JPH04318247A - Air-fuel ratio study control device for internal combustion engine - Google Patents

Abstract

PURPOSE:To study a study correction value of adjacent regions according to a proper value, when regions on a study map are studied in the case of an air-fuel ratio study classified by operational regions. CONSTITUTION:Two air-fuel ratio study maps for dividing an operational region into 16 regions and 256 regions are provided. Here, when a study on 256 regions is performed, whether concerned regions K, I and adjacent unstudied regions m, n are contained or not in the same region on the study map, classified by 16 regions, is discriminated (S43). Here, when regions B, A, contained with the concerned region, and regions m/4, n/4, contained with the adjacent region, are equal study correction coefficients KBLRC2K, I of the concerned region are left as applied (S44) to also the adjacent region, and when different, study correction coefficients KBLRC2m, n of the adjacent region are rewritten (S45) so that a correction level, containing a study correction coefficient KBLRC1 on the study map classified by the 16 regions, is equal in the concerned region and the adjacent regions.

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, a device for correcting the amount of fuel supplied so that the air-fuel ratio of an intake air-fuel mixture in an automobile internal combustion engine matches a target air-fuel ratio. Regarding.

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]

[Problem to be Solved by the Invention] Incidentally, the air-fuel ratio learning correction coefficient KBLRC for each operating region is set to accommodate differences in air-fuel ratio correction requests due to differences in operating conditions, as described above. , divide the driving range as finely as possible and calculate the learning correction coefficient KBLR for each driving range.
It is desirable to have learning settings for C. however,
If the driving area is divided into small areas and the learning correction coefficient KBLRC is learned for each narrow driving area, the learning opportunities in each driving area will decrease, the convergence of learning will deteriorate, and the learning area will be different from the learned area and the unlearned area. As a result, large air-fuel ratio differences occur between operating ranges.

[0007] Therefore, the present applicant has provided a plurality of learning maps in which the same operating region based on engine operating conditions is divided into a plurality of regions based on unit operating regions of mutually different sizes. Learning is performed from a learning map that divides driving areas into larger unit driving areas within the map, and as the learning progresses, learning moves to smaller unit driving areas, corresponding to the corresponding area of each learning map. We have previously proposed an air-fuel ratio learning control device that searches for learning correction values to be used and sets the final air-fuel ratio learning correction value based on these learning correction values (Japanese Patent Application No. 1-282883). reference).

According to such air-fuel ratio learning, learning convergence is ensured by learning for each large unit operating region at the initial stage of learning, and as the learning progresses, learning for each unit operating region is performed in finer detail to prevent differences in operating conditions. This allows learning to accurately respond to differences in correction requests. however,
Even with the above configuration, when learning a learning map that finely divides driving areas, it is difficult to obtain equal learning opportunities in each unit driving area, so the learning correction value for the corresponding unit driving area is rewritten. Sometimes, control is performed to rewrite unit operation areas surrounding the applicable unit operation area to the same learning correction value as the applicable area, and approximately appropriate values are set even for areas where learning opportunities are difficult to obtain. I did it like that.

[0009] By the way, as described above, when a plurality of learning maps are constructed with different numbers of divisions, the area around the corresponding unit driving area on the learning map that finely divides the driving area becomes larger. They are not necessarily included in the same unit operation area on the learning map that divides the operation area, and if the same learning correction value as the unit operation area is applied to all surrounding areas, an error will occur. Something happened.

For example, as shown in FIG. 17, the learning results of a corresponding area a on a learning map that divides a driving area into smaller areas that are included in area A on a learning map that divides a driving area into relatively large areas. , when trying to apply the same to the surrounding eight adjacent areas, some of the adjacent areas are included in area B, which is different from area A. Here, if the learned value in area A is K1, the learned value in area B is K1', and the currently learned learning value corresponding to area a is K2, then within the same area A among the areas adjacent to area a, As for the area included in area a, the final learning correction value will be set based on K1 and K2 in the same way as the corresponding area, but the area included in area B among the areas adjacent to area a. Regarding the area, the final learning correction value will be set based on K1' and K2. Therefore, even though the area is adjacent to area a, there will be a difference in the final learning correction result depending on whether it is included in area A or area B. Even though the same learning value was applied to adjacent areas based on the assumption that the required levels were similar,
As a result, there is a problem in that a step difference occurs in the correction level between adjacent regions, and such a step difference causes an air-fuel ratio step difference.

