JPH04241756A - Air-fuel ratio study control device for internal combustion engine - Google Patents

Abstract

PURPOSE:To avoid generation of a large step difference of air-fuel ratio by using a result, studied, for instance, at acceleration time, at deceleration time and steady time, in the case of air-fuel ratio study control for studying an air-fuel ratio study correction value classified by plurally divided operational regions. CONSTITUTION:A study map for storing an air-fuel ratio study correction value, classified by plurally divided operational regions, is individually provided in accordance with respective operative conditions of acceleration, steady, deceleration, and a study map is selected from the above-mentioned study map based on the actual operative condition.(in step S102) A study is performed(in steps S103, 104, 105) and classified by the above-mentioned selected study map further to perform fuel correction based on an air-fuel ratio study correction value in the concerned region of the selected study map.(in step S106)

Description

[Detailed description of the invention]

0001

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an air-fuel ratio learning control device for an internal combustion engine, and more particularly to a device that can prevent erroneous air-fuel ratio learning due to air-fuel ratio deviations during engine acceleration and deceleration.

0002

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

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

[0004] Here, the deviation of the air-fuel ratio feedback correction coefficient LMD from the reference value (target convergence value) is learned for each of a plurality of predetermined operating regions to determine a learning correction coefficient KBLRC, and the basic fuel The injection amount Tp is corrected by the learning correction coefficient KBLRC to obtain the correction coefficient LMD.
The base air-fuel ratio obtained without the control is made to substantially match the target air-fuel ratio, and during the air-fuel ratio feedback control, the fuel injection amount Ti is calculated by further correcting it using the correction coefficient LMD.

[0005]Thereby, it is possible to perform correction corresponding to the required value of air-fuel ratio correction that differs depending on the operating conditions, stabilize the air-fuel ratio feedback correction coefficient LMD near the reference value, and improve air-fuel ratio controllability.

[0006]

[Problems to be Solved by the Invention] By the way, during transient operation of the engine, due to the influence of fuel wall flow and the difference in intake air flow rate between when the fuel injection amount Ti is set and when the intake valve is opened, During acceleration, the air-fuel ratio becomes leaner, and conversely, during deceleration, the air-fuel ratio becomes richer, and the leaner or richer tendency becomes more pronounced as the degree of acceleration and deceleration increases.

As described above, 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 leanening, the air-fuel ratio feedback correction coefficient LMD is set as shown in FIG. 17, for example.
At this time, learning is performed to eliminate the air-fuel ratio deviation that occurs due to the transient operation. For this reason, when deceleration or steady operation is performed next in the region where lean air-fuel ratio due to acceleration operation has been learned, the fuel injection amount will be changed based on the learned correction value that is significantly different from the request, as shown in Fig. 18. The problem is that using the learned correction value will actually aggravate the air-fuel ratio deviation, resulting in a large air-fuel ratio difference and increasing CO, HC, or NOx in the exhaust gas. was there.

In particular, when the map for storing the learning correction coefficient KBLRC is configured with a learning map in which the entire driving range is divided into small unit areas and a learning map in which the entire driving range is divided into larger unit areas, the performance becomes even better. With a map that learns the air-fuel ratio for each small operating region, there are few learning opportunities for each operating region, and since the operating region is narrow, air-fuel ratio deviations are picked up in spots, so the air-fuel ratio due to transient engine operation is There is a problem in that it is easy to learn the deviation, and a larger air-fuel ratio step will occur the next time the erroneous learning region is entered.

The present invention has been developed in view of the above-mentioned problems. For example, when a large air-fuel ratio deviation occurs due to accelerated operation of the engine, learning is performed to eliminate such air-fuel ratio deviation, and the next time in the same operating range, It is an object of the present invention to enable a learning correction to be performed in accordance with the driving situation at that time, without being influenced by the learning result during acceleration, even if the vehicle is operated in a steady state or decelerated.

0010

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

Further, the air-fuel ratio feedback correction value setting means compares the actual air-fuel ratio detected by the air-fuel ratio detection means with the target air-fuel ratio, and adjusts the basic air-fuel ratio so that the actual air-fuel ratio approaches the target air-fuel ratio. Sets the air-fuel ratio feedback correction value for correcting the fuel supply amount. On the other hand, the air-fuel ratio learning correction value storage means stores a learning map that rewriteably stores an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each operating range divided into a plurality of engine operating conditions. The learning map selection means selects one of the plurality of learning maps based on the acceleration/deceleration degree of the engine detected by the acceleration/deceleration degree detection means.

The air-fuel ratio learning correction value correcting means learns the deviation of the air-fuel ratio feedback correction value from the target convergence value, and adjusts the air-fuel ratio learning stored in the corresponding operating region on the selected learning map. The correction value is corrected and rewritten in a direction that reduces the deviation. Here, the fuel supply amount setting means determines the final fuel supply based on the basic fuel supply amount, the air-fuel ratio feedback correction value, and the air-fuel ratio learning correction value of the corresponding operating region on the selected learning map. The fuel supply control means drives and controls the fuel supply means based on the set fuel supply amount.

