JPH04175434A - Air-fuel ratio learning controller for internal combustion engine - Google Patents

Abstract

PURPOSE:To suppress an error learning of the air-fuel ratio slippage in a transition by converting the correction degree of the air-fuel ratio learning correction value depending on the acceleration or deceleration degree of an engine or the a deflection of the air-fuel ratio feedback correction value, and converting the learning running speed corresponding to the acceleration or deceleration degree of the engine. CONSTITUTION:Depending on the engine operation condition detected by a means A, a standard fuel feeding amount is set by a means B, and an air-fuel ratio feedback correction value to correct the standard fuel feeding amount is set by a means D by comparing the air-fuel ratio detected by a device C and the object air-fuel ratio. And an air-fuel ratio learning correction value to correct the standard fuel feeding amount is stored in a means E to rewrite freely, and the air-fuel ratio learning correction value is corrected by a means F by learning the deflection of the air-fuel ratio feedback correction value from the object convergent value. Furthermore, depending on the fuel feeding amount set by a means G, a fuel feeding means I is driven and controlled by a means H. And depending on the acceleration or deceleration operation degree of the engine detected by a means J, the correcting degree of the air-fuel ratio learning correction value is converted, and the learning running speed is converted by a means K corresponding to the acceleration or deceleration degree of the engine.

Description

DETAILED DESCRIPTION OF THE INVENTION <Industrial Application Field> The present invention relates to an air-fuel ratio learning control device for an internal combustion engine, and more particularly to a device that can prevent erroneous air-fuel ratio learning due to air-fuel ratio deviations during engine acceleration/deceleration. Regarding.

<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,
As disclosed in Japanese Patent Laid-Open No. 0-90944, Japanese Patent Laid-Open No. 61-190142, etc., learning control of the air-fuel ratio is employed.

The air-fuel ratio feedback correction control is performed by setting a basic fuel injection amount Tp, which 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, in the engine exhaust system. The actual air-fuel ratio is corrected by the air-fuel ratio feedback correction coefficient LMD, which is set by proportional/integral control based on the rich/lean ratio with respect to the target air-fuel ratio (for example, the stoichiometric air-fuel ratio) determined by the oxygen sensor. This provides feedback control to the target air-fuel ratio.

Here, the deviation from the reference value (target convergence value) of the air-fuel ratio feedback correction coefficient LMD is learned for each predetermined plurality of operating regions, and the learning correction coefficient KBLRC is calculated.
is determined, and the basic fuel injection amount Tp is determined based on the learning correction coefficient KBL.
The base air-fuel ratio obtained without the correction coefficient LMD is corrected by RC so that it substantially matches the target air-fuel ratio, and during air-fuel ratio feedback control, the fuel injection amount Ti is calculated by further correcting with the correction coefficient LMD. It is.

As a result, it is possible to perform correction corresponding to the required value of air-fuel ratio correction that differs depending on the operating conditions, and the air-fuel ratio feedback correction coefficient L
It is possible to stabilize MD near the reference value and improve air-fuel ratio controllability.

<Problem to be solved by the invention> By the way, during transient engine operation, the influence of fuel wall flow,
Due to the difference in intake air flow rate between when the fuel injection amount Ti is set and when the intake valve is opened, the air-fuel ratio becomes leaner during acceleration, and conversely, the air-fuel ratio becomes richer during deceleration. As the degree of acceleration/deceleration increases, the lean or rich tendency becomes more pronounced.

In this way, when a relatively large air-fuel ratio deviation occurs during transient operation of the engine, the air-fuel ratio feedback correction coefficient LMD is set to increase or decrease in the direction of eliminating the air-fuel ratio deviation.
When the air-fuel ratio is equalized during engine acceleration, the air-fuel ratio feedback correction coefficient LMD behaves as shown in FIG. Sometimes it is done and remembered. Therefore, when deceleration operation or steady operation is performed in the area where the lean air-fuel ratio has been learned due to acceleration operation, the fuel injection amount is determined based on the learned correction value which is significantly different from the request, as shown in Fig. 14. The problem is that using the learned correction value actually aggravates the air-fuel ratio deviation, causing a large air-fuel ratio difference and increasing CO2HC or NOx in the exhaust gas. there were.

In particular, if the map for storing the learning correction coefficient KBLRC is configured with a learning map that divides the entire driving area into small unit areas and a learning map that divides the entire driving area into larger unit areas, it is possible to With maps that learn the air-fuel ratio at each operating range, there are few learning opportunities for each operating range, and since the operating range is narrow, air-fuel ratio deviations are picked up spot-on. However, there was a problem in that the next time the system entered the erroneous learning region, a larger air-fuel ratio difference would occur.

The present invention has been developed in view of the above-mentioned problems. When a large air-fuel ratio deviation occurs due to transient operation of the engine, a learning value that is significantly different from the learning value corresponding to steady operation is performed based on this deviation. An object of the present invention is to provide an air-fuel ratio learning control device that can avoid this.

<Means for Solving the Problems> Therefore, an air-fuel ratio learning control device for an internal combustion engine according to the present invention is configured 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 detected engine operating condition. Set the basic fuel supply amount based on.

Further, the air-fuel ratio detection means detects the air-fuel ratio of the engine intake air-fuel mixture, and the air-fuel ratio feedback correction value setting means compares the detected air-fuel ratio with a target air-fuel ratio to set the actual air-fuel ratio to the target air-fuel ratio. An air-fuel ratio feedback correction value for correcting the basic fuel supply amount is set to increase or decrease so that it approaches the air-fuel ratio.

On the other hand, the air-fuel ratio learning correction value storage means rewritably stores an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each predetermined operating region based on engine operating conditions, and the air-fuel ratio learning correction value is stored in a rewritable manner. The value correction means learns the deviation of the air-fuel ratio feedback correction value from the target convergence value, and corrects the air-fuel ratio learning correction value stored in the air-fuel ratio learning correction value storage means in a direction to reduce the deviation. rewrite.

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 stored in the air-fuel ratio learning correction value storage means. A fuel supply amount is set, and the fuel supply means drives and controls the fuel supply means based on the set fuel supply amount.

Here, the learning speed changing means based on the degree of acceleration/deceleration adjusts the degree of correction of the air-fuel ratio learning correction value by the air-fuel ratio learning correction value correcting means based on the degree of engine acceleration/deceleration detected by the acceleration/deceleration degree detection means. By changing this, the learning progress speed is changed according to the degree of acceleration/deceleration of the engine.

