.
A basic fuel injection amount Tp (←K×Q/N; K is a constant) corresponding to is calculated.

In the next step 83, the step 82
The final fuel injection amount (fuel supply amount) Ti is calculated by applying various corrections to the basic fuel injection amount Tp calculated in step. Here, the correction value used to correct the basic fuel injection amount Tp is:
The learning correction coefficient KBLRC, the air-fuel ratio feedback correction coefficient LMD, and various correction coefficients COEF that are set including a basic correction coefficient based on the cooling water temperature Tw detected by the water temperature sensor 15, a post-start increase correction coefficient, etc. ,
This is a correction amount Ts for correcting a change in the effective injection time of the fuel injection valve 6 due to a change in battery voltage, and Ti←Tp
×LMD×KBLRC×COEF+Ts is calculated to update the final fuel injection amount Ti at predetermined intervals.

When a predetermined fuel injection timing is reached, the control unit 12 adjusts the latest calculated fuel injection amount T.
A drive pulse signal with a pulse width corresponding to i is sent to the fuel injector 6.
and controls the amount of fuel supplied to the engine 1. Further, the program shown in the flowchart of FIG. 11 is a process based on the "stress" (the integrated value of the deviation of the air-fuel ratio feedback correction coefficient LMD from the target convergence value) sampled according to the program shown in the flowcharts of FIGS. 3 and 4. This is a program that executes as a background job (BGJ).

In step 91, the parameters shown in FIGS. 3 and 4 are used as parameters indicating the degree of inappropriateness of the air-fuel ratio learning correction value.
Compare the stress set by the program shown in the flowchart with a predetermined value (for example, 0.8) to determine the air-fuel ratio feedback correction coefficient L when learning is almost completed.
It is determined whether the degree of variation in MD (degree of variation in base air-fuel ratio) is greater than or equal to a predetermined value.

Here, when the stress exceeds a predetermined value, it is determined that the learning result is inappropriate and an air-fuel ratio deviation has occurred, although the learning is almost completed, and the learning correction coefficient KBLRC φ is The process advances to step 92 in order to perform the learning again. In step 92, flags Fφ, F[0,0] to F[3,3], FF[0,
0] to FF[16, 16] are all reset to zero, and the flag fla is set to 1 when most areas on the 16 area learning map have been learned, as described later.
g (the flag determined in step 15 in FIG. 4) is also reset to zero, and since learning will be restarted from the beginning as described above, stress is also reset to zero.

In this way, if the degree of deviation of the air-fuel ratio feedback correction coefficient LMD from the reference value becomes larger than a predetermined value, relearning can be performed. When the fuel ratio suddenly changes, learning for each large operating range is performed again, so the air-fuel ratio can be quickly converged to the target air-fuel ratio.

Next, a description will be given of the correction of the learning correction coefficient for a larger sectional operation area based on the subdivision area, which is performed according to the programs shown in the flowcharts of FIGS. 12 to 14. The program shown in the flowcharts of FIGS. 12 to 14 is executed as a background job (BGJ). First, in step 101, when the learning correction coefficient KBLRCφ corresponding to all operating regions has been learned, the program is executed as a background job (BGJ). It is determined which flag Fφ is set to 1, and if the flag Fφ is zero, the program is terminated as is, but if it is 1, the program proceeds to step 102 and subsequent steps.

In step 102, various parameters used in this program, such as Sum, W, X, and Y, are initialized. Here, Sum is the integrated value of the learning correction coefficient KBLRC1 stored in the area that has been learned in each driving area of the 16 area learning map, and the initial value is 1.
0 is set, and the W is used to count up the number of samples in the integration calculation, and is set to 1 as an initial value. Furthermore, X and Y designate grid positions in the 16-area learning map, and zero is set to each as an initial value.

In the next step 103, the flag F[X, Y] (learned discrimination flag for each area on the 16 area learning map) whose coordinate position is X, Y reset to zero in the above step 102 is determined. and searches for a learned area in the driving area on the 16 area learning map indicated by X and Y. Since X and Y have initial values of zero, when they are unlearned in the [0, 0] operating range, X is incremented by 1 in step 104 to become [1, 0], and in step 105,
is not 4, the process returns to step 103.
A determination is made.

In this way, X becomes 1 at step 104.
When the updated result becomes 4, X is reset to zero in step 106, and Y is increased by 1, and the process returns to step 103 again until it is determined that Y is 4 in step 107, and then proceeds to step 104. Since X is increased by 1, the flag F [X, Y] in each driving region in the 16 region learning map is determined by repeating the process of fixing Y and changing X. .