[0013]

[Operation] According to this configuration, a plurality of learning maps are provided for storing the air-fuel ratio learning correction value in a rewritable manner, depending on the degree of acceleration/deceleration of the engine, and one of them is selected according to the actual degree of acceleration/deceleration. Since two learning maps are selected, for example, the learning result of the air-fuel ratio deviation that occurs during acceleration is stored in the learning map corresponding to acceleration, and conversely, the learning result during deceleration is stored in the learning map corresponding to deceleration. Moreover, when actually using it to correct the fuel supply amount, the air-fuel ratio learning correction value on the learning map corresponding to the actual degree of acceleration/deceleration is referred to, so the learned result during acceleration can be used for example. It will not be used during deceleration or steady state.

[0014]

[Examples] Examples of the present invention will be described below. In FIG. 2 showing one embodiment, air is taken into an internal combustion engine 1 from an air cleaner 2 via an intake duct 3, a throttle valve 4, and an intake manifold 5. As shown in FIG. A branch portion of the intake manifold 5 is provided with a fuel injection valve 6 as a fuel supply means for each cylinder. The fuel injection valve 6 is a normally closed 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 a spark plug 7, which ignites a spark to ignite and burn the air-fuel mixture. Then, exhaust gas is discharged from the engine 1 via an exhaust manifold 8, an exhaust duct 9, a three-way catalyst 10, and a muffler 11. The control unit 12 includes a CPU,
Equipped with a microcomputer including ROM, RAM, A/D converter, and input/output interface, it receives input signals from various sensors, processes them as described later, and controls the operation of the fuel injection valve 6. Control.

The various sensors mentioned above include the intake duct 3
An air flow meter 13 is provided therein, and outputs a signal corresponding to the intake air flow rate Q of the engine 1. In addition, a crank angle sensor 14 is provided, and in the case of a 4-cylinder engine,
A reference signal REF for every 180 degrees of crank angle and a unit signal POS for every 1 degree or 2 degrees of crank angle are output. Here, the engine rotational speed N can be calculated by measuring the period of the reference signal REF or the number of occurrences of the unit signal POS within a predetermined period of time.

A water temperature sensor 15 is also provided to detect the temperature Tw of cooling water in the water jacket of the engine 1. Here, the air flow meter 13, crank angle sensor 14, water temperature sensor 15, etc. correspond to 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, and detects the air-fuel ratio of the intake air-fuel mixture via the oxygen concentration in the exhaust gas. The oxygen sensor 16 detects whether the actual air-fuel ratio is rich or lean with respect to the stoichiometric air-fuel ratio (target air-fuel ratio in this embodiment) by utilizing the fact that the oxygen concentration in the exhaust gas changes suddenly after reaching the stoichiometric air-fuel ratio. It is a publicly known thing.

[0018] Here, the control unit 12
The CPU of the microcomputer built into the
Arithmetic processing is performed according to the programs on the ROM shown in the flowchart of FIG. 13, and the fuel injection amount Ti is set while executing air-fuel ratio feedback correction control and air-fuel ratio learning correction control for each operating region, and fuel is supplied to the engine 1. control.

In this embodiment, 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, and learning map are used. The function as the selection means is provided in the form of software as shown in the flowcharts of FIGS. 4 to 13, and the air-fuel ratio learning correction value storage means corresponds to a RAM with a backup function of the microcomputer.

Before explaining in detail the air-fuel ratio learning control of this embodiment shown in the flowcharts of FIGS. 4 to 13,
An overview of the air-fuel ratio learning control in this embodiment will be explained with reference to FIG. In FIG. 3, first, in step 101 (indicated as S101 in the figure; the same applies hereinafter), it is determined whether the actual air-fuel ratio detected by the oxygen sensor 16 is rich or lean with respect to the target air-fuel ratio. , based on this determination, the air-fuel ratio feedback correction coefficient LMD (initial value = 1.0
).

[0021] In the next step 102, the engine acceleration/
It determines whether the operation is steady or decelerated, selects one of three preset learning maps based on the determination result, and selects the area corresponding to the current driving condition on the selected learning map. Stored air-fuel ratio learning correction coefficient K
Rewrite and correct BLRC. The learning map has multiple operating ranges using the basic fuel injection amount Tp (←K×Q/N, K is a constant) calculated from the intake air flow rate Q and the engine rotational speed N, and the engine rotational speed N as parameters. the air-fuel ratio learning correction coefficient KBLR for each of the plurality of operating regions.
A corresponding area on the learning map is specified from the latest basic fuel injection amount Tp and engine rotational speed N, and correction is performed using the air-fuel ratio feedback correction coefficient LMD in the specified area. The requested value is rewritten and stored as the air-fuel ratio learning correction value.

