JP6036519B2 - Fuel injection control device - Google Patents

Description

  The present invention relates to a fuel injection control apparatus that executes learning injection and detects actual injection characteristics of a fuel injection valve.

  Conventionally, a change in the actual injection characteristic of the actual injection amount with respect to the command injection amount of the fuel injection valve caused by machine difference or change over time is learned, and the injection amount correction amount is calculated so that the actual injection amount becomes the command injection amount It is known.

  In Patent Document 1, based on the fluctuation amount of the engine speed that fluctuates due to the addition of learning pilot injection, the actual amount corresponding to the engine work amount as the fluctuation amount of the engine operating state that occurs according to the actual injection amount. The work equivalent amount is calculated. Then, the basic injection characteristic is acquired from the actual injection characteristic representing the correlation between the command injection amount and the work equivalent amount, and the difference between the basic injection characteristic and the actual injection characteristic is calculated as the correction amount.

  In Patent Document 1, a plurality of learning injections are executed for each command injection amount, a work equivalent amount is calculated from the fluctuation amount of the engine speed, and the variation in the entire data of the work equivalent amount corresponding to the command injection amount is within an allowable range. If it exceeds, the entire corresponding data is not adopted as learning data, and the learning injection is retried. Thereby, in patent document 1, it is going to prevent the fall of learning accuracy.

JP 2012-21514 A

  However, in the learning method of Patent Document 1 that does not employ the entire corresponding data as the learning data when the variation of the entire data exceeds the allowable range, an originally inappropriate work equivalent amount calculated as the learning data is Conversely, it may be within the allowable range due to disturbance. Therefore, the reliability of learning becomes low.

  In addition, even if multiple learning injections are executed for each command injection amount, if the variation in data of work equivalent amounts corresponding to the command injection amount exceeds the allowable range, data acquired by multiple learning injections Since the learning injection is retried without adopting the entirety as learning data, the number of learning injections increases. As a result, the establishment frequency of learning decreases.

  The present invention has been made to solve the above problem, and an object of the present invention is to provide a fuel injection control device having high reliability of injection amount learning and high occurrence frequency of learning.

According to the fuel injection control device of the present invention, as the amount of fluctuation in the operating state of the internal combustion engine that occurs according to the actual injection amount when the fuel injection valve executes the learning injection, the physical quantity that is the fluctuation amount of each of the plural types of physical quantities. The fluctuation amount is acquired, and first correlation data representing the correlation between the command injection amount and the physical fluctuation amount is acquired. Further, second correlation data representing a correlation between a plurality of physical fluctuation amounts corresponding to the command injection amount is acquired. Then, any one of the first correlation data is set as the target correlation data, the data exceeding the range allowed as appropriate data in the target correlation data, and other than the target correlation data among the first correlation data and the second correlation data The third correlation data is acquired by excluding the data of the target correlation data corresponding to the data exceeding the range allowed as appropriate data in the correlation data from the target correlation data. Based on the acquired third correlation data, an actual injection characteristic representing a correlation between the command injection amount and the fluctuation amount of the operating state is detected.

  As described above, any one of the first correlation data is set as the target correlation data, and not only the data exceeding the range permitted as the appropriate data in the target correlation data but also the appropriate data in other correlation data other than the target correlation data. Data of the target correlation data corresponding to data exceeding the allowable range is excluded from the target correlation data.

  As a result, if only the target correlation data is included, the data of the target correlation data corresponding to the data excluded in the other correlation data is included in the target correlation data, even if the data falls within the allowable range due to the disturbance. Excluded. Therefore, the reliability of the third correlation data acquired by excluding data exceeding the allowable range in the target correlation data and other correlation data from the target correlation data is increased.

  In addition, since the data of each physical fluctuation amount exceeds the allowable range, not the variation of the entire physical fluctuation data corresponding to the command injection amount, the corresponding data is excluded, so the data within the allowable range is used for learning. Can be used as data. Therefore, since learning can be performed by effectively using data within the allowable range, the frequency of establishment of learning is improved.

