JP2631579B2 - Air-fuel ratio learning control device for internal combustion engine - Google Patents

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 system for an internal combustion engine, and more particularly, to correcting a fuel supply amount so that an air-fuel ratio of an intake air-fuel mixture in a vehicle internal combustion engine matches a target air-fuel ratio. For the air-fuel ratio learning control.

[0002]

2. Description of the Related 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 Application Laid-Open Nos. 60-90944 and 61-1.
As disclosed in Japanese Patent Publication No. 90142 or the like, there is an apparatus that employs air-fuel ratio learning control.

In the air-fuel ratio feedback correction control, a rich / lean actual air-fuel ratio with respect to a target air-fuel ratio (for example, a stoichiometric air-fuel ratio) is determined by an oxygen sensor provided in an engine exhaust system, and the air-fuel ratio feedback correction is performed based on the determination result. The coefficient LMD is set by proportional / integral control or the like, and a basic fuel injection amount Tp calculated from parameters of the engine operating state (for example, intake air flow rate Q and engine speed N) related to the amount of air taken into the engine is calculated as follows: The actual air-fuel ratio is feedback-controlled to the target air-fuel ratio by correcting with the air-fuel ratio feedback correction coefficient LMD.

Here, a deviation of the air-fuel ratio feedback correction coefficient LMD from a reference value (target convergence value) is learned for each of a plurality of operating regions to obtain a learning correction coefficient KBLRC (air-fuel ratio learning correction value). 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 substantially coincides with the target air-fuel ratio. During the air-fuel ratio feedback control, the correction coefficient LMD is further increased. To calculate the fuel injection amount Ti.

[0005] This makes it possible to perform fuel correction corresponding to a different correction request for each operating condition, stabilize the air-fuel ratio feedback correction coefficient LMD near the reference value, and improve the air-fuel ratio controllability.

[0006]

The air-fuel ratio learning correction coefficient KBLRC for each operation region is set to correspond to the difference in the air-fuel ratio correction request due to the difference in the operating conditions as described above. It is desired that the learning is performed by dividing the operation region as finely as possible. However, the operating region is finely divided and the learning correction coefficient KBLRC is set for each narrow operating region.
Learning, the learning opportunity in each driving area decreases, the convergence of learning deteriorates,
Since the learned region and the unlearned region are mixed, a large air-fuel ratio step occurs between the operation regions.

Therefore, the present applicant is provided with a plurality of learning maps in which the number of sections of the driving region is different, and the number of sections is smaller in the plurality of learning maps, and the driving area of the learning unit is wider. An air-fuel ratio learning control device has been proposed in which learning is performed from a map, and as the learning progresses, the air-fuel ratio learning control device is configured to shift to air-fuel ratio learning on a learning map in which the number of divisions is larger and the driving region of the learning unit is narrower. Ganpei 1-2
No. 82883).

According to such air-fuel ratio learning, learning convergence is ensured by performing learning in large unit operation regions in the initial stage of learning, and air-fuel ratio learning is performed in finer unit operation regions as learning progresses. Learning can be accurately performed in response to differences in correction requests due to differences in operating conditions. However, the configuration including a plurality of learning maps for storing the air-fuel ratio learning correction value for each operation region as described above has a disadvantage that a large memory capacity is required.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and has as its object to realize air-fuel ratio learning that achieves both convergence of learning and air-fuel ratio learning accuracy while saving memory capacity. And It is another object of the present invention to make it possible to quickly make the learning converge when the base air-fuel ratio changes abruptly while performing accurate air-fuel ratio learning for each operating condition.

[0010]

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, an operating condition detecting means detects an engine operating condition including at least an operating parameter related to an amount of air taken into the engine, and a basic fuel supply amount setting means detects a basic fuel supply amount based on the detected engine operating condition. Set the fuel supply.

