JP2019132866A - Air flow rate meter - Google Patents

Abstract

To provide an air flow rate meter that can improve correction accuracy of an air flow rate.SOLUTION: An AFM is configured to measure an air flow rate on the basis of an output value of a sensing unit to be arranged in the environment where air flows. The AFM comprises: an average air flow rate computation unit 37 that calculates an average air flow rate from the output value; a pulsation maximum value computation unit 36 that calculates a pulsation maximum value being a maximum value of the air flow rate from the output value; and a pulsation amplitude computation unit 38 that takes a difference between the pulsation maximum value and the average air flow rate to thereby compute a pulsation amplitude of the air flow rate. The AFM further comprises: a pulsation error prediction unit 39 that predicts a pulsation error of the air flow rate in correlation with the pulsation amplitude; and a pulsation error correction unit 40 that uses the pulsation error to correct the air flow rate so that the pulsation error is small.SELECTED DRAWING: Figure 2

Description

  The present disclosure relates to an air flow measurement device.

  Conventionally, there is an internal combustion engine control device disclosed in Patent Document 1 as an example of an air flow rate measuring device. This control device calculates a pulsation amplitude ratio and a pulsation frequency, and calculates a pulsation error from the pulsation amplitude ratio and the pulsation frequency. Then, the control device refers to the correction coefficient necessary for correcting the pulsation error from the pulsation amplitude ratio and the pulsation frequency from the pulsation error correction map, and calculates the amount of air corrected for the pulsation error.

JP 2014-20212 A

  The control device calculates the pulsation amplitude ratio by dividing the pulsation amplitude amount that is the difference between the maximum flow rate and the minimum flow rate during pulsation by the average air amount at that time. However, the minimum flow rate tends to be a measurement of an unstable phenomenon in terms of fluid mechanics, and the measurement reproducibility and measurement accuracy are inherently deteriorated. Therefore, in the control device, the calculation accuracy of the pulsation amplitude ratio is lowered, and the correction accuracy may be deteriorated accordingly.

  The present disclosure has been made in view of the above-described problems, and an object thereof is to provide an air flow rate measuring device capable of improving the correction accuracy of the air flow rate.

In order to achieve the above object, the present disclosure
An air flow rate measuring device for measuring an air flow rate based on an output value of a sensing unit (10) arranged in an environment where air flows,
An average air amount calculation unit (37) that calculates an average air amount that is an average value of the air flow rate from the output value;
A pulsation amplitude calculator (36, 38) that calculates a pulsation maximum value that is the maximum value of the air flow rate from the output value, and calculates the pulsation amplitude of the air flow rate by taking the difference between the pulsation maximum value and the average air amount;
A pulsation error prediction unit (39, 39a to 39e) for predicting a pulsation error of the air flow rate correlated with the pulsation amplitude;
And a pulsation error correction unit (40) for correcting the air flow rate so as to reduce the pulsation error using the pulsation error predicted by the pulsation error prediction unit.

  As described above, the present disclosure calculates the pulsation amplitude of the air flow rate by taking the difference between the average air amount and the pulsation maximum value. The pulsation maximum value, which is the maximum value of the air flow rate, has higher measurement accuracy than the minimum value of the air flow rate. For this reason, in the present disclosure, it is possible to obtain a pulsation amplitude in which the influence of the minimum value of the air flow rate with low measurement accuracy is reduced. The present disclosure predicts a pulsation error correlated with the pulsation amplitude and corrects the air flow rate so as to reduce the pulsation error. Therefore, the present disclosure can improve the correction accuracy of the air flow rate. That is, the present disclosure can obtain an air flow rate with reduced pulsation error.

  The reference numerals in parentheses described in the claims and in this section indicate the correspondence with the specific means described in the embodiments described later as one aspect, and are within the technical scope of the present disclosure. It is not intended to limit.

It is a block diagram which shows schematic structure of AFM in 1st Embodiment. It is a block diagram which shows schematic structure of the process part in 1st Embodiment. It is a wave form diagram for demonstrating the calculation method of the pulsation amplitude in 1st Embodiment. It is a wave form diagram for demonstrating the determination method of the measurement period in 1st Embodiment. It is a wave form chart for explaining a calculation method of average air volume in a 1st embodiment. It is a block diagram which shows schematic structure of the process part in 2nd Embodiment. It is a block diagram which shows schematic structure of the process part in 3rd Embodiment. It is a block diagram which shows schematic structure of the process part in 4th Embodiment. It is a wave form diagram for demonstrating the calculation method of the pulsation frequency in 4th Embodiment. It is a wave form diagram for demonstrating the other calculation method of the pulsation frequency in 4th Embodiment. It is a block diagram which shows schematic structure of the process part in the modification of 4th Embodiment. It is a block diagram which shows schematic structure of the process part in 5th Embodiment. It is drawing which shows the two-dimensional map in 5th Embodiment. It is drawing which shows the pulsation amplitude-pulsation error for every average air quantity and pulsation frequency in 5th Embodiment. It is drawing which shows the three-dimensional map in the modification 1 of 5th Embodiment. It is drawing which shows the average air volume and the pulsation amplitude-pulsation error for every pulsation frequency in the modification 2 of 5th Embodiment. It is drawing which shows the three-dimensional map in the modification 2 of 5th Embodiment. It is drawing which shows the average air volume and the pulsation rate-pulsation error for every pulsation frequency in the modification 3 of 5th Embodiment. It is drawing which shows the three-dimensional map in the modification 3 of 5th Embodiment. It is a block diagram which shows schematic structure of the process part in 6th Embodiment. It is a wave form diagram which shows the maximum flow volume and average air volume in case the measurement period in 6th Embodiment is short. It is a wave form diagram which shows the long measurement period in 6th Embodiment. It is a block diagram which shows schematic structure of the process part in 7th Embodiment. It is drawing which shows the three-dimensional map in 7th Embodiment. It is drawing which shows the pulsation amplitude-pulsation error in 7th Embodiment. It is a block diagram which shows schematic structure of AFM in 8th Embodiment. It is a block diagram which shows schematic structure of the process part in 9th Embodiment. It is a wave form diagram which shows the relationship between the air flow rate and time in 9th Embodiment.

  Hereinafter, a plurality of modes for carrying out the present disclosure will be described with reference to the drawings. In each embodiment, portions corresponding to the matters described in the preceding embodiment may be denoted by the same reference numerals and redundant description may be omitted. In each embodiment, when only a part of the configuration is described, the other configurations described above can be applied to other portions of the configuration.

(First embodiment)
The air flow rate measuring apparatus according to the first embodiment will be described with reference to FIGS. In the present embodiment, as shown in FIG. 1, an example in which an air flow measurement device is applied to an AFM (air flow meter) 100 is employed. That is, the AFM 100 corresponds to an air flow rate measuring device. The AFM 10 is mounted on a vehicle equipped with, for example, an internal combustion engine (hereinafter referred to as an engine). The AFM has a thermal air flow measurement function for measuring the flow rate of intake air (hereinafter referred to as air flow rate) taken into the cylinders of the engine. Therefore, the AFM 100 can be said to be a hot-wire air flow meter. The air flow rate can also be said to be an intake air flow rate.

  The AFM 100 mainly includes a sensing unit 10 and a processing unit 20. The AFM 100 is electrically connected to an ECU (Electronic Control Unit) 200. ECU 200 corresponds to an internal combustion engine control device and is an engine control device having a function of controlling the engine based on a detection signal from AFM 100 or the like. This detection signal is an electric signal indicating the air flow rate corrected by the pulsation error correction unit 40 described later.

  The sensing unit 10 is disposed in an intake duct such as an outlet of an air cleaner or an intake pipe as an environment in which air flows. As disclosed in JP-A-2006-109625, for example, the sensing unit 10 is disposed in the intake duct while being attached to a passage forming member, for example. That is, the sensing unit 10 is a passage forming member in which a bypass passage (sub air passage) and a sub bypass passage (secondary air passage) through which a part of the intake air flowing through the inside of the intake duct (main air passage) passes are formed. By being attached, it is arranged in the sub-bypass passage. However, the present disclosure is not limited to this, and the sensing unit 10 may be directly disposed in the main air passage.