The present invention has been made in view of the above problems, and when rewriting the air-fuel ratio learning correction value corresponding to the corresponding area on the learning map as described above, the learning correction value of the corresponding area is It is an object of the present invention to enable the air-fuel ratio learning correction value corresponding to each adjacent region to be rewritten to an appropriate value based on the above.

0012

[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 detection means detects engine operating conditions including at least operating parameters related to the amount of air taken into the engine, and the basic fuel supply amount setting means is configured based on the detected engine operating conditions. Set the basic fuel supply amount.

Further, the air-fuel ratio feedback correction value setting means compares the actual air-fuel ratio detected by the air-fuel ratio detection means with the target air-fuel ratio, and adjusts the basic air-fuel ratio so that the actual air-fuel ratio approaches the target air-fuel ratio. Sets the air-fuel ratio feedback correction value for correcting the fuel supply amount. On the other hand, the air-fuel ratio learning correction value storage means includes a plurality of learning maps formed by dividing the same operating region based on engine operating conditions into a plurality of regions based on mutually different size unit operating regions, and the plurality of learning maps An air-fuel ratio learning correction value for correcting the basic fuel supply amount for each unit operating region is rewritably stored.

The air-fuel ratio learning means learns the deviation of the air-fuel ratio feedback correction value from the target convergence value, and stores the deviation of the air-fuel ratio feedback correction value from the target convergence value in correspondence with the corresponding unit operating region of each learning map in the air-fuel ratio learning correction value storage means. Air-fuel ratio learning, which corrects and rewrites the air-fuel ratio learning correction value in a direction that reduces the deviation, is performed with priority given to a larger unit operating region.

[0015] Here, the adjacent area rewriting means is configured to use the air-fuel ratio learning means to rewrite the corresponding unit operation in a learning map in which there is another learning map that divides the operation area into a larger unit operation area among the plurality of learning maps. When the air-fuel ratio learning correction value corresponding to a region is rewritten, the air-fuel ratio learning correction value in each of a plurality of unit operating regions on the same learning map whose operating conditions are close to the relevant unit operating region where the rewriting is performed, A comprehensive calculation based on the air-fuel ratio learning correction value corresponding to the unit operating area on the learning map to be rewritten and the air-fuel ratio learning correction value corresponding to the unit operating area on other learning maps in which the unit operating area is included, respectively. The correction level is rewritten so that it becomes approximately the same level as the correction level in the corresponding unit operation region where the rewriting is performed.

[0016] The fuel supply amount setting means determines 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 of each of the plurality of learning maps in the air-fuel ratio learning correction value storage means. The fuel supply control means drives and controls the fuel supply means based on the set fuel supply amount.

[0017]

[Operation] According to the air-fuel ratio learning control device having such a configuration, a plurality of unit operating regions (hereinafter referred to as adjacent regions) having operating conditions similar to the corresponding unit operating region are displayed on the same learning map together with the corresponding unit operating region. ), when rewriting the air-fuel ratio learning correction value corresponding to the corresponding area, instead of storing the air-fuel ratio learning correction value corresponding to the corresponding area as it is in the adjacent area, a unit operation larger than the learning map to be rewritten is used. Referring to a learning map in which driving areas are divided into multiple areas (hereinafter referred to as upper learning map), the overall correction level including the learning correction value on the upper learning map is calculated based on the adjacent area and the unit operation. This rewrites the air-fuel ratio learning correction value of the adjacent region so that it becomes substantially the same between the two regions.

That is, the air-fuel ratio learning correction value that has been learned corresponding to the unit operating area on the upper learning map in which the applicable unit operating area is included, and the new air-fuel ratio learning correction value that has been rewritten corresponding to the applicable operating area. Find a comprehensive learning correction level based on the fuel ratio learning correction value, and calculate the air-fuel ratio learning correction value that has been learned corresponding to the unit operation area on the upper learning map that includes the adjacent area, and the air-fuel ratio learning correction value that corresponds to the adjacent area. The learning correction value corresponding to the adjacent area is set so that the overall correction level based on the learning correction value is approximately the same level as the overall correction level in the corresponding area. Here, the area on the upper learning map that includes the adjacent area,
If the area on the upper learning map that includes the relevant area is the same, the same learning correction value will be set for the adjacent area, but if the area included on the upper learning map is different and the different area When different learning values are learned between adjacent regions, learning correction values having a difference corresponding to the difference in learning correction values are set for adjacent regions.