The air-fuel ratio feedback correction value setting means compares the air-fuel ratio of the engine intake air-fuel mixture detected by the air-fuel ratio detection means with the target air-fuel ratio so that the actual air-fuel ratio approaches the target air-fuel ratio. , An air-fuel ratio feedback correction value for correcting the basic fuel supply amount is set. The learning correction value storage means rewritably stores an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each of a plurality of operating regions based on engine operating conditions,
The air-fuel ratio learning means learns a deviation of the air-fuel ratio feedback correction value from the target convergence value, and calculates the air-fuel ratio learning correction value stored in the learning correction value storage means corresponding to the corresponding operating region. Correct and rewrite in the direction to reduce the deviation.

On the other hand, when the air-fuel ratio feedback correction value substantially coincides with the target convergence value, the learned area storage means discriminates the corresponding operating area on the learned correction value storage means at that time as a learned area. Then, the determination result is stored for each operation area of the learning correction value storage means. Also,
The estimation learning means learns the air-fuel ratio learning correction value of the relevant operation area rewritten by the air-fuel ratio learning means as an air-fuel ratio learning correction value corresponding to another operation area whose operating conditions are closer to the relevant operation area. The air-fuel ratio learning correction value in the correction value storage means other than the corresponding operation region is rewritten.

The estimation learning control means based on the number of learned regions includes:
As the number of learned regions stored in the learned region storage unit increases, the number of operating regions in which the air-fuel ratio learning correction value is rewritten together with the corresponding operating region by the estimation learning unit is reduced. The fuel supply amount setting means determines the final fuel based on the basic fuel supply amount, the air-fuel ratio feedback correction value, and the air-fuel ratio learning correction value stored in the learning correction value storage means corresponding to the operating region. Set the supply amount,
The fuel supply control means drives and controls the fuel supply means based on the set fuel supply amount.

Here, as shown by a dotted line in FIG. 1, instead of the learned area storage means and the estimated learning control means based on the number of learned areas, an appropriate judgment means and an estimated learning control means based on appropriate judgment are provided. You may comprise. The appropriateness determining means determines the appropriateness of the result of the air-fuel ratio learning based on the deviation of the air-fuel ratio feedback correction value from the target convergence value when the corresponding operating region is switched on the learning correction value storage means. The estimating learning control means sets the maximum number of operating areas in which the air-fuel ratio learning correction value is rewritten together with the corresponding operating area by the estimating learning means when the appropriateness determining means determines that the learning result is inappropriate. Then, the number of the operation areas is reduced as the learning progresses.

[0015]

The required level of the air-fuel ratio learning correction value should also be approximated between the operating regions on the learning correction value storage means where operating conditions are close. Therefore, when the number of learned regions is small, it is considered that the required level of the air-fuel ratio learning correction value is substantially the same even in other operating regions whose operating conditions are close to the corresponding operating region, and the corresponding operating region is considered. The air-fuel ratio learning correction value is applied to the surrounding operation region. Therefore, in the initial stage of the learning in which the number of learned regions is small, the air-fuel ratio learning correction value in the operating region where the learning opportunity is not obtained is not left as it is, and it is possible to quickly learn at least a level close to the required value. Thus, the convergence of learning is ensured. Further, when the number of learned regions increases, the number of operating regions that are rewritten along with the corresponding operating region decreases, so that learning can be performed in detail corresponding to the requirements of each operating region.

On the other hand, when it is necessary to change the air-fuel ratio feedback correction value from the target convergence value when the corresponding operating region is switched on the learning correction value storage means, the air corresponding to the corresponding operating region after the switching is changed. It is predicted that the fuel ratio learning correction value is inappropriate. Therefore, the appropriateness of the air-fuel ratio learning result is determined based on the deviation of the air-fuel ratio feedback correction value from the target convergence value when the corresponding operation region is switched on the learning correction value storage means, and the learning result is inappropriate. In the case, the maximum number of operating regions that are rewritten together with the corresponding operating region in order to achieve quick convergence is reduced, and the number of the operating regions is reduced as learning progresses.
Accurate learning for each operation area can be performed.

[0017]

Embodiments of the present invention will be described below. In FIG. 2 showing one embodiment, air is sucked into an internal combustion engine 1 from an air cleaner 2 through an intake duct 3, a throttle valve 4 and an intake manifold 5. Each branch of the intake manifold 5 is provided with a fuel injection valve 6 as fuel supply means for each cylinder. The fuel injection valve 6 is an electromagnetic fuel injection valve that is energized by a solenoid and opens, and is deenergized and closed by being energized by a drive pulse signal from a control unit 12, which will be described later. Fuel which is pressure-fed from a fuel pump (not shown) and adjusted to a predetermined pressure by a pressure regulator is intermittently injected and supplied to the engine 1.