Here, if there is a region in the driving region of the 16-region learning map in which the flag F[X, Y] is determined to be 1, the process proceeds from step 103 to step 108. In step 108, the flag F[X,Y] is determined to be 1, and the area [X, Y] on the learned 16-area learning map is determined to be 1.
, Y] corresponding learning correction coefficient KBLRC1 [X, Y]
is added to the accumulated value Sum up to that point, and the sampling number W is increased by 1 in order to count the number of additions (in other words, the accumulated number of samples).

Then, in the next step 109, the 25 areas included in the [X, Y] area on the 16 area learning map are
6. Detect the integrated value Sump of the learning correction coefficient KBLRC2 and the number Z of learned regions corresponding to each of the 16 regions on the region learning map. Then, in the next step 110, the learned area [X, Y]
Z, which is the number of learned areas among the 16 areas included in , is compared with a predetermined value (for example, 12), and if there is a learned area that exceeds the predetermined value, the process proceeds to step 111.

In step 111, the learning correction coefficient K stored in the learned area included in the [X, Y] area is
Average value (Sump/Z) of BLRC2 and target convergence value Ta
If the absolute value of the deviation from rget is a predetermined value (for example, 0.04
). If the deviation exceeds a predetermined value, the correction amount that should originally be borne by the learning correction coefficient KBLRC1 [X, Y] of the 16-area learning map is reduced to the learning correction coefficient KBLRC2 of the 256-area learning map.
The process proceeds to step 112 in order to transfer the added amount to the learning correction coefficient KBLRC1.

[0076] In step 112, the learning correction coefficient KBLRC1 [X, Y] stored in the area [X, Y] on the 16-area learning map that has been determined to have been learned this time is updated according to the following formula. do. KBLRC1[X,Y]←KBLRC1[X,
Y] + (Sump /Z-Target) x γ, that is, by the above calculation formula, the average correction amount borne by the 256 area learning map is calculated by 16
This is transferred to the region learning map side and brings the correction amount by the 256 region learning map closer to the target convergence value Target=1.0.

[0077] In this way, when the average correction amount borne by the 256 area learning map is transferred to the 16 area learning map corresponding to the area including them, the correction amount is reduced by 2.
56 Since it is necessary to reversely correct the learning correction coefficient KBLRC2 on the region learning map, the process of step 113 is performed. That is, in the next step 113, step 11
Learning correction coefficient KBLRC1 [X, Y] corrected by 2
The amount added to the learning correction coefficient KBLRC1 is subtracted from the learning correction coefficient KBLRC2 stored in each of the 16 areas on the 256-area learning map included in the area.

In this way, the learning correction coefficient KBLRC
After the correction amount for 1 is inversely corrected for the learning correction coefficient KBLRC2, the process returns to step 104 to search for a learned area on the 16 area learning map, and when a learned area is detected, the learning correction coefficient KBLRC2 is corrected. The process described above will be repeated each time. On the other hand, when it is determined in step 110 that the number of learned regions Z is less than or equal to the predetermined value,
And, in step 111, the learning correction coefficient KBLRC2 on the area learning map 256 is sufficiently adjusted to the target Target.
If it is determined that it is close to , steps 112 and 113 are skipped and the process returns to step 104.

Then, after checking the learned areas for all areas on the 16 area learning map, step 107
The process then proceeds to step 114. Step 11
In step 4, it is determined whether W, which is the count up of the number of learned areas on the 16-area learning map, exceeds a predetermined value (for example, 12), and it is determined whether most of the areas on the 16-area learning map have been learned. Determine if there is.

Here, if the W is less than the predetermined value, the flag is set to zero in step 115 and the process ends; however, if the W exceeds the predetermined value, the process proceeds to step 116. 1 for the flag flag
After setting, from step 117 onwards, processing is performed to correct the learning correction coefficient KBLRC φ corresponding to all driving regions based on the data of the learned regions of the 16 region learning map.

In step 117, the absolute value of the deviation between the average value (Sum/W) of the learning correction coefficient KBLRC1 in the area determined to have been learned on the 16 area learning map and the target convergence value Target is set to a predetermined value. Determine whether or not the value is less than or equal to the value. If the deviation is less than a predetermined value, the program is terminated, but if it is greater than or equal to the predetermined value, the correction amount that should originally be borne by the learning correction coefficient KBLRC φ corresponding to all operating regions is is assumed to have been transferred to the learning correction coefficient KBLRC1, and the process proceeds to step 118 in order to have the learning correction coefficient KBLRCφ bear the burden of this transfer.

In step 118, the learning correction coefficient KBLRC φ is updated according to the following equation. KBLRCφ← KBLRCφ+(Sum/Z-Tar
get) x γ2, that is, the average correction amount in the learned areas on the 16-area learning map is transferred to the learning correction coefficient KBLRCφ that corrects in all driving areas, and the learning correction coefficient KBLRC1 is adjusted to the target convergence. Value Target=1.0
It is intended to be learned in the vicinity.