[0019]

[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.

[0020] 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 period of 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.

[0024] 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. 14, and the fuel injection amount Ti is set while executing air-fuel ratio feedback correction control and air-fuel ratio learning correction control for each operating region, and fuel is supplied to the engine 1. control.

In this embodiment, the functions as 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, and adjacent area rewriting means are as follows. Said FIGS. 3 to 14
As shown in the flowchart, the air-fuel ratio learning correction value storage means corresponds to a RAM with a backup function of a microcomputer (not shown) built into the control unit 12.

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. still,
The initial value of the air-fuel ratio feedback correction coefficient LMD (target convergence value Tar of the correction coefficient LMD by air-fuel ratio learning control)
get) is 1.0.

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 in accordance with the oxygen concentration in the exhaust gas is read. Then, in the next step 2, the voltage signal from the oxygen sensor 16 read in step 1 and the target air-fuel ratio (
Slice level (for example, 500 mV) equivalent to the stoichiometric air-fuel ratio
) to determine whether the air-fuel ratio of the engine intake air-fuel mixture is rich or lean with respect to the target air-fuel ratio.

When the voltage signal from the oxygen sensor 16 is greater than the slice level and it is determined that the air-fuel ratio is rich, the process proceeds to step 3, where it is determined whether or not this rich determination is the first time. 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. The fact that this is the first rich determination means that a lean determination was made previously, and this increases the air-fuel ratio feedback correction coefficient LMD (= fuel injection amount T
Since the correction coefficient LMD is then controlled to decrease when it is determined to be rich, the value before the initial decrease control for rich determination is the maximum value of the correction coefficient LMD. .

In the next step 5, a predetermined proportionality constant P is subtracted from the previous correction coefficient LMD to obtain a correction coefficient LM.
Aim to control the decrease of D. Further, in step 6, 1 is set to "P addition" which is a flag indicating that proportional control has been executed. On the other hand, when 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 to obtain a correction coefficient. This step 7 is executed every time this program is executed until the LMD is updated and the air-fuel ratio is no longer rich and turned to lean.
The control to decrease I×Ti is repeated.

Furthermore, 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, step 8 is first performed in the same manner as in the rich determination. It is determined whether the current lean determination is the first time or not. If it is the first time, the process proceeds to step 9 and the correction coefficient LMD up to the previous time, that is, the correction coefficient L that was controlled to gradually decrease during the rich determination is determined.
Set MD to minimum value b.

In the next step 10, the fuel injection amount Ti is increased by updating the previous correction coefficient LMD by adding a proportional constant P, and in step 11, it is shown that the proportional control has been executed. The flag "P minute addition" is set to 1. If it is determined in step 8 that the lean determination is not the first time, proceed to step 12;
A 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.

[0032] 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. 15, two learning maps are used for the air-fuel ratio learning correction value, which divides the entire operating range into a plurality of unit operating ranges using the basic fuel injection amount Tp and the engine rotational speed N as parameters. One is a 16-area learning map that divides the entire operating area into 16 unit operating areas and stores the learning correction coefficient KBLRC1 for each unit operating area, and the other is a 16-area learning map that divides the entire operating area into 256 unit operating areas. The learning correction coefficient KBL is divided into operating regions and calculated for each unit operating region.
It is a 256 region learning map that stores RC2, and 16
One unit operation area of the area learning map is further subdivided into 16 areas by 256 area learning maps. That is, two learning maps are provided in which the same operating region based on the engine operating conditions (Tp, N) is divided into a plurality of regions based on unit operating regions of mutually different sizes.