Each combustion chamber of the engine 1 is provided with an ignition plug 7 for igniting a mixture by igniting a spark. Then, exhaust gas is discharged from the engine 1 through the exhaust manifold 8, the exhaust duct 9, the three-way catalyst 10, and the muffler 11. The control unit 12 includes a CPU, RO
A microcomputer including an M, a RAM, an A / D converter, an input / output interface, and the like is provided. The microcomputer receives input signals from various sensors, performs arithmetic processing as described later, and operates the fuel injection valve 6. Control.

The various sensors include an 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, the crank angle sensor 14 is provided, and in the case of the four cylinders of the present embodiment, outputs a reference signal REF for every 180 ° of the crank angle and a unit signal POS for every 1 ° or 2 ° of the crank angle. Here, the engine speed N can be calculated by measuring the period of the reference signal REF or the number of occurrences of the unit signal POS within a predetermined time. Further, a water temperature sensor 15 for detecting a cooling water temperature Tw of the water jacket of the engine 1 is provided.

Here, the air flow meter 13, the crank angle sensor 14, the water temperature sensor 15 and the like correspond to the operating condition detecting means in the present embodiment, and the operating parameters related to the amount of air taken into the engine are the same as those in the present embodiment. In the example, it is the intake air flow rate Q and the engine speed N. Further, an oxygen sensor 16 as an air-fuel ratio detecting means is provided at a collecting portion of the exhaust manifold 8, and detects an air-fuel ratio of the intake air-fuel mixture through an oxygen concentration in the exhaust gas. The oxygen sensor 16 detects rich / lean of the actual air-fuel ratio with respect to the stoichiometric air-fuel ratio by utilizing the sudden change in the oxygen concentration in the exhaust gas at the stoichiometric air-fuel ratio (the target air-fuel ratio in this embodiment). In this embodiment, a relatively high voltage signal is output when the air-fuel ratio is richer than the stoichiometric air-fuel ratio, and a low voltage signal near 0 V is output when the air-fuel ratio is lean. I do.

Here, the CPU of the microcomputer incorporated in the control unit 12 performs arithmetic processing according to the programs on the ROM shown in the flowcharts of FIGS. The fuel injection amount Ti is set while executing the fuel ratio 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, air-fuel ratio learning means, estimation learning means, learned area storage means The control unit 12 has a function as an estimated learning control unit based on the number of learned regions, a proper judgment unit, an estimated learning control unit based on proper judgment, and a learning correction value storage unit.

The program shown in the flowchart of FIG. 3 is a program for setting an air-fuel ratio feedback correction coefficient LMD (air-fuel ratio feedback correction value) to be multiplied by a basic fuel injection amount (basic fuel supply amount) Tp by proportional / integral control. And is executed every one revolution (1 rev) of the engine 1. First, in step 1 (referred to as S1 in the figure, the same applies hereinafter), a voltage signal output from the oxygen sensor (O ₂ / S) 16 according to the oxygen concentration in the exhaust gas is read.

Then, in the next step 2, step 1
The voltage signal from the oxygen sensor 16 read in step 2 and the slice level corresponding to the target air-fuel ratio (theoretical air-fuel ratio)
Compare with When it is determined that the voltage signal from the oxygen sensor 16 is higher than the slice level and the air-fuel ratio is richer than the stoichiometric air-fuel ratio, the process proceeds to step 3, where it is determined whether the current rich determination is the first time.

When the rich determination is performed for the first time, the routine proceeds to step 4, where the air-fuel ratio feedback correction coefficient LMD set up to the previous time is set to the maximum value a. In the next step 5, the reduction coefficient LMD is controlled by subtracting a predetermined proportionality constant P from the correction coefficient LMD up to the previous time. In step 6, the flag FP is set to 1 so as to determine that the proportional control of the correction coefficient LMD has been performed, in other words, that the air-fuel ratio has undergone rich / lean inversion.