Here, similarly to the case where the learning correction coefficient KBLRC1 is corrected based on the learning correction coefficient KBLRC2 of the same area, it is necessary to inversely correct the learning correction coefficient KBLRC1 by the amount corrected by the learning correction coefficient KBLRCφ. In step 119, the amount (Su
Correct by subtracting m/Z-Target)×γ2.

[0084] In the above embodiment, the air flow meter 1
Although the basic fuel injection amount Tp is calculated based on the intake air flow rate Q and the engine rotation speed N detected in step 3, in addition to this, the combination of the intake pressure PB and the engine rotation speed N,
Alternatively, the basic fuel injection amount Tp may be calculated based on the throttle valve opening TVO and the engine rotational speed N.

In addition, in this embodiment, a plurality of learning maps are individually provided, but even if the learning map is composed of one, the flowcharts shown in FIGS. 15 and 16 may be used. A similar effect can be obtained by determining the relevant area by setting the hysteresis characteristic.

[0086]

[Effects of the Invention] As explained above, according to the present invention, when determining an operating region that includes actual operating conditions on a learning map in which air-fuel ratio learning correction values are stored in an updatable manner for each divided operating region, The relevant area can be discriminated using hysteresis characteristics according to the width of each driving area, and hunting in the relevant area to be learned can be effectively prevented while providing learning opportunities in relatively narrow driving areas. On the other hand, the hysteresis characteristics can be set according to the width of each operating region without requiring a large storage capacity.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing the configuration of the present invention.

FIG. 2 is a system schematic diagram showing an embodiment of the present invention.

FIG. 3 is a flowchart showing air-fuel ratio feedback control.

FIG. 4 is a flowchart showing air-fuel ratio feedback control.

FIG. 5 is a flowchart showing air-fuel ratio learning control.

FIG. 6 is a flowchart showing air-fuel ratio learning control.

FIG. 7 is a flowchart showing air-fuel ratio learning control.

FIG. 8 is a flowchart showing air-fuel ratio learning control.

FIG. 9 is a flowchart showing the final setting of an air-fuel ratio learning correction coefficient.

FIG. 10 is a flowchart showing setting of fuel injection amount.

FIG. 11 is a flowchart showing contents related to learning iterative control.

FIG. 12 is a flowchart showing correction control of learning results.

FIG. 13 is a flowchart showing correction control of learning results.

FIG. 14 is a flowchart showing correction control of learning results.

FIG. 15 is a flowchart showing determination of a relevant area.

FIG. 16 is a flowchart showing determination of a relevant area.

FIG. 17 is a diagram showing the state of the learning map in the example.

FIG. 18 is a diagram showing the state of threshold data of a learning map in an example.

FIG. 19 is a state diagram showing hysteresis characteristics.

FIG. 20 is a diagram showing estimation learning of adjacent regions on a learning map.

FIG. 21 is a diagram illustrating a conventional method of determining a relevant area.

[Explanation of symbols]

1. Institution 6 Fuel injection valve 12 Control unit 13 Air flow meter 14 Crank angle sensor 15 Water temperature sensor 16 Oxygen sensor

Claims (1)

[Claims] 1. Engine operating condition detection means for detecting engine operating conditions including at least operating parameters related to the amount of air taken into the engine; A basic fuel supply amount setting means for setting the fuel supply amount, an air-fuel ratio detection means for detecting the air-fuel ratio of the engine intake air-fuel mixture, and a comparison between the air-fuel ratio detected by the air-fuel ratio detection means and the target air-fuel ratio. an air-fuel ratio feedback correction value setting means for setting an air-fuel ratio feedback correction value for correcting the basic fuel supply amount so that the actual air-fuel ratio approaches the target air-fuel ratio; and a preset engine operating condition parameter threshold value. storage means for rewritably storing an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each of a plurality of operating regions divided based on data; and a predetermined ratio of deviation between the threshold data in the storage means. a correction value calculation means for calculating a correction value for correcting the threshold value data, and a change in the corresponding driving region based on the threshold data preset in the storage means and the correction value calculated by the correction value calculation means. Applicable region determining means for determining a relevant operating region among a plurality of operating regions in the storage means based on threshold data corrected in a direction to inhibit the air-fuel ratio feedback correction value; air-fuel ratio learning means for correcting and rewriting an air-fuel ratio learning correction value corresponding to the relevant operating region determined by the relevant region determining means in a direction that reduces the deviation; and the basic fuel supply amount and air-fuel ratio feedback. a fuel supply amount setting means for setting a final fuel supply amount based on the correction value and the air-fuel ratio learning correction value for the corresponding operating region in the storage means; and a fuel supply amount setting means based on the fuel supply amount set by the fuel supply amount setting means. 1. An air-fuel ratio learning control device for an internal combustion engine, comprising: fuel supply control means for driving and controlling a fuel supply means.