JPH04318244A - Self-diagnostic device in fuel supplier of internal combustion engine - Google Patents

Abstract

PURPOSE:To early diagnose in good response worsening of air-fuel ratio controllability due to trouble and deterioration of parts, in the case of a fuel supplier having a study correcting function of air-fuel ratio. CONSTITUTION:An air-fuel ratio study correction coefficient KBLRC2 for correcting a basic fuel injection amount Tp is studied and classified by operational regions divided into a plurality of parts based on the basic fuel injection amount Tp and an engine speed N. Here, a mean AVTp of an updated step difference of a study result in the case of dividing the operational region based on the basic injection amount Tp and a mean AVTp of an updated step difference of a study result in the case of dividing the operational region based on the engine speed N are respectively obtained. Based on these updated step difference mean AVTp, AVN and a mean value of the study correction coefficient KBLRC2 classified by the operation region, a change of base air-fuel ratio based on part trouble and deterioration in a fuel supply system is diagnosed to generate an alarm when the change of the above-mentioned base air- fuel ratio is discriminated.

Description

[Detailed description of the invention]

0001

[Field of Industrial Application] The present invention relates to a self-diagnosis device for a fuel supply system for an internal combustion engine, and more specifically, the present invention relates to a self-diagnosis device for a fuel supply system for an internal combustion engine, and more specifically, it corrects the fuel supply amount so that the air-fuel ratio of the intake air-fuel mixture in an internal combustion engine for an automobile matches the target air-fuel ratio. The present invention relates to self-diagnosis of a fuel supply system configured to perform self-diagnosis.

0002

[Prior Art] Conventionally, in an electronically controlled fuel injection system for an internal combustion engine having an air-fuel ratio feedback correction control function,
JP-A-60-90944, JP-A-61-1901
As disclosed in Japanese Patent No. 42, etc., there are some that employ learning control of the air-fuel ratio. In the air-fuel ratio feedback correction control, an oxygen sensor installed in the engine exhaust system determines 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 based on the determination result, the air-fuel ratio feedback correction coefficient LMD is determined. Parameters of engine operating conditions that are set by proportional/integral control and are related to the amount of air sucked into the engine (for example, intake air flow rate Q and engine rotational speed N)
By correcting the basic fuel injection amount Tp calculated from the above using the air-fuel ratio feedback correction coefficient LMD, the actual air-fuel ratio is feedback-controlled to the target air-fuel ratio.

[0003] Here, the deviation of the air-fuel ratio feedback correction coefficient LMD from the reference value (target convergence value) is learned for each of the plurality of operating regions, and is determined as a learning correction coefficient KBLR.
C (air-fuel ratio learning correction value) is determined, and the basic fuel injection amount Tp is corrected by the learning correction coefficient KBLRC so that the base air-fuel ratio obtained without the correction coefficient LMD approximately matches the target air-fuel ratio, and the air-fuel ratio During feedback control, the fuel injection amount Ti is calculated by further correcting it using the correction coefficient LMD.

[0004] This makes it possible to perform fuel correction in response to different correction requests for each operating condition, stabilize the actual air-fuel ratio near the stoichiometric air-fuel ratio, maintain good conversion efficiency in the three-way catalytic converter, and It is possible to control the concentration of harmful components to a low level.

[0005]

[Problem to be Solved by the Invention] By the way, failure or deterioration occurs in the components of the fuel supply control system such as the air flow meter that detects the amount of intake air, the fuel injection valve, the fuel pump, etc., and the base air-fuel ratio becomes lower than the target air-fuel ratio. If the air-fuel ratio deviates significantly from the target, even if the air-fuel ratio learning progresses and the air-fuel ratio feedback correction coefficient LMD converges to the target on average, the exhaust properties may actually worsen (exhaust harmful components (increased concentration). Therefore, the changes in the base air-fuel ratio are self-diagnosed, and based on the diagnosis results, failures and
By encouraging replacement of deteriorated parts and maintenance,
There was a desire to avoid operation in situations where controllability to the target air-fuel ratio (exhaust properties) is poor.

[0006] Here, as the above-mentioned diagnosis method, in a fuel supply device having an air-fuel ratio learning correction function as described above,
When the average value of the learned correction coefficient KBLRC, in which the required correction value for obtaining the target air-fuel ratio is learned for each operating region, has a deviation of more than a predetermined value from the initial value, the parts of the fuel supply system We considered a method of estimating that a large change in the base air-fuel ratio occurred due to failure or deterioration of the air-fuel ratio.