On the other hand, if it is determined in step 3 that the rich determination is not the first time, the process proceeds to step 7, in which a value obtained by multiplying the integration constant I by the latest fuel injection amount Ti is subtracted from the correction coefficient LMD up to the previous time. To update the correction coefficient LMD. When it is determined in step 2 that the voltage signal from the oxygen sensor 16 is smaller than the slice level and the air-fuel ratio is lean with respect to the target, similarly to the rich determination, first in step 8, It is determined whether or not the lean determination is the first time, and if it is the first time, the process proceeds to step 9 and the correction coefficient LMD up to the previous time is set to the minimum value b.

In the next step 10, an increase in the fuel injection amount Ti is corrected by adding the proportionality constant P to the correction coefficient LMD up to the previous time and updating it. In a step 11, it is determined that the proportional control has been executed. To the flag FP
Is set. 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 integration constant I by the latest fuel injection amount Ti is added to the correction coefficient LMD up to the previous time, and the correction coefficient LMD is gradually increased. To increase.

Correction coefficient LM at the first time of rich / lean discrimination
When the proportional control of D is executed, various processes related to the air-fuel ratio learning control described later are further performed. First, in step 13, the air-fuel ratio learning correction coefficient KBLRC is set to be decreased according to the increase in the number of learned regions on the air-fuel ratio learning map that stores the air-fuel ratio learning correction coefficient KBLRC in an updatable manner for each of the plurality of operating regions,
When zero is set, it is determined whether or not an estimated learning counter ZZ indicating that learning has been completed in almost all operating regions is zero.

The setting control of the estimation learning counter ZZ will be described later in detail with reference to the flowchart of FIG. Further, the end of learning in the present embodiment is a state in which the target air-fuel ratio (the stoichiometric air-fuel ratio) is obtained without correction by the air-fuel ratio feedback correction coefficient LMD, in other words,
The air-fuel ratio feedback correction coefficient LMD is equal to the target convergence value = 1.
When the value is substantially equal to 0, it is determined that the corresponding operating region at that time has been learned.

The learning map divides the operating region into 256 regions of 16 × 16 based on the basic fuel injection amount Tp and the engine rotation speed N in the present embodiment, and a learning correction coefficient for each of the divided operating regions. KBLRC (initial value = 1.0) is stored in an updatable manner. When it is determined in step 13 that the estimated learning counter ZZ is set to zero,
Proceeding to step 14, it is determined whether or not the relevant operating region on the learning map when the previous proportional control was performed is the same as the current relevant operating region.

Here, when it is determined that the corresponding area has been switched, the process proceeds to step 15, where the average of the maximum value a and the minimum value b of the correction coefficient LMD set in steps 4 and 9 and the correction value are calculated. Target convergence value of coefficient LMD (= 1.0)
Is set based on the absolute value of the deviation from the learning result. That is, since it is determined in step 13 that the learning is substantially completed in the entire operation region, if the learning is performed with high accuracy, the correction coefficient LMD is substantially equal to the target convergence value (= 1.0
) Should be stable, and it can be estimated that there is a difference between the learning result and the required level as the deviation from the target convergence value increases. Therefore, in step 15, the greater the average value of the correction coefficient LMD has a larger deviation from the target convergence value, the larger the Δ stress is set, and the increase in the Δ stress indicates that the learning result is inappropriate. The increase is shown.

The Δ stress set in step 15 is added to the previous integrated value “stress” in the next step 16, and the result of this addition is newly set in “stress”.
The Δ stress is integrated for each proportional control of the correction coefficient LMD. Thus, for example, an air flow meter
When the base air-fuel ratio changes as a whole due to deterioration of the fuel injection valve 13 and the fuel injection valve 6, the target air-fuel ratio cannot be obtained only by the correction using the learned learning correction coefficient KBLRC, and the correction coefficient LMD needs to be corrected. , A relatively large Δstress is set every time the corresponding operation area is switched on the learning map, and the “stress” is rapidly increased by sequentially integrating the Δstresses. Therefore, as described later, when the "stress" becomes equal to or higher than a predetermined level, it is determined that the learned result is inappropriate, and the air-fuel ratio learning is restarted from the beginning.