Furthermore, when a learning speed changing means based on the deviation shown in FIG. The learning progress speed is changed in accordance with the deviation by changing the degree of correction of the air-fuel ratio learning correction value by the air-fuel ratio learning correction value correction means based on the deviation of the average value of the fuel ratio feedback correction value.

Further, in the configuration of the present invention shown in FIG. 1, the air-fuel ratio learning correction value storage means includes a plurality of types of learning maps in which the same operating region is divided based on unit operating regions of mutually different sizes, Preferably, the air-fuel ratio learning correction value is rewritably stored for each divided operating region of each of the plurality of learning maps.

<Operation> According to this configuration, during air-fuel ratio learning in which the deviation of the air-fuel ratio feedback correction value from the target convergence value is learned and the air-fuel ratio learning correction value is corrected in a direction to reduce this deviation, the degree of acceleration/deceleration of the engine is adjusted. The degree of correction of the air-fuel ratio learning correction value is changed accordingly, and the learning progress speed is thereby changed according to the degree of acceleration/deceleration of the engine.

Therefore, when the degree of acceleration/deceleration of the engine is large and a large air-fuel ratio deviation occurs, it is possible to slow down the learning progress and avoid erroneous learning due to air-fuel ratio deviation during transient periods, and vice versa during steady state. It becomes possible to accelerate the progress of learning and achieve rapid convergence of learning.

Furthermore, when the degree of acceleration/deceleration of the engine is large, a large air-fuel ratio deviation occurs, and the air-fuel ratio feedback correction value shifts accordingly. The learning progress speed (degree of correction of the air-fuel ratio learning correction value) may be changed depending on the deviation of the air-fuel ratio learning correction value.

Furthermore, the air-fuel ratio learning correction value storage means includes a plurality of types of learning maps in which the same operating region is divided based on unit operating regions of mutually different sizes, and the air-fuel ratio learning correction value storage means is provided with a plurality of types of learning maps, each of which divides the same operating region based on unit operating regions of mutually different sizes. If the air-fuel ratio learning correction value is stored in a rewritable manner, the learning convergence speed can be improved and the air-fuel ratio side
In addition to being able to suppress steps with II precision, by controlling the change in the air-fuel ratio learning progress speed as described above, even if a learning map that finely divides the operating range is used, air-fuel ratio deviations during transient times can be learned as they are. There is no need to worry.

<Example> The present invention will be explained in detail below.

In FIG. 2 showing one embodiment, an internal combustion engine l is connected to an air cleaner 2 through an intake duct 3. Air is sucked in through the throttle valve 4 and the intake manifold 5. A fuel injection valve 6 as a fuel supply means is provided in a branch portion of the intake manifold 5 for each cylinder. The fuel injection valve 6 is a normally closed type electromagnetic fuel injection valve that opens when the solenoid is energized and closes when the energization is stopped. The valve is opened, and fuel is injected and supplied under pressure 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. and,
From the engine I, an exhaust manifold 8, an exhaust duct 9. Exhaust gas is discharged via a three-way catalyst 10 and a muffler 1).

The control unit 12 includes a CPU and a ROM.

It is equipped with a microcomputer including a RAM, an A/D converter, and an input/output interface, and receives input signals from various sensors, performs arithmetic processing as described below, and controls the operation of the fuel injection valve 6. .

As the various sensors mentioned above, an air flow meter 13 is provided in the intake duct 3, and outputs a signal corresponding to the intake air flow rate Q of the engine I.

In addition, a crank angle sensor 14 is provided, and in the case of a 4-cylinder engine, a reference signal REF for each crank angle of 180',
A unit signal PO8 is output for every 1° or 2° of crank angle. Here, the engine rotation speed N can be calculated by measuring the cycle of the reference signal REF or the number of occurrences of the unit signal Pos within a predetermined time.

Further, a water temperature sensor 15 is provided to detect the cooling water temperature Tw of the water jacket of the engine 1.

Here, the air flow meter 13. Crank angle sensor 14. The water temperature sensor 15 and the like correspond to engine operating condition detection means.

Further, an oxygen sensor 16 as an air-fuel ratio detecting means is provided at the gathering part of the exhaust manifold 8 to detect the air-fuel ratio of the intake air-fuel mixture through the oxygen concentration in the exhaust gas. The oxygen sensor 16 is a known sensor that detects whether the actual air-fuel ratio is rich or lean with respect to the stoichiometric air-fuel ratio by utilizing the fact that the oxygen concentration in the exhaust gas changes suddenly from the stoichiometric air-fuel ratio.

Here, the CPU of the microcomputer built in the control unit 12 performs arithmetic processing according to the programs on the ROM shown in the flowcharts of FIGS. 3 to 10, and performs air-fuel ratio feedback correction control and air-fuel ratio The fuel injection amount Ti is set while executing the learning correction control, and the fuel supply to the engine 1 is controlled.

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, learning correction value correction means, acceleration/deceleration degree detection means, learning by acceleration/deceleration degree The functions of speed changing means and learning speed changing means based on deviation are provided in software as shown in the flowcharts of FIGS. 3 to 10, and the air-fuel ratio learning correction value storage means is provided by a microcomputer RAM or equivalent.

The program shown in the flowchart of FIG. 3 is a program that sets the air-fuel ratio feedback correction coefficient LMD (air-fuel ratio feedback correction value), which is multiplied by the basic fuel injection amount Tp, by proportional/integral control. (l rev). Note that the initial value of the air-fuel ratio feedback correction coefficient LMD is 1.

First, step 1 (indicated as Sl in the figure).

The same applies hereafter) reads the voltage signal output from the oxygen sensor (Oz/S) 16 according to the oxygen concentration in the exhaust gas.

Then, in the next step 2, the voltage signal from the oxygen sensor 16 read in step 1 is compared with a slice level (for example, 500 mV) corresponding to the target air-fuel ratio (theoretical air-fuel ratio), and the It is determined whether the fuel ratio 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 this is the current rich determination or 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 the rich determination is the first time means that the lean determination was made until the previous time, and as a result, the air-fuel ratio feedback correction coefficient LMD was controlled to increase (=increase correction of the fuel injection amount Ti). When rich is determined, the correction coefficient LMD is then controlled to decrease, so the value before the first decrease control of 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 control the correction coefficient LMD to decrease. Further, in step 6, 1 is set to P-minute 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 used as the correction coefficient L up to the previous time.
The correction coefficient LMD is updated by subtracting it from MD, and the reduction control by lXTi is repeated in step 7 every time this program is executed until the air-fuel ratio is eliminated from the rich state and reversed to lean.