However, in the learning transient state until the learning correction coefficient KBLRC (air-fuel ratio learning correction value) for each operating region is learned corresponding to the changed base air-fuel ratio, the learning correction in the unlearned region is Since the coefficient KBLRC does not accurately indicate changes in the base air-fuel ratio,
Until learning progresses sufficiently, the learning correction coefficient KBL
It is not possible to diagnose large changes in the base air-fuel ratio based on the average value of RC, and the air-fuel ratio controllability is There was a problem in that the vehicle was driven in a deteriorated condition.

The present invention has been made in view of the above-mentioned problems, and provides a diagnosis that can quickly diagnose situations where the base air-fuel ratio has changed significantly due to component failure or deterioration without waiting for learning to progress in each operating region. The purpose is to provide equipment.

[0009]

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

The air-fuel ratio feedback correction value setting means compares the air-fuel ratio of the engine intake air-fuel mixture detected by the air-fuel ratio detection means with a target air-fuel ratio so as to bring the actual air-fuel ratio closer to the target air-fuel ratio. An air-fuel ratio feedback correction value for correcting the basic fuel supply amount is set in . Also,
The storage means rewriteably stores an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each operating region divided into a plurality of operating regions based on engine operating conditions, and the air-fuel ratio learning means The deviation of the fuel ratio feedback correction value from the target convergence value is learned, and the air-fuel ratio learning correction value stored in the storage means corresponding to the corresponding operating region is corrected and rewritten in a direction that reduces the deviation.

The fuel supply amount setting means determines the final fuel supply amount based on the basic fuel supply amount, the air-fuel ratio feedback correction value, and the air-fuel ratio learning correction value stored in the corresponding operating region in the storage means. is set, and the fuel supply control means drives and controls the fuel supply means based on the set fuel supply amount. Here, the update level difference calculation means calculates the average value of the updated level difference of the learning results in each operating region of the storage means, and the update level difference diagnostic means calculates the average value of the updated level difference of the learning result in each operating region of the storage means, and the update level difference diagnosis unit calculates the average value of the updated level difference based on the calculated average value of the updated level difference. Diagnose defects in the feeding device.

Furthermore, as shown by the dotted line in FIG. 1, instead of the diagnosis means using the update step, an average learning correction value calculating means and a diagnosis means using the average value and the update step may be provided. Here, the average learning correction value calculation means calculates the average value of the air-fuel ratio learning correction values for each operating region in the storage means, and the diagnosis means based on the average value and update step difference is calculated by the update step difference calculation means. A defect in the fuel supply device is diagnosed based on the average value of the updated step difference and the average value of the air-fuel ratio learning correction value calculated by the average learning correction value calculating means.

[0013]

[Operation] In the air-fuel ratio learning, the required correction level to obtain the target air-fuel ratio is learned for each operating region, so if the required correction level is learned in each operating region, the learning result is updated. This reduces the need to update the learning results, and the difference in updating the learning results becomes sufficiently small. On the other hand, if a learning result that meets the correction request for each operating region is not obtained, try changing the air-fuel ratio feedback correction value significantly from the target convergence value to bring the actual air-fuel ratio closer to the target air-fuel ratio. Therefore, the update step of learning results becomes large.

Therefore, when the update step is large, it indicates that the air-fuel ratio learning correction has not been performed in accordance with the correction requests for each operating region, and that the control to stabilize the air-fuel ratio to the target air-fuel ratio has not been performed. Before the learning in each area converges to the level corresponding to the base air-fuel ratio after the change, it is possible to quickly diagnose a situation where the controllability to the target air-fuel ratio is deteriorating based on the update step. can.

Even if there is a change in the base air-fuel ratio due to component failure or deterioration, if learning is performed in accordance with the base air-fuel ratio characteristics after such change, the update step in the air-fuel ratio learning correction value will be small. Furthermore, if the deterioration progresses slowly, the learning of the air-fuel ratio learning correction value will progress gradually with small update steps, making it impossible to diagnose component failure or deterioration of the fuel supply system based on the update steps. Therefore, by performing a self-diagnosis by combining the average value of the air-fuel ratio learning correction value with the update step, if the base air-fuel ratio changes due to component failure or deterioration, only the deterioration of exhaust properties during the learning process can be detected. Instead, it is now possible to diagnose potential deterioration in air-fuel ratio controllability once learning has converged.