The program shown in the flow charts of FIGS. 4 and 5 is an air-fuel ratio learning program for updating and setting the learning correction coefficient KBLRC for each operation region, and is executed every predetermined short time (for example, 10 ms). First, in step 21, the flag FP that is set to 1 when the proportional control of the correction coefficient LMD is performed in the flowchart of FIG. 3 is determined, and when the flag FP is set to zero, the program is directly executed. Is terminated, and the flag FP is set to 1
Is set in step 22, the flag FP
Steps related to air-fuel ratio learning after resetting
Proceed to the process after 23. Therefore, the air-fuel ratio learning described later is
The correction coefficient LMD is executed every proportional control (every time the air-fuel ratio is rich / lean inverted).

In step 23, based on the latest calculated basic fuel injection amount Tp and the engine speed N, the operating area (the applicable operating area) on the learning map corresponding to the current operating condition is determined by the grid position. [I] Specify as [K]. In the next step 24, the average value of the correction coefficient LMD (a + b) /
It is determined whether or not 2 is approximately 1.0. When the average value of the correction coefficient LMD is approximately 1.0, the approximate target air-fuel ratio is obtained by correcting only the learning correction coefficient KBLRC, and the current operating condition corresponds to the area on the learning map to which the current operating condition corresponds. It is presumed that the learning correction coefficient KBLRC is appropriate. Therefore, the process proceeds to step 25, and the corresponding operation region [I]
1 is set in the learned flag Flag [I] [K] corresponding to [K].
Is set so that an area in which the learned flag is set to 1 is determined to be a learned area.

In step 26, in the program shown in the flowchart of FIG.
The estimated learning counter ZZ is set based on the number of learned regions Status in the 256 regions on the learning map detected based on lag. Here, the estimation learning counter ZZ
Is set to decrease as the number of learned regions Status increases, and is set to zero when the number of learned regions Status is near the maximum number of 256.

The estimated learning counter ZZ is a learning correction coefficient KB for one of the corresponding operating areas in the 256 areas on the learning map.
When rewriting the LRC, this parameter determines the number of other areas with similar operating conditions to which the learning correction coefficient KBLRC corresponding to the relevant operating area is directly applied. When the estimated learning counter ZZ is zero, only the relevant operating area is used. Is learned, but the number of regions in which the learning correction coefficient KBLRC is updated at one time increases as the estimated learning counter ZZ increases. In other words, the range of the operating region that is learned at a time is gradually narrowed as the number of learned regions increases.

After step 27, various processes related to air-fuel ratio learning according to the estimated learning counter ZZ are set as described above. In step 27, the 256
The learning correction coefficient KBLRC [I] [K] stored corresponding to the corresponding operation area in the area is corrected by a correction coefficient LMD.
A predetermined ratio (1/8 in this embodiment) of the deviation of the average value from the target convergence value (= 1.0) is added, and the addition result is set as a new learning correction coefficient KBLRC.

In the next step 28, a value obtained by adding the estimated learning counter ZZ to [I] indicating the corresponding position on the 16 grids divided by the basic fuel injection amount Tp in the learning map is used as the basic fuel injection amount Tp. A value obtained by subtracting the estimated learning counter ZZ from the grid position [I] is set to p _max indicating the maximum position of the grid range to be learned at a time on the grid to be partitioned, and the grid divided by the basic fuel injection amount Tp. P indicating the minimum position of the grid range to be learned at a time on
Set to _min . That is, p including the corresponding grid position [I]
_{The range from min to} p _{max is} to be learned this time on the grid divided by the basic fuel injection amount Tp.

In steps 29 to 32, when p _min and p _max set in step 28 are set beyond the setting range of φ to 15, they are zero which is the minimum allowable value or the maximum allowable value. Perform processing to regulate to 15. Similarly, in step 33, the learning area on the grid divided by the engine rotation speed N is defined as q _min (← K−ZZ) to q _max (← K +
ZZ), and in steps 34 to 37, processing for restricting the learning area settings q _{min to} q _max to within the setting range of φ to 15 is performed.

