KR102529444B1 - Learning method of ash deposition in dpf - Google Patents

Learning method of ash deposition in dpf Download PDF

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KR102529444B1
KR102529444B1 KR1020180045665A KR20180045665A KR102529444B1 KR 102529444 B1 KR102529444 B1 KR 102529444B1 KR 1020180045665 A KR1020180045665 A KR 1020180045665A KR 20180045665 A KR20180045665 A KR 20180045665A KR 102529444 B1 KR102529444 B1 KR 102529444B1
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learning
ash
differential pressure
dpf
slope
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KR20190122033A (en
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허동한
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현대자동차주식회사
기아 주식회사
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N9/00—Electrical control of exhaust gas treating apparatus
    • F01N9/002—Electrical control of exhaust gas treating apparatus of filter regeneration, e.g. detection of clogging
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N3/00—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust
    • F01N3/02—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for cooling, or for removing solid constituents of, exhaust
    • F01N3/021—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for cooling, or for removing solid constituents of, exhaust by means of filters
    • F01N3/023—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for cooling, or for removing solid constituents of, exhaust by means of filters using means for regenerating the filters, e.g. by burning trapped particles
    • F01N3/0232—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for cooling, or for removing solid constituents of, exhaust by means of filters using means for regenerating the filters, e.g. by burning trapped particles removing incombustible material from a particle filter, e.g. ash
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/04—Methods of control or diagnosing
    • F01N2900/0402—Methods of control or diagnosing using adaptive learning
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
    • F01N2900/06—Parameters used for exhaust control or diagnosing
    • F01N2900/16—Parameters used for exhaust control or diagnosing said parameters being related to the exhaust apparatus, e.g. particulate filter or catalyst
    • F01N2900/1611—Particle filter ash amount
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Processes For Solid Components From Exhaust (AREA)
  • Filtering Of Dispersed Particles In Gases (AREA)

Abstract

본 발명은 DPF 내부에 퇴적된 ASH량을 학습하는 ASH 학습방법에 있어서, ASH량 학습이 가능한 상태인지 판단하는 학습조건 판단단계, DPF를 통과하는 유량과 상기 DPF의 전후단의 압력 차이값을 이용하여 상기 ASH량을 학습하는 ASH 학습단계를 포함한다.In an ASH learning method for learning the amount of ASH accumulated inside a DPF, the present invention uses a learning condition determination step for determining whether the amount of ASH can be learned, and a difference between the flow rate passing through the DPF and the pressure at the front and rear ends of the DPF. and an ASH learning step of learning the amount of ASH by doing so.

Description

DPF(DIESEL PARTICULATE FILTER) 내부에 퇴적된 ASH 학습방법{LEARNING METHOD OF ASH DEPOSITION IN DPF}ASH learning method deposited inside DPF (DIESEL PARTICULATE FILTER) {LEARNING METHOD OF ASH DEPOSITION IN DPF}

본 발명은 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법에 관한 것으로, 보다 상세하게는 학습 계산 가능범위를 좁혀 1차식 형태로 단순화한 차압곡선의 기울기(차압/체적유량)를 이용하여 학습값을 계산하는 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법에 관한 것이다.The present invention relates to an ASH learning method deposited inside a diesel particulate filter (DPF), and more specifically, learning using the slope (differential pressure/volume flow rate) of a differential pressure curve simplified to a linear form by narrowing the possible range of learning calculations It relates to an ASH learning method deposited inside a DPF (Diesel Particulate Filter) that calculates a value.

일반적으로 엔진 작동에 따른 오염 물질을 처리하는 DPF(Diesel Particulate Filter, 이하 DPF라 함)는, 그 내부를 구성하는 담체의 입구부와 출구부의 막힘 부위가 반전되는 구조를 이용한 매연 정화 장치로서, 엔진 배기가스 내 오염 물질이 담체 내부의 다공 홀을 통과함에 따라 시간 경과 후, 담체 내에 포집된 검댕이 그을림인 수트(soot) 포집 량이 일정 수준 이상이 되면, 온도를 발화 온도 이상으로 상승시켜 수트 성분을 제거하게 된다. In general, DPF (Diesel Particulate Filter, hereinafter referred to as DPF), which treats pollutants according to engine operation, is a soot purification device using a structure in which the inlet and outlet parts of the carrier constituting the inside are reversed. As the contaminants in the exhaust gas pass through the porous holes inside the carrier, after a period of time, when the amount of soot, which is the soot collected in the carrier, exceeds a certain level, the soot component is removed by raising the temperature above the ignition temperature. will do

그러나, 엔진 배기가스 내 오염 물질은 수트와 같이 일정 온도 이상에서 타버리지 않는 오염 물질이 생성되는데, 이러한 오염 물질은 차량의 윤활유와, 엔진 실린더 라이너(liner)의 금속 성분으로부터 발생하는 금속 산화물 성분인 애쉬(ASH)로서, 이와 같은 애쉬는 금속 산화물이기에 산소와 질소(NO2)에 의한 산화반응(재생)에 의해 제거될 수 없는 물질이다.However, contaminants in engine exhaust gas, such as soot, are generated that do not burn at a certain temperature or higher. These contaminants are metal oxide components generated from vehicle lubricants and metal components of engine cylinder liners. Ash (ASH) is a material that cannot be removed by an oxidation reaction (regeneration) by oxygen and nitrogen (NO 2 ) because such ash is a metal oxide.