[0016]

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

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

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

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

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

[0021] 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. While controlling the system, it also performs self-diagnosis of the fuel supply system.

In this embodiment, the basic fuel supply amount setting means, the fuel supply amount setting means, the fuel supply control means, the air-fuel ratio feedback correction value setting means, the air-fuel ratio learning means, the updating step calculating means, and the updating step-based diagnosis As shown in the flowcharts of FIGS. 3 to 13, the control unit 12 has functions as a means for calculating an average learning correction value, a means for calculating an average learning correction value, and a means for diagnosing using an average value and an update step. is assumed to correspond to a RAM with a backup function of a microcomputer (not shown) built into the control unit 12.

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

[0024] Then, in the next step 2, step 1
The voltage signal from the oxygen sensor 16 read in and the slice level (for example, 50
0 mV). When it is determined that the voltage signal from the oxygen sensor 16 is greater than the slice level and the air-fuel ratio is richer than the stoichiometric air-fuel ratio, the process proceeds to step 3, where it is determined whether or not the current rich determination is the first.

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

On the other hand, if it is determined in step 3 that the rich determination is not the first time, the process proceeds to step 7, and the value obtained by multiplying the integral constant I by the latest fuel injection amount Ti is subtracted from the previous correction coefficient LMD. and updates the correction coefficient LMD. Further, when it is determined in step 2 that the air-fuel ratio is lean with respect 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 advances to step 9 and the correction coefficient LMD up to the previous time is set to the minimum value b.

In the next step 10, the proportionality constant P is added to the previous correction coefficient LMD to update it, and in step 11
Now, the flag FP is set 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.

[0028] At the first time of rich/lean discrimination, the correction coefficient LM
When proportional control D is executed, various processes related to air-fuel ratio learning correction control, which will be described later, are further performed. In this embodiment, as shown in FIG. 14, the entire operating range is divided into 16 unit operating ranges, and the learning correction coefficient KBLRC1 is rewritably stored for each unit operating range.
The air-fuel ratio learning control device comprises a region learning map and a 256 region learning map that divides the entire operating region into 256 unit operating regions and stores the learning correction coefficient KBLRC2 for each unit operating region in an updatable manner. be done. Further, the learning correction coefficient KBLRCφ is applied under all operating conditions without dividing the operating range, and is separately set to learn the learning correction coefficient KBLRCφ, which indicates an average correction request in all operating ranges.

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

In step 15, it is determined whether or not most of the driving areas on the 16 area learning map have been learned by checking a learned flag F[B,
Make a judgment based on A]. 16 on the 16 area learning map
When most of the regions have been learned, the process proceeds to step 16, where it is determined whether the corresponding driving region on the 256 region learning map is the same as the previous time. Then, the process proceeds to step 17 only when there is a change in the relevant operating range.

In step 17, the latest correction coefficient LMD
Target convergence value Target (=1
．． Based on the absolute value of the deviation with respect to 0), a map of Δstress indicating the degree of inappropriateness of the learned value is referred to, and Δstress is set to increase in accordance with an increase in the deviation of the correction coefficient LMD from the target convergence value Target. Then, the Δ stress obtained this time is added to the "stress" in which the integrated value of the Δ stress is set.

As will be described later, when the stress exceeds a predetermined value, the already learned air-fuel ratio learning correction coefficient KBLRC
It is determined that this is inappropriate and the student is forced to start over from the beginning. The programs shown in the flowcharts of FIGS. 5 to 8 are air-fuel ratio learning programs for each operating region, and are executed at predetermined minute intervals (for example, 10 ms).

In step 21, the flag FP is determined, and when FP is 1, the process proceeds to step 22, and after resetting FP to zero, various processing by this program is performed, and when it is zero, this program is immediately terminated. let When the FP is reset to zero in step 22, in the next step 23, the learning correction coefficient KBLRC φ (initial value 1
．． A flag Fφ indicating whether or not 0) has been learned is determined.

[0034] Here, when the flag Fφ is zero and the learning correction coefficient KBLRCφ has not been completed,
Proceeding to step 24, it is determined whether the average value (←(a+b)/2) of the maximum and minimum values a and b of the correction coefficient LMD is approximately 1.0. If (a+b)/2 is not approximately 1.0, the process proceeds to step 26, and the predetermined coefficient Add the multiplied value to the previous learning correction coefficient KBLRC φ, and use the addition result as the new learning correction coefficient KBLRC φ←KBLR
Set as C φ+X{(a+b)/2-Target}.

