JPH01106948A - Control device for learning of internal combustion engine - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed description of the invention] <Industrial application field> The present invention relates to an air-fuel ratio (fuel injection amount) for an internal combustion engine.

This invention relates to a learning control device for a feedback control system such as ignition timing and idle speed.

<Prior art> As a conventional learning control device for an internal combustion engine,
203828, JP 51-211738, JP 60-90944.

There is one disclosed in Japanese Patent Application Laid-Open No. 61-190141.

These are feedback correction values that are set using proportional/integral control, etc., while comparing the basic control amount, which is set in accordance with the control target value such as the air-fuel ratio, based on the operating state of the engine, with the control target value and the actual value. In systems that perform feedback control of the air-fuel ratio, etc. to the control target value by correcting the control amount and controlling this control amount, the deviation from the reference value of the feedback correction value during feedback control is calculated based on the engine operating state. Learn for each area and determine the learning value for each area.
When calculating the control amount, the basic control amount is corrected by the area-specific learning value so that the control amount obtained without correction by the feed pack correction value matches the control target value, and during feedback control. The control amount is calculated by further correcting this using a feedback correction value.

According to this, it is possible to eliminate the follow-up delay of feedback control during transient operation during feedback control, and it is possible to accurately obtain a desired control output when feedback control is stopped.

Therefore, it absorbs variations in component parts such as electronically controlled fuel injection devices, and also absorbs changes over time such as engine filling efficiency, atmospheric pressure, etc.
It is used to maintain the maximum performance of the engine over a long period of time by correcting changes in operating environment conditions such as temperature and humidity. .

<Problems to be Solved by the Invention> However, such conventional learning control devices use a so-called iterative learning method using a data map, that is, data map grid divisions are set according to engine operating conditions, and feedback control deviation amount in each learning area is calculated. Since this method repeatedly updates the information based on learning experience, it has the disadvantage that if each learning area classification is set in detail in order to improve learning correction accuracy, the learning update speed becomes slow.

In other words, learning correction accuracy and learning speed are contradictory conditions.

In view of these conventional problems, it is an object of the present invention to provide a learning control device for an internal combustion engine that can greatly improve the learning speed while increasing the learning correction accuracy and is highly reliable through self-diagnosis. purpose.

<Means for Solving the Problems> In order to achieve the above object, the present invention configures a learning control device for an internal combustion engine including the following means A-I, as shown in FIG.

(A) Basic control amount setting means for setting the basic control amount corresponding to the control target value of the controlled object of the internal combustion engine. (B) Comparing the control target value and the actual value and moving the actual value closer to the control target value. Feedback correction value setting means for increasing or decreasing the feedback correction value by a predetermined amount (C) Rewritable factor-specific learned value storage means for storing a plurality of factor-specific learned values (D) Setting the basic control amount to the feedback correction value and (E) a control amount calculating means for calculating a control amount by correcting the plurality of factor-specific learned values and using arithmetic formulas set accordingly. Control means for controlling (F) Deviation detecting means for detecting the deviation of the feedback correction value from the reference value (G) Analyzing the causes of the deviation based on various information and detecting the deviation by factor based on the analysis result (H) Factor-specific learning value updating means for correcting and rewriting the factor-specific learning value in the storage means based on each of the plurality of parameters. (1) The factor-specific learning value is set in advance. Abnormality determination means (operation) that determines the component related to the learned value for each factor as abnormal when the abnormality determination value exceeds the abnormality determination value determined by the factor. The feedback correction value setting means B compares the control target value and the actual value and increases or decreases the feedback correction value by a predetermined amount based on, for example, proportional/integral control in the direction of bringing the actual value closer to the control target value. and set. The control amount calculation means corrects the basic control amount using the feedpack correction value, and further sets the basic control amount based on a plurality of factor-specific learning values stored in the factor-specific learning value storage means C. The control amount is calculated by correcting it using the optimal calculation formula.

Then, the control means E operates according to this control amount to control the controlled object of the internal combustion engine.

On the other hand, the deviation detection means F detects the deviation of the feedback correction value from the reference value. And factor analysis means G
The system inferentially analyzes the factors that led to the deviation based on various information (for example, at least one of the following: engine operating condition, deviation amount, deviation direction, deviation speed, deviation direction, etc.) according to predetermined analysis rules. Based on the analysis results, the deviation is separated into multiple parameters according to factors. Then, the factor-specific learning value updating means H corrects and rewrites the factor-specific learning value in the storage means C based on each of the plurality of separated parameters.