Furthermore, when it is determined in step 2 that the air-fuel ratio is close to the target, in the same manner as in the rich determination, it is first determined in step 8 whether or not this lean determination is the first time. If it is the first time, the process proceeds to step 9 and sets the correction coefficient LMD up to the previous time, that is, the correction coefficient LMD that was controlled to gradually decrease at the time of rich judgment, to the minimum value, and in step IO, the correction coefficient LMD up to the previous time is set to the minimum value. Correction coefficient LMD
By adding and updating the proportional constant P, the fuel injection amount Ti is increased and corrected, and in step 1), the P addition, which is a flag indicating that the proportional control has been executed, 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

When the proportional control of the correction coefficient LMD is executed in the first rich/lean determination, various processes related to the air-fuel ratio learning correction control, which will be described later, are further performed.

In this embodiment, as shown in Fig. 1), there are two learning maps for the air-fuel ratio learning correction value that divide the entire operating range using the basic fuel injection amount Tp and the engine rotational speed N as parameters. , one uses a 4x4 grid to divide the entire operating area into 16 unit areas, and the other uses a 16x16 grid to partition the entire operating area into 256 unit operating areas. One operating area of the map is further subdivided into 4x4 areas by a learning map with a 16x16 grid. Further, as described above, an air-fuel ratio learning correction value that is applied to all operating ranges is also set.

First, in step 13, the deviation Δα of the average value of the air-fuel ratio feedback correction coefficient LMD is calculated according to the following formula.

In the above calculation formula, ABdmy is the average value (= (a+b)/2) of the correction coefficient LMD obtained during the previous execution of proportional control, and is the value obtained by subtracting the previous average value from the latest average value of the correction coefficient LMD, that is, The amount of variation in the average level of the correction coefficient LMD is assumed to be Δα.

In the next step 14, the latest average value of the correction coefficient LMD = (a+b)/2 is ABdm in order to be used for calculating Δα in step 13 when proportional control is executed next time.
Set to y.

Then, in step 15, coefficients X,
I and X2 are determined by searching the map based on the Δα.

The coefficients X, XI, and X2 are values that determine the degree of modification of the learning correction coefficient based on the deviation of the correction coefficient LMD from the target convergence value, and the larger X, Xl, and X2 are, the greater the degree of modification of the learning correction coefficient. Therefore, the speed of learning progress becomes faster.

Incidentally, during steady operation, the correction coefficient LMD generally stabilizes at a predetermined level, but during transient operation, a large air-fuel ratio deviation occurs, and the correction coefficient LMD changes greatly in order to eliminate such air-fuel ratio deviation.

Therefore, during transient operation, the Δα increases, but in this embodiment, when the Δα is large, the coefficients
1. X2 is set small (as a result, when the correction coefficient LMD fluctuates greatly, the degree of correction of the learning correction coefficient is reduced, slowing down the learning progress speed, and reducing the air-fuel ratio deviation during transients. is not significantly reflected in the learning correction coefficient.

Therefore, when the correction coefficient LMD is stable, such as during steady operation, it is possible to increase the degree of correction of the learning correction coefficient to increase the speed of learning progress, and at the same time, during transient operation, the correction coefficient LMD fluctuates greatly. When the engine is running, the learning progress speed is forcibly slowed down to prevent air-fuel ratio deviations caused by transient operation from being erroneously learned.

In the next step 16, it is determined whether the count value cnt, which is used to determine whether the vehicle is stably stationary in one of the operating regions divided by a 4×4 grid, is zero.

In the flowchart of FIG. 4, which will be described later, when the corresponding operating region among the operating regions divided by 4×4 grids changes at predetermined minute intervals, the count value cnt
is set to a predetermined value (for example, 4), and if it is determined in step 16 that the count value cnt is not zero, the process proceeds to step 17 and the process of decreasing the count value cnt by 1 is performed. After coming to stop in one driving area in the 4x4 grid, the count value cnt
is decreased by 1 every time the correction coefficient LMD is proportionally controlled, and when the count value cut is zero, it can be assumed that it is stably stationary in one area in the 4 x 4 grid. This is so that it can be done.

By determining whether or not the count value cnt is zero, it is determined whether learning is to be updated as described later, and learning is not performed in the initial stage when the operating range changes. be.

In step 18, as described below, the 16X 16
A flag flag, which is set to 1 when most of the air-fuel ratio learning correction values corresponding to each operating region of the lattice learning map have been learned, is determined.

When the flag flag is 1 and the learning of the 16×16 grid learning map is almost completed, the process advances to step 19.

In step 19, it is determined whether or not the vehicle remains in the same region as the previous time within the operating region divided into 16×16 grids, and if it is not the same region as the previous time, the process proceeds to step 20.

In step 20, the difference between the absolute value of the deviation of the latest correction coefficient LMD average value from the target convergence value (=1) and the absolute value of the previous deviation is calculated, and based on this value, the degree of inappropriateness of the learning value is determined. Set the Δ stress by referring to the Δ stress map.

In step 18, it is determined that most of the air-fuel ratio learning correction values corresponding to the respective operating regions of the 16×16 grid learning map have been learned.
The correction coefficient LMD should be stable at a substantially constant level even if the operating range changes, but if there is a range where learning has not progressed sufficiently depending on the operating range, the air-fuel ratio difference that occurs when entering that range is Correction coefficient LM
Since D changes, if the correction coefficient LMD changes greatly when the operating range changes, the Δ stress is set to be large.

Furthermore, in step 20, the obtained Δ stress is integrated,
The integration result is stored as stress.

As will be described later, when this stress exceeds a predetermined value, it is determined that the already learned air-fuel ratio learning correction coefficient is inappropriate, and the learning is performed again.

The program shown in the flowchart of FIG. 4 is an air-fuel ratio learning program for each operating region, and is executed at predetermined minute intervals (for example, every 10 m5).

In step 21, it is determined whether the P-minute addition is a flag that is set to 1 when the air-fuel ratio feedback correction coefficient LMD is proportionally controlled in the flowchart of FIG. 3, and when the P-minute addition is 1, After proceeding to step 22 and resetting the P-minute addition to zero, various processing by this program is performed, and when it is zero, this program is immediately terminated.

When the P addition is reset to zero in step 22, in the next step 23, a flag is set to indicate whether the learning correction coefficient KBLRCφ (initial value l), which is the air-fuel ratio learning correction value common to all operating ranges, has been learned. Determine Fφ.

Here, the flag Fφ is zero and the learning correction coefficient KBL
If learning of RCφ has not been completed, the process proceeds to step 24, and 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 l.