The block diagram which shows the fuel-injection system by this embodiment. The flowchart which shows the injection quantity learning process. The time chart which shows the change of the rail pressure by the presence or absence of learning injection. The characteristic view which shows the correlation with instruction | command injection amount and rail pressure fall amount. The characteristic view which shows the correlation with instruction | command injection amount and work equivalent amount. The characteristic view which shows the correlation with rail pressure fall amount and work equivalent amount. The characteristic view which shows the correlation with instruction | command injection amount and work equivalent amount. FIG. 5 is a characteristic diagram showing a deviation between basic injection characteristics and actual injection characteristics.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
A fuel injection system according to this embodiment is shown in FIG.
(Fuel injection system 10)
The fuel injection system 10 is for injecting fuel into, for example, a four-cylinder diesel engine (hereinafter also simply referred to as “engine”) 2 for an automobile. The fuel injection system 10 includes a fuel supply pump 14, a common rail 20, a fuel injection valve 30, and an electronic control unit (ECU) 40.

  The fuel supply pump 14 incorporates a feed pump that pumps fuel from the fuel tank 12. The fuel supply pump 14 is a known pump that pressurizes the fuel sucked into the pressurizing chamber from the feed pump when the plunger reciprocates as the cam of the camshaft rotates.

  The metering valve 16 serving as a metering actuator is installed on the suction side of the fuel supply pump 14 and controls the amount of fuel sucked by each plunger of the fuel supply pump 14 in the suction stroke by current control. . By adjusting the fuel intake amount, the fuel discharge amount from each plunger of the fuel supply pump 14 is adjusted. The amount of fuel discharged from each plunger of the fuel supply pump 14 may be measured by a metering valve installed on the discharge side of the fuel supply pump 14.

  The common rail 20 is a hollow member that accumulates fuel discharged from the fuel supply pump 14. The common rail 20 is provided with a pressure sensor 22 that detects internal fuel pressure (rail pressure), and a pressure limiter 24 that opens when the rail pressure exceeds a predetermined pressure and discharges fuel in the common rail 20. .

  The engine 2 is provided with a rotational speed sensor 32 that detects an engine rotational speed (NE) as a sensor that detects an operating state. Further, as other sensors for detecting the driving state, an accelerator sensor for detecting an accelerator opening (ACCP) that is an operation amount of an accelerator pedal by a driver, a temperature of cooling water (water temperature), a temperature of intake air (intake air temperature). The fuel injection system 10 is provided with a temperature sensor or the like for detecting the above.

  The fuel injection valve 30 is installed in each cylinder of the engine 2 and injects fuel accumulated in the common rail 20 into the cylinder. The fuel injection valve 30 is, for example, a known electromagnetic valve that controls the lift of the nozzle needle that opens and closes the nozzle hole with the pressure in the control chamber. The injection amount of the fuel injection valve 30 is controlled by the pulse width of the injection command signal commanded from the ECU 40. As the pulse width of the injection command signal increases, the injection amount increases.

  The ECU 40 is mainly configured by a microcomputer centering on a CPU, RAM, ROM, flash memory and the like. The ECU 40 executes various control of the fuel injection system 10 based on output signals taken from various sensors including the pressure sensor 22 and the rotation speed sensor 32 by the CPU executing a control program stored in the ROM or flash memory. Run.

  For example, the ECU 40 controls the energization amount to the metering valve 16 so that the rail pressure detected by the pressure sensor 22 becomes the target pressure, and regulates the discharge amount of the fuel supply pump 14. The ECU 40 sets a current value for controlling the metering valve 16 based on a characteristic map representing a correlation between the current value for controlling the metering valve 16 and the discharge amount.

  Further, the ECU 40 controls the fuel injection amount of the fuel injection valve 30, the fuel injection timing, and the pattern of the multistage injection in which the pilot injection, the pre-injection before the main injection, the after injection, the post injection after the pilot injection, etc. To do.

  The ECU 40 stores a so-called TQ map indicating the correlation between the pulse width (T) of the injection command signal for instructing the fuel injection valve 30 and the injection amount (Q) in a ROM or flash memory for each predetermined pressure range of the rail pressure. I remember it. Then, when the injection amount of the fuel injection valve 30 is determined based on the engine speed and the accelerator opening, the ECU 40 refers to the TQ map of the corresponding pressure range according to the rail pressure detected by the pressure sensor 22, The pulse width of the injection command signal that commands the determined injection amount to the fuel injection valve 30 is acquired from the TQ map.

(Injection amount learning process)
Next, an injection amount learning process executed by the ECU 40 will be described. In the flowchart of FIG. 2, “S” represents a step.

  The learning injection is commanded to detect an actual fluctuation amount that is a difference in the operating state of the engine 2 between when the learning injection is present and when it is not. In the present embodiment, as will be described below, as the actual fluctuation amount of the operating state of the engine 2 caused by executing the learning injection, the work calculated based on the rail pressure reduction amount and the fluctuation amount of the engine speed. Get a substantial amount.