  The sensing unit 10 includes a well-known heating resistor, a resistance temperature detector, and the like. The sensing unit 10 outputs a sensor signal (output value, output signal) corresponding to the air flow rate flowing through the sub-bypass channel to the processing unit 20. It can be said that the sensing unit 10 outputs an output value, which is an electrical signal corresponding to the air flow rate flowing through the sub-bypass channel, to the processing unit 20.

  By the way, in the intake duct, intake pulsation including backflow occurs due to the reciprocating motion of the piston in the engine. The sensing unit 10 is affected by the intake pulsation, and an error in the true air flow rate occurs in the output value. In particular, the sensing unit 10 is susceptible to intake pulsation when the throttle valve is operated to the fully open side. Hereinafter, the error due to the intake pulsation is also referred to as a pulsation error Err. The true air flow rate is an air flow rate that is not affected by intake pulsation.

  The processing unit 20 measures the air flow rate based on the output value of the sensing unit 10 and outputs the measured air flow rate to the ECU 200. The processing unit 20 includes at least one arithmetic processing unit (CPU) and a storage device that stores programs and data. For example, the processing unit 20 is realized by a microcomputer including a storage device that can be read by a computer. The processing unit 20 performs various calculations by the arithmetic processing unit executing programs stored in the storage medium, measures the air flow rate, and outputs the measured air flow rate to the ECU 200.

  The storage device is a non-transitional tangible storage medium that stores a computer-readable program and data in a non-temporary manner. The storage medium is realized by a semiconductor memory or a magnetic disk. This storage device can also be called a storage medium. The processing unit 20 may include a volatile memory that temporarily stores data.

  The processing unit 20 has a function of correcting an output value in which the pulsation error Err has occurred. In other words, the processing unit 20 corrects the air flow rate at which the pulsation error Err has occurred so as to approach the true air flow rate. Therefore, the processing unit 20 outputs an air flow rate obtained by correcting the pulsation error Err to the ECU 200 as a detection signal. It can be said that the processing unit 20 outputs an electric signal indicating the air flow rate to the ECU 200.

  The processing unit 20 operates as a plurality of functional blocks by executing a program. In other words, the processing unit 20 includes a plurality of functional blocks 31 to 41 as shown in FIG. The processing unit 20 includes a sensor output A / D conversion unit 31, a sampling unit 32, and an output air flow rate conversion table 33 as functional blocks. The processing unit 20 performs A / D conversion on the output value output from the sensing unit 10 by the sensor output A / D conversion unit 31. The processing unit 20 samples the A / D converted output value by the sampling unit 32, and converts the output value into an air flow rate by the output air flow rate conversion table 33.

  Furthermore, the processing unit 20 includes, as functional blocks, a sampling storage unit 34, an upper extreme value determination unit 35, a pulsation maximum value calculation unit 36, an average air amount calculation unit 37, a pulsation amplitude calculation unit 38, a pulsation error prediction unit 39, a pulsation An error correction unit 40 and a post-pulsation correction flow rate output unit 41 are included.

  The sampling storage unit 34 stores a plurality of sampling values between the two upper extreme values determined by the upper extreme value determination unit 35. For example, as illustrated in FIG. 4, the upper extreme value determination unit 35 determines, as a first upper extreme value, a sampling value at which the air flow rate corresponding to the sampling value switches from rising to lowering among the plurality of sampling values. Then, the upper extreme value determination unit 35 determines, as a second upper extreme value, a sampling value at which the air flow rate corresponding to the sampling value is switched from rising to lowering among the plurality of sampling values. In other words, the upper extreme value determination unit 35 determines the sampling value at the first peak time as the first upper extreme value, and determines the sampling value at the second peak time, which is the next peak time, as the second upper extreme value. . The sampling storage unit 34 stores a sampling value between the first upper extreme value and the second upper extreme value.

  This is because the measurement period (calculation period) of the average air amount Gave and the pulsation maximum value Gmax is determined, and the average air amount Gave and the pulsation maximum value Gmax are calculated in this measurement period. Here, the measurement period is between the first upper extreme value and the second upper extreme value. As the number of samplings is as large as possible, the average air amount Gave and the pulsation maximum value Gmax can be calculated more accurately. The average air amount Gave is an average value of the air flow rate in a predetermined period. On the other hand, the pulsation maximum value Gmax can be said to be the maximum value of the air flow rate when the air is pulsating.

  The pulsation maximum value calculation unit 36 acquires the maximum value of the air flow rate from the plurality of sampling values stored in the sampling storage unit 34. That is, the pulsation maximum value calculation unit 36 obtains the maximum value of the air flow rate in the measurement period, that is, the pulsation maximum value Gmax that is the maximum flow rate, from the output value of the sensing unit 10. In the following, the minimum value of the air flow rate during the measurement period is also referred to as a pulsation minimum value.

  The pulsation maximum value calculator 36 may acquire a plurality of upper extreme values in the air flow rate from the output value, and obtain the pulsation maximum value Gmax from the average value of the plurality of upper extreme values. For example, the pulsation maximum value calculator 36 acquires the first upper extreme value and the second upper extreme value, and calculates the average value of the first upper extreme value and the second upper extreme value as the pulsation maximum value. As a result, the pulsation maximum value calculator 36 can reduce the influence even if an error occurs in either the first upper extreme value or the second upper extreme value. For this reason, the pulsation maximum value calculation unit 36 can improve the calculation accuracy of the pulsation maximum value Gmax. Note that the pulsation maximum value calculator 36 may acquire three or more upper extreme values and obtain the pulsation maximum value Gmax from the average value of the three or more acquired upper extreme values.

  The average air amount calculation unit 37 calculates an average value of the air flow rate from the plurality of sampling values stored in the sampling storage unit 34. That is, the average air amount calculation unit 37 calculates the average air amount Gave of the air flow rate during the measurement period from the output value of the sensing unit 10.

For example, the average air amount calculation unit 37 calculates the average air amount Gave by using the integrated average. Here, as an example, the calculation of the average air amount Gave using the waveform shown in FIG. 5 will be described. In this example, the measurement period is from time T1 to time Tn, the air flow rate at time T1 is G1, and the air flow rate at time Tn is Gn. Then, the average air amount calculation unit 37 calculates the average air amount Gave using Equation 1. In this case, the average air amount Gave in which the influence of the pulsation minimum value with a relatively low detection accuracy is reduced can be calculated when the number of samplings is larger than when the number of samplings is small.

In addition, the average air amount calculation unit 37 may calculate the average air amount Gave by averaging the pulsation minimum value that is the minimum value of the air flow rate during the measurement period and the pulsation maximum. That is, the average air amount calculation unit 37 calculates the average air amount Gave using Equation 2.

  Further, the average air amount calculation unit 37 calculates the average air amount Gave without using the pulsation minimum value whose detection accuracy is lower than the maximum value of the air flow rate, or several air amounts before and after the pulsation minimum value and the pulsation minimum value. It may be calculated. As will be described later, the processing unit 20 calculates the pulsation amplitude A from the average air amount Gave and the pulsation maximum value Gmax. Therefore, the processing unit 20 can calculate the pulsation amplitude A in which the influence of the pulsation minimum value is reduced by the average air amount calculation unit 37 calculating the average air amount Gave without using the pulsation minimum value. In other words, when calculating the pulsation amplitude A, the processing unit 20 does not use the pulsation minimum value with low detection accuracy, but uses the average air amount Gave and the pulsation maximum value Gmax with relatively high detection accuracy. By calculating A, the calculation accuracy of the pulsation amplitude A can be improved.