In this embodiment, three learning maps are separately provided, one for acceleration, one for steady state, and one for deceleration.
A corresponding learning map is selected according to the determination result of acceleration, steady state, and deceleration in step 102, and the corresponding area on the selected learning map is learned (steps 103 to 105). Next step 106
Now, the basic fuel injection amount Tp is corrected based on the air-fuel ratio feedback correction coefficient LMD and the air-fuel ratio learning correction coefficient KBLRC corresponding to the corresponding area on the selected learning map, and the final fuel injection amount Ti is set. Then, the amount of fuel injected by the fuel injection valve 6 is controlled based on the fuel injection amount Ti.

As described above, learning maps for learning the air-fuel ratio are provided separately for acceleration, steady state, and deceleration, and the corresponding learning map is selected according to the transient operating state at that time, and the learning map is displayed on the selected learning map. and the air-fuel ratio learning correction coefficient K stored on the selected learning map.
The fuel injection amount is corrected based on BLRC. Therefore, for example, the learning result of the air-fuel ratio feedback correction coefficient LMD, which is set to compensate for the lean air-fuel ratio that occurs during acceleration, is not used during steady operation or deceleration operation, and the result learned during acceleration is the same. Since it is used when acceleration is resumed in the operating range, the accuracy of air-fuel ratio learning control including during transient times is improved.

In particular, if the learning areas are divided into fine sections so that differences in correction requests due to differences in operating conditions can be responded to accurately, there will be fewer learning opportunities for each operating area, and deviations in the base air-fuel ratio during transient periods will be reduced. It will be easier to learn, but if the learning map is set in advance according to the degree of acceleration and deceleration of the engine, even if the learning area is set narrowly, it will be easier to learn when accelerating as shown above. Since the results are not used during steady state or deceleration, there is no need to constantly widen the operating range to avoid learning base air-fuel ratio deviations during transient times, and it is possible to respond accurately to correction requests for different operating conditions. You will be able to do this.

[0025] In the above, three learning maps are provided corresponding to the three operating states of acceleration, steady state, and deceleration, but in the detailed embodiment described below, there are three learning maps, regardless of the transient state. Learning maps to be learned are provided individually, and the three learning maps for each of the transient driving conditions divide the entire driving range into 256 regions, whereas the learning maps to be learned regardless of the transient driving conditions are divided into 256 areas. is 16
After learning the learning correction coefficient KBLRC1 corresponding to the corresponding area on the learning map (hereinafter referred to as the 16-area learning map), which is divided into 16 areas, all driving areas are divided into 16 areas. Classified learning map (
Hereinafter, it will be abbreviated as 256 segment learning map. ) to learn the learning correction coefficient KBLRC2.

Furthermore, in this embodiment, as shown in FIG. 14, the learning correction coefficient KBLR is used regardless of the operating conditions.
C φ is set separately, and the learning correction coefficient KBL
After RC φ is learned, the learning is moved to the 16-segment learning map, and then to the 256-segment learning map, and after the learning corresponding to the large area has progressed, the learning for the smaller area is started. Move to learning.

Next, the air-fuel ratio learning control, the outline of which is shown in the flowchart of FIG. 3, will be explained in detail in accordance with the flowcharts shown in FIGS. 4 to 13. The program shown in the flowcharts of FIGS. 4 and 5 is a program that sets the air-fuel ratio feedback correction coefficient LMD (air-fuel ratio feedback correction value) multiplied by the basic fuel injection amount Tp by proportional/integral control. Executed every revolution (1 rev). Note that the air-fuel ratio feedback correction coefficient L
The initial value of MD is 1.0.

First, in step 1, the oxygen sensor (O2
/S) Reads the voltage signal output from 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 or not this rich determination is the first time. When the rich determination is made for the first time, the process proceeds to step 4, where the air-fuel ratio feedback correction coefficient LMD set up to the previous time is set to the maximum value a. The fact that this is the first rich determination means that a lean determination was made previously, and this increases the air-fuel ratio feedback correction coefficient LMD (= fuel injection amount T
Since the correction coefficient LMD is then controlled to decrease when it is determined to be rich, the value before the initial decrease control for rich determination is the maximum value of the correction coefficient LMD. .

In the next step 5, a predetermined proportionality constant P is subtracted from the previous correction coefficient LMD to obtain a correction coefficient LM.
Aim to control the decrease of D. Further, in step 6, 1 is set to P-minute addition, which is a flag indicating that proportional control has been executed. On the other hand, if it is determined in step 3 that the rich determination is not the first time, the process proceeds to step 7, and the integral constant I
The correction coefficient LMD is updated by subtracting the value obtained by multiplying the latest fuel injection amount Ti from the previous correction coefficient LMD, and this program continues until the rich state of the air-fuel ratio is resolved and the air-fuel ratio is reversed to lean. In this step 7 each time I
Repeat the reduction control by ×Ti.