The rail pressure decrease amount is a physical variation amount representing the variation amount of the rail pressure, which is a physical amount, and the work equivalent amount is a physical variation amount calculated based on the variation amount of the engine speed, which is a physical amount.
In S400 and S402, the ECU 40 determines whether or not the following learning conditions (1) and (2) are satisfied.
(1) Every predetermined traveling distance, for example, every 5000 km, or every predetermined driving time, for example, every 500 hours.
(2) When the engine operating state is stable. For example, the rotation speed, rail pressure, and injection amount fluctuations are within predetermined ranges.

  Even if the learning condition is not satisfied (S400: No or S402: No), or even when the learning condition is satisfied (S400: Yes and S402: Yes), the learning of the injection amount is completed for all the cylinders. Then, when there is no cylinder for which the injection amount has not been learned (S404: No), the ECU 40 ends this process.

When the learning condition is satisfied (S400: Yes and S402: Yes) and there is a cylinder whose injection amount has not been learned (S404: Yes), the ECU 40 sets the initial value α for the learning command injection amount Q (S406). ). For example, 0 mm ³ is set as the initial value α.

Then, the ECU 40 increases the command injection amount until the same injection amount learning stability condition as S402 is not satisfied (S408: No) or until the command injection amount Q exceeds the maximum command injection amount Qmax (S420: Yes). However, the processing of S408 to S420 is repeated. 5 mm ³ is set as the maximum command injection amount Qmax.

  When the injection amount learning stability condition is satisfied (S408: Yes), the ECU 40 reduces the rail pressure as the operation state of the engine 2 when the learning injection is present and when there is no learning injection in the unlearned cylinder of the injection amount. The amount is acquired (S410).

  FIG. 3 shows the rail pressure drop amount in the target cylinder of the learning injection as an example showing the change in the physical quantity when the learning injection is present and when it is not. The upper part of FIG. 3 shows the amount of decrease in rail pressure when the learning injection is executed, and the lower part of FIG. 3 shows the change in rail pressure when the learning injection is not executed.

  The learning injection is executed at an injection timing different from the normal injection. As shown in FIG. 3, the range of the detection period of the rail pressure drop amount when there is a learning injection and when there is no learning injection is that the learning cylinder is instructed to perform learning injection and then the other cylinders are instructed to inject. Until it is done.

  Next, the ECU 40 sets the rail pressure decrease amount when there is learning injection to ΔPci, the rail pressure decrease amount when there is no learning injection to ΔPc0, and the rail pressure decrease amount ΔPc due to learning injection as first correlation data as the following equation (1) ) (S412).

ΔPc = ΔPci−ΔPc0 (1)
For the unlearned cylinder, the ECU 40 detects the fluctuation amount of the engine speed between when the learning injection is present and when there is no learning injection within the same detection period as the detection of the rail pressure drop amount, and the fluctuation of the engine speed. Based on the amount, a work equivalent amount corresponding to the work amount of the engine 2 when the learning injection is present and when it is absent is calculated (S414).

  Further, the work equivalent amount ΔT, which is the difference between the work equivalent amounts when the learning injection is present and when it is absent, is calculated as first correlation data (S416). In the present embodiment, the correlation data representing the correlation between the command injection amount and the work equivalent amount is set as target correlation data that is a target for detecting the actual injection characteristics.

When the ECU 40 repeats the learning injection with the current command injection amount Q a predetermined number of times and executes the processes of S410 to S416 for each learning injection, the ECU 40 increases the command injection amount Q by, for example, 1 mm ³ as a predetermined increase amount β ( S418) Until the command injection amount Q exceeds the maximum command injection amount Qmax (S420: Yes), the processing of S408 to S416 is repeated.

  When the command injection amount Q exceeds the maximum command injection amount Qmax (S420: Yes), in S422, the ECU 40 indicates the correlation between the command injection amount and the rail pressure decrease amount, and the correlation indicating the correlation between the command injection amount and the work equivalent amount. Regression analysis is performed on the data by the least square method or the like, and regression lines 100 and 110 are calculated as shown in FIGS. Further, 95% confidence intervals 102 and 112 are calculated for each correlation data, and inappropriate data exceeding the confidence intervals 102 and 112 are excluded.