  As shown in FIG. 3, the pulsation amplitude calculation unit 38 calculates (acquires) the pulsation amplitude A of the air flow rate by taking the difference between the pulsation maximum value Gmax and the average air amount Gave. That is, the pulsation amplitude calculation unit 38 obtains the single amplitude of the air flow rate, not the full amplitude of the air flow rate. This is to reduce the influence of the pulsation minimum value with relatively low detection accuracy as described above. The pulsation maximum value calculator 36 and the pulsation amplitude calculator 38 correspond to the pulsation amplitude calculator in the claims.

  The pulsation error predicting unit 39 predicts the pulsation error Err of the air flow rate correlated with the pulsation amplitude A. The pulsation error prediction unit 39 predicts the pulsation error Err of the air flow rate correlated with the pulsation amplitude A using, for example, a map in which the pulsation amplitude A and the pulsation error Err are associated with each other. That is, when the pulsation amplitude A is obtained by the pulsation amplitude calculation unit 38, the pulsation error prediction unit 39 extracts the pulsation error Err correlated with the obtained pulsation amplitude A from the map. It can also be said that the pulsation error prediction unit 39 acquires the pulsation error Err correlated with the pulsation amplitude A.

  In this case, the AFM 100 includes a map in which a plurality of pulsation amplitudes A and pulsation errors Err correlated with the pulsation amplitudes A are associated with each other. The map can be created by confirming the relationship between each pulsation amplitude A and the pulsation error Err correlated with each pulsation amplitude A through experiments or simulations using an actual machine. That is, it can be said that each pulsation error Err is a value obtained for each pulsation amplitude A when an experiment or simulation using an actual machine is performed by changing the value of the pulsation amplitude A. Note that the map in the embodiment described below can be similarly created by experiments or simulations using actual machines.

  As described above, the AFM 100 is disposed in the intake duct with the sensing unit 10 attached to the passage forming member. Therefore, the AFM 100 not only increases the pulsation error Err as the pulsation amplitude A increases due to the influence of the shape of the passage forming member, but also decreases the pulsation error Err as the pulsation amplitude A increases. There is also a possibility. For this reason, in the AFM 100, the relationship between the pulsation amplitude A and the pulsation error Err may not be expressed as a function. Therefore, the AFM 100 is preferable because it can predict the accurate pulsation error Err by using the map as described above. In the map, a plurality of pulsation amplitudes A and correction amounts Q correlated with the pulsation amplitudes A may be associated with each other.

  However, the AFM 100 may be able to express the relationship between the pulsation amplitude A and the pulsation error Err as a function, such as when the sensing unit 10 is directly disposed in the main air passage. In this case, the AFM 100 may calculate the pulsation error Err using this function. Since the AFM 100 does not need to have a map by calculating the pulsation error Err using a function, the capacity of the storage device can be reduced. This also applies to the following embodiments. That is, in the following embodiment, the pulsation error Err may be obtained using a function instead of a map.

  The pulsation error Err is the difference between the uncorrected air flow rate obtained from the output value and the true air flow rate. That is, the pulsation error Err corresponds to the difference between the air flow rate whose output value is converted by the output air flow rate conversion table 33 and the true air flow rate. Therefore, the correction amount Q for bringing the uncorrected air amount close to the true air flow rate can be obtained if the pulsation error Err is known.

  The pulsation error correction unit 40 uses the pulsation error Err predicted by the pulsation error prediction unit 39 to correct the air flow rate so that the pulsation error Err becomes small. That is, the pulsation error correction unit 40 corrects the air flow rate so that the air flow rate affected by the intake pulsation approaches the true air flow rate. Here, the average air amount Gave is adopted as a correction target of the air flow rate.

  For example, the pulsation error correction unit 40 obtains the correction amount Q from the predicted pulsation error Err using a calculation, a map in which the pulsation error Err and the correction amount Q are associated, or the like. For example, the pulsation error correction unit 40 can correct the air flow rate so that the pulsation error Err is reduced by adding the correction amount Q to the average air amount Gave.

  That is, when the correction amount Q is minus Q1, the pulsation error correction unit 40 adds minus Q1 to the average air amount Gave, that is, subtracts Q1 from the average air amount Gave, thereby correcting the pulsation error Err. A later air flow rate can be obtained. Further, when the correction amount Q is plus Q2, the pulsation error correction unit 40 can obtain the corrected air flow rate in which the pulsation error Err is reduced by adding Q2 to the average air amount Gave. However, the present disclosure is not limited to this, and can be adopted as long as the air flow rate can be corrected so that the pulsation error Err becomes small.

  In the present embodiment, the air flow rate is corrected so that the pulsation error Err is reduced with the average air amount Gave as a target. However, the present disclosure is not limited to this. As indicated by a broken line in FIG. 2, the pulsation error correction unit 40 may correct the air flow rate so that the pulsation error Err is reduced with respect to a value before being calculated by the average air amount calculation unit 37.

  The pulsation corrected flow rate output unit 41 outputs an electrical signal indicating the air flow rate corrected by the pulsation error correction unit 40. In the present embodiment, a pulsation corrected flow rate output unit 41 that outputs the air flow rate corrected by the pulsation error correction unit 40 to the ECU 200 is employed.

  Thus, the AFM 100 calculates the pulsation amplitude A of the air flow rate by taking the difference between the average air amount Gave and the pulsation maximum value Gmax. This pulsation maximum value Gmax has higher measurement accuracy than the minimum value of the air flow rate. For this reason, the AFM 100 can obtain the pulsation amplitude A in which the influence of the minimum value of the air flow rate with low measurement accuracy is reduced. The AFM 100 predicts the pulsation error Err corresponding to the pulsation amplitude A, and corrects the air flow rate so that the predicted pulsation error Err becomes small. Therefore, the AFM 100 can improve the correction accuracy of the air flow rate. That is, the AFM 100 can obtain an air flow rate with a reduced pulsation error Err. It can also be said that the AFM 100 can improve the robustness in obtaining an argument for correcting the air flow rate.

  In the present embodiment, as an example, the AFM 100 including the sensing unit 10 in addition to the processing unit 20 is employed. However, in the present disclosure, the air flow rate is measured based on the output value of the sensing unit 10, and the pulsation maximum value calculation unit 36, the average air amount calculation unit 37, the pulsation amplitude calculation unit 38, the pulsation error prediction unit 39, the pulsation The processing unit 20 including the error correction unit 40 may be provided.

  The preferred embodiments of the present disclosure have been described above. However, the present disclosure is not limited to the above embodiment, and various modifications can be made without departing from the spirit of the present disclosure. Below, 2nd Embodiment-8th Embodiment is described as another form of this indication. The above-described embodiment and the second to eighth embodiments can be implemented independently, but can also be implemented in combination as appropriate. The present disclosure is not limited to the combinations shown in the embodiments, and can be implemented by various combinations.

  Note that the functions realized by the processing unit 20 may be realized by hardware and software different from those described above, or a combination thereof. The processing unit 20 may communicate with, for example, another control device, such as the ECU 200, and the other control device may execute part or all of the processing. When the processing unit 20 is realized by an electronic circuit, the processing unit 20 can be realized by a digital circuit including a large number of logic circuits or an analog circuit.

(Second Embodiment)
An AFM (hereinafter simply referred to as AFM) according to the second embodiment will be described with reference to FIG. The AFM is different from the AFM 100 in a part of the processing unit 20. As shown in FIG. 6, the AFM is different from the AFM 100 in that it includes a pulsation rate calculation unit 38a and a pulsation error prediction unit 39a. The AFM includes a pulsation rate calculation unit 38 a in addition to the AFM 100, and it can be said that the AFM includes a pulsation error prediction unit 39 a instead of the pulsation error prediction unit 39 of the AFM 100.

  The pulsation rate calculating unit 38a calculates the pulsation rate P of the air flow rate by dividing the pulsation amplitude A by the average air amount Gave. That is, the pulsation rate calculation unit 38 a predicts the pulsation amplitude A of the air flow rate in the same manner as the pulsation amplitude calculation unit 38 and divides the pulsation amplitude A by the average air amount Gave obtained by the average air amount calculation unit 37. By doing so, the pulsation rate P can be obtained. Specifically, the pulsation rate P = (Gmax−Gave) / Gave × 100. Thus, the pulsation rate P is a parameter having a correlation with the pulsation amplitude A.