Furthermore, the learning correction coefficient KBLRC φ is applied under all operating conditions without dividing the operating range as described above.
The learning correction coefficient KBLRC for the corresponding driving area is determined from the two learning maps.
1, KBLRC2, and the basic fuel injection amount Tp is corrected by these and the learning correction coefficient KBLRCφ. When proportional control of the air-fuel ratio feedback correction coefficient LMD is performed, first, in step 13, a count value cnt is set for determining whether or not the state is stably stopped in one operating region on the 16-region learning map. Make a determination.

In the program 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) is set, and in step 13 the count value cn
If it is determined that t is not zero, the process proceeds to step 14 to decrease the count value cnt by 1.
After coming to stop in one operation area on the 6-area learning map, the count value cnt will be decreased by 1 every time the correction coefficient LMD is proportionally controlled, and when the count value cnt is zero, it will stop on the 16-area learning map. It is designed so that it can be regarded as being stably stationary in one operating range.

[0035] By determining whether or not the count value cnt is zero, it is determined whether or not learning is to be updated as described later, and the corresponding driving region has changed on the 16-region learning map. Learning is not performed in the initial stage. In step 15, as will be described later, the learning correction coefficient K corresponding to most driving regions of the 16 region learning map is
The flag flag, which is set to 1 when BLRC1 has been learned, is determined.

When the flag flag is 1 and the learning of the 16 area learning map is almost completed, 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, based on the absolute value of the deviation of the latest correction coefficient LMD average value (a+b)/2 from the target convergence value Target (=1.0), a map of Δ stress indicating the inappropriateness of the learning value is referred to. and the target convergence value Targ of the correction coefficient LMD
The Δ stress is set to increase in accordance with the increase in the deviation with respect to et.

In step 15, the learning correction coefficient KBLR corresponding to each driving region of the 16 region learning map is determined.
Since it is determined that most of C1 has been learned, the correction coefficient LMD should normally remain stable around the target convergence value Target even if the operating conditions change, but if the operating conditions change. If the correction coefficient LMD changes significantly when
Set stress higher.

Then, the Δstress obtained this time is added to the "stress" in which the integrated value of the Δstress is set. As described later, when the stress exceeds a predetermined value,
It is determined that the already learned air-fuel ratio learning correction coefficient 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, a flag "P addition" which is set to 1 when the air-fuel ratio feedback correction coefficient LMD is proportionally controlled by the program shown in the flowcharts of FIGS. 3 and 4 is determined. When the minute addition is 1, the program proceeds to step 22 and resets the P minute addition to zero, and then performs various processes according to this program, and when it is zero, the program is immediately terminated.

When the P addition is reset to zero in step 22, in the next step 23, the learning correction coefficient KBLRCφ (initial value 1.0), which is the air-fuel ratio learning correction value common to all operating ranges, has been learned. A flag Fφ indicating whether or not is determined is determined. Here, when the flag Fφ is zero and learning of the learning correction coefficient KBLRCφ is not completed,
Proceeding to step 24, it is determined whether the average value (←(a+b)/2) of the maximum and minimum values a and b of the correction coefficient LMD 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 calculated 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. Therefore, when learning and updating the learning correction coefficient KBLRC φ, even if learning values have been updated in the 16-area learning map and the 256-area learning map, all the data must be reset. The learning correction coefficient KBLRC φ is learned so that the correction coefficient LMD converges to the target convergence value Target using only the coefficient KBLRC φ.

In step 24, (a+b)/2 is approximately 1.
If it is determined that the flag Fφ is
Set 1 to 1, and the learning correction coefficient KBLRCφ corresponding to all operating ranges has been learned.In other words,
It can be determined that the air-fuel ratio feedback correction coefficient LMD has converged to approximately 1 as a result of learning and updating the learning correction coefficient KBLRCφ.

On the other hand, in step 23, the flag Fφ is set to 1.
If it is determined that the learning correction coefficient KBLRCφ corresponding to the entire operating range has been learned, the operating range is now determined based on the basic fuel injection amount Tp and the engine rotational speed N. Air-fuel ratio learning is performed for each operating region divided into multiple regions. First, in step 27, on the 256 area learning map, the area [K,
I] is detected. Here, as shown in FIG. 15, 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. .