  As shown in FIG. 6, the correlation data as the second correlation data representing the correlation between the rail pressure drop amount and the work equivalent amount includes the correlation data between the command injection amount and the rail pressure drop amount, and the command injection amount and the work amount. In the correlation data with the corresponding amount, the regression line 120 is calculated after excluding the data corresponding to the data excluded because it exceeds the 95% confidence intervals 102, 112, and inappropriate data exceeding the confidence interval 122 is obtained. exclude.

  The data exceeding the confidence interval 102 in the correlation data between the command injection amount and the rail pressure decrease amount includes, for example, noise in the output signal of the pressure sensor 22 that detects the rail pressure, and the fuel between the common rail 20 and the fuel injection valve 30. It is caused by disturbance such as leakage.

  The data exceeding the confidence interval 112 in the correlation data between the command injection amount and the work equivalent amount is, for example, due to disturbances such as torsional vibration of the crankshaft and noise of the output signal of the rotation speed sensor 32 that detects the engine rotation speed. Occur.

  The ECU 40 acquires correlation data between the command injection amount and the work equivalent amount as third correlation data based on the correlation data between the rail pressure drop amount and the work equivalent amount from which inappropriate data is excluded in S422. A regression line is calculated from the correlation data, and the actual injection characteristic 130 shown in FIG. 7 is detected (S424).

As shown in FIG. 8, when the actual injection characteristic 130 obtained in this way does not pass through the origin of the command injection amount = 0 mm ³ and the work equivalent amount = 0, the correlation between the command injection amount and the work equivalent amount. It is determined that the deviation of the actual injection characteristic 130 caused by the machine difference or the change over time with respect to the basic injection characteristic 140 representing the difference is caused by the deviation in the offset direction, not the inclination of the characteristic.

As a result, the basic injection characteristic 140 is estimated by translating the actual injection characteristic 130 in the offset direction, that is, in the increase / decrease direction of the command injection amount so as to pass through the origin of the command injection amount = 0 mm ³ and work equivalent amount = 0. be able to. For example, when the primary expression of the actual injection characteristic is expressed by the following expression (2), the basic injection characteristic 140 is expressed by the following expression (3).

ΔW = α × Q + β (2)
ΔW = α × Q (3)
Q: Command injection amount.
ΔW: work equivalent when learning injection is present.
α: Slope of injection characteristics.
β: intercept of actual injection characteristics.

Therefore, the injection amount ΔQ, which is a difference between the actual injection characteristic 130 and the basic injection characteristic 140, can be calculated from the following equation (4).
α × Q2 + β = α × Q1
α (Q2−Q1) = − β
(Q2−Q1) = − β / α
ΔQ = −β / α (4)
The ECU 40 performs the above-described injection amount learning when the learning condition is satisfied, and stores the injection amount ΔQ learned for each rail pressure at that time. In this case, the ECU 40 learns the injection amount ΔQ from the basic injection amount characteristic and the actual injection amount characteristic for each predetermined pressure range by adjusting the discharge amount of the fuel supply pump 14 to increase or decrease the rail pressure. Also good.

  The ECU 40 corrects the injection amount in the normal injection by correcting the command injection amount at the corresponding rail pressure based on the injection amount ΔQ calculated as a learning value for correcting the injection amount by the equation (4).

  The injection amount correction may be performed based on the common injection amount ΔQ calculated as the learning value regardless of the engine operation region, or may be performed based on the injection amount ΔQ learned for each engine operation region. .

  In the embodiment described above, not only the data exceeding the confidence interval 112 in the target correlation data representing the correlation between the command injection amount and the work equivalent amount, but also the confidence interval 102 in the correlation data between the command injection amount and the rail pressure decrease amount. And the data of the target correlation data corresponding to the data exceeding the confidence interval 122 in the correlation data between the rail pressure drop amount and the work equivalent amount are excluded from the target correlation data.

  As a result, if there is only one target correlation data, even if the originally inappropriate data falls within the confidence interval due to disturbance, the target correlation is used if the data is excluded in the corresponding other correlation data. Excluded from the data. Therefore, the reliability of the correlation data acquired by excluding inappropriate data from the target correlation data is increased.

  Further, since the corresponding data is excluded when the individual data exceeds the confidence interval rather than the variation of the entire data, the data within the confidence interval can be adopted as the learning data. Therefore, learning can be performed by effectively using data within the confidence interval, so that the frequency of establishment of learning is improved.