  The pulsation error prediction unit 39a predicts the pulsation error Err correlated with the pulsation rate P as the pulsation error Err correlated with the pulsation amplitude A. In this case, the pulsation error prediction unit 39a predicts the pulsation error Err correlated with the pulsation rate P using, for example, a map in which the pulsation rate P and the pulsation error Err are associated with each other. That is, when the pulsation rate P is obtained by the pulsation rate calculation unit 38a, the pulsation error prediction unit 39a extracts the pulsation error Err correlated with the obtained pulsation rate P from the map. In this case, the AFM includes a map in which a plurality of pulsation rates P and a pulsation error Err correlated with each pulsation rate P are associated. That is, it can be said that each pulsation error Err is a value obtained for each pulsation rate P when an experiment or simulation using an actual machine is performed by changing the value of the pulsation rate P.

  The AFM of the second embodiment configured as described above can achieve the same effects as the AFM 100.

(Third embodiment)
The AFM according to the third embodiment (hereinafter simply referred to as AFM) will be described with reference to FIG. The AFM is different from the AFM 100 in a part of the processing unit 20. As shown in FIG. 7, the AFM is different from the AFM 100 in that the average air amount Gave obtained by the average air amount calculation unit 37 is input to the pulsation error prediction unit 39b.

  The pulsation error prediction unit 39b predicts the pulsation error Err using the average air amount Gave and the pulsation amplitude A. That is, the pulsation error prediction unit 39b predicts the pulsation error Err correlated with the average air amount Gave in addition to the pulsation amplitude A.

  In this case, the pulsation error prediction unit 39b uses, for example, a map in which the pulsation error Err is associated with the average air amount Gave and the pulsation amplitude A, and the pulsation error Err correlated with the average air amount Gave and the pulsation amplitude A. Predict. That is, when the average air amount Gave is obtained by the average air amount calculation unit 37 and the pulsation amplitude A is obtained by the pulsation amplitude calculation unit 38, the pulsation error prediction unit 39b obtains the obtained average air amount Gave and pulsation amplitude A. Is extracted from the map.

  In this case, the AFM includes a two-dimensional map in which a plurality of combinations of a plurality of average air amounts Gave and a pulsation amplitude A are associated with a pulsation error Err correlated with each combination. The two-dimensional map here is, for example, an average air amount Gave1 to Gaven on one axis, a pulsation amplitude A1 to An on the other axis, and each combination of the average air amount Gave1 to Gaven and pulsation amplitude A1 to An. Are associated with each of the pulsation errors Err1 to Errn. For example, the pulsation error Err1 is associated with the average air amount Gave1 and the pulsation amplitude A1. Further, a pulsation error Errn is associated with the average air amount Gaven and the pulsation amplitude An. Each of the pulsation errors Err1 to Errn is obtained by each combination of the pulsation amplitude A and the average air amount Gave when the pulsation amplitude A and the average air amount Gave are changed and an experiment or simulation using an actual machine is performed. The value can be said.

  The AFM according to the present embodiment configured as described above can achieve the same effects as the AFM 100. Further, the pulsation error Err is also affected by the average air amount Gave. Therefore, in this embodiment, since the pulsation error Err correlated with the pulsation amplitude A and the average air amount Gave is predicted and corrected using the pulsation error Err, only the pulsation error Err corresponding to the pulsation amplitude A is used. Therefore, it is possible to perform correction with higher accuracy than in the case where correction is performed.

  Further, the pulsation error prediction unit 39b may predict the pulsation error Err using the average air amount Gave and the pulsation rate P. This can be regarded as an example in which the third embodiment and the second embodiment are combined.

  In this case, the pulsation error prediction unit 39b uses, for example, a map in which the pulsation error Err is associated with the average air amount Gave and the pulsation rate P, and the pulsation error Err correlated with the average air amount Gave and the pulsation rate P. Predict. That is, when the average air amount Gave is obtained by the average air amount calculation unit 37 and the pulsation rate P is obtained by the pulsation rate calculation unit 38a, the pulsation error prediction unit 39b obtains the obtained average air amount Gave and the pulsation rate P. Is extracted from the map.

  In this case, the AFM includes a two-dimensional map in which a plurality of combinations of the average air amount Gave and the pulsation rate P and pulsation errors Err correlated with each combination are associated. In this two-dimensional map, for example, the average air amount Gave1 to Gaven is taken on one axis, the pulsation rates P1 to Pn are taken on the other axis, and the pulsation error Err1 is associated with each combination of the average air amount Gave and the pulsation rate P. -Errn are associated with each other. For example, the pulsation error Err1 is associated with the pulsation frequency F, the average air amount Gave1, and the pulsation rate P1. Further, the pulsation error Errn is associated with the average air amount Gaven and the pulsation rate Pn. Each of the pulsation errors Err1 to Errn is obtained by each combination of the pulsation rate P and the average air amount Gave when the pulsation rate P and the average air amount Gave are changed and an experiment or simulation using an actual machine is performed. The value can be said.

  Therefore, this AFM can achieve the same effects as the AFM 100. Furthermore, since this AFM predicts a pulsation error Err correlated with the pulsation rate P and the average air amount Gave and corrects it using the pulsation error Err, the correction is performed using only the pulsation error Err corresponding to the pulsation rate P. This makes it possible to perform correction with higher accuracy than in the case of doing so.

(Fourth embodiment)
The AFM according to the fourth embodiment (hereinafter simply referred to as AFM) will be described with reference to FIGS. The AFM is different from the AFM 100 in a part of the processing unit 20. As shown in FIG. 8, the AFM includes a frequency analysis unit 42 and is different from the AFM 100 in that the pulsation frequency F obtained by the frequency analysis unit 42 is input to the pulsation error prediction unit 39c.

  The pulsation error prediction unit 39c predicts the pulsation error Err using the pulsation amplitude A and the pulsation frequency F. That is, the pulsation error prediction unit 39b predicts the pulsation error Err correlated with the pulsation frequency F in addition to the pulsation amplitude A. The frequency analysis unit 42 corresponds to the frequency acquisition unit in the claims. The pulsation frequency F is the frequency of the pulsation waveform in the air, and can also be said to be the frequency of the air flow rate. The pulsation frequency F includes not only the primary wave but also higher-order frequencies such as a secondary wave and a tertiary wave.

  The frequency analysis unit 42 calculates the pulsation frequency F from the plurality of sampling values stored in the sampling storage unit 34. For example, as shown in FIG. 9, the frequency analysis unit 42 calculates the pulsation frequency F based on the interval between two peaks. In the example of FIG. 9, the first peak time T11 is the first peak time, and the second peak time T12 is the second peak time. In this case, the pulsation frequency F [Hz] = 1 / (T12−T11). Therefore, the frequency analysis unit 42 can obtain the pulsation frequency F by calculating 1 / (T12−T11).

  Moreover, the frequency analysis part 42 may calculate the pulsation frequency F by the time over the threshold value Gs, as shown in FIG. In the example of FIG. 10, the first time that intersects the threshold Gs is the first intersection time T21, and the second time that intersects the threshold Gs is the second intersection time T22. In this case, the pulsation frequency F [Hz] = 1 / (T22−T21). Therefore, the frequency analysis unit 42 can obtain the pulsation frequency F by calculating 1 / (T22−T21). Further, the frequency analysis unit 42 may calculate the pulsation frequency F by Fourier transform.

  The pulsation error prediction unit 39c predicts the pulsation error Err correlated with the pulsation frequency F and the pulsation amplitude A using, for example, a map in which the pulsation error Err is associated with the pulsation frequency F and the pulsation amplitude A. That is, when the pulsation frequency F is obtained by the frequency analysis unit 42 and the pulsation amplitude A is obtained by the pulsation amplitude calculation unit 38, the pulsation error prediction unit 39c correlates with the obtained pulsation frequency F and pulsation amplitude A. The error Err is extracted from the map.