Furthermore, when it is determined in step 2 that the air-fuel ratio is lean relative 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 b, and in step 10, the correction coefficient LMD up to the previous time By adding and updating the proportional constant P to the correction coefficient LMD, an increase in the fuel injection amount Ti is corrected, and in step 11, the above P, which is a flag indicating that proportional control has been executed,
Set the minute addition to 1.

When 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 is Gradually increase LMD. 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.

First, in step 13, a count value c is used to determine whether or not the corresponding driving region is stable on the 16-segment learning map that is learned regardless of the transient state.
Determine nt. In the program shown in the flowcharts of FIGS. 6 to 10, which will be described later, when the corresponding operating region on the 16-segment learning map changes at predetermined minute intervals, the count value cnt is set to a predetermined value (for example, 4).
is set, and if it is determined in step 13 that the count value cnt is not zero, step 1
The process advances to step 4 and the process of decreasing the count value cnt by 1 is performed. Therefore, after coming to stop in one operating region on the 16-section learning map, the count value cnt will be decreased by 1 every time the correction coefficient LMD is proportionally controlled, and when the count value cnt is zero, the count value cnt will be reduced by 1 for every proportional control of the correction coefficient LMD. It is arranged so that it can be regarded as a state in which it remains stably in one area on the learning map.

[0034] 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 region changes. It's like this. In step 15, as will be described later, it is determined whether most of the air-fuel ratio learning corresponding to each operating region on the 16-class learning map has been learned. As will be described later, flags F[B, A] are set for each area of the 16-segment learning map to indicate whether learning has been completed or not.
The determination in step 15 above can be made based on the flag F[B, A].

When most of the 16-section learning map has been learned, the process advances to step 16. In step 16, it is determined whether the operating conditions are approximately the same as during the overall proportional control.
6. Determine whether the corresponding area on the classification learning map is the same as the previous time or not, and if the operating conditions have changed, proceed to step 17. In step 17, the absolute value of the deviation of the latest correction coefficient LMD average value (a+b)/2 from the target convergence value (initial value 1.0) is determined, and based on this value, the degree of inappropriateness of the learned value is indicated. Refer to the Δ stress map and set the Δ stress.

That is, since the 16-segment learning map has almost been learned, it is desirable that the base air-fuel ratio due to changes in operating conditions be suppressed to a sufficiently small level.
If the air-fuel ratio feedback correction coefficient LMD changes significantly from the target convergence value to compensate for the deviation of the base air-fuel ratio as the operating conditions change, this indicates that the current air-fuel ratio learned value is inappropriate.

Therefore, when the average value of the correction coefficient LMD has a larger deviation from the target convergence value, the Δ stress is set larger, and the integrated value of the Δ stress obtained in the next step 18 is When the "stress" exceeds a predetermined value, it is determined that the air-fuel ratio learning has not been performed with sufficient accuracy, and the learning is restarted from the beginning as will be described later.

The program shown in the flowcharts of FIGS. 6 to 10 is an air-fuel ratio learning program for each operating region.
It is executed every predetermined minute time (for example, 10ms). In step 21, the amount of change ΔTp in the basic fuel injection amount Tp during the recent 100 ms is calculated. The amount of change Δ
Tp is used to determine the degree of acceleration/deceleration of the engine, as will be described later.

In step 22, it is determined whether P is added, which is a flag that is set to 1 when the air-fuel ratio feedback correction coefficient LMD is proportionally controlled by the program shown in the flowcharts of FIGS. 4 and 5. Minute addition is 1
When this is the case, the process proceeds to step 23, where the P-minute addition is reset to zero, and then 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 23, in the next step 24, the learning correction coefficient KBLRCφ (initial value 1.0), which is the air-fuel ratio learning correction value common to all operating ranges, has been learned. A flag Fφ indicating whether or not is determined is determined. Here, when the flag Fφ is set to 1, it indicates that the learning correction coefficient KBLRCφ has been learned, and when the flag Fφ is zero, it indicates that the learning correction coefficient KBLRCφ has been learned. If not, the process proceeds to step 25, where it is determined whether the average value (←(a+b)/2) of the maximum and minimum values a and b of the correction coefficient LMD is approximately 1.

When (a+b)/2 is not approximately 1, the process proceeds to step 27, and the correction coefficient LMD is calculated from (a+b)/2.
The value obtained by subtracting the target convergence value Target (1.0 in this example) multiplied by a predetermined coefficient X is added to the previous learning coefficient KBLRC φ, and the addition result is updated as a new learning coefficient KBLRC φ. Set. KBLRC φ←KBLRC φ+X{(a+b)/2
-Target} Also, in step 27, 25
6 Learning correction coefficient KBLRC1 and learning correction coefficient KBL stored in each driving area of the classification learning map
Reset all RC2 to the initial value of 1.0.

