JPH01106949A - Internal combustion engine learning control device - 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 feed pump control system such as ignition timing and idle speed.

<Prior art> As a conventional learning control device for an internal combustion engine,
203828, JP-A-59-211738, and JP-A-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 using the learning value for each area so that the control amount obtained by calculating the control amount without correction using the feedback correction value matches the control target value. is further corrected using a feedback correction value to calculate the control amount.

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, the present invention provides a learning control device for an internal combustion engine that can significantly improve learning speed while increasing learning correction accuracy and is highly reliable through self-diagnosis. The purpose is to

<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, which includes the following means A-, as shown in FIG.

(8) Basic control 1ffi 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 learned value updating means that corrects and rewrites the factor-specific learned value in the storage means based on each of the plurality of parameters. (I) Factor-specific learned value and preset settings. Comparison means (J) for comparing the determined abnormality judgment value with the abnormality judgment value (J) Timekeeping means (K) for measuring the duration of the state in which the factor-based learning value exceeds the abnormality judgment value (K) The measurement time of the timekeeping means exceeds a predetermined time Abnormality determination means (operation) for determining a component related to the learned value for each factor to be abnormal when The correction value setting means B compares the control target value and the actual value and sets the feedback correction value by increasing or decreasing a predetermined amount based on, for example, proportional/integral control in a direction that brings the actual value closer to the control target value. Then, the control amount calculation means corrects the basic control amount with the feedback correction value, and further calculates the optimal value set according to the 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 a 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, by detecting the deviation (error amount) of feedback control, inferring it using various information and a database, analyzing the factors, and accurately correcting it using an arithmetic formula suitable for each factor, learning is possible. This achieves both correction accuracy and learning speed.

In addition, the comparison means (■) compares each factor-specific learning value with a preset abnormality judgment value, and if the result shows that the factor-specific learning value exceeds the abnormality judgment value, the timer (J) measures the duration of this state. When the measurement continues for a predetermined time or longer, the abnormality determining means determines that the component related to the factor-based learning value has deteriorated or failed. This makes it possible to detect not only absolute failures, but also changes in characteristics due to component deterioration, and to diagnose deterioration.
In addition, erroneous abnormality determinations can be prevented 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 1 is connected to an air cleaner 2 through 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 is energized to open the solenoid and then closed by being de-energized. Fuel is injected and supplied under pressure from the 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 NOx to convert it into other harmless substances, and when the air-fuel mixture is burned at the stoichiometric air-fuel ratio. The reconversion efficiency is 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 ink face, 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, the 0□ sensor 16 is located 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 0□ 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 in the control unit 12 executes programs (fuel injection amount calculation routine, air-fuel ratio feedback control routine, optimal learning routine) on the ROM 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 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 comparison means.

The functions as a time measurement means and an abnormality determination means are achieved by the program. Further, a RAM is used as the factor-specific learning value storage means, and the stored contents are retained even after the engine key switch is turned off using a backup power source.

Next, the state of the arithmetic processing of the microcomputer in the control unit 12 will be explained with reference to the flowcharts of 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.
= -Q/N (K is a constant) is calculated. This step 2
The part corresponds to the basic control amount setting means.

In step 3, a water temperature correction coefficient corresponding to the water temperature Tw is used, an air-fuel ratio correction coefficient corresponding to the engine speed N and the basic fuel injection amount Tp, and various correction coefficients including 4R, etc.COE F = 1
+KTw+ K, m+...' are 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, X2 =1.

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=X2·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 and feedback control in the high rotation or high load region, and obtains a richer output air-fuel ratio by setting the air-fuel ratio correction coefficient □ to suppress 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 02 sensor 16 is ■. 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 voltage V rof corresponding to the stoichiometric air-fuel ratio.

When the air-fuel ratio is lean (■.z<Vrof), the process proceeds from step 13 to step 14 to determine whether or not it is the time of reversal from rich to lean (immediately after the reversal), and when the air-fuel ratio is reversed, the process proceeds to step 15. After storing the deviation of the previous air-fuel ratio feedback correction coefficient α from the reference value 1 as a=α−1 for the optimum learning routine shown in FIG. Increase the value by a predetermined proportionality constant of 2 minutes. At the time of reversal, the process proceeds to step 17, where the air-fuel ratio feedback correction coefficient α is increased by a predetermined integral constant of 1 minute relative 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 (V 02 > V r-f), the process proceeds from step 13 to step 18 to determine whether it is the time of reversal from lean to rich (immediately after reversal), and when the air-fuel ratio is reversed, the process proceeds to step 19. Proceeding to step 20, after storing the previous deviation of the air-fuel ratio feedback correction coefficient α from the reference value 1 as b-α-1 for the optimum learning routine shown in FIG. is decreased by a predetermined proportionality constant P from the previous value. At the time of reversal, 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.
In this way, 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 at the time of reversal, 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. At this time, 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. 7. 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 to check the output voltage V of the 02 sensor 16.
Engine speed N and basic fuel injection amount while O2 is reversed
Movement of rp (N,, 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 for K and + is 1 or less.

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 the proportion of each factor is expressed as KI and Kz.