  In this case, the AFM includes a two-dimensional map in which a plurality of combinations of the pulsation frequency F and the pulsation amplitude A are associated with the pulsation error Err correlated with each combination. The two-dimensional map here has, for example, pulsation frequencies F1 to Fn on one axis, pulsation amplitudes A1 to An on the other axis, and pulsation errors Err1 to Errn for each combination of pulsation frequency F and pulsation amplitude A. Each of which is associated. For example, the pulsation error Err1 is associated with the pulsation frequency F1 and the pulsation amplitude A1. The pulsation error Errn is associated with the pulsation frequency Fn and the pulsation amplitude An. Each of the pulsation errors Err1 to Errn is a value obtained by each combination of the pulsation frequency F and the pulsation amplitude A when an experiment or simulation using an actual machine is performed by changing the values of the pulsation frequency F and the pulsation amplitude A. It can be said.

  The AFM according to the present embodiment configured as described above can achieve the same effects as the AFM 100. Further, the pulsation error Err is also affected by the pulsation frequency F. Therefore, in this embodiment, since the pulsation error Err correlated with the pulsation amplitude A and the pulsation frequency F is predicted and corrected using the pulsation error Err, only the pulsation error Err corresponding to the pulsation amplitude A is used. The correction can be performed with higher accuracy than the correction.

  Further, the pulsation error prediction unit 39c may predict the pulsation error Err using the pulsation frequency F and the pulsation rate P. This can be regarded as an example in which the fourth embodiment and the second embodiment are combined.

  In this case, the pulsation error prediction unit 39c predicts the pulsation error Err correlated with the pulsation frequency F and the pulsation rate P using, for example, a map in which the pulsation error Err is associated with the pulsation frequency F and the pulsation rate P. To do. That is, when the pulsation frequency F is obtained by the frequency analysis unit 42 and the pulsation rate P is obtained by the pulsation rate calculation unit 38a, the pulsation error prediction unit 39c correlates with the obtained pulsation frequency F and the pulsation rate P. The error Err is extracted from the map.

  In this case, the AFM includes a two-dimensional map in which a plurality of combinations of the pulsation frequency F and the pulsation rate P are associated with the pulsation error Err correlated with each combination. The two-dimensional map here has, for example, pulsation frequencies F1 to Fn on one axis, pulsation rates P1 to Pn on the other axis, and pulsation errors Err1 to Errn for each combination of pulsation frequency F and pulsation rate P. Each of which is associated. For example, the pulsation error Err1 is associated with the pulsation frequency F1 and the pulsation rate P1. A pulsation error Errn is associated with the pulsation frequency Fn and the pulsation rate Pn. Each of the pulsation errors Err1 to Errn is a value obtained by each combination of the pulsation frequency F and the pulsation rate P when an experiment or simulation using an actual machine is performed by changing the values of the pulsation frequency F and the pulsation rate P. It can be said.

  Therefore, this AFM can achieve the same effects as the AFM 100. Further, since this AFM predicts a pulsation error Err correlated with the pulsation rate P and the pulsation frequency F and corrects it using the pulsation error Err, the correction is performed using only the pulsation error Err corresponding to the pulsation rate P. It is possible to perform correction with higher accuracy than in the case.

(Modification)
Here, the modification of 4th Embodiment is demonstrated using FIG. The frequency analysis unit 42a of this modification is different from the frequency analysis unit 42 in that a pulsation frequency is acquired based on a signal from the ECU 200.

  For example, the frequency analysis unit 42a acquires a signal indicating the rotation speed of the engine output shaft (that is, the engine rotation speed), a sensor signal of the crank angle sensor, and the like from the ECU 200. Then, the frequency analysis unit 42a calculates the pulsation frequency based on the signal acquired from the ECU 200. In this case, the frequency analysis unit 42a may acquire the pulsation frequency F using, for example, a map in which the engine rotation speed and the pulsation frequency F are associated with each other.

  The modified AFM can provide the same effects as those of the fourth embodiment. Furthermore, since the AFM according to the modification obtains the pulsation frequency based on information from the ECU 200, the processing load in the AFM is less than when the pulsation frequency is calculated from a plurality of sampling values stored in the sampling storage unit 34. Can be reduced.

(Fifth embodiment)
The AFM of the fifth embodiment (hereinafter simply referred to as “AFM”) will be described with reference to FIGS. 12, 13, and 14. The AFM is different from the AFM 100 in a part of the processing unit 20. As shown in FIG. 12, the AFM has a point that the average air amount Gave obtained by the average air amount calculation unit 37 and the pulsation frequency F obtained by the frequency analysis unit 42 are input to the pulsation error prediction unit 39d. And different.

  The pulsation error prediction unit 39d predicts the pulsation error Err using the pulsation frequency F, the average air amount Gave, and the pulsation amplitude A. That is, in addition to the pulsation amplitude A, the pulsation error prediction unit 39d predicts a pulsation error Err that is also correlated with the pulsation frequency F and the average air amount Gave. Therefore, the fifth embodiment can be regarded as an embodiment in which the first embodiment, the third embodiment, and the fourth embodiment are combined.

In this case, the pulsation error prediction unit 39d uses, for example, the two-dimensional map shown in FIG. 13 and the error prediction formula shown in Equation 3 to correlate the pulsation error with the pulsation frequency F, the average air amount Gave, and the pulsation amplitude A. Err is predicted.

In the error prediction formula, Cnn is the slope and Bnn is the intercept.

  The relationship between the pulsation error Err [%] and the pulsation amplitude A is different for each combination of a plurality of pulsation frequencies F and a plurality of average air amounts Gave, as shown in FIG. The solid line in FIG. 14 shows the relationship between the corrected pulsation error Err and the pulsation amplitude A. On the other hand, the broken line indicates the relationship between the pulsation error Err and the pulsation amplitude A before correction, that is, the pulsation characteristic.

  As shown in FIG. 13, the map associates a combination of slope Cnn and intercept Bnn that correlates with each combination of average air amount Gave and pulsation frequency F. More specifically, the two-dimensional map has, for example, average air amounts Gave1 to Gaven on one axis, pulsation frequencies F1 to Fn on the other axis, and average air amounts Gave1 to Gaven and pulsation frequencies F1 to Fn. Each combination of slope Cnn and intercept Bnn is associated with the combination. Each of the inclination Cnn and the intercept Bnn can be obtained by an experiment or simulation using an actual machine.

  Thus, it can be said that the map is for obtaining the slope Cnn and the intercept Bnn when calculating the pulsation error Err. In other words, in the map, the coefficient in the error prediction formula is associated with each average air amount Gave and each pulsation frequency F.

  For example, in the case of the pulsation amplitude A1, the pulsation frequency F1, and the average air amount Gave1, the pulsation error prediction unit 39d acquires the slope C11 and the intercept B11 by using a map. That is, the relationship between the pulsation amplitude A and the pulsation error Err can be represented by a solid line in the leftmost graph of FIG. Then, the pulsation error prediction unit 39d can obtain the pulsation error Err by calculating C11 × pulsation amplitude A1 + B11 using Equation 3.

  The AFM according to the present embodiment configured as described above can achieve the same effects as the AFM 100. Furthermore, in this embodiment, since the pulsation error Err correlated with the pulsation amplitude A, the average air amount Gave, and the pulsation frequency F is predicted and corrected using the pulsation error Err, only the pulsation error Err corresponding to the pulsation amplitude A is obtained. It is possible to perform correction with higher accuracy than in the case where correction is performed using.

  Further, the pulsation error prediction unit 39d may use the pulsation rate P in place of the pulsation amplitude A. This can be regarded as an example in which the second embodiment, the third embodiment, and the fourth embodiment are combined.