When (a+b)/2 is not approximately l, the process proceeds to step 26, and the value obtained by subtracting the target convergence value Target (1.0 in this embodiment) of the correction coefficient LMD from (a+b)/2 is multiplied by a predetermined coefficient Change the value to the previous learning coefficient KBLRC
φ and use the addition result as a new learning coefficient BLRCφ
Set as .

Here, the learning correction coefficient KB applied to all the above operating regions
The coefficient X used when updating the learning of LRCφ is variably set according to the deviation of the correction coefficient LMD in the flowchart of FIG. 3, as described above.

Also, in step 26, a 4x4 grid map and a 16x
Learning correction coefficient KBLRC1, learning correction coefficient KBLRC stored in each operating area of the 16 grid map
Reset all 2 to the initial value 1. Therefore, when learning and updating the learning correction coefficient KBLRCφ, even if learning values are stored in the 4×4 lattice map and 16×16 lattice wrap, the data is reset and new learning is performed.

When it is determined in step 24 that (a+b)/2 is approximately l, the flag Fφ is set to l in step 25, and the learning correction coefficient KBLRCφ corresponding to the entire operating range is set.
In other words, the learning correction coefficient K
It can be determined that the air-fuel ratio feedback correction coefficient LMD has converged to approximately l as a result of learning and updating BLRCφ.

On the other hand, if it is determined in step 23 that the flag Fφ is 1, the learning correction coefficient K corresponding to the entire operating range
Since this indicates that the learning of BLRCφ has been completed, the operating region is divided into a plurality of regions based on the basic fuel injection amount Tp and the engine rotational speed N, and air-fuel ratio learning is performed for each region.

First, in step 28, a counter value i for determining which position of the 16 grids the current basic fuel injection amount (basic fuel supply amount) Tp is included in is set to zero, and in the next step 29, , it is determined whether the counter value i exceeds 15 or not, and if it does not exceed 15, step 30
Then, the threshold value 'rp (i) of the basic fuel injection amount Tp corresponding to the count value i is compared with the most recently calculated basic fuel injection amount Tp.

When it is determined in step 30 that the latest basic fuel injection amount Tp is smaller than the threshold value Tp (i), the process advances to step 33 and the count value i at that time is calculated as the latest basic fuel injection amount Tp. The position of the included region is set to I. That is, the maximum basic fuel injection amount Tp of each region is set in advance as the threshold value Tp (i), and the latest basic fuel injection amount Tp and the threshold value Tp ( i) are compared in order from the smallest side, and when Tp(i)>Tp for the first time, i is set to I as indicating the number of the Tp block.

Further, when it is determined in step 30 that Tp (i)≦Tp, the process proceeds to step 31, where the count value i is incremented by 1, and Tp(i), which is one step larger, is compared with the latest Tp. do it like this.

Then, when the count value i is counted up to 16 in step 31, 16 grids from 0 to 15 (16
In this state, a basic fuel injection amount Tp larger than the initially set maximum value of the basic fuel injection amount Tp range divided into blocks) has been calculated, and at this time, i is set to 15 in step 32.
is set before proceeding to step 33, - the initialized Tp Pro 1. Maximum Tp area of current Tp
It is assumed that the

Next, to divide into 16 blocks based on engine speed N,
In the same manner as the above-mentioned determination of whether the basic fuel injection amount Tp is professional or not, the block number that includes the latest engine rotational speed N is determined based on the count value. First, in step 34, the count value k is initially set to zero, and until it is determined in step 35 that the count value k exceeds 15, a comparison is made with the threshold value N (k) in step 36. , the count value k when N (k)>N for the first time is set to K indicating the block number of N in step 39, and N
When (k)≦N, the count value k is incremented by 1 in step 37. Also, the count value k is 15
When the count value k is exceeded, the process proceeds to step 38 and the count value k is
Set 15 to , and proceed to step 39.

In this way, the basic fuel injection amount Tp and the engine rotation speed N
The coordinate (K , I).

If the corresponding position of the 16×16 grid is found as described above, one operating region in the 4×4 operating region map as shown in Figure 1) is 16×4×4=4 in the 16×16 operating region. Since 16 areas are divided into 1 block, the above 1. Based on K, the position to which the current operating conditions apply can be identified in the 4x4 grid learning map.

That is, in step 40, the block number I of Tp is divided by 4, and the integer value obtained by rounding down the decimal point of the result is set to A, and in step 41, the block number K of N is similarly set. Divide by 4 and set the integer value of the result, rounded down to the decimal point, to B. As a result, the area position of the 4X4 grid map to which the current operating conditions apply is (B).

A] coordinates.

In the next step 42, using (B, A) indicating the position of the corresponding area on the 4x4 grid map, in order to determine whether or not the corresponding operating area on the 4x4 grid has changed, Add the value multiplied by 16 and B and the result is AB
Set to .

Then, in step 43, A
By comparing BOLD with the latest AB, it is determined whether or not the current area is the same as the previous one.

A B ′:l-A B OLD, and when the operating region is different from the previous one in the 4×4 grid, step 44
The count value cnt is set to a predetermined value (for example, 4).

In step 45, AB calculated in step 42 this time is set to ABOLD as the previous value for the next determination in step 43.

In step 46, [B, A
) is used as the coordinate to determine whether or not the air-fuel ratio learning has been completed in the operating range that includes the current operating conditions.
) is zero and learning has not been completed in one operating region of the 4×4 lattice map that includes the current operating conditions, the process advances to step 47.

In step 47, <i the count value cnt is zero.

If it is not zero and there is a fluctuation in the corresponding area in the 4x4 grid map, this program is terminated and the count value cnt is zero and remains stably in the corresponding operating area. Proceed to step 48 only when there is.

In step 48, the average value (a+b)/2 of the maximum and minimum values a and b of the air-fuel ratio feedback correction coefficient LMD sampled in the flowchart of FIG.
The center value of the correction coefficient LMD is the initial value (
=1) Determine the progress of learning based on whether or not it is nearby,
If it is not recognized that the value is approximately 1 and learning has not been completed, the process proceeds to step 50.

In step 50, the current (
For the learning correction coefficient KBLRCI stored corresponding to the area B and A), the target convergence value Target (1.0 in this example) is subtracted from the average value of the maximum and minimum values a and b. Add the values multiplied by a predetermined coefficient
A new learning correction coefficient KBLRCI corresponding to .

Incidentally, the coefficient Xl used for the learning update of the learning correction coefficient KBLRCI stored for each region of the 4×4 grid map is also based on the deviation of the correction coefficient LMD, similar to the coefficient X used for learning update of the learning correction coefficient KBLRCφ. When the correction coefficient LMD is fluctuating, the above-mentioned X1 is set to a small value, and when the correction coefficient LMD is fluctuating due to transient operation, the learning coefficient is set based on this. This makes it possible to prevent erroneous learning of 8LRC1.