Further, in step 26, the learning correction coefficient KBLRC1 and the learning correction coefficient KBLRC2 stored for each driving area of the 16-area learning map and the 256-area learning map are all reset to the initial value of 1.0. If it is determined in step 24 that (a+b)/2 is approximately 1, then in step 25 the flag Fφ is set to 1.
Set the learning correction coefficient K corresponding to all driving ranges.
It is determined that learning of BLRCφ has been completed.

On the other hand, in step 23, the flag Fφ is set to 1.
If it is determined that this is the case, then air-fuel ratio learning is performed for each operating region, which is divided into a plurality of operating regions based on the basic fuel injection amount Tp and the engine rotational speed N. First, step 2
In 7, on the 256 area learning map, the area [K, I] to which the current driving conditions apply is set in advance as the 256 driving area.
Basic fuel injection amount Tp set to divide into regions
The determination is made based on the threshold values Tp[i] and N[i] of the engine rotational speed N. Here, as shown in FIG. 14, K indicates the position of the corresponding region in 16 regions divided using the engine rotational speed N as a parameter, and I indicates the position of the corresponding region in the 16 regions divided using the basic fuel injection amount Tp as a parameter. shows.

In the next step 28, the corresponding area [K
,I] on the 16-area learning map,
Detect the corresponding area [B, A] on the 16-area learning map. Then, in the next step 29, it is determined whether the corresponding area on the 16-area learning map is the same as the previous one. Then, when the corresponding driving region changes on the 16-region learning map, the process proceeds to step 30, and the step 1
A predetermined value (for example, 4) is set to the count value cnt, which is decremented by 1 at 4.

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

In step 33, the progress of learning is determined based on whether the average value (a+b)/2 of the air-fuel ratio feedback correction coefficient LMD is close to the initial value (=1.0), which is the target convergence value Target. . Here, the correction coefficient L
If the average value of MD is found to be approximately 1.0 and learning has not been completed, the process directly proceeds to step 35, and if the average value of correction coefficient LMD is found to be approximately 1.0 and learning has been completed, At step 34, flag F[B,
A] is set to 1, and the process proceeds to step 35.

In step 35, a target convergence value Target is calculated from the average value of the maximum and minimum values a and b for the learning correction coefficient KBLRC1 stored corresponding to the area [B, A] on the 16-area learning map. A predetermined coefficient X is added to the value obtained by subtracting
Add the values multiplied by 1 and calculate the result as the learning correction coefficient KBLRC1[B,A]←KBLRC1[B,A]+X corresponding to the current driving area [B, A] on the 16-area learning map.
1 {(a+b)/2-Target} and update the map data.

[B,
During the learning of the A] area, the learning correction coefficients KBLRC2 of the 16 areas included in this [B, A] area in the 256 area learning map are all reset to the initial value 1.0 in step 36. On the other hand, when it is determined in step 31 that the flag F [B, A] is 1, the process proceeds to step 37 and the driving region [B, A] on the 16 region learning map is further subdivided into 16 regions 256
Move on to learning area learning maps.

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

On the other hand, in step 37, the average value of the correction coefficient LMD (a+b)/2 is set as the target convergence value Target.
1.0, the process proceeds to step 39, where it is determined that the learning of the driving area [K, I] that includes the current driving conditions of the area learning map 256 has been completed. Set flag FF [K, I] to 1 as follows. Then, from step 40 onwards, for each of the predetermined driving areas [K, I] on the 256 area learning map for which it was determined that learning has been completed this time, a plurality of driving areas with similar driving conditions adjacent to each other on the same map are Then, control is performed to set an appropriate learning correction coefficient KBLRC2 estimated based on the learning correction coefficient KBLRC2 in the corresponding region [K, I].

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

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

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

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

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

The results learned as described above are shown in FIG.
The learning correction coefficient KBLRC corresponding to the actual driving conditions is read out from the learning results. To briefly describe the processing contents of the program shown in the flowchart of FIG. 9, the target convergence value Tar of the learning correction coefficient KBLRC1
By adding the deviation from get to the learning correction coefficient KBLRC2 of each of the 16 regions included in the driving region to which the learning correction coefficient KBLRC1 is applied,
The correction burden due to the learning correction coefficient KBLRC1 is transferred to the learning correction coefficient KBLRC2 side, and after this correction is transferred, all the learning correction coefficients KBLRC1 are reset to the target convergence value Target, and double Avoid air/fuel ratio correction. That is, 16 area learning maps and 256
The learning results for each area learning map are
The 256 areas on the 256 area learning map are summarized into learning correction coefficients KBLRC2 for each area.