Therefore, the learning correction coefficient KBLRC φ
When learning and updating the data, even if learning values are stored in the 16-class learning map and the 256-class learning map, the data is reset and new learning is performed. If it is determined in step 25 that (a+b)/2 is approximately 1, the flag Fφ is set to 1 in step 26, thereby completing the learning of the learning correction coefficient KBLRCφ corresponding to all operating regions. In other words, it can be determined that the air-fuel ratio feedback correction coefficient LMD has converged to approximately 1 as a result of learning and updating the learning correction coefficient KBLRCφ.

On the other hand, in step 24, the flag Fφ is set to 1.
If it is determined that the learning correction coefficient KBLRCφ corresponding to the entire operating range has been learned, the operating range is now determined based on the basic fuel injection amount Tp and the engine rotational speed N. It is divided into multiple regions and air-fuel ratio learning is performed for each region. First, in step 28, based on the latest calculated basic fuel injection amount Tp and engine rotational speed N, coordinates indicating the relevant area on the 256 classification learning map [
K, I] (see FIG. 15). Here, the above K is
The current engine rotational speed N indicates a corresponding block, and I indicates the block corresponding to the current basic fuel injection amount Tp.

In the next step 29, the step 21
The amount of change ΔTp in the basic fuel injection amount Tp calculated in is compared with a positive predetermined value Tp1, and if the basic fuel injection amount Tp is increasing at a rate greater than a predetermined rate and Tp>Tp1, the predetermined value is Assuming that it is in an acceleration state, step 30
Proceed to. In step 30, the learning map preset for acceleration is selected from among the 256 categorical learning maps that are set according to the transient state as described above, and later a new learning map is selected. Until this happens, the air-fuel ratio learning correction is performed on the learning map for acceleration, and the fuel correction is performed based on the learning correction coefficient KBLRC2 retrieved from the learning map for acceleration. Make it.

On the other hand, when it is determined in step 29 that Tp≦Tp1, the process proceeds to step 31, where the negative predetermined value Tp2 is compared with the change amount ΔTp, and the basic fuel injection amount Tp is decreased at a rate greater than a predetermined value. It is changing and Tp<Tp
2, it is assumed that the vehicle is in a predetermined deceleration state and the process proceeds to step 32. In step 32, a learning map preset for deceleration is selected from among the 256 classification learning maps that are set according to the transient state as described above.

Furthermore, when it is determined in step 31 that Tp≧Tp2, it is assumed that the basic fuel injection amount Tp is in a steady state where it is substantially constant, and the process proceeds to step 33 where the transient state is determined as described above. There are three settings according to 2
56 Select a learning map preset for steady-state use from among the classification learning maps. That is, based on the amount of change ΔTp in the basic fuel injection amount Tp, the operating state of the engine is determined into three states: acceleration, steady state, and deceleration, and three 256 states are determined.
The learning map corresponding to the discrimination result is selected from among the classification learning maps. For example, during acceleration, learning correction of the learning correction coefficient KBLRC2 is performed on the learning map preset for acceleration, and , the air-fuel ratio learning correction is performed with reference to the learning map for acceleration.

In this embodiment, the transient state of the engine is determined based on the amount of change ΔTp in the basic fuel injection amount Tp, but the transient state is determined based on changes in the throttle valve opening, intake air flow rate, etc. Alternatively, the classification of the transient state may be subdivided into three or more parts, and 256 classification learning maps corresponding to the number of subdivisions may be provided. Furthermore, the number of operating region divisions is not limited to 256 regions.

In the next steps 34 and 35, 1
Coordinates indicating the corresponding area on the 6-section learning map [B, A
]. In step 28 described above, the coordinates [K, I] indicating the corresponding area on the 256-segment learning map are obtained, and as shown in FIG. 15, one driving area on the 16-segment learning map is the 256-segment learning map. Since 16 areas are divided into one block, the area to which the current driving conditions apply can be specified in the 16-segment learning map based on I and K.

That is, in step 34, 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 35, the block number K of N is set. Divide by 4 in the same way, and set the integer value of the result, rounded down to the decimal point, to B. As a result, the area position on the 16-segment learning map to which the current driving condition applies is represented by the coordinates [B, A].

In the next step 36, it is determined whether or not the corresponding driving area has changed on the 16-segment learning map using the coordinates [B, A] indicating the position of the corresponding area on the 16-segment learning map. Therefore, the value obtained by multiplying A by 16 is added to B, and the result is set in AB. Then, in step 37, ABOL, which is the previously calculated AB,
By comparing D with the latest AB, it is determined whether the area to which this current application applies is the same as the previous one. AB
If ≠ABOLD and the corresponding area on the 16-segment learning map is different from the previous one, the count value cnt is set to a predetermined value (for example, 4) in step 38.

In step 39, A calculated in step 37 this time is used for the next determination in step 43.
Set B to ABOLD as the previous value. In step 40, a flag F [B, A] is determined, which indicates whether or not the driving region including the current driving conditions specified with coordinates [B, A] in the 16-segment learning map has been learned. If this flag F[B, A] is zero and learning has not been completed in one driving area of the 16-segment learning map that includes the current driving conditions, the process proceeds to step 41.