If it is determined in step 48 that (a+b)/2 or approximately 1, it means that the learning in the operating region of the 4×4 grid map that includes the current operating conditions has been completed, so in step 49 the flag is flagged. F (B, A) is set to 1, and it is determined that learning has been completed for areas where flag F (B, A) is 1.

During learning of such a 4x4 grid map, the learning correction coefficient K for a finer 16x16 grid map is
As for BLRC2, in step 51, all of these are set to the initial value l.

In this way, when there is a region in the 4X4 grid map where learning has not been completed, when the driving region becomes stable, (
A predetermined percentage of the deviation from the target value Target of a + b)/2 is calculated using the previously stored learning correction coefficient KBLR.
By adding and updating the CI, the learning correction coefficient KBLR is used instead of the air-fuel ratio feed pack correction coefficient LMD.
It is assumed that the target air-fuel ratio is obtained through correction by CI, and that the learning of the operating region is completed when the air-fuel ratio feedback correction coefficient LMD substantially converges to the initial value 1, which is the target convergence value.

On the other hand, if it is determined in step 46 that the flag F (B, A) is 1, and the learned learning correction coefficient KBLRCI is stored in the corresponding driving region in the current state of the 4x4 grid map, the learning correction coefficient KBLRCI is stored. Operating area CB, A on the current 4x4 grid map where the coefficient KBLRCI is stored
) is further subdivided into 16 regions using a 4X4 grid.16X 1
Move on to learning the 8-lattice learning map.

In step 52, the average value of the correction coefficient LMD (a
+b)/2 determines whether or not it approximately matches the target convergence value of 1, and determines whether (a + b)/2 is not approximately 1 and requires correction by the air-fuel ratio feedback correction coefficient LMD. If so, the process advances to step 53.

In step 53, the target convergence value Ta is calculated from (a+b)/2.
The value obtained by subtracting rget (1.0 in this embodiment) and multiplying by a predetermined coefficient The learning correction coefficient KBLRC2 (K, I
), and the addition result is set as a new correction coefficient KBLRC2 (K, .■) in the relevant operating region (K, I).

KB[, RC2(K, [3+X2 (--'(
'arget) Again, the above learning correction coefficient KBLRC
The coefficient X used for learning update of 2(K, l)
2 is the correction coefficient LMD, similar to the coefficients X and Xl described above.
The learning progress rate is set based on the deviation of the learning rate and is controlled variably.

On the other hand, if it is determined in step 52 that (a+b)/2, which is the average value of the correction coefficient LMD, substantially matches the target convergence value Target of 1, the process proceeds to step 54;
Flag FF (K, I) is set to 1 so that it is determined that learning of the operating region (K, I) that includes the current operating conditions of the 16×16 grid map has been completed.

Then, from step 55 onward, it is determined that the current learning has been completed, and the corresponding flag FF (K, I) is set to 1 in the predetermined operation area (K,
Based on I), if there is an operation area adjacent to this area (K, I) for which learning has not been completed (see Figure 12), the current operation area (K, I) is added to that operation area.
Learning correction coefficient KBLR stored corresponding to I)
Control is performed to store C2.

In step 55, the position of the region including the current operating conditions is indicated in the 16×16 grid map (K, I).
The values obtained by subtracting 1 from each are set in m and n, and in the next step 56, it is determined whether m=+2 or not.

When the process proceeds from step 55 to step 56, the determination in step 56 is NO, so the process proceeds to step 57, where one of the 16×16 grids indicated by (m, n) is selected.
Flag F
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 58.

In this step 58, the area address (m, n) on the 16×16 grid is changed to the area address [m, n) on the 4×4 grid.
/4, n/4) and the area (B) on the 4x4 grid map to which this is currently determined to be applicable.

A].

In other words, (K, 13 is an area included in CB, A), but the area around (K, I) is included in an area adjacent to (B, A) on the 4x4 grid map. This is because the area may be included in the same CB, A) ((m/4.n/4) = CB, A), the process proceeds to step 59 and is determined to have been learned this time. (K,
I) Let the learning correction coefficient KBLRC2 corresponding to the region be the learning value for the (m, n) region.

On the other hand, in step 58, (m) adjacent to (K, I).

n] is determined to be included in a different area on the 4X4 lattice map, the process proceeds to step 60, and the learning correction coefficient KB calculated by the following formula is applied to the area (m, n).
Store LRC2.

KBLRC2 (m, n) ” KBLRCI (B, A) 10 KBLRC2 (K, I
)-KBLRCI (m/4.n/4) The formula for calculating the above KBLRC2Cm, n) is [
Since K, I) and (m, n) are adjacent areas on a 16x16 grid, the final correction request is based on the assumption that they should be similar, and the 4 areas each includes. ×4 grid map learning correction coefficient KBLRC
BLRCI (B,
A), KBLRCI (m/4. n/4) is set to be approximated by the following formula.

KBLRCI (B, A) +KBLRC2 (K,
I) =KBLRCI Cm/4, n/4
) +KBLRC2[m,, n) As described above, when the area (m, n) has been learned, the learned value is not updated, and when it is not yet learned,
KBLRC2(K, I) based on KBLRC2(K, I)
After updating m, n), in step 61, m is incremented by 1 and the process returns to step 56, where m= is increased by +1.
2, that is, with n constant, m is moved in a range of +1 centered on K, and whether learned or unlearned is determined for each driving region.

Then, as a result of the 1-up processing of m in step 61, when it is determined in step 56 that m=+2, the process proceeds to step 62, and it is determined whether n=1+2, and n≠1+2. If so, m is set to -1 again in step 63, and n is incremented by 1 in the next step 64, and then the process proceeds to step 57.

Therefore, initially, let n=1-1 and m is centered at K±
Next, we set n=I and moved m within a range of ±1 around K, and then set n=I+1 to distinguish adjacent areas. The learning correction coefficient KBLRC2 (K, I) is set when the eight operating regions surrounding (K, I) (see Figure 12) have not been learned.
) as the learning correction coefficient KBLRC for that operating region.
2(m, n).

In this way, if the learning results of the learned area are applied to the surrounding unlearned areas, even if the driving area is subdivided like a 16x16 grid and there are few learning opportunities for each driving area. , it is possible to prevent differences in air-fuel ratio controllability from occurring between operating regions.

When it is determined in step 62 that n=1+2, it means that the determination processing for all eight operating regions surrounding (K, I) has been completed, so at this time, step 5
Proceed to step 3 and select the learning correction coefficient KBLRC2, which is determined to have already been learned in this area (K, I).
The learning accuracy is further improved by updating the learning.