The program shown in the flowchart of FIG. 9 is processed in the background, and first, in step 51, counters i and j for indicating each area on the 256 area learning map are reset to zero, and then Then, in step 52, it is determined whether the counter i has exceeded 15. When the counter i is 15 or less, the process proceeds to step 53, where the learning correction coefficient KBLRC2 [j, i] corresponding to the area on the 256 area learning map indicated by [j, i] is set to K2, Also,
Area [j on the 16-area learning map that includes [j, i]
/4, i/4] corresponding to the learning correction coefficient KBLRC1[
j/4, i/4] to K1.

Next, in step 54, the learning correction coefficient KBLRC2[j,i] is updated according to the following equation. KBLRC2[j,i]←K2+(K1-Target
) Using the above formula, 256 If the learning correction coefficient KBLRC2[j,i] on the area learning map is updated, the learning correction coefficient K
The correction burden due to BLRC1 is the learning correction coefficient KBLR.
This will be transferred to the C2 side.

After the learning correction coefficient KBLRC2 is updated in step 54, the counter i is incremented by 1 in step 55, and the process returns to step 52. Here, counter i is 15
When the counter j exceeds 15, the process proceeds from step 52 to step 56, where it is determined whether the counter j has exceeded 15 or not. Then, when counter j is 15 or less,
At step 57, the counter i is reset to zero, and
By incrementing the counter j by 1 and returning to step 52 again, step 5 is performed for all 256 areas.
The update in step 4 is performed.

256 When the update of the learning correction coefficient KBLRC2 on the area learning map is completed and it is determined in step 56 that the counter j has exceeded 15, the entire correction burden due to the learning correction coefficient KBLRC1 is replaced by the learning correction coefficient. KB
Since this has been transferred to the LRC2 side, in step 58, 1 on the 16 area learning map is
A process is performed to reset all the learning correction coefficients KBLRC1 corresponding to each of the six regions to the target convergence value Target (=1.0), that is, to the initial value.

The process of transferring the correction burden due to the learning correction coefficient KBLRC1 to the learning correction coefficient KBLRC2 side and resetting all the learning correction coefficients KBLRC1 to their initial values is completed, and the learning results are calculated for each learning map and for each driving region. 256 Learning correction coefficient KBLRC on the area learning map
2, in the next step 59, the 256
A learning correction coefficient KBLRC2 corresponding to the current driving condition is read out from the region learning map by performing linear interpolation.

Learning correction coefficient K corresponding to actual driving conditions
When reading BLRC2 from the map by linear interpolation,
In the next step 60, the final learning correction coefficient KBLRC is set as follows. KBLRC ←KBLRC φ+KBLRC2-Tar
get learning correction coefficient KBLRC is the basic fuel injection amount Tp
is a correction term multiplied by each learning correction coefficient KB
LRC φ, KBLRC2 sets the initial value to the target convergence value Target (=1.
0 ), the target convergence value Target (=1.0 ) has been subtracted.

The learning correction coefficient KBLRC finally set by the program shown in the flowchart of FIG. 9 above is as shown in FIG.
This is used in the fuel injection amount setting program shown in the flowchart of No. 0. The fuel injection amount setting program shown in the flowchart of FIG.
s), and first, step 81
Now, the engine rotation speed N calculated based on the intake air flow rate Q detected by the air flow meter 13 and the detection signal from the crank angle sensor 14 is input.

Then, in the next step 82, the basic fuel injection amount Tp (←K×Q /N; K is a constant). In the next step 83, various corrections are made to the basic fuel injection amount Tp calculated in step 82 to calculate the final fuel injection amount (fuel supply amount) Ti. Here, the correction values used to correct the basic fuel injection amount Tp are the learning correction coefficient KBLRC, the air-fuel ratio feedback correction coefficient L
Cooling water temperature T detected by MD and water temperature sensor 15
various correction coefficients COEF that are set including a basic correction coefficient based on w, a post-start increase correction coefficient, etc., and a correction amount Ts for correcting changes in the effective injection time of the fuel injection valve 6 due to changes in battery voltage. Yes, Ti←Tp×LMD×
The final fuel injection amount Ti is updated at predetermined time intervals by calculating KBLRC×COEF+Ts.