  In the above embodiment, the basic injection characteristic is estimated from the actual injection characteristic detected in the fuel injection state during traveling. Thereby, since it is not necessary to measure the basic injection characteristics in advance, the man-hour for measuring the basic injection characteristics for each model can be omitted. In any model, the difference in the command injection amount between the basic injection characteristic and the actual injection characteristic can be calculated as a learning value for correcting the injection amount of the fuel injection valve.

  Then, by correcting the command injection amount based on the calculated learning value, it is possible to execute a minute injection within a desired error range when performing a minute injection such as pilot injection or post injection during normal injection. . Thereby, it can prevent that very small amount injection becomes non-injection.

  Further, since the learning injection is executed in the fuel injection state during traveling, the execution frequency of the learning injection can be increased. Furthermore, the learning injection can be executed in a wide fuel pressure range in the fuel injection state during traveling.

[Other Embodiments]
In the above embodiment, the correlation between the learning command injection amount and the work equivalent amount is set as the actual injection characteristic. In addition to this, the correlation between the command injection amount and the rail pressure decrease amount may be the actual injection characteristic. Further, the actual injection amount may be calculated from either the work equivalent amount or the rail pressure decrease amount, and the correlation between the command injection amount and the actual injection amount may be used as the actual injection characteristic.

  In addition to the rail pressure drop amount and the work equivalent amount, other physical quantities are adopted as physical quantities representing the actual fluctuation amount of the engine operating state that occurs in accordance with the actual injection quantity by executing the learning injection, and the command injection quantity and Correlation data with three or more physical quantities may be acquired. Alternatively, instead of at least one of the rail pressure drop amount and the work equivalent amount, another physical quantity may be adopted.

  In the above embodiment, the learning injection is executed in the fuel injection state during traveling. In addition to this, as long as the noise, vibration, drivability, and the like due to the learning injection are within the allowable range, the learning injection may be executed in the deceleration no-injection operation state and the idle operation state, for example.

  As described above, the present invention is not limited to the above-described embodiment, and can be applied to various embodiments without departing from the gist thereof.

2: diesel engine (internal combustion engine), 30: fuel injection valve, 40: ECU (fuel injection control device, fluctuation amount acquisition means, first correlation means, second correlation means, third correlation means, characteristic detection means, characteristic estimation means)

Claims (4)

Injection command means (S410) for instructing the fuel injection valve to perform learning injection with different command injection amounts;
Fluctuation amount acquisition that acquires a physical variation amount that is a variation amount of each of a plurality of types of physical amounts as a variation amount of the operating state of the internal combustion engine that occurs according to the actual injection amount when the fuel injection valve performs the learning injection Means (S410 to S416);
First correlation means (S422) for acquiring first correlation data representing a correlation between the command injection amount commanded by the injection command means and the physical fluctuation amount acquired by the fluctuation amount acquisition means;
Second correlation means (S422) for acquiring second correlation data representing the correlation between the plurality of physical fluctuation amounts corresponding to the command injection amount;
Any one of the first correlation data is set as target correlation data, the data exceeding the range allowed as appropriate data in the target correlation data, and the first correlation data and the second correlation data other than the target correlation data wherein the of the target correlation data data, third correlation means for obtaining a third correlation data was excluded from the target correlation data corresponding to more than acceptable range as appropriate data in the correlation data of the other data (S422 )When,
Characteristic detection means (S424) for detecting an actual injection characteristic representing a correlation between the command injection amount and the fluctuation amount of the operating state based on the third correlation data acquired by the third correlation means;
A fuel injection control device (40) comprising: The fluctuation amount acquisition means includes a rail pressure drop amount representing a fluctuation amount of pressure in the common rail that accumulates fuel injected by the fuel injection valve, and a work equivalent amount calculated based on the fluctuation amount of the engine speed. Obtained as the physical variation amount,
The first correlation means acquires correlation data representing the correlation between the command injection amount and the rail pressure decrease amount, and the correlation between the command injection amount and the work equivalent amount, as the first correlation data,
The second correlation means acquires the second correlation data representing a correlation between the rail pressure decrease amount corresponding to the command injection amount and the work equivalent amount.
The fuel injection control device according to claim 1.   3. The fuel injection control device according to claim 1, wherein a predetermined confidence interval obtained by performing regression analysis on each correlation data is set as the allowable range. 4.   4. A characteristic estimation means (S426) for translating the actual injection characteristic to estimate a basic injection characteristic representing a correlation between the command injection amount and the fluctuation amount of the operating state. The fuel injection control device according to any one of the above.

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