In this way, in this embodiment, first, the learning correction coefficient KBLRCφ corresponding to all driving regions is learned, and then learning is performed for each driving region divided by a 4×4 grid learning map. For areas that have already been trained using the x4 grid learning map, we have divided that area into 4 x 4 grids for learning, so that learning can progress from large driving areas to small driving areas. The convergence of the air-fuel ratio is ensured by learning over a large operating range, and as the learning progresses, learning is performed for each smaller operating range, so the accuracy can be adjusted to the difference in the required correction value due to the difference in operating range. I can handle it well.

Three learning correction coefficients KB learned as described above
The final setting of the air-fuel ratio learning correction coefficient KBLRC based on LRCφ, KBLRCl, and KBLRC2 is performed according to the flowchart in FIG.

The flowchart in FIG. 8 is processed as a background job, and first, in step 71,
The learning correction coefficient KBLRCI stored in the area (B, A) of the 4×4 grid map is read out, and the next step 7
In step 2, the learning correction coefficient KBLRC2 stored in the area [K, I) of the 16×16 grid map is read out. still,
B×4+A and KX16+1 shown in the flowchart are for converting the addresses indicating the respective grid positions into addresses on the memory.

In step 73, KBLRCφ+KBLRCl +
The final learning correction coefficient KBLRC is set as KBLRC2-2,0→KBLRC.

The learning correction coefficient KBLRC set in the flowchart of FIG. 8 is used to calculate and set the fuel injection amount Ti in the program shown in the flowchart of FIG.

The program shown in the flowchart of FIG. 5 is executed at predetermined minute intervals (for example, 10 m5). First, in step 81, the intake air flow rate Q detected by the air flow meter 13 and the intake air flow rate Q from the crank angle sensor 14 are calculated. Input the engine rotation speed N calculated based on the detection signal.

Then, in the next step 82, the basic fuel injection amount T corresponding to the intake air flow rate Q per unit rotation is determined based on the intake air flow rate Q and the engine rotational speed N input in step 81.
p (←KxQ/N: is a constant) is calculated.

In the next step 83, various corrections are made to the basic fuel injection amount Tp calculated in step 82 to calculate the final fuel injection amount (fuel supply amount) Ti.

Here, the correction values used for correcting the basic fuel injection amount Tp are the learning correction coefficient KBLRC learned and set according to the flowchart in FIG. 4, the air-fuel ratio feedback correction coefficient LMD calculated according to the flowchart in FIG. 3, and the water temperature sensor. Based on the cooling water temperature Tw detected at 15 (various correction coefficients C0EF set including the basic correction coefficient, post-start increase correction coefficient, etc.), changes in the effective injection time of the fuel injection valve 6 due to changes in battery voltage are calculated. A correction molecule s for correction, Ti”TpxLMDxKBLRCXC0EF
+Ts is calculated and the final fuel injection amount Ti is updated at predetermined intervals.

At a predetermined fuel injection timing, the control unit 12 outputs a drive pulse signal with a pulse width corresponding to the latest calculated fuel injection amount Ti to the fuel injection valve 6 to control the amount of fuel supplied to the engine 1. do.

The program shown in the flowchart of FIG. 6 is a program that performs processing based on the stress sampled according to the flowchart of FIG. 3, and is executed as a background job (BGJ).

In step 91, the stress required is determined when most of the 16x16 lattice map has been learned and the flag flag (setting of this flag will be explained in detail later based on the flowchart in FIG. 7) is set to 1. (degree of air-fuel ratio deviation) and a predetermined value (for example, 0.8
) to determine whether the degree of air-fuel ratio deviation when learning is almost completed is greater than or equal to a predetermined value.

Here, when the stress exceeds a predetermined value, it is determined that although learning has almost completed, the learning result is inappropriate and an air-fuel ratio deviation has occurred, and learning is performed again (learning repetition). The process advances to step 92 in order to do so.

In step 92, a flag Fφ is set for determining whether or not air-fuel ratio learning for each operating region has been completed.

F C0,0) ~ F (3-, 3), FF (0,
0) to FF (16, 16) are all reset to zero, and the flag flag and 4x4 are set to 1 when most of the operating range of the 16x16 grid map has been learned.
16X1 included in one operating area of the grid map
When most of the subdivision operation areas (16 areas) of the 6-lattice map have been learned, the flag flag (0°0) to (3
, 3) is reset to zero.

Furthermore, since learning is repeated again from all operating ranges as described above, stress is also reset to zero.

In this way, if the degree of deviation (stress) of the air-fuel ratio feedback correction coefficient LMD from the reference value becomes larger than a predetermined value, relearning can be performed, for example, if an accident occurs such as a hole in the intake system. When the air-fuel ratio suddenly changes, learning for each large operating region is performed again, so the air-fuel ratio can be quickly converged.

Next, a description will be given of correction of the learning correction coefficient for a larger segmented operation area based on the subdivision area, which is performed according to the program shown in the flowchart of FIG.

The program shown in the flowchart of FIG. 7 is executed as a background job (BGJ). First, in step 101, if the learning correction coefficient KBLRCφ corresponding to all operating ranges has been learned, 1 is set. The flag Fφ to be set is determined, and the flag Fφ
If is zero, the program is terminated as is, but if it is l, the process 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 learning correction coefficient KBLRC stored in the area that has been learned in each operating area of the 4x4 grid map.
It is the integrated value of I, and is set to 1.0 as the initial value,
The W is used to count up the number of samples in the integration calculation, and is set to l as an initial value. Further, X and Y designate grid positions in the 4×4 grid map, and zero is set to each as an initial value.

In the next step 103, a flag F (X,
Y) (4x4 lattice learned discrimination flag) is determined,
Search for a learned area in the 4X4 grid operating area indicated by X and Y. Since X and Y have initial values of zero, (
0, O), X is incremented by 1 to become (1, 0) in step 104, and in step 105 it is determined that X is not 4, so the determination in step 103 is made again. will be held. \ In this way, when X is incremented by 1 in step 104 and becomes 4, in step 106 X is reset to zero and Y is incremented by 1, until step 107 determines that Y is 4. Returning to step 103 again, and proceeding to step 104, X will be increased by 1, so as a result, by repeating fixing Y and changing X, 4
Flag F (X
, Y).

Here, flag F (X
, Y) is determined to be 1, the process proceeds to step 108.

In step 108, it is determined that the flag F (X, Y) is 1, and the BLRCI is added to the learning correction coefficient stored in the learned driving region to the accumulated value Sum up to that point. In order to count the number of times (in other words, the cumulative number of samples), the sampling number W is incremented by 1.