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

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

Here, when the stress exceeds a predetermined value, it is determined that the learning result is inappropriate and an air-fuel ratio deviation has occurred, although the learning is almost completed, and the learning correction coefficient KBLRC φ is The process advances to step 92 in order to perform the learning again. In step 92, flags Fφ, F[0,0] to F[3,3], FF[0,
0] to FF[16, 16] are all reset to zero, and since learning will be restarted from the beginning as described above, stress is also reset to zero.

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

Next, self-diagnosis control of the fuel supply system will be explained according to the programs shown in the flowcharts of FIGS. 12 and 13. The program shown in the flowcharts of FIGS. 12 and 13 is processed as a background job (BGJ). First, in step 101, the update steps of the learning results for each area on the 256 area learning map are accumulated. Reset the SIG to zero.

In the next steps 102 and 103, counters p and q for indicating the area position on the 256 area learning map are reset to zero, respectively. Then, in step 104, it is determined whether the processing in each of the 16 regions divided by the basic fuel injection amount Tp has been completed, depending on whether the counter p has been counted up to 16 or more.

When the counter p is less than 16, the process advances to step 105, and it is then determined whether the counter q, which indicates 16 areas divided by the engine rotational speed N, has been counted up to 16 or more. If it is determined in step 105 that the counter q is less than 16, the process proceeds to step 106. Here, the flag FF [q, p] indicating the learning state in the area on the 256 area learning map indicated by [q, p] is determined, and the flag FF [
q, p] is 1 and the region [q, p] has been learned, in step 107, the integrated value Sigm (←Sigm
+KBLRC2[q,p]) and next step 1
At step 08, the counter z for counting the cumulative number is incremented by 1, and the process proceeds to step 109.

On the other hand, in step 106, the flag FF[q
, p] is zero and the region [q, p] has not been learned, steps 107 and 108 are skipped and the process proceeds to step 109. In step 109, the counter q is incremented by 1, and the process returns to step 105. Through such arithmetic processing, the counter q is counted up from zero to 15 with the counter p fixed, and the integrated value Sigm of the learned correction coefficient KBLRC2 is determined in the 16 regions where the basic fuel injection amount Tp is the same. .

When it is determined in step 105 that the counter q has exceeded 16, the process advances to step 110. In step 110, the learned correction coefficient KBL is calculated by dividing the integrated value Sigm by the number of samples z.
Find the average value of RC2, and use the latest average value and the same T
The deviation from the average value Sigmold found last time in the p region (deviation between the previous learning result and the latest learning result) is set as the update step of the learning result, and the update step of this learning result is integrated and calculated. The integration result is SIG (←SI
G+(Sigm/z-Sigmold)).

[0067] Then, the next steps 111 to 114
Now, while resetting counter q to zero, counter p
is increased by 1, and the integrated value Sigma and integrated number z are increased by 1.
By resetting to zero, the integrated value Sigm and the integrated number z are determined again in the next Tp region. In addition, in step 114, the average (Sigm /z) of the latest integrated value Sigm is calculated as S
igmold, and then when the average value of the learned learning correction coefficient KBLRC2 of the same Tp area is calculated,
The update step is determined based on the Sigmold.

After the process in step 115, the process returns to step 104 again, and all update steps in each of the 16 regions divided by the basic fuel injection amount Tp are integrated, and in step 104, if the counter p is 16 or more, it is determined that the counter p is 16 or more. Once it is determined, the process advances to step 116. Step 1
16, by dividing SIG, which is the integrated value of the update step difference in each of the 16 regions divided by the basic fuel injection amount Tp, by 16, the average value AVTp of the update step difference for each operating region divided by the basic fuel injection amount Tp is calculated. seek. Then, in order to obtain the average value AVN of the updated step difference in 16 regions divided by the engine rotational speed N, the process proceeds to step 117 and subsequent steps.

In steps 117 to 132, the counter q is fixed and the counter p is incremented by 1, and the learned correction coefficient KBLRC2 in the same 16 areas of engine rotation speed N is integrated, and the engine rotation speed is The update step difference in each of the 16 regions divided by speed N is determined, and the average AVN of the update step difference is determined in the same manner as steps 101 to 116 described above.

In step 133, the basic fuel injection amount T
AVTp, which is the average update step when operating regions are divided by p; learning correction coefficient KBLRC φ (average value of air-fuel ratio learning correction values for each operating region) indicating an average correction request under all operating conditions; Based on this, it is determined whether or not the fuel supply system is included in the defect determination area (the shaded area in FIG. 15) of the fuel supply system shown in FIG. In FIG. 15, when the update level difference is below a predetermined level and the average correction request (learning correction coefficient KBLR
The period when C φ) is below a predetermined level is defined as a normal region of the fuel supply system, and the other regions with large update steps and regions with a large average correction request are defined as defective regions of the fuel supply system.