In the next step 38, p _min and q _min indicating the area having the smallest lattice number in the learning range set as described above are counted by counters i and j, respectively.
Set to. In step 39, the p _min is initially set the counter i is equal to or smaller than a maximum value p _max, when it is p _max or less, the routine proceeds to step 40.

[0041] At step 40, the q _min is initially set counter j is equal to or smaller than a maximum value q _max, wherein, when the counter j is judged to be less than q _max, the step Proceeding to 41, the learning correction coefficient KBLRC corresponding to the area position designated by [i] [j] is rewritten to the learning correction coefficient KBLRC updated and set as a value corresponding to the area in step 27.

In the next step 42, the counter j is incremented by one, and the process returns to step 40 again, where the counter j becomes q _max
Until the value exceeds, the routine proceeds to step 41, where the same learning correction coefficient KBLRC is set at the area position indicated by [i] [j]. When it is determined in step 40 that the counter j exceeds _qmax , the process proceeds to step 43, where the counter i is incremented by one, and the counter j is reset to _qmin .
Proceed to step 39. Then, at step 39 until the counter i is judged to have exceeded the p _max, the counter j while fixing the counter i is changed from q _min to q _max, area position indicated by [i] [j] Is repeated to set the same learning correction coefficient KBLRC.

With this learning, [p _min ] including the corresponding operation region [I] [K] in the 256 regions on the learning map.
In each of the operation ranges [q _min ] to [p _max ] [q _max ] (the other operation regions close to the operation conditions of the corresponding operation region), the same learning correction as that corresponding to the corresponding operation region [I] [K] is performed. Coefficient KB
LRC is set, and the estimated learning counter ZZ is set.
When the number of learned regions is set to be relatively large, the same learning correction coefficient KBLRC is applied over a wide range including the corresponding operating region [I] [K], and good learning convergence is obtained. As the learned area increases and the estimated learning counter ZZ decreases, the learning range is narrowed. Eventually, the 256 areas divided on the learning map are individually learned.

Therefore, with the configuration including only the learning map in which the operation region is finely divided, it is possible to achieve both the convergence of learning and the learning accuracy, and if the unit operation region for storing the learning correction coefficient KBLRC is different, The memory capacity can be saved as compared with the case where the learning is performed with a plurality of learned maps. The program shown in the flowchart of FIG. 6 is a program in which a learned area on a learning map in which the operating area is divided into 256 areas is set as a learned flag Flag which is set and stored for each operating area.
Is a program for detecting the number of learned areas based on the background job (BG
J).

First, in step 51, counters i and j for individually instructing each of the 256 areas are reset to zero, and a counter Z for counting the number of learned areas is reset to zero. In steps 52-58,
While the counter i is fixed, the counter j is changed from zero.
By counting up to 15, and when the learned flag Flag corresponding to the area indicated by [i] [j] is 1, the process of incrementing the counter Z by 1 is repeated, so that learning is performed in all 256 areas. The completed flag Flag,
The number of learned operating regions (where the learned flag Flag is set to 1) is set in the counter Z.

In step 59, the value of the counter Z is set to the number of learned regions Status, and the estimated learning counter ZZ is set in step 26 in the flowchart of FIG. 4 based on the number of learned regions Status. I do. In the next step 60, it is determined whether or not “stress” updated and set for each proportional control of the correction coefficient LMD in the flowchart of FIG. 3 exceeds a predetermined level. The "stress" is learned as the required correction level when the corresponding operation region is switched, because the learning result becomes inappropriate due to a change in the base air-fuel ratio even though all the regions of the learning map have been learned. If it becomes necessary to largely change the correction coefficient LMD in order to compensate for the level difference with the learning correction coefficient KBLRC, the correction coefficient LMD is set to be increased each time the operation region is switched. Therefore, if the “stress” exceeds a predetermined level, it is predicted that the learned result does not correspond to the actual base air-fuel ratio.