Then, in the next step 109, (
In order to discriminate one by one the 16 regions in the 16X16 lattice included in the X, Y) region, initial values of zero are set for each of α and β, and learning correction is performed for the regions that have already been learned among the 16 regions. An initial value of 1.0 is set to Sump, which sets the integrated value of the coefficient KBLRC2, and an initial value of 1 is set to Z, which counts the number of samples of the integrated value Sump.

Then, in step 1)0 to step 1)5,
FF (α+
4X, β+4Y), and the learning correction coefficient KBLRC2 stored in the learned area is calculated as the integrated value S.
ump is accumulated, and the accumulated sample number Z is counted up.

In this way, when the learned regions among the 16 regions of the 16×16 lattice map that are included in the learned regions of the 4×4 lattice map are picked up, in the next step 1) 6, the number of samples Z is set to a predetermined value. It is determined whether the value exceeds a value (for example, 12).

Here, if the number of samples 2 is less than the predetermined value, (X
As for the Y) area, the learning of the 16 more subdivided areas has not progressed sufficiently, so in step 1) 7, the flag F (X, Y) is set to zero and relearning is performed. do it like this.

After setting the flag flag (X, Y) to zero in step 1) 7, return to step 104 again and set the 4×4
Search for other areas that have been learned on the grid map.

On the other hand, when it is determined in step 1) 6 that the number of samples Z exceeds the predetermined value, the flag f is determined in step 1) 8.
lagF (X, Y) is set to 1, and in the next step 1) 9, the average value (S ump/Z ) of the learning correction coefficient KBLRC2 stored in the learned area and the target convergence value 'rarget are calculated. It is determined whether the absolute value of the deviation is less than a predetermined value (for example, 0.04).

If the deviation exceeds a predetermined value, then 4×
The correction amount to be borne by the learning correction coefficient KBLRCI of the 4-grid map is the learning correction coefficient KBL of the 16x16 grid map.
Assuming that it is added to RC2, step 120 is performed to transfer the added amount to the learning correction coefficient KBLRCI side.
Proceed to.

In step 120, the 4
The learning correction coefficient KBLRCI (X, Y) stored in the area (X, Y) on the ×4 grid map is learned and updated according to the following formula.

KBLRCI (X, Y) ← In other words, by the above calculation formula, the average correction amount borne by the 16x16 grid map is transferred to the 4x4 grid map corresponding to the area including them, and the 16x16 grid is This is to bring the correction amount by the map closer to the target convergence value.

In this way, when the average correction amount borne by the 16x16 grid map is transferred to the 4x4 grid map corresponding to the area including them, the learning correction coefficient KBLRC2 on the 16x16 grid map is calculated accordingly. Since it is necessary to reversely correct the difference, the processing from step 121 onwards is performed.

That is, the learning correction coefficient KBL corrected in step 120
The learning correction coefficient KB is calculated from the learning correction coefficient KBLRC2 stored in each of the 16 areas on the 16×16 grid map included in the area of RCI (X, Y).
The amount added to the LRCI is subtracted (steps 121 to 126).

In this way, after correcting the learning correction coefficient KBLRCI by inversely correcting the learning correction coefficient KBLRC2, the process returns to step 104, searches for a learned area on the 4x4 grid map, and When a completed area is detected, the above-described process is repeated each time.

Then, after checking the learned areas for all areas on the 4x4 grid map, steps 107 to 12
We will move on to 7.

In step 127, W, which counts up the number of learned areas on the 4x4 grid map, is set to a predetermined value (for example, 12
), and it is determined whether most areas of the 4×4 grid map have been learned.

Here, if the W is less than or equal to the predetermined value, the flag is set to zero in step 128 and the process ends; however, if the W exceeds the predetermined value, the flag is set to zero in step 129. After setting 1 to
After step 130, the learning correction coefficient K corresponding to all operating regions is calculated based on the data of the learned region of the 4×4 grid map.
Processing to correct BLRCφ is performed.

In step 130, the learning correction coefficient KBLRCI in the area determined to have been learned on the 4×4 grid map is
The average value (S um/W ) and the target convergence value Targ
It is determined whether the absolute value of the deviation from et is less than a predetermined 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 learning correction coefficient K corresponding to all operating regions should be
The correction amount to be borne by the BLRCφ side is the learning correction coefficient KB.
It is assumed that this has been transferred to the LRCI side, and the process proceeds to step 131 in order to have the learning correction coefficient KBLRCφ bear the burden of this transfer.

In step 131, the learning correction coefficient K is calculated according to the following formula.
The learning update of BLRCφ is performed.

KBLRCφ← In other words, the average correction amount in the learned area on the 4×4 grid map is transferred to the learning correction coefficient KBLRCφ that is corrected in all operating areas, and the learning correction coefficient KBLRCI or near the target convergence value is calculated. The aim is to ensure that the students are able to learn the basics.

Here, similarly to the case where the learning correction coefficient KBLRCI is corrected based on the learning correction coefficient KBLRC2 of the same area,
It is necessary to inversely correct each learning correction coefficient KBLRCI by the amount corrected by the learning correction coefficient KBLRCφ. The amount of correction added to the correction coefficient KBLRCφ (Sum/Z
-Target) Correct by subtracting Xγ2.

In the above embodiment, the learning update degree is changed based on the deviation of the correction coefficient LMD to variably control the learning progress speed, but the coefficient X,
X1. The learning progress speed may be controlled by variably setting X2.

In other words, when the engine is suddenly accelerated or decelerated, the air-fuel ratio deviates significantly from the target air-fuel ratio, and if the above-mentioned air-fuel ratio learning is performed at this time, the air-fuel ratio deviation during transient operation can be resolved. If such a large correction is learned and the same operating range is used during steady operation or slow acceleration/deceleration, a large air-fuel ratio difference will occur, so the learning progress speed should be slowed down during sudden acceleration or deceleration. is desirable.

Therefore, the coefficients X, Xl, and X2 are set according to the programs shown in the flowcharts of FIG. 9 and FIG. 1θ.

The program shown in the flowchart of FIG. 9 is executed at predetermined minute intervals (for example, 10 m5), and first, in step 131, it is determined whether or not the timer tmr is 100. 1 and timer t
If mr is not 100, the timer tmr is incremented by 1 in step 132 and the process ends. However, when the timer tmr is counted up to 100, the timer tmr is reset to zero in step 133, and then the timer tmr is incremented by 1 in step 134. Basic fuel injection amount T
The amount of change ΔTp in p is calculated.