In step 133, when it is determined that there is a defect in the fuel supply system based on the updated step difference AVTp and the learning correction coefficient KBLRCφ, the process proceeds to step 136, where it is determined that the air-fuel ratio controllability has deteriorated due to the change in the base air-fuel ratio. For example, a warning lamp installed near the driver's seat of the vehicle warns the driver of the situation. On the other hand, step 133
If it is determined that there is no defect in the fuel supply system, the process proceeds to step 134, where the fuel supply is performed based on the average update step difference AVN and the learning correction coefficient KBLRCφ in the operating range divided by the engine speed N. Determine system failure. Here too, if a defect is determined, step 1
The process proceeds to step 36 to issue a warning of deterioration in air-fuel ratio controllability, but if no defect in the fuel supply system is determined in both step 133 and step 134, it is assumed that air-fuel ratio control is being performed as expected. Well, step 135
, and select a process that does not issue a warning of deterioration in air-fuel ratio controllability.

As described above, if a malfunction in the fuel supply system (deterioration in air-fuel ratio controllability) is diagnosed based on the difference in the learning result of the learning correction coefficient KBLRC2, it is possible to diagnose a malfunction in the fuel supply system (deterioration in air-fuel ratio controllability). When a change in the base air-fuel ratio occurs and the learning correction coefficient KBLRC is updated to absorb the change in the base air-fuel ratio, the change in the base air-fuel ratio in all operating regions is
Changes in the base air-fuel ratio can be diagnosed before being absorbed into the LRC2.

In other words, when there is a change in the base air-fuel ratio, until the learning in each operating region has completely progressed,
Since the learned area corresponding to the base air-fuel ratio after the change and the unlearned area corresponding to the base air-fuel ratio before the change coexist, the change in the base air-fuel ratio is calculated based on the average value of the learning correction coefficient KBLRC2. It cannot be captured accurately. However, in this embodiment, the learned learning correction coefficient K
Since changes in the base air-fuel ratio are captured based on the update step of BLRC2 (step of learning results), learning corresponding to the changed base air-fuel ratio is performed in at least some regions, and the update step of the learning correction coefficient KBLRC2 is updated in such regions. Since this occurs in response to the change in the base air-fuel ratio, changes in the base air-fuel ratio can be diagnosed with good response, and it is possible to quickly warn that the air-fuel ratio controllability is deteriorating due to component failure or deterioration.

Furthermore, even if changes in the base air-fuel ratio are learned in all operating ranges, the air-fuel ratio feedback correction coefficient LMD is stabilized at the target, and large update steps in the learning correction coefficient KBLRC do not occur, Since the learning correction coefficient KBLRC φ applied to the operating conditions shows a change commensurate with the change in the base air-fuel ratio, it is now possible to diagnose a defect in the fuel supply system based on the learning correction coefficient KBLRC φ. Further, the learning correction coefficient KBL
In diagnosis based on RC φ, even if failure diagnosis cannot be performed based on the update step of learning results because the deterioration of parts of the fuel supply system progresses at a slow rate and the change in the base air-fuel ratio is gradual, the base air-fuel ratio A defect can be diagnosed when the change becomes large. Therefore, even if the air-fuel ratio feedback correction coefficient LMD is on average converging to the target, it is possible to diagnose a state in which exhaust properties are worsening due to a defect in the fuel supply system.

In addition, as shown in FIGS. 15 and 16, the update step difference AVTp, AVN and the learning correction coefficient KBLRC φ
Based on this, it is possible to determine the direction of change in the base air-fuel ratio, and it is also possible to estimate the deterioration characteristics of the exhaust properties due to the change in the base air-fuel ratio. As described above, according to this embodiment, when a failure or deterioration of a fuel supply system component occurs and the base air-fuel ratio changes accordingly, the change in the base air-fuel ratio is reflected in each operating region on the learning map. It is possible to detect and warn of changes in the base air-fuel ratio (deterioration in controllability to the target air-fuel ratio) even before the system is learned, and to prevent continuation of operation in a state where controllability to the target air-fuel ratio has deteriorated. It is possible to avoid this.