  In this case, the pulsation error prediction unit 39d predicts the pulsation error Err using the pulsation frequency F, the average air amount Gave, and the pulsation rate P. That is, the pulsation error prediction unit 39d predicts the pulsation error Err using the pulsation rate P instead of the pulsation amplitude A. In this case, in the error prediction formula shown in Equation 3, the term of the pulsation amplitude A is changed to the pulsation rate P. This also has the same effect.

(Modification 1)
Here, the modification 1 of 5th Embodiment is demonstrated using FIG. The pulsation error prediction unit 39d of this modification is different from the fifth embodiment in that the pulsation error Err (correction amount Q) is predicted using a three-dimensional map.

  In this case, the pulsation error prediction unit 39d uses, for example, a map in which the correction amount Q is associated with the pulsation amplitude A, the average air amount Gave, and the pulsation frequency F, and the pulsation amplitude A, the average air amount Gave, and the pulsation frequency. A correction amount Q correlated with F is acquired.

  The AFM is provided with a two-dimensional map for each pulsation amplitude A in which a plurality of combinations of the average air amount Gave and the pulsation frequency F and a correction amount Q correlated with each combination are associated as shown in FIG. A three-dimensional map is provided. For example, in the two-dimensional map related to the pulsation amplitude A1, the average air amounts Gave1 to Gaven are taken on one axis, the pulsation frequencies F1 to Fn are taken on the other axis, and each of the average air amounts Gave1 to Gaven and the pulsation frequencies F1 to Fn are taken. Each of the correction amounts Q111 to Q1nn is associated with the combination. Each of the correction amounts Q111 to Q1nn has the average air amount Gave and the pulsation frequency when the average air amount Gave and the pulsation frequency F are changed in the case of the pulsation amplitude A1 and an experiment or simulation using an actual machine is performed. It can be said that it is a value obtained by each combination of F. The same applies to the two-dimensional map related to the pulsation amplitude A2 and thereafter.

  When the pulsation error prediction unit 39d acquires the pulsation amplitude A, the average air amount Gave, and the pulsation frequency F, the pulsation error prediction unit 39d acquires a correction amount Q associated with these parameters using a three-dimensional map. For example, the pulsation error prediction unit 39d acquires the correction amount Q111 when the pulsation amplitude A1, the average air amount Gave1, and the pulsation frequency F1 are acquired.

  This modification 1 can have the same effect as the fifth embodiment.

  In the first modification, the pulsation rate P may be used instead of the pulsation amplitude A. In this case, the pulsation error prediction unit 39d is provided with a two-dimensional map in which a plurality of combinations of the average air amount Gave and the pulsation frequency F and a correction amount Q correlated with each combination are associated with each pulsation rate P. A correction amount Q is acquired using a three-dimensional map. This also has the same effect.

(Modification 2)
Next, a second modification of the fifth embodiment will be described with reference to FIGS. The pulsation error prediction unit 39d of this modification differs from the fifth embodiment in that the error prediction formula used when predicting the pulsation error Err is changed according to the magnitude of the pulsation amplitude A.

In this case, the pulsation error prediction unit 39d correlates with the pulsation frequency F, the average air amount Gave, and the pulsation amplitude A using, for example, the three-dimensional map shown in FIG. The estimated pulsation error Err is predicted.

In the error prediction formula shown in Equation 4, Dnn is the slope and Enn is the intercept. The expression 3 is the same as described above.

  As shown in FIG. 16, the relationship between the pulsation error Err and the pulsation amplitude A is different for each combination of a plurality of pulsation frequencies F and a plurality of average air amounts Gave. Further, the relationship between the pulsation error Err and the pulsation amplitude A has a different tendency with the threshold value As as the boundary even if the average air amount Gave and the pulsation frequency F are the same. That is, the relationship between the pulsation error Err and the pulsation amplitude A is reversed with the threshold value As as the boundary. When the pulsation amplitude A is smaller than the threshold value As, the pulsation error Err decreases as the pulsation amplitude A increases. When the pulsation amplitude A is larger than the threshold value As, the pulsation error Err increases as the pulsation amplitude A increases. The solid line in FIG. 16 shows the relationship between the corrected pulsation error Err and the pulsation amplitude A. On the other hand, the broken line indicates the relationship between the pulsation error Err and the pulsation amplitude A before correction, that is, the pulsation characteristic.

  For this reason, as shown in FIG. 17, the map includes a two-dimensional map in the case of pulsation amplitude A <threshold value As and a two-dimensional map in the case of pulsation amplitude A> threshold value As. The two-dimensional map in the case of pulsation amplitude A <threshold As is the same as the two-dimensional map shown in FIG. 13, and the combination of the slope Cnn and the intercept Bnn that correlates with each combination of the average air amount Gave and the pulsation frequency F. Associated. On the other hand, the two-dimensional map in the case of pulsation amplitude A> threshold As is associated with a combination of slope Dnn and intercept Enn that correlates with each combination of average air amount Gave and pulsation frequency F. The two-dimensional map in this case is different from the two-dimensional map in the case where the slope Dnn and the intercept Enn satisfy the pulsation amplitude A <the threshold value As.

  In this case, for example, in the case of the pulsation amplitude A1 (<As), the pulsation frequency F1, and the average air amount Gave1, the pulsation error prediction unit 39d acquires the slope C11 and the intercept B11 by using a map. Then, the pulsation error prediction unit 39d can obtain the pulsation error Err by calculating C11 × pulsation amplitude A1 + B11 using Equation 3. For example, in the case of pulsation amplitude A2 (> As), pulsation frequency F1, and average air amount Gave1, the pulsation error prediction unit 39d acquires the slope D11 and the intercept E11 by using a map. Then, the pulsation error prediction unit 39d can obtain the pulsation error Err by calculating D11 × pulsation amplitude A2 + E11 using Equation 4.

  This modification 2 can have the same effect as the fifth embodiment. Further, the pulsation error Err has a different tendency depending on the pulsation amplitude A even if the pulsation frequency F and the average air amount Gave are the same. Therefore, since the modified example 2 switches the error prediction formula according to the magnitude of the pulsation amplitude A, appropriate correction can be performed.

  In the second modification, the error prediction formula can be switched depending on whether or not the backflow occurs by setting the threshold value As to the average air amount Gave. For this reason, the second modification can be corrected with high accuracy even when a backflow occurs.

(Modification 3)
Next, Modification 3 of the fifth embodiment will be described with reference to FIGS. The pulsation error prediction unit 39d of this modification is different from Modification 2 in that the error prediction formula used when predicting the pulsation error Err is changed according to the magnitude of the pulsation rate P.

  In this case, the pulsation error prediction unit 39d uses, for example, a three-dimensional map shown in FIG. 18 and an error prediction formula similar to Equations 3 and 4 to calculate the pulsation frequency F, the average air amount Gave, and the pulsation rate P. Predict correlated pulsation error Err. The error prediction formula used here is obtained by changing the term of the pulsation amplitude A in Equations 3 and 4 to the pulsation rate P. Below, what changed the term of the pulsation amplitude A in Formula 3 to the pulsation rate P is called Formula 13, and what changed the term of the pulsation amplitude A in Formula 4 to the pulsation rate P is also called Formula 14.

  As shown in FIG. 18, the relationship between the pulsation error Err and the pulsation rate P is different for each combination of a plurality of pulsation frequencies F and a plurality of average air amounts Gave. Further, the relationship between the pulsation error Err and the pulsation rate P has a different tendency with the threshold value Ps as a boundary even if the average air amount Gave and the pulsation frequency F are the same. That is, the relationship between the pulsation error Err and the pulsation rate P is reversed with the threshold value Ps as a boundary. When the pulsation rate P is smaller than the threshold value As, the pulsation error Err decreases as the pulsation rate P increases. When the pulsation rate P is larger than the threshold value Ps, the pulsation error Err increases as the pulsation rate P increases. The solid line in FIG. 18 shows the relationship between the corrected pulsation error Err and the pulsation rate P. On the other hand, the broken line indicates the relationship between the pulsation error Err before correction and the pulsation rate P, that is, the pulsation characteristic.