In step 41, the count value cnt
It is determined whether or not the count value cnt is zero, and if it is not zero and there is a fluctuation in the corresponding area in the 16-segment learning map, this program is immediately terminated and the count value cnt is zero and stable in the corresponding operating area. Proceed to step 42 only when the vehicle is stopped. In step 42, 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 by the program shown in the flowcharts of FIGS. 4 and 5, that is, the center value of the correction coefficient LMD. is the initial value (=1
) The progress of learning is determined based on whether or not it is near 1.
If it is not recognized that it is, and learning has not been completed, the process advances to step 44.

In step 44, maximum and minimum values a,
The value obtained by subtracting the target convergence value Target from the average value of b is multiplied by a predetermined coefficient Save new settings as .

[0054] KBLRC1←KBLRC1+X1 {(a+b)/2
-Target} Meanwhile, in step 42, (a+b)/
If it is determined that 2 is approximately 1, it means that learning in the corresponding area of the 16-segment learning map that includes the current driving conditions has been completed, so in step 43, flag F[B, A] is set to 1. The flag F[B, A] is set to 1 so that it is determined that learning has been completed for areas where the flag F[B, A] is 1.

During the learning of such a 16-segment learning map, the learning correction coefficients KBLRC2 in the 16 areas included in the area [B, A] on the more detailed 256-segment learning map are all initialized in step 44. Reset to value 1.0. In this way, when there is an area where learning has not been completed on the 16-segment learning map,
When the operating region is stable, the air-fuel ratio feedback correction coefficient is updated by adding a predetermined percentage of the deviation from the target value Target of (a+b)/2 to the learning correction coefficient KBLRC1 stored up to then. The target air-fuel ratio is obtained by correction using the learning correction coefficient KBLRC1 instead of LMD, and the learning for that 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. shall be.

On the other hand, in step 40, the flag F [B, A
] is determined to be 1, and the learned learning correction coefficient KBLRC1 is stored in the corresponding driving area on the 16-segment learning map, then the learning correction coefficient KBLRC1 is stored in one driving area [B, A] on the 16-segment learning map. Contains 256 sections Move to learning the corresponding area on the learning map. In step 45, it is determined whether or not (a+b)/2, which is the average value of the correction coefficient LMD, substantially matches the target convergence value of 1.0.
If (a+b)/2 is not approximately 1.0 and is in an unlearned state requiring correction by the air-fuel ratio feedback correction coefficient LMD, the process proceeds to step 46.

In step 46, the value obtained by subtracting the target convergence value Target (1.0 in this embodiment) from (a+b)/2 is multiplied by a predetermined coefficient It is added to the learning correction coefficient KBLRC2 [K, I] stored corresponding to the driving region [K, I] that includes the current driving conditions on the map, and the result of this addition is added to the corresponding driving region [K, I]. Update setting is made as a new correction coefficient KBLRC2 [K, I].

KBLRC2[K,I]←KBLRC2[K
,I]+X2 {(a+b)/2-Target}
On the other hand, if it is determined in step 45 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 47. In step 47, the flag FF is set so that it is determined that the learning of the operating region [K, I] that includes the current operating conditions on the selected 256 classification learning map based on the transient operating state has been completed. Set [K, I] to 1. In addition, since three 256 classification learning maps are provided based on the transient state, the flag FF [K, I]
are the same area for each 256 segmented learning map [K, I]
will be set individually for each.

Then, from step 48 onwards, it is determined that the current learning has been completed, and the corresponding flag FF [K, I] is set to 1. Based on the predetermined driving region [K, I] of the 256 classification learning map, If there is an operation area adjacent to area [K, I] for which learning has not been completed (see Fig. 16), the current operation area [K, I] is added to that operation area.
I] learning correction coefficient KBLRC stored corresponding to
2 is stored.

[0060] In step 48, the area position including the current driving condition is indicated in the 256 segment learning map.
The values obtained by subtracting 1 from [K, I] are set to m and n, and in the next step 49, it is determined whether m=K+2. When the process proceeds from step 48 to step 49, the determination in step 49 is NO, so the process proceeds to step 50, where it is determined whether the learning of the area on the 256 segmented learning map has been completed or not. It is determined whether the flag FF [m, n] is 1 or zero.

Here, if the flag FF [m, n] is zero and the learning has not been completed, the process advances to step 51. In step 51, the corresponding coordinates [m, n] on the 256-segment learning map are converted to the corresponding coordinates [m/4, n/4] on the 16-segment learning map, which is currently determined to be applicable. Area on the 16-section learning map [
B, A].