In the next step 28, on the 16-area learning map, areas [B, A
] is detected. Here, too, B indicates the grid position where the engine rotational speed N is a parameter, and A indicates the grid position where the basic fuel injection amount Tp is the parameter. and,
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. And 1
When the corresponding driving region changes on the 6-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, a flag F [B, A], and this flag F[
B, A] is zero and the current operating conditions are included16
If learning has not been completed in one unit operation area on the area learning map, the process advances to step 32.

In step 32, the count value cnt
If the count value cnt is not zero and there is a change in the corresponding area in the 16-area learning map, this program is terminated, and the count value cnt is zero and the 16-area learning map is changed. Step 3 only when the corresponding operating region above is stable.
Proceed to step 3. In step 33, the maximum and minimum values a,
The average value (a+b)/2 of 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 it is determined that the average value of the correction coefficient LMD is approximately 1.0 and learning has been completed. If it is recognized that
At step 4, the flag F[B, A] is set to 1, and then the process proceeds to step 35.

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

[0048] 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 . As mentioned above, if there is a region in the 16-region learning map where learning has not been completed, the air-fuel ratio feedback correction coefficient L
By adding and updating a predetermined percentage of the deviation from the target value Target of the average value (a+b)/2 of MD to the learning correction coefficient KBLRC1 stored up to then,
Instead of the air-fuel ratio feedback correction coefficient LMD, the target air-fuel ratio is obtained by correction using the learning correction coefficient KBLRCφ and the learning correction coefficient KBLRC1, and the air-fuel ratio feedback correction coefficient LMD is set to an initial value of 1.0, which is the target convergence value Target. It is assumed that the learning of the operating region is completed at the time of substantially convergence.

On the other hand, in step 31, the flag F [B, A
] is determined to be 1, and when the learned learning correction coefficient KBLRC1 is stored in the corresponding driving area of the 16-area learning map, the process proceeds to step 37, and the current 16th learning correction coefficient KBLRC1 is stored in the corresponding driving area of the 16-area learning map. The driving region [B, A] on the region learning map is further subdivided into 16 regions. 256 Move to learning of the region learning map.

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.

[0051] 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.

[0052] From step 40 onwards, for the predetermined driving area [K, I] on the 256 area learning map for which it is determined that the learning has been completed this time, multiple driving areas with similar driving conditions are adjacent to each other on the same map. (See Figure 16) 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 the 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] The above calculation formula for KBLRC2 [m, n] is, since [K, I] and [m, n] are adjacent areas on the 256 area learning map, the final correction request is This is based on the assumption that they should be similar, and since the learning correction coefficients KBLRC1 of the 16 area learning maps included in each map are different, each KBLR is different.
The sum of C1 [B, A] and KBLRC1 [m/4, n/4] is set to approximate as shown in the following formula.

KBLRC1 [B, A] + KBLRC2 [K, I]
= KBLRC1 [m/4, n/4] + KBLRC2 [m
, n] Note that the final learning correction coefficient KBLRC is the learning correction coefficient KBLRC as described later in this embodiment.
Since φ, KBLRC1, and KBLRC2 are added and set, the learning correction coefficient KBLRC φ is used in the process to make the final correction levels in adjacent areas on the 256 area learning map approximately the same as described above. Although it is necessary to take this into account, the learning correction coefficient KBLRC φ is omitted because it is applied to all operating ranges. In addition, for example, the learning correction coefficient KBLRC φ, KBLRC1, KB
LRC2 are multiplied together to obtain the final learning correction coefficient K
If BLRC is configured, KBLRC2 [
m, n]←KBLRC1[B,A]×KBLRC2[K
, I]/KBLRC1 [m/4, n/4] to update the learning correction coefficient KBLRC2 of the adjacent region [m, n].

In this way, the learned correction coefficient KBLRC2 in the relevant area is unconditionally applied to the unlearned area adjacent to the relevant area learned on the 256 area learning map.
Instead, the learning correction coefficient KBLRC2 in the adjacent area is updated and set so that the final correction level is approximately the same in the relevant area and the adjacent area. The learning results can be spread to adjacent areas with high accuracy, and even when the operating area is subdivided and there are few learning opportunities for each operating area, as in the 256-area learning map, the air-fuel ratio controllability can be improved between operating areas. It is possible to prevent a difference in level from occurring.