In this way, the deviation (error amount) of feedback control is detected, and this is inferred and factor-analyzed using various information and databases, and highly accurate (
By performing the correction, both learning correction accuracy and learning speed can be achieved.

In addition, the abnormality determination means I compares each factor-specific learning value with each preset abnormality determination value, and when there is a factor-specific learning value that exceeds the abnormality determination value, the abnormality determination means I is judged to have deteriorated or failed.

As a result, not only absolute failures but also changes in characteristics due to component deterioration can be detected, deterioration diagnosis can be performed, and reliability can be improved.

<Embodiment> An embodiment in which a learning control device according to the present invention is applied to an air-fuel ratio feedback control system of an internal combustion engine having an electronically controlled fuel injection device will be described below.

In FIG. 2, an engine l has an air cleaner 2 to an intake duct 3. Air is taken in via the throttle valve 4 and the intake manifold 5. A fuel injection valve 6 as a control means is provided in a branch portion of the intake manifold 5 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. Fuel is injected and supplied by a pump and adjusted to a predetermined pressure by a pressure regulator. Although this example is a multi-point injection system, it may also be a single-point injection system in which a single fuel injection valve is provided in common to all cylinders, such as upstream of a throttle valve.

An ignition plug 7 is provided in the combustion chamber of the engine 1, which ignites a spark to ignite and burn the air-fuel mixture.

From engine 1, exhaust manifold 8° exhaust duct 9. Exhaust gas is discharged via a three-way catalyst 10 and a muffler 11. The three-way catalyst 10 is an exhaust purification device that oxidizes Co and HC in the exhaust components and reduces NO to other harmless substances, and burns the air-fuel mixture at the stoichiometric air-fuel ratio. Sometimes both conversion efficiencies are the best.

The control unit 12 includes a CPU and a ROM.

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

As the various sensors described above, a hot wire type or flap type air flow meter 13 is provided in the intake duct 3, and outputs a voltage signal according to the intake air flow rate Q.

Further, a crank angle sensor 14 is provided, and in the case of a four-cylinder engine, outputs a reference signal for every 180 degrees of crank angle and a unit signal for every 1 degree or 2 degrees of crank angle. Here, the engine speed N can be calculated by measuring the cycle of the reference signal or the number of unit signals generated within a predetermined time.

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

Furthermore, an O2 sensor 16 is installed at the collecting part of the exhaust manifold 8.
is provided to detect the air-fuel ratio of the air-fuel mixture taken into the engine 1 via the 02 concentration in the exhaust gas. If the 02 sensor 16 is equipped with a NOx reduction catalyst layer as proposed in Japanese Patent Application No. 62-65844, more accurate detection will be possible. 'Here, the CPU of the microcomputer built into the control unit 12 is
Programs on the ROM (fuel injection amount calculation routine, air-fuel ratio feedback control routine, optimal learning routine) shown as flowcharts in FIGS. 3 to 6.

It performs arithmetic processing according to a self-diagnosis routine (self-diagnosis routine) and controls fuel injection.

The functions of the basic control amount setting means, the feedback correction value setting means, the control amount calculation means, the deviation detection means, the factor analysis means, the learning value updating means for each factor, and the abnormality determination means are as follows.
This is achieved by the program. Further, the factor-specific learning value storage means uses a RAM, and uses a backup power source to retain the stored contents even after the engine key switch is turned off.

Next, the state of the arithmetic processing of the microcomputer in the control unit 12 will be explained with reference to the flowcharts shown in FIGS. 3 to 6.

FIG. 3 shows a fuel injection amount calculation routine, which is executed at predetermined time intervals.

In step 1 (denoted as Sl in the figure; the same applies hereinafter), the intake air flow rate Q is detected based on the signal from the air flow meter 13, and the engine speed N is calculated based on the signal from the crank angle sensor 14. .

Water temperature T detected based on the signal from the water temperature sensor 15
Enter w etc.

In step 2, the basic fuel injection amount Tp corresponding to the intake air amount per unit rotation is determined from the intake air flow rate Q and the engine rotation speed N.
=K - Q/N (K is a constant) is calculated. This step 2 corresponds to the basic control amount setting means.