[0062] That is, [K, I] is an area included in [B, A], but the area surrounding [K, I] is an area adjacent to [B, A] on the 16-segment learning map. This is because the area may be included in the same [B, A] ([m/4, n/4] = [B, A]), the process advances to step 52, and the area that has been learned this time is It was determined that there was [K,
The learning correction coefficient KBLRC2 corresponding to the area [I] is updated and stored as a learning value for the area [m, n]. On the other hand, when it is determined in step 51 that [m, n] adjacent to [K, I] is included in a different area on the 16-segment learning map, the process proceeds to step 53, where the area [m, n]
, n] is the learning correction coefficient KBLRC calculated by the following formula.
2 is stored.

[0063] KBLRC2 [m, n]←KBLRC1 [B, A
]+KBLRC2[K,I]-KBLRC1[m/4
. This is based on the assumption that the learning correction coefficients KBLRC1 of the 16-segment learning maps included in each map are different.
/4] is set to be approximated as shown in the following formula.

[0064] KBLRC1 [B, A] + KBLRC2 [
K, I] = KBLRC1 [m/4, n/4] + KBLR
C2 [m, n] If the [m, n] area has been learned as described above, the learned value will not be updated, and if it has not been learned, KBLRC2 will be updated based on KBLRC2 [K, I]. When [m, n] is updated, in step 54, m is increased by 1 and the process is repeated in step 4.
9, until m=K+2, that is, with n constant, m is moved within a range of ±1 around K, and whether learned or unlearned is determined for each driving region.

When it is determined in step 49 that m=K+2 as a result of the 1-up processing of m in step 54, the process proceeds to step 55, and it is determined whether n=I+2, and n≠I+2. , step 5
In step 6, m is set to K-1 again, and in the next step 57, n is incremented by 1, and then the process proceeds to step 50. Therefore,
At first, we set n=I-1 and moved m within a range of ±1 centering on K to determine adjacent areas, but next we set n=I and moved m within a range of ±1 centering K. Then, next, let n=I+1 and move m around K.
1, and as a result, if the eight operating regions surrounding [K, I] (see Figure 16) have not been learned, the value based on the learning correction coefficient KBLRC2 [K, I] is used as the learning correction for that operating region. It is stored as a coefficient KBLRC2 [m, n].

[0066] In this way, if the learning results of the learned area are applied to the surrounding unlearned areas, if the driving area is subdivided as in the 256 segmented learning map and there are few learning opportunities for each driving area. Even in this case, it is possible to prevent differences in air-fuel ratio controllability from occurring between operating ranges. When it is determined in step 55 that n=I+2, [K,
This means that the determination processing for all eight operating regions surrounding [I] has been completed, so at this time, the process advances to step 46,
The learning correction coefficient KBLRC2, which is determined to have already been learned in the current region [K, I], is updated to further improve the learning accuracy.

In this way, in this embodiment, first, after learning the learning correction coefficient KBLRC φ corresponding to all driving regions, learning of the corresponding region on the 16-segment learning map is performed, and then For areas that have already been learned on the learning map, we have divided those areas into 16 areas and have them learn by area, so the learning progresses 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 learning progresses, detailed learning is performed for each operating range, so it can accurately respond to differences in required correction values due to different operating ranges. can.

Three learning correction coefficients KBLRC φ, KBLRC1, KBLRC2 learned as described above
The setting of the final air-fuel ratio learning correction coefficient KBLRC based on is performed according to the program shown in the flowchart of FIG. The program shown in the flowchart of FIG. 11 is processed as a background job. First, in step 71, the learning correction coefficient K stored in the corresponding area [B, A] on the 16-segment learning map is
BLRC1 is read out, and in the next step 72, the learning correction coefficient KBL stored in the corresponding [K, I] on the 256 classification learning map selected according to the transient operating state is read out.
Read RC2. In addition, B×4 shown in the flowchart
+A and K×16+I are for converting the coordinates indicating the respective corresponding area positions into addresses on the memory.

In step 73, KBLRC φ+KB
The final learning correction coefficient KBLRC is set as LRC1+KBLRC2-2.0 →KBLRC. The learning correction coefficient KBLRC set in the program shown in the flowchart of FIG. 11 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. 12 is executed at predetermined minute intervals (for example, 10 ms). First, in step 81, the intake air flow rate Q detected by the air flow meter 13 and the crank angle sensor 14 are Engine rotation speed N calculated based on the detection signal from
Enter. 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 (←K×Q/N; K is a constant) is calculated.

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

When a predetermined fuel injection timing is reached, the control unit 12 adjusts the latest calculated fuel injection amount T.
A drive pulse signal with a pulse width corresponding to i is sent to the fuel injector 6.
and controls the amount of fuel supplied to the engine 1. The program shown in the flowchart of FIG. 13 is a program that performs a learning repetition process based on the stress sampled according to the program shown in the flowcharts of FIGS. 4 and 5.
J) is executed.

In step 91, the stress indicating the inappropriateness of the air-fuel ratio learning correction value and a predetermined value (for example, 0.
8). Here, when the stress exceeds a predetermined value, it is determined that the learning result is inappropriate and the desired air-fuel ratio correction has not been performed, although learning is almost completed, and learning is performed again (learning repetition). To do so, proceed to step 92.