  For this reason, as shown in FIG. 19, the map includes a two-dimensional map in the case of pulsation rate P <threshold value Ps and a two-dimensional map in the case of pulsation rate P> threshold value Ps. The two-dimensional map in the case of pulsation rate P <threshold value Ps is the same as the two-dimensional map shown in FIG. 13, and the combination of slope Cnn and intercept Bnn that correlates with each combination of average air amount Gave and pulsation frequency F. Associated. On the other hand, in the two-dimensional map in the case of pulsation rate P> threshold value Ps, a combination of slope Dnn and intercept Enn that correlates with each combination of average air amount Gave and pulsation frequency F is associated. The two-dimensional map in this case is different from the two-dimensional map in the case where the slope Dnn and the intercept Enn are pulsation rate P <threshold value Ps.

  In this case, for example, in the case of the pulsation rate P1 (<Ps), the pulsation frequency F1, and the average air amount Gave1, the pulsation error prediction unit 39d acquires the slope C11 and the intercept B11 by using a map. Then, the pulsation error prediction unit 39d can obtain the pulsation error Err by calculating C11 × pulsation rate P1 + B11 using Equation 13. For example, in the case of the pulsation rate P2 (> Ps), the pulsation frequency F1, and the average air amount Gave1, the pulsation error prediction unit 39d acquires the slope D11 and the intercept E11 by using a map. Then, the pulsation error prediction unit 39d can obtain the pulsation error Err by calculating D11 × pulsation rate P2 + E11 using Equation 14.

  As described above, the pulsation error prediction unit 39d according to Modification 3 predicts the pulsation error Err correlated with the pulsation rate P using a plurality of error prediction formulas. Each of the plurality of error prediction formulas has a different tendency of change in pulsation error Err with respect to change in pulsation rate P. Then, the pulsation error prediction unit 39d changes the error prediction formula according to the magnitude of the pulsation rate P.

  Modification 3 can achieve the same effects as those of the fifth embodiment. Further, the pulsation error Err has a different tendency depending on the pulsation rate P even if the pulsation frequency F and the average air amount Gave are the same. Therefore, since the modified example 3 switches an error prediction formula according to the magnitude of the pulsation rate P, appropriate correction can be performed.

  Further, in the third modification, by setting the threshold value Ps to 100%, the error prediction formula can be switched depending on whether or not a backflow occurs. For this reason, the modification 3 can perform highly accurate correction even when a backflow occurs.

  Furthermore, in the second modification, it is necessary to change the threshold value As according to the average air amount Gave. However, in Modification 3, by setting the threshold value Ps to 100%, the error prediction formula can be switched depending on whether or not a backflow occurs. For this reason, in the third modification, the calculation can be simplified and the amount of memory can be reduced.

(Modification 4)
Further, the pulsation error prediction unit 39d may calculate the correction amount Q by the following regression equation.
Correction amount Q = αG + βF + γA
G: average air amount, F: pulsation frequency, A: pulsation amplitude, α, β, γ: constant constants α, β, γ are values determined by the passage forming member.

(Sixth embodiment)
Here, the AFM of the sixth embodiment (hereinafter simply referred to as AFM) will be described with reference to FIGS. 20, 21, and 22. The AFM is different from the AFM 100 in a part of the processing unit 20. The AFM differs from the first embodiment in how to determine the measurement period for measuring the average air amount Gave and the pulsation maximum value Gmax. The AFM includes a pulsation cycle calculation unit 34 a and a measurement period calculation unit 34 b instead of the sampling storage unit 34 and the upper extreme value determination unit 35.

  The pulsation cycle calculation unit 34a calculates the pulsation cycle of air. More specifically, the pulsation cycle calculation unit 34 a calculates the pulsation cycle using the air flow rate converted by the output air flow rate conversion table 33.

  The measurement period calculation unit 34b changes the measurement period for obtaining the average air amount Gave and the pulsation maximum value Gmax according to the pulsation cycle obtained by the pulsation cycle calculation unit 34a. Specifically, when the pulsation period is long, the measurement period calculation unit 34b makes the measurement period longer than when the pulsation period is short. For example, the measurement period calculation unit 34b sets the pulsation cycle (one cycle) obtained by the pulsation cycle calculation unit 34a as the measurement period.

  For example, as shown in FIG. 21, when the measurement period is short with respect to the pulsation cycle, an error occurs between the true pulsation maximum value Gmax and the pulsation maximum value Gmax during the measurement period. Similarly, an error occurs between the true average air amount Gave and the average air amount Gave during the measurement period. Therefore, in this case, the accuracy of the pulsation error Err (correction amount Q) decreases.

  Further, as shown in FIG. 22, when the measurement period is long with respect to the pulsation cycle, the time until the average air amount Gave and the pulsation maximum value Gmax are obtained becomes long. Therefore, in this case, the time until the pulsation error Err (correction amount Q) is obtained is long, and the responsiveness is deteriorated.

  However, since the AFM changes the measurement period according to the pulsation cycle as described above, it is possible to improve the calculation accuracy of the average air amount Gave and the maximum pulsation value Gmax and improve the responsiveness. Naturally, the AFM of the sixth embodiment can achieve the same effects as the AFM 100.

  In the present embodiment, as shown in FIG. 20, an example in which the pulsation error Err is obtained using the pulsation amplitude A is employed. However, the present disclosure is not limited to this, and in the second to fifth embodiments, a pulsation period calculation unit 34a and a measurement period calculation unit 34b are employed instead of the sampling storage unit 34 and the upper extreme value determination unit 35. May be.

(Seventh embodiment)
Here, the AFM of the seventh embodiment (hereinafter simply referred to as AFM) will be described with reference to FIGS. 23, 24, and 25. The AFM is different from the AFM 100 in a part of the processing unit 20. Further, as shown in FIG. 23, the AFM includes a duct diameter storage unit 43 that stores the diameter of the duct (duct diameter H) on which the AFM is mounted. As shown in FIG. 23, the AFM is different from the AFM 100 in that the duct diameter H stored in the duct diameter storage unit 43 is input to the pulsation error prediction unit 39e.

  The relationship between the pulsation error Err and the pulsation amplitude A is different for each combination of a plurality of pulsation frequencies F and a plurality of average air amounts Gave. Further, the relationship between the pulsation error Err and the pulsation amplitude A differs depending on the duct diameter H because the flow velocity distribution in the duct changes depending on the duct diameter H even if the average air amount Gave and the pulsation frequency F are the same. FIG. 25 shows the relationship between the pulsation error Err and the pulsation amplitude A when a certain duct diameter is H. This is different depending on the duct diameter H as shown in FIG.

  Therefore, the pulsation error prediction unit 39e predicts the pulsation error Err using the pulsation amplitude A, the pulsation frequency F, the average air amount Gave, and the duct diameter H. That is, the pulsation error predicting unit 39e predicts the pulsation error Err correlated with the duct diameter H in addition to the pulsation amplitude A, the pulsation frequency F, and the average air amount Gave. In this case, the pulsation error prediction unit 39e uses, for example, a pulsation amplitude A, a pulsation frequency F, an average air amount Gave, and a duct diameter H by using the three-dimensional map shown in FIG. Predict correlated pulsation error Err.

  As shown in FIG. 24, the AFM is a two-dimensional map in which a plurality of combinations of average air amount Gave and pulsation frequency F and a combination of slope Cnnn and intercept Bnnn correlated with each combination are associated with each duct diameter H. Are provided with a three-dimensional map. More specifically, each two-dimensional map takes, for example, an average air amount Gave1 to Gaven on one axis and a pulsation frequency F1 to Fn on the other axis, and an average air amount Gave1 to Gaven and a pulsation frequency F1 to Fn. Each combination is associated with a combination of the slope Cnnn and the intercept Bnnn. Each of the inclination Cnnn and the intercept Bnnn can be obtained by an experiment or simulation using an actual machine. Each two-dimensional map is the same as in FIG.