If it is determined in step 60 that the "stress" exceeds a predetermined level, step 61
In the learning map, the learning correction coefficient KB is set together with the corresponding area on the learning map by setting zero to the number of learned areas Status.
While the number of regions in which the LRC is rewritten is maximized, all the learned flags Flag [φ] [φ] to [15] [15] of each operation region in which 1 is set as being learned are reset to zero, The learning is performed again from the learning of learning a wide range including the corresponding driving region at a time.

Therefore, when the learning result becomes inappropriate due to a change in the base air-fuel ratio, the re-learning proceeds promptly in units of a wide operating range. The deterioration can be suppressed to a minimum, and as the learning progresses and the number of learned areas increases, the learning area becomes narrower (the number of areas learned together with the corresponding area decreases), and correction for each operating condition is performed. Correction that meets the request can be performed again. Therefore, while the air-fuel ratio learning is basically performed for each of the finely divided operation regions, the learning can be quickly converged when the base air-fuel ratio changes abruptly.

In step 62, the "stress"
Is reset to zero, and after the re-learning performed as described above converges in all regions, the inappropriateness of the learning result is added to the “stress”. The program shown in the flowchart of FIG. 7 is a program for setting the fuel injection amount, and is executed every predetermined short time (for example, 10 ms).

First, in step 71, an air flow meter
The engine speed N and the like calculated based on the intake air flow rate Q detected by the engine 13 and the signal from the crank angle sensor 14 are read. In step 72, based on the intake air flow rate Q and the engine speed N, the basic fuel injection amount Tp (← Q / N ×
K; K is a constant).

In step 73, the air-fuel ratio feedback correction coefficient LM controlled by the proportional integral control according to the flowchart of FIG.
Read D. In step 74, various correction coefficients including a basic correction coefficient and a transient correction coefficient based on the coolant temperature detected by the water temperature sensor 15 are set. In step 75,
A voltage correction amount Ts for correcting a change in the effective valve opening time of the fuel injection valve 6 due to a change in the battery voltage is set.

In step 76, a corresponding operating region on the learning map is specified from the basic fuel injection amount Tp and the engine speed N calculated in step 72, and stored in correspondence with the corresponding operating region. Learning correction coefficient KBLRC
Is read. Then, in step 77, the basic fuel injection amount Tp is changed to the air-fuel ratio feedback correction coefficient LMD, various correction coefficients COEF, the voltage correction amount Ts, and the learning correction coefficient KBLRC.
And the final fuel injection amount Ti (← 2Tp ×
KBLRC × LMD × COEF + Ts) is calculated.

At a predetermined injection timing synchronized with the engine rotation, the control unit 12 executes the above-described step 77.
Then, a drive pulse signal corresponding to the fuel injection amount Ti set latest is output to the fuel injection valve 6 to supply the fuel injection to the engine. In this embodiment, the basic fuel injection amount Tp
The operating region is divided into 256 regions based on the engine speed N and the engine speed N, and the learning correction coefficient KBLRC is learned for each of these operating regions. However, it is apparent that the number of segments and operating conditions are not limited to the above. It is. Further, the setting of the basic fuel injection amount Tp is not limited to the setting based on the intake air flow rate Q and the engine speed N. For example, the basic fuel injection amount Tp is set based on the suction negative pressure and the engine speed N. It may be.

[0054]

As described above, according to the present invention, it is necessary to perform the air-fuel ratio learning which accurately copes with the difference of the correction request due to the difference of the operation condition while securing the learning convergence at the beginning of the learning. There is an effect that it can be realized while saving the capacity. In addition, there is an effect that the re-learning can be promptly converged when the learning result becomes inappropriate due to a change in the base air-fuel ratio while performing the air-fuel ratio learning corresponding to the operating conditions in detail.

[Brief description of the drawings]

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

FIG. 2 is a system schematic diagram showing one 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 learning control.

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

FIG. 6 is a flowchart showing detection control of the number of learned regions.

FIG. 7 is a flowchart showing setting control of a fuel injection amount.