In step 3, the water temperature correction coefficient is adjusted according to the water temperature Tw.
Various correction coefficients COE F = 1 including air-fuel ratio correction coefficient KMR, etc. according to engine speed N and basic fuel injection amount Tp
+Ktw+KMR+... is set.

In step 4, the latest air-fuel ratio feedback correction coefficient α (reference value 1) set by the air-fuel ratio feedback control routine shown in FIG. 4, which will be described later, is read.

In step 5, the voltage correction numerator s is calculated based on the battery voltage.
Set. This is to correct changes in the injection flow rate of the fuel injection valve 6 due to changes in battery voltage.

In step 6, the factor-specific learning values X, , X2 are read from a predetermined address of the RAM serving as the factor-specific learning value storage means. Note that when learning has not started, the initial value is
+ = O, Xz = 1 are stored.

In step 7, the fuel injection amount Ti is calculated according to the following equation. This step 7 corresponds to the control amount calculation means.

Ti=Xz・Tp−COEF・α×(Ts+X,) In step 8, the calculated Ti is set in the output register. As a result, when a predetermined engine rotation synchronization (for example, every rotation) fuel injection timing is reached, a drive pulse signal with a pulse width of the latest set Ti is applied to the fuel injection valve 6.
is given and fuel injection is performed.

FIG. 4 shows an air-fuel ratio feedback control routine, which is executed in rotational synchronization or time synchronization, and thereby sets the air-fuel ratio feedback correction coefficient α. Therefore, this routine corresponds to feedback correction value setting means.

In step 11, it is determined whether predetermined air-fuel ratio feedback control conditions are satisfied. Here, the predetermined air-fuel ratio feedback control condition is that the engine speed N is equal to or less than a predetermined value, and the basic fuel injection amount Tp representing the load is equal to or less than a predetermined value. If such conditions are not met, this routine is terminated. In this case, the air-fuel ratio feedback correction coefficient α is the previous value (or reference value 1)
is clamped, and air-fuel ratio feedback control is stopped. This stops the air-fuel ratio feedback control in the high rotation or high load region, obtains a rich output air-fuel ratio using the air-fuel ratio correction coefficient KMR, and suppresses the rise in exhaust temperature.
This is to prevent seizure of the engine 1 and burnout of the three-way catalyst 10.

When the air-fuel ratio feedback control conditions are met, step 1
Proceed to step 2 and beyond.

In step 12, the output voltage of the sensor 16 is 0□■. 2 is read, and in the next step 13, it is determined whether the air-fuel ratio is rich or lean by comparing it with the slice level voltages r and f corresponding to the stoichiometric air-fuel ratio.

When the air-fuel ratio is lean (■o2〈■r, f), the process proceeds from step 13 to step 14, where it is determined whether or not it is the time of reversal from rich to lean (immediately after the reversal). 5 to store the previous deviation of the air-fuel ratio feedback correction coefficient α from the reference value 1 as a=α-1 for the optimum learning routine shown in FIG. The coefficient α is increased by a predetermined proportionality constant of 2 with respect to the previous value. Otherwise, the process proceeds to step 17, where the air-fuel ratio feedback correction coefficient α is increased by a predetermined integral constant of 1 with respect to the previous value, and thus the air-fuel ratio feedback correction coefficient α is increased at a constant slope. Note that P>>I.

When the air-fuel ratio is rich (Voz>V-f), the process proceeds from step 13 to step 18 to determine whether or not it is the time of reversal from lean to rich (immediately after the reversal), and when the air-fuel ratio is reversed, the process proceeds to step 19. After storing the deviation of the previous air-fuel ratio feedback correction coefficient α from the reference value 1 as b=α−1 for the optimum learning routine shown in FIG. The value is decreased by a predetermined proportionality constant P. Otherwise, the process proceeds to step 21, where the air-fuel ratio feedback correction coefficient α is decreased by a predetermined integral constant of 1 minute from the previous value, and thus the air-fuel ratio feedback correction coefficient α is decreased at a constant slope.

Figure 5 shows the optimal learning routine, which is executed at predetermined intervals,
As a result, the factor-specific learning values X, , X2 are set and updated.