At step 92, flags Fφ,
F[0,0]~F[3,3],FF[0,0]~FF[
16, 16] are all reset to zero, and the stress is also reset to zero. In this way, if the air-fuel ratio learning correction is unable to compensate for changes in the base air-fuel ratio due to differences in operating conditions, and the stress becomes greater than a predetermined value, re-learning can prevent holes in the intake system, for example. When the air-fuel ratio suddenly changes due to an accident such as the engine opening, learning for each large operating region is performed again, so the air-fuel ratio can be quickly converged.

In addition, in the above embodiment, in addition to the learning map that can be used depending on the transient state, there is also a learning map that is learned independently of the transient state and that divides the driving range into relatively large areas. However, the configuration may include only learning maps corresponding to transient states. In addition, in this embodiment, the learning maps that can be used depending on the transient state are:
All of the operating areas were divided into the same number of areas, but
The number of divided regions and the grid position may be changed depending on the transient state.

[0076]

As explained above, according to the present invention, a learning map is provided individually according to the acceleration/deceleration degree of the engine, and an air-fuel ratio learning correction value on the learning map corresponding to the acceleration/deceleration degree at that time is learned. In addition, since the fuel supply amount is corrected using the air-fuel ratio learning correction value on the corresponding learning map, even if the operating region is divided into fine sections, the results learned during acceleration, for example, will not be used during steady state or deceleration. It is possible to prevent large air-fuel ratio differences from occurring, and improve the learning controllability of the air-fuel ratio, including during transient times.

[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 present invention.

FIG. 3 is a flowchart showing an overview of air-fuel ratio control in the embodiment.

FIG. 4 is a flowchart showing the details of air-fuel ratio feedback control in the embodiment.

FIG. 5 is a flowchart showing the contents of air-fuel ratio feedback control in the embodiment together with FIG. 4;

FIG. 6 is a flowchart showing the contents of air-fuel ratio learning control in the embodiment.

FIG. 7 is a flowchart showing the contents of air-fuel ratio learning control in the embodiment.

FIG. 8 is a flowchart showing the contents of air-fuel ratio learning control in the embodiment.

FIG. 9 is a flowchart showing the contents of air-fuel ratio learning control in the embodiment.

FIG. 10 is a flowchart showing the contents of air-fuel ratio learning control in the embodiment.

FIG. 11: Final learning correction coefficient KBL in the example
A flowchart showing how RC is set.

FIG. 12 is a flowchart showing how the fuel injection amount is set in the embodiment.

FIG. 13 is a flowchart showing control details related to repetition of air-fuel ratio learning in the embodiment.

FIG. 14 is a diagram showing the configuration of a learning map in the example.

FIG. 15 is a diagram showing the division state of the learning map and the coordinates of the corresponding area in the example.

FIG. 16 is a diagram showing coordinate positions of operating regions adjacent to learned operating regions in the example.

FIG. 17 is a time chart for explaining problems with conventional learning control.

FIG. 18 is a time chart for explaining problems with conventional learning control.

[Explanation of symbols]

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

Claims (1)

[Claims] 1. Engine operating condition detection means for detecting engine operating conditions including at least operating parameters related to the amount of air taken into the engine; and basic fuel consumption based on the engine operating conditions detected by the engine operating condition detection means. A basic fuel supply amount setting means for setting the supply amount, an air-fuel ratio detection means for detecting the air-fuel ratio of the engine intake mixture, and an actual air-fuel ratio by comparing the air-fuel ratio detected by the air-fuel ratio detection means with the target air-fuel ratio. air-fuel ratio feedback correction value setting means for setting an air-fuel ratio feedback correction value for correcting the basic fuel supply amount so that the air-fuel ratio of the engine approaches the target air-fuel ratio; an air-fuel ratio learning correction value storage means comprising a plurality of learning maps for rewritably storing air-fuel ratio learning correction values for correcting the basic fuel supply amount for each region; acceleration/deceleration degree detection means for detecting the acceleration/deceleration degree; learning map selection means for selecting one of the plurality of learning maps in the air-fuel ratio learning correction value storage means based on the detected acceleration/deceleration degree; Learning the deviation of the air-fuel ratio feedback correction value from the target convergence value, and correcting the air-fuel ratio learning correction value stored in the corresponding operating region on the selected learning map in a direction to reduce the deviation. an air-fuel ratio learning correction value correction means for rewriting, the basic fuel supply amount,
a fuel supply amount setting means for setting a final fuel supply amount based on an air-fuel ratio feedback correction value and an air-fuel ratio learning correction value for a corresponding operating region on the selected learning map; and a fuel supply amount setting means. 1. An air-fuel ratio learning control device for an internal combustion engine, comprising: fuel supply control means for driving and controlling the fuel supply means based on the fuel supply amount set by the means.