[Explanation of symbols]

 1 engine 6 fuel injection valve 12 control unit 13 air flow meter 14 crank angle sensor 15 water temperature sensor 16 oxygen sensor

Claims (2)

(57) [Claims] An operating condition detecting means for detecting an engine operating condition including at least an operating parameter related to an amount of air taken into an engine, and a basic fuel supply based on the engine operating condition detected by the operating condition detecting means. Basic fuel supply amount setting means for setting the amount, air-fuel ratio detecting means for detecting the air-fuel ratio of the engine intake air-fuel mixture, and comparing the air-fuel ratio detected by the air-fuel ratio detecting means with the target air-fuel ratio to determine the actual 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 as to bring the air-fuel ratio closer to the target air-fuel ratio; and an operating region divided into a plurality of regions based on engine operating conditions. A learning correction value storing means for rewritably storing an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each time; and a target convergence value of the air-fuel ratio feedback correction value. Air-fuel ratio learning means for learning the deviation of the air-fuel ratio learning correction value stored in the learning correction value storage means corresponding to the corresponding operation area in the direction of reducing the deviation and rewriting the air-fuel ratio learning correction value; When the air-fuel ratio feedback correction value substantially matches the target convergence value, the corresponding operating region on the learning correction value storage unit at that time is determined as a learned region, and the determination result is stored in the learning correction value storage unit. The learned area storage means for storing each operating area, and the air-fuel ratio learning correction value of the corresponding operating area rewritten by the air-fuel ratio learning means corresponds to another operating area having operating conditions closer to the corresponding operating area. Estimating learning means for rewriting air-fuel ratio learning correction values other than the corresponding operating area on the learning correction value storage means as the air-fuel ratio learning correction values to be learned, and learning information stored in the learned area storage means. Learning learning control means for reducing the number of operating areas in which the air-fuel ratio learning correction value is rewritten together with the corresponding operating area by the estimation learning means in accordance with an increase in the number of learned areas; Fuel supply amount setting means for setting a final fuel supply amount based on the air / fuel ratio learning correction value stored in the fuel ratio feedback correction value and learning correction value storage means in correspondence with the corresponding operating region; 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 setting means. 2. An operating condition detecting means for detecting an engine operating condition including at least an operating parameter related to an amount of air taken into the engine, and a basic fuel supply based on the engine operating condition detected by the operating condition detecting means. Basic fuel supply amount setting means for setting the amount, air-fuel ratio detecting means for detecting the air-fuel ratio of the engine intake air-fuel mixture, and comparing the air-fuel ratio detected by the air-fuel ratio detecting means with the target air-fuel ratio to determine the actual 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 as to bring the air-fuel ratio closer to the target air-fuel ratio; and an operating region divided into a plurality of regions based on engine operating conditions. A learning correction value storing means for rewritably storing an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each time; and a target convergence value of the air-fuel ratio feedback correction value. Air-fuel ratio learning means for learning a deviation of the air-fuel ratio learning correction value stored in the learning correction value storage means corresponding to the corresponding operation area in a direction to reduce the deviation and rewrite the air-fuel ratio learning correction value; The air-fuel ratio learning correction value of the relevant operation area rewritten by the air-fuel ratio learning means is stored in the learning correction value storage means as an air-fuel ratio learning correction value corresponding to another operation area whose operating conditions are closer to the relevant operation area. Estimation learning means for rewriting the air-fuel ratio learning correction value other than the relevant operation area, and air-fuel ratio correction based on the deviation from the target convergence value of the air-fuel ratio feedback correction value when the relevant operation area is switched on the learning correction value storage means. A properness judging means for judging the appropriateness of the fuel ratio learning result; and, when the appropriateness judging means judges that the learning result is inappropriate, the estimation learning means makes the judgment. Estimation learning control means based on proper judgment for reducing the number of the operation areas in which the air-fuel ratio learning correction value is rewritten together with the operation area to the maximum number, and thereafter reducing the number of the operation areas with the progress of the learning, the basic fuel supply amount, the air-fuel ratio Fuel supply amount setting means for setting a final fuel supply amount based on the air-fuel ratio learning correction value stored in the feedback correction value and learning correction value storage means in correspondence with the corresponding operating region; An air-fuel ratio learning control device for an internal combustion engine, comprising: a fuel supply control unit that drives and controls the fuel supply unit based on the fuel supply amount set by the unit.