In step 31, it is determined whether a predetermined learning condition is satisfied. Here, the predetermined learning condition is that the air-fuel ratio feedback control is being performed and the rich/lean signal of the 02 sensor 16 is inverted at an appropriate period. If such conditions are not met, this routine is terminated.

If the predetermined learning conditions are met, the process advances to step 32 and the output voltage of the sensor 16 is set to 0□■. 2 is reversed, and if it is not reversed, the process proceeds to step 33, where the engine speed N and basic fuel injection amount Tp are sampled as data on the engine operating state at that time.

0□Output voltage of sensor 16■. When 2 is reversed, the process proceeds to step 34 to obtain the average value of a and b for optimal learning. In this case, a and b are the upper and lower peak values of the deviation of the air-fuel ratio feedback correction coefficient α from the reference value 1 from reversal to reversal of the increase/decrease direction of the air-fuel ratio feedback correction coefficient α, as shown in FIG. , and by calculating these average values, the average deviation Δα of the air-fuel ratio feedback correction coefficient α from the reference value 1 is detected.

Therefore, step 15.19 in FIG. 4 and step 34 in FIG. 5 correspond to the deviation detection means.

Next, proceed to step 35 and 0□ Output voltage of sensor 16■
. Engine speed N and basic fuel injection amount while 2 is reversed
Movement of rp (N8.N2..., Tp,, Tpz...
) to identify the engine operating state (N, Tp).

Next, proceed to step 36 and check the engine operating state (N, Tp).
The learning weight assigned to each area is searched for by referring to the map from the area of . however,
2 is less than or equal to 1 for Kl +.

Here, the factors that led to the deviation Δα are mainly caused by the fuel injection valve 6 (hereinafter referred to as F/I critical factor), and factors caused by the air flow meter 13 including changes in air density (hereinafter referred to as F/I critical factors). Q factor) and their respective proportions are expressed as K + and K 2.

Then, based on a rule of thumb, we estimate that the F/I critical factor is high in the low-speed, low-load region, and that the Q factor is large in the high-speed, high-load region, so we assign + and KzO values to each area, and use this map. By referring to this, factor analysis is performed based on the engine operating status.

As a result, the deviation Δα becomes the parameter of the F/I factor,
It is now possible to separate Δα and the parameter of the Q factor into 2 ・Δα, and in the next step 37, it is separated into each parameter as Δα,= ・Δα, Δα2=2 ・Δα.

Therefore, steps 35 to 37 correspond to factor analysis means.

In addition to being performed based on the engine operating state as described above, the factor analysis may also be performed based on the amount of deviation, direction of deviation, speed of deviation, direction of deviation change, etc., and by inference from these databases.

Next, the process proceeds to step 38, where the factor-specific learned value X=,
The difference Δ is added to the learning value X2 of the other Q factor.
Update α2 by adding M2. M I and M t are learning weighting coefficients.

X + = X r + M +・Δα1X, = Xt
+Mz・Δα2 Furthermore, the learning value for each of these factors X1. Based on the self-diagnosis routine shown in FIG. 6 based on XI, the fuel injection valves 6. A self-diagnosis of the air flow meter 13 is performed.

Next, the process proceeds to step 39, where these factor-specific learning values X+ and Xz are written to a predetermined address on the RAM to rewrite the data. This RAM is a backup memory, and the stored contents are retained even after the engine key switch is turned off.

Therefore, steps 38 and 39 correspond to the factor-specific learning value updating means.

In this way, the F/I prisoner requirement learning value X and the learning value X2 of the Q factor are determined, but correction based on these is based on the factor This is done using a separate optimal calculation formula.

That is, for the learned value XI of the F/I factor, the learned value X2 of the Q factor is added as an addition term to the basic fuel injection amount Tp.
As for the multiplication term for the basic fuel injection amount Tp,
An arithmetic expression is set, and the optimum correction is performed using this formula.

FIG. 6 shows a self-diagnosis routine, which is executed at predetermined intervals.
This causes the fuel injection valve 6. Failure and deterioration diagnosis of the air flow meter 13 is performed.

In step 41, the factor-specific learning value X related to the fuel injection valve 6 set in the optimum learning routine of FIG. 5 is compared with the upper limit judgment value XIM□ of the preset abnormality judgment values.