In this embodiment, the learning correction coefficients KBLRC1 and KBLRC2 in each of the two learning maps are
In addition, learning correction coefficient KBLRC applied to all operating conditions
φ is learned, but for example, if the operating region is 16
It has only a learning map divided into regions, calculates the updated step difference in each driving region on the learning map, calculates the average value of the learning correction coefficient KBLRC for each driving region on the map, and calculates the average value of the learning correction coefficient KBLRC for each driving region on the map. A self-diagnosis of the fuel supply system may be performed.

[0077] Furthermore, when calculating the update step difference of learning results for each operating region, the update step difference AVTp for each operating region is divided by the basic fuel injection amount Tp as described above, and the update step difference is divided by the engine rotational speed N. Although the updated step difference AVN for each operating region may be obtained separately, the updated step difference AVN may be obtained for each unit operating region divided by the basic fuel injection amount Tp and the engine rotational speed N, and the average value of these is used as the final value. It may also be used as an update step.

Furthermore, in this embodiment, the average value of the air-fuel ratio learning correction value (the learning correction coefficient KBLRC φ corresponding to the average value) is used in addition to the updated step difference, but the information on only the updated step difference is used for diagnosis. It may be possible to have the user perform a self-diagnosis based on the following. If only updated steps are used, it may not be possible to make a defective judgment once learning has converged, but for example, once a defect has been determined based on updated steps, such defective judgments may be canceled until maintenance is performed. If you prevent this from happening, parts of the fuel supply system may malfunction or
It is possible to avoid operating with deteriorated air-fuel ratio controllability.

[0079]

As explained above, according to the present invention, in a fuel supply system having an air-fuel ratio learning correction function, the base air-fuel ratio changes due to failure or deterioration of the components of the fuel supply system, and the air-fuel ratio control It is now possible to quickly diagnose the state of deterioration in exhaust performance before the learning for each driving area has sufficiently progressed, and it is possible to provide an early warning of driving in a state where exhaust performance has deteriorated. becomes.

[Brief explanation of drawings]

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

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

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

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

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

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

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

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

FIG. 9 is a flowchart showing correction and readout control of learning results.

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

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

FIG. 12 is a flowchart showing self-diagnosis of the fuel supply system.

FIG. 13 is a flowchart showing self-diagnosis of the fuel supply system.

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

FIG. 15 is a diagram showing a failure diagnosis area in this embodiment.

FIG. 16 is a diagram showing the deterioration of exhaust characteristics in the defect diagnosis region.

[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 (2)

[Claims] 1. Engine operating condition detection means for detecting engine operating conditions including at least operating parameters related to the amount of air taken into the engine; A basic fuel supply amount setting means for setting the fuel supply amount, an air-fuel ratio detection means for detecting the air-fuel ratio of the engine intake air-fuel mixture, and a comparison between the air-fuel ratio detected by the air-fuel ratio detection means and the target air-fuel ratio. an air-fuel ratio feedback correction value setting means for setting an air-fuel ratio feedback correction value for correcting the basic fuel supply amount so that the actual air-fuel ratio approaches the target air-fuel ratio; a storage means for rewritably storing an air-fuel ratio learning correction value for correcting the basic fuel supply amount for each operating region; and a storage means for learning a deviation of the air-fuel ratio feedback correction value from a target convergence value. an air-fuel ratio learning means for correcting and rewriting the air-fuel ratio learning correction value stored in correspondence with the relevant operating region in a direction that reduces the deviation; and the basic fuel supply amount, the air-fuel ratio feedback correction value, and the memory. a fuel supply amount setting means for setting the final fuel supply amount based on the air-fuel ratio learning correction value stored in the corresponding operating region in the means; a fuel supply control means for driving and controlling a fuel supply means; and an update step calculation means for calculating an average value of update steps of learning results in each operating region of the storage means. and update step difference diagnosis means for diagnosing a defect in the fuel supply device based on the average value of the update step differences calculated by the update step difference calculating means. Self-diagnosis device in supply equipment. 2. In place of the diagnosis means based on the update step difference, an average learning correction value calculation means for calculating an average value of the air-fuel ratio learning correction values for each operating region in the storage means, and an update step difference calculation means. average value and update step diagnosis means for diagnosing a defect in the fuel supply device based on the average value of the updated step differences calculated and the average value of the air-fuel ratio learning correction value calculated by the average learning correction value calculation means; A self-diagnosis device for a fuel supply system for an internal combustion engine according to claim 1, further comprising: a self-diagnosis device for a fuel supply system for an internal combustion engine.