Then, from a rule of thumb, we estimate that the F/I critical factor is high in the low-speed, low-load region, and the Q factor is large in the high-speed, high-load region, and we assign a value of 2 to each area. By referring to the map, factor analysis is performed based on the engine operating status.

As a result, the deviation Δα becomes the parameter of the F/I factor,
・Δα and 2 ・Δα can be separated into Q factor parameters, and in the next step 37, Δα,
= ・Δα, Δα2− 2 ・Δα, and separate into each parameter.

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 learned values X, , Xz for each factor stored in a predetermined address on the RAM are read out, and the deviation Δα is applied to one F/I prisoner learned value XI as shown in the following equation.
The difference Δ is added to the learning value X2 of the other Q factor.
Update α2 by adding M2. M,,M,is a learning weighting coefficient.

X + = X r + M +・Δα1X t =
X z + M z・Δα2 Furthermore, the learning value for each of these factors
Based on the self-diagnosis routine shown in FIG. 6 based on 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 learned value X+ of the F/I factor and the learned value X2 of the Q factor are determined, and the correction based on these is optimal for each factor, as shown in step 7 in Figure 3. This is done using arithmetic expressions.

That is, the learned value X1 of the F/I factor is added to the basic fuel injection amount Tp, and the learned value X2 of the Q factor is added to the basic fuel injection amount Tp.
As a 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 XI related to the fuel injection valve 6 set in the optimum learning routine of FIG. 5 is compared with the lower limit judgment value X, 4AX of the abnormality judgment values set in advance.

Here, when X, ≧XIMAR, the process proceeds to step 42, where it is compared with the lower limit judgment value
When , that is, the factor-specific learning value X1 is
If it is within the range of X1MAX, the count value C1 of the timer is cleared in step 43, and in step 44 it is determined that the fuel injection valve 6 is normal and OK is displayed. - way,
In step 41.42, X + > X IMAX,
When X + < X IMIN, the process proceeds to step 45 and starts the timer. In step 46, it is determined whether the timer count value C3 is greater than or equal to a predetermined value Cs. Here, when C+<Cs, proceed to step 48, and CI≧Cs
When this happens, the process proceeds to step 47, where it is determined that the fuel injection valve 6 has deteriorated, and NG is displayed.

In steps 48 to 54, the air flow meter 13 is similarly diagnosed.

That is, steps 48 and 49 cause the air flow meter 13 to
The factor-specific learning value X2 related to is X2□8≦X2≦X2,
Determine whether or not there is 4AX Te, X2H, N≦x2≦X2
If it is MAX, the timer count C2 is cleared in step 50, and OK is displayed on the air flow meter 13 in step 51. Also, X2<X2M□, X2 >XtMa
If x, the process proceeds to step 52 to start a timer, and when the count value 02 becomes equal to or higher than the predetermined value C3 in step 53, NG is displayed in step 54.

Therefore, steps 41, 42, 48, and 49 correspond to comparison means, steps 45.52 correspond to timekeeping means, and steps 46.53 correspond to abnormality determination means.

Note that the set time from when the factor-based learning value exceeds the abnormality determination value until the abnormality determination is made may be different for each component.

Figure 8 shows that the effect of this learning control is +16 for the old seal.
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, learning value by factor X1. By examining the value of Xz, it is possible to know changes in characteristics due to component deterioration, which could not be detected conventionally, 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. Furthermore, since it is determined that an abnormality occurs when the abnormal state of the factor-based learning values X+ and Xz continues steadily, erroneous determination can be prevented and highly reliable self-diagnosis can be performed.

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 learning speed is significantly improved by analyzing the factors that led to the deviation and learning by factor. be able to.

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.

Furthermore, since it is determined that the abnormality is abnormal if the abnormal state continues for a predetermined period of time or more, accurate self-diagnosis can be performed.

[Brief Description of the Drawings] 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 present invention, Figs. 3 to 6 are flow charts showing control contents, FIG. 7 is a diagram showing how the air-fuel ratio feedback 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...0□Sensor patent applicant Japan Electronics Co., Ltd. Agent Patent attorney Fujio Sasashima

Claims (1)

[Claims] (1) Basic control amount setting means for setting a basic control amount corresponding to the control target value of the controlled object of the internal combustion engine, and feedback in the direction of bringing the actual value closer to the control target value by comparing the control target value and the actual value. Feedback correction value setting means for increasing or decreasing a correction value by a predetermined amount; Rewritable factor-specific learned value storage means for storing a plurality of factor-specific learned values; a control amount calculation means that calculates a control amount by correcting it with arithmetic formulas set accordingly based on learned values for each factor; and a control means that operates in accordance with the control amount to control a controlled object of the internal combustion engine. and a deviation detection means for detecting a deviation of the feedback correction value from a reference value, and a factor for analyzing the cause of the deviation based on various information and separating the deviation into a plurality of parameters for each factor based on the analysis result. analysis means; 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; and comparing the factor-specific learning value with a preset abnormality judgment value. a comparison means; a timer for measuring the duration of time in which the factor-specific learned value exceeds the abnormality determination value; What is claimed is: 1. A learning control device for an internal combustion engine, comprising: abnormality determining means for determining a component as abnormal;