Here, when X, ≧X4□, the process proceeds to step 42, where it is compared with the lower limit judgment value
If it is within the range of AX, it is determined in step 43 that the fuel injection valve 6 is normal, and OK is displayed. On the other hand, in step 41.42, X,>X,,□*XI<XIMI
If N, the process proceeds to step 44, where it is determined that the fuel injection valve 6 has deteriorated, and NG is displayed.

In steps 45 to 48, the air flow meter 13 is similarly diagnosed.

That is, steps 45 and 46 cause the air flow meter 13 to
The factor-specific learning value X2 related to is XtNtH≦X2≦X□
Determine whether □, XtH1N≦X2≦X tW
If it is AX, in step 47 the air flow meter 13 is set to O.
Display K, Xz < XzMIN, Xz >
If XzMAxT is near, NG is displayed in step 48.

Therefore, steps 41, 42, 45, and 46 correspond to the abnormality determining means.

Figure 8 shows that the effect of this learning control is +16 on the landmark.
The engine with a rich tendency of % approaches the engine with the median variation of ∆ mark after about 4 learnings, and the engine with a lean tendency of 16% with ∆ mark after about 3 learnings.
This shows how the engine approaches the median dispersion value of the marks, clearly showing the improvement in learning speed by this learning control.

In addition, by examining the values of the factor-based learning values X+ and Xz, it is possible to know changes in characteristics due to component deterioration, which could not be detected in the past, and to understand the deterioration status of the component parts. Therefore, problems in drivability, deterioration in fuel efficiency, etc. caused by deterioration of parts can be prevented, and the performance of the device can also be improved.

In this embodiment, a so-called L-Jetro system that has an air flow meter and detects the intake air flow rate is shown as the electronically controlled fuel injection system, but a so-called D-Jetro system that detects the intake manifold negative pressure is used. It can be applied to various systems such as the so-called α-N method based on the throttle valve opening (α) and the engine speed (N).

Moreover, it can be applied not only to air-fuel ratio feedback control, but also to ignition timing control based on knocking detection and idling speed feedback control via an auxiliary air valve.

<Effects of the Invention> As explained above, according to the present invention, instead of the conventional method of learning by area, the method of analyzing the factors that caused the deviation and learning for each factor improves the learning correction accuracy. Learning speed can be significantly increased without slowing down.

Furthermore, such learning control can reduce the number of matching steps, simplify component management, and make maintenance free. Furthermore, the capacity of the backup memory can also be reduced.

Furthermore, by performing self-diagnosis of the component parts based on the learned values for each factor, it becomes possible to diagnose the deterioration of the parts.

[Brief explanation of the drawing]

Fig. 1 is a functional block diagram showing the configuration of the present invention, Fig. 2 is a system diagram showing an embodiment of the invention, Figs. 3 to 6 are flow charts showing control details, and Fig. 7 is air-fuel ratio feedback. FIG. 8 is a diagram showing how the correction coefficient changes, and FIG. 8 is a diagram showing the effect of learning control. 1... Engine 6... Fuel injection valve 12... Control unit 13... Air flow meter
14... Crank angle sensor 16... 02 sensor patent applicant Japan Electronics Co., Ltd. Agent Patent attorney Sasashima Fujio Copper 8 NOx [Figure 7 (gr/km)

Claims (1)

[Scope of Claims] Basic control amount setting means for setting a basic control amount corresponding to a control target value of a controlled object of an internal combustion engine, and comparing the control target value and the actual value to bring the actual value closer to the control target value. feedback correction value setting means for increasing or decreasing the feedback correction value by a predetermined amount in the direction; rewritable factor-specific learning value storage means for storing a plurality of factor-specific learning values; and setting the basic control amount to the feedback correction value. and a control amount calculation means that calculates a control amount by correcting the plurality of factor-specific learned values using calculation formulas set accordingly, and operates in accordance with the control amount to control a controlled object of the internal combustion engine. a control means for detecting a deviation of the feedback correction value from a reference value, a deviation detection means for detecting a deviation of the feedback correction value from a reference value, and analyzing the cause of the deviation based on various information and converting the deviation into a plurality of parameters for each factor based on the analysis result. factor analysis means for separating; factor-specific learning value updating means for correcting and rewriting the factor-specific learning value in the storage device based on each of the plurality of parameters; A learning control device for an internal combustion engine, comprising: abnormality determining means for determining a component related to the factor-specific learning value as abnormal when the value exceeds the factor-specific learning value.