  For example, in the case of the duct diameter H1, the pulsation amplitude A1, the pulsation frequency F1, and the average air amount Gave1, the pulsation error prediction unit 39e acquires the slope C111 and the intercept B111 by using a map. Then, the pulsation error prediction unit 39e can obtain the pulsation error Err by calculating C111 × pulsation amplitude A1 + B111 using Equation 3.

  The AFM according to the present embodiment configured as described above can achieve the same effects as the AFM 100. Furthermore, in this embodiment, the pulsation error Err correlated with the pulsation amplitude A, the average air amount Gave, the pulsation frequency F, and the duct diameter H is predicted and corrected using the pulsation error Err. The correction can be performed with higher accuracy than the correction using only the pulsation error Err.

(Eighth embodiment)
Here, a modification of the eighth embodiment will be described with reference to FIG. The eighth embodiment is different from the first embodiment in that the sensing unit 10 is provided in the AFM 110 and the processing unit 20 is provided in the ECU 210. That is, in the present embodiment, the present disclosure can be regarded as an example in which the present disclosure is applied to the processing unit 20 provided in the ECU 210. Note that the present disclosure (air flow measurement device) may include the sensing unit 10 in addition to the processing unit 20.

  For this reason, AFM110 and ECU210 can have the same effect as AFM100. Furthermore, since the AFM 110 does not include the processing unit 20, the processing load can be reduced as compared with the AFM 100.

  The eighth embodiment can also be applied to the second to seventh embodiments. In this case, the processing unit in each embodiment is provided in the ECU 210. Therefore, the ECU 210 performs calculation of the pulsation rate P, analysis of the pulsation frequency F, and the like.

(Ninth embodiment)
Here, the AFM of the ninth embodiment (hereinafter simply referred to as AFM) will be described with reference to FIGS. As shown in FIG. 27, the AFM is different from the AFM 100 in that the AFM includes a filter unit 44. That is, it can be said that the AFM of the present embodiment includes the processing unit 20 including the filter unit 44.

  The filter unit 44 is provided before the sampling storage unit 34 and the upper extreme value determination unit 35. The filter unit 44 performs a filtering process on the output value (output signal) and outputs the processed output value. In the present embodiment, the processing unit 20 in which the filter unit 44 is provided between the output air flow rate conversion table 33, the sampling storage unit 34, and the upper extreme value determination unit 35 is employed. The post-processing output value can also be said to be a post-processing output signal.

  The filter unit 44 can employ, for example, a low-pass filter. In the waveform shown in FIG. 28, the broken line is the output signal before the filtering process, and the solid line is the output signal after the filtering process. Note that the output signal after the filter processing in FIG. 28 is a processed output signal when a low-pass filter with a time constant of 3 ms is adopted as the filter unit 44.

  For this reason, the average air amount calculation unit 37 calculates the average air amount Gave from the processed output value as the output value. The pulsation maximum value calculator 36 obtains the pulsation maximum value Gmax from the processed output value as the output value.

  The AFM of the ninth embodiment configured as described above can achieve the same effects as the AFM 100. Furthermore, as shown in FIG. 28, the AFM of the ninth embodiment can reduce the effect of output disturbance due to electrical noise or turbulence even when noise is superimposed on the output value. The detection accuracy of the upper extreme value can be improved.

  The ninth embodiment can be applied to the second to eighth embodiments. In this case, the processing unit in each embodiment includes the filter 44.

  DESCRIPTION OF SYMBOLS 10 ... Sensing part, 20 ... Processing part, 31 ... Sensor output A / D conversion part, 32 ... Sampling part, 33 ... Output air flow rate conversion table, 34 ... Sampling storage part, 34a ... Pulsation period calculating part, 34b ... Measurement period Calculation unit 35 ... Upper extremum value determination unit 36 ... Pulsation maximum value calculation unit 37 ... Average air amount calculation unit 38 ... Pulsation amplitude calculation unit 38a ... Pulsation rate calculation unit 39, 39a-39e ... Pulsation error prediction 40: Pulsation error correction unit 41: Flow rate output unit after pulsation correction 42, 42a Frequency analysis unit 43 Duct diameter storage unit 100 110 AFM 200 200 210 ECU

Claims (10)

An air flow rate measuring device for measuring an air flow rate based on an output value of a sensing unit (10) arranged in an environment where air flows,
An average air amount calculation unit (37) that calculates an average air amount that is an average value of the air flow rate from the output value;
A pulsation amplitude calculation unit (36, 36) that calculates a pulsation maximum value that is the maximum value of the air flow rate from the output value and calculates a pulsation amplitude of the air flow rate by taking a difference between the pulsation maximum value and the average air amount. 38)
A pulsation error prediction unit (39, 39a to 39e) for predicting a pulsation error of the air flow rate correlated with the pulsation amplitude;
An air flow rate measuring device comprising: a pulsation error correction unit (40) that corrects the air flow rate so that the pulsation error is reduced using the pulsation error predicted by the pulsation error prediction unit. A pulsation rate calculating section (38a) for calculating the pulsation rate of the air flow rate by dividing the pulsation amplitude by the average air amount;
The air flow rate measuring device according to claim 1, wherein the pulsation error prediction unit (39a) predicts the pulsation error correlated with the pulsation rate as the pulsation error correlated with the pulsation amplitude.   The air flow rate measuring device according to claim 1 or 2, wherein the pulsation error prediction unit further predicts the pulsation error correlated with the average air amount. A frequency acquisition unit (42, 42a) for acquiring a pulsation frequency which is a frequency of a pulsation waveform in the air;
The air flow rate measuring device according to any one of claims 1 to 3, wherein the pulsation error prediction unit further predicts the pulsation error correlated with the pulsation frequency acquired by the frequency acquisition unit. The internal combustion engine control device that controls the internal combustion engine using the air flow rate corrected by the pulsation error correction unit is configured to be able to acquire a signal indicating the operating state of the internal combustion engine,
The air flow rate measuring device according to claim 4, wherein the frequency acquisition unit acquires the signal from the internal combustion engine control device, and acquires the pulsation frequency based on the acquired signal. A pulsation period calculation unit (34a) for calculating the pulsation period of the air;
A measurement period calculation unit (34b) that changes the measurement period for obtaining the average air amount and the pulsation maximum value according to the pulsation period calculated by the pulsation period calculation unit,
The air flow rate measurement device according to any one of claims 1 to 5, wherein the measurement period calculation unit makes the measurement period longer when the pulsation period is longer than when the pulsation period is short. The sensing unit is mounted in a duct and measures the flow rate of air flowing in the duct.
A duct diameter storage unit (43) for storing the duct diameter of the duct;
The air flow measurement device according to any one of claims 1 to 6, wherein the pulsation error prediction unit further predicts the pulsation error correlated with the duct diameter stored in the duct diameter storage unit. The pulsation error prediction unit is configured to predict the pulsation error correlated with the pulsation amplitude using a plurality of error prediction formulas,
Each of the plurality of error prediction formulas has a different tendency of the change of the pulsation error with respect to the change of the pulsation amplitude,
The air flow rate measuring apparatus according to any one of claims 1 to 7, wherein the pulsation error prediction unit changes the error prediction formula according to a magnitude of the pulsation amplitude. A filter processing unit that performs filtering on the output value and outputs the processed output value;
The average air amount calculation unit calculates the average air amount from the post-processing output value as the output value,
The air flow measurement device according to any one of claims 1 to 8, wherein the pulsation amplitude calculation unit obtains the pulsation maximum value from the post-processing output value as the output value.   The pulsation amplitude calculation unit includes a pulsation maximum value calculation unit (36) that obtains a plurality of upper extreme values in the air flow rate from the output value and obtains the pulsation maximum value from an average value of the plurality of upper extreme values. The air flow rate measuring device according to any one of claims 1 to 9.

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