As described above, when the [m, n] area has been learned, the learned value is not updated, and when the area has not been learned, the KBLRC2[m, n] is updated, in step 46, m is increased by 1, and step 41 is performed again.
Returning to step 2, m is moved within a range of ±1 around K with n constant until m=K+2, and whether learned or unlearned is determined for each driving region.

When it is determined in step 41 that m=K+2 as a result of the 1-up processing of m in step 46, the process proceeds to step 47, and it is determined whether 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. Therefore,
At first, we set n=I-1 and moved m within a range of ±1 centering on K to determine adjacent areas, but next we set n=I and moved m within a range of ±1 centering K. Then, next, let n=I+1 and move m around K.
1, and as a result, if the eight operating regions surrounding [K, I] (see Figure 16) have not been learned, the value based on the learning correction coefficient KBLRC2 [K, I] is used as the learning correction for that operating region. It is stored as a coefficient KBLRC2 [m, n].

[0061] When it is determined in step 47 that n=I+2, it means that the determination processing for all eight operating regions surrounding [K, I] has been completed. Learning correction coefficient KBLR that is determined to have already been learned in the area [K, I]
Perform learning update of C2. In this embodiment, the learning map that divides the driving area into a plurality of areas includes two learning maps, a 16-area learning map and a 256-area learning map.
When updating the learning correction coefficient KBLRC2 of the unlearned area adjacent to the learned area on the 256 area learning map, the 16 area learning map and the 64 area learning map are provided. The learning correction coefficient KBL of adjacent unlearned areas is adjusted so that the overall correction level by the learning correction coefficient KBLRC in each of the maps and the 256 area learning maps is approximately the same.
Just rewrite RC2.

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 calculates 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, the learning can be re-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 in 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).

[0076] 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.

[0078] 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.

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.

[0081] 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 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.

[0086] 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.

[0087]

Effects of the Invention As explained above, according to the present invention, in air-fuel ratio learning configured with a plurality of learning maps, the air-fuel ratio corresponding to the corresponding area on the learning map in which operating areas are divided relatively finely is determined. When rewriting the learning correction value, the air-fuel ratio learning correction value corresponding to each adjacent area can also be rewritten to an appropriate value based on the learning correction value of the corresponding area, and a learning map that finely divides the operating area can be used. This has the effect of suppressing the occurrence of differences in correction levels between regions and making it possible to perform good air-fuel ratio learning correction.

[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 diagram showing the state of the learning map in the example.

FIG. 16 is a diagram showing the state of the learning map in the example.

FIG. 17 is a state diagram for explaining problems in conventional learning control.

[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. 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; the air-fuel ratio learning correction for correcting the basic fuel supply amount for each unit operating area of the plurality of learning maps; an air-fuel ratio learning correction value storage means for rewritably storing a value; and a corresponding unit operating region of each learning map in the air-fuel ratio learning correction value storage means for learning the deviation of the air-fuel ratio feedback correction value from a target convergence value; an air-fuel ratio learning means for performing air-fuel ratio learning that corrects and rewrites the air-fuel ratio learning correction value stored corresponding to the deviation in a direction that reduces the deviation, giving priority to a larger unit operating region; When the air-fuel ratio learning correction value corresponding to the corresponding unit operating area is rewritten by the air-fuel ratio learning means in a learning map in which there is another learning map that divides the operating area into larger unit operating areas among the learning maps of , the air-fuel ratio learning correction value in each of a plurality of unit operating regions on the same learning map whose operating conditions are similar to the unit operating region to be rewritten is set to The overall correction level based on the fuel ratio learning correction value and the air-fuel ratio learning correction value corresponding to the unit operating area on the other learning map in which the unit operating area is included is determined in the unit operating area in which the rewriting is performed. an adjacent area rewriting means for rewriting the area to be approximately at the same level as the correction level; and the basic fuel supply amount;
a fuel supply amount setting means for setting a final fuel supply amount based on an air-fuel ratio feedback correction value and an air-fuel ratio learning correction value for each corresponding operating region of a plurality of learning maps in the air-fuel ratio learning correction value storage means; An air-fuel ratio learning control device for an internal combustion engine, comprising: fuel supply control means for driving and controlling the fuel supply means based on the fuel supply amount set by the fuel supply amount setting means.