Specifically, the basic fuel injection amount TI)OLD when the previous timer tmr reached 100 is subtracted from the latest basic fuel injection amount Tp, and the result is set as the change amount in the basic fuel injection amount TI) to ΔTp. set.

In the next step 135, the timer tmr is set to 100 again.
In order to calculate ΔTp when the count is increased, the latest basic fuel injection amount Tp is set as the previous time Tp.
Set to OLD.

On the other hand, in the program shown in the flowchart of FIG. 1O, the coefficients X and XI are determined based on the amount of change ΔTp in the basic fuel injection amount Tp determined as described above.

Configure X2.

Incidentally, the coefficients X, XI, and X2 are calculated in step 26.
Step 50. It is used in step 53 to determine the degree of modification of each learning correction coefficient KBLRCφ, KBLRC1, and KBLRC2.

The program shown in the flowchart of FIG.
It is executed every rotation, and step 141
Then, coefficients X and XI according to ΔTp.

Based on the map in which X2 is stored, coefficients X, X1 . Obtain with reference to X2.

Here, the coefficient X increases as ΔTp increases.

Xl and X2 are set to small values close to zero, and conversely, when ΔTp approaches zero, the
．． X2 is set to a value close to 1.0.

Therefore, when ΔTp is close to zero during steady operation, the learning progress speed can be accelerated, and conversely, during transient operation, △
When Tp is large, the learning progress speed is slowed down to prevent erroneous learning of air-fuel ratio deviations due to transients.

In particular, in a configuration like this embodiment, in which the unit area for dividing the operating area is set to multiple sizes, and is provided with a learning map that divides the operating area more finely and a learning map that divides the operating area relatively broadly, Since it is easy to incorrectly learn air-fuel ratio deviations using a detailed learning map, it is possible to prevent incorrect learning by varying the learning progress speed according to the air-fuel ratio feedback correction coefficient, MD deviation, or engine acceleration/deceleration as described above. The effect of evasion is large.

In the above case, the amount of change ΔTp in the basic fuel injection amount Tp was used as a parameter indicating the degree of acceleration/deceleration of the engine, but in addition to this, changes in the opening degree of the throttle valve and changes in the engine rotation speed were also detected. Based on this, the coefficients X, X1.
X2 may be set variably.

<Effects of the Invention> As explained above, according to the present invention, based on the acceleration/deceleration degree of the engine or the deviation of the air-fuel ratio feedback correction value,
By changing the degree of correction of the air-fuel ratio learning correction value, and thereby changing the learning progress speed according to the acceleration/deceleration degree of the engine, it is possible to prevent air-fuel ratio deviations from being erroneously learned during transient operation. This has the effect of preventing large air-fuel ratio differences from occurring due to erroneous learning during acceleration (deceleration) being used during deceleration (acceleration) or during steady state.

In particular, it is equipped with multiple types of learning maps in which the same operating region is divided into unit operating regions of different sizes, and the air-fuel ratio learning correction value can be rewritten for each divided operating region of each of the plurality of learning maps. In this case, the air-fuel ratio deviation during transient operation is likely to be erroneously learned using a learning map that finely divides the operating range. By providing multiple learning maps with different sizes of segmented operating regions, it is possible to improve the learning speed and ensure fine correction accuracy for each operating region, while preventing the occurrence of air-fuel ratio differences due to erroneous learning during transient periods. It becomes like this.

[Brief explanation of the drawing]

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 invention, Figs. ) and FIG. 12 are diagrams for explaining the state of the learning map in the above embodiment, respectively, and FIG.
1 and 14 are time charts for explaining the problems of conventional learning control, respectively. l... Engine 6... Fuel injection valve 12... Control unit 13... Air flow)-91
4...Crank angle sensor 15...Water temperature sensor
16...Oxygen sensor patent applicant Fujio Sasashima, agent and patent attorney for Japan Electronics Co., Ltd.

Claims (3)

[Claims] (1) An 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, and a basic fuel supply amount based on the engine operating conditions detected by the engine operating condition detection means. basic fuel supply amount setting means for setting the air-fuel ratio; air-fuel ratio detection means for detecting the air-fuel ratio of the air-fuel mixture taken into the engine; and comparing the air-fuel ratio detected by the air-fuel ratio detection means with a target air-fuel ratio to determine the actual air-fuel ratio. air-fuel ratio feedback correction value setting means for increasing or decreasing an air-fuel ratio feedback correction value for correcting the basic fuel supply amount so as to bring the fuel ratio closer to the target air-fuel ratio; an air-fuel ratio learning correction value storage means for rewritably storing an air-fuel ratio learning correction value for correcting a basic fuel supply amount; and learning a deviation of the air-fuel ratio feedback correction value from a target convergence value, and reducing the deviation. an air-fuel ratio learning correction value correcting means for correcting and rewriting the air-fuel ratio learning correction value stored in the air-fuel ratio learning correction value storage means in a direction in which the basic fuel supply amount, the air-fuel ratio feedback correction value, and the air-fuel ratio learning correction value are a fuel supply amount setting means for setting a final fuel supply amount based on an air-fuel ratio learning correction value for a corresponding operating region stored in a fuel ratio learning correction value storage means; A fuel supply control means for driving and controlling the fuel supply means based on the fuel supply amount; an acceleration/deceleration degree detection means for detecting the degree of acceleration/deceleration of the engine; and an engine acceleration detected by the acceleration/deceleration degree detection means. Learning speed changing means based on the degree of acceleration/deceleration that changes the learning progress speed according to the degree of acceleration/deceleration of the engine by changing the degree of correction of the air-fuel ratio learning correction value by the air-fuel ratio learning correction value correction means based on the degree of deceleration operation. An air-fuel ratio learning control device for an internal combustion engine, comprising: and. (2) Instead of the acceleration/deceleration driving degree detecting means and the learning speed changing means based on the degree of acceleration/deceleration, the air-fuel ratio learning correction is performed by the air-fuel ratio learning correction value correcting means based on the deviation of the average value of the air-fuel ratio feedback correction value. 2. The air-fuel ratio learning control device for an internal combustion engine according to claim 1, further comprising deviation-based learning speed changing means for changing the learning progress speed according to the deviation by changing the degree of correction of the value. (3) The air-fuel ratio learning correction value storage means includes a plurality of types of learning maps in which the same operating region is divided based on unit operating regions of mutually different sizes, and the plurality of learning maps each have a divided operation. 3. The air-fuel ratio learning control device for an internal combustion engine according to claim 1, wherein the air-fuel ratio learning control device is configured to rewritably store the air-fuel ratio learning correction value for each region.