이와 같이 금속 산화물인 애쉬가 퇴적되면 DPF 담체 내 수트(soot) 포집을 위한 유효 체적이 감소되는데, 이러한 DPF 담체의 유효 체적의 감소로 인해 배기 가스에 의해 압력 차이 증가를 가져오면, DPF 담체 내 수트 포집 예측량 증가로 인한 재생 주기 감소를 가져오고, 이로 인해 연비 악화나 오일 열화 현상이 발생하거나 심할 경우에는 재생 종료를 인식하지 못하여 작동 시 에러를 발생시키게 된다.When ash, which is a metal oxide, is deposited in this way, the effective volume for collecting soot in the DPF carrier is reduced. This results in a decrease in the regeneration cycle due to an increase in the predicted amount of collection, and as a result, deterioration in fuel efficiency or oil degradation occurs, or in severe cases, an error occurs during operation because the end of regeneration is not recognized.

본 발명은 DPF 내 퇴적된 ASH에 의한 차압곡선의 변화를 정확하게 계산하여, soot 의 예측량을 보다 정확하게 할 수 있는 DPF 내부에 퇴적된 ASH 학습방법을 제공하는 것이다.An object of the present invention is to provide a learning method for ASH accumulated inside a DPF, which can more accurately predict a soot by accurately calculating a change in a differential pressure curve by ASH accumulated within a DPF.

ASH 학습 가능 조건을 판단하는 학습조건 판단단계, ASH 학습 계산단계, ASH 학습 종료 조건을 판단하는 종료조건 판단단계, ASH 학습 종료단계를 포함하고, 학습 계산단계는 학습 전 차압곡선의 기울기(차압/체적유량)와 학습 후 차압곡선의 기울기(차압/체적유량)의 비율로 학습값을 계산하는 것을 특징으로 하는 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법이다.It includes a learning condition determination step for determining an ASH learning possible condition, an ASH learning calculation step, an end condition determination step for determining an ASH learning end condition, and an ASH learning end step. It is an ASH learning method deposited inside a diesel particulate filter (DPF), characterized in that the learning value is calculated by the ratio of the differential pressure curve (differential pressure/volume flow rate) and the slope of the differential pressure curve after learning.

본 발명에 따른 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법은 DPF 내부에 퇴적된 ASH에 의한 차압곡선의 변화를 정확하게 계산하여, soot의 예측량을 보다 정확하게 산출할 수 있다.The method of learning the ASH deposited inside the DPF (Diesel Particulate Filter) according to the present invention can accurately calculate the change in the differential pressure curve by the ASH deposited inside the DPF, and more accurately calculate the predicted amount of soot.

차량 마일리지가 증가하더라도 지속적인 반복 학습을 통해 ASH 학습값과 soot의 예측량을 보다 정확하게 산출할 수 있다.Even if the vehicle mileage increases, the ASH learning value and the predicted amount of soot can be more accurately calculated through continuous iterative learning.

정확한 soot량의 예측을 통해 재생주기 단축에 의한 오일증가 등 품질 문제 발생 빈도를 저감시킬 수 있다.Accurate prediction of soot amount can reduce the frequency of quality problems such as oil increase due to shortened regeneration cycle.

도 1은 본 발명에 따른 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법의 순서도의 일 실시예이다.
도 2는 본 발명에 따른 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법의 적용 전(an-1)과 적용한 후

Figure 112018039117374-pat00001
의 기울기(1차곡선)와 종래의 차압곡선(2차 곡선)을 비교한 그래프이다.
도 3은 본 발명에 따른 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법을 적용한 학습값이 오차범위 내에서 일정값을 갖는 것을 보여주는 그래프(a)와, 차압곡선이 실제 측정한 차압곡선과 근접하게 위치하는 것을 보여주는 그래프(b)이다.
도 4는 DPF에 ASH가 퇴적되는 과정을 보여주는 그림이다.1 is an embodiment of a flowchart of an ASH learning method deposited inside a diesel particulate filter (DPF) according to the present invention.
Figure 2 is before (a n-1 ) and after application of the ASH learning method deposited inside the DPF (Diesel Particulate Filter) according to the present invention
Figure 112018039117374-pat00001
It is a graph comparing the slope of (first order curve) and the conventional differential pressure curve (second order curve).
Figure 3 is a graph (a) showing that the learning value applied with the ASH learning method deposited inside the DPF (Diesel Particulate Filter) according to the present invention has a constant value within the error range, and the differential pressure curve is actually measured It is graph (b) showing that they are located close to each other.
4 is a diagram showing a process in which ASH is deposited on a DPF.

본 발명을 충분히 이해하기 위해서 본 발명의 바람직한 실시 예를 첨부 도면을 참조하여 설명한다. 본 발명의 실시 예는 여러 가지 형태로 변형될 수 있으며, 본 발명의 범위가 아래에서 상세히 설명하는 실시 예로 한정되는 것으로 해석되어서는 안 된다. 본 실시 예는 당업계에서 평균적인 지식을 가진 자에게 본 발명을 보다 완전하게 설명하기 위해서 제공되는 것이다. 따라서 도면에서의 요소의 형상 등은 보다 명확한 설명을 강조하기 위해서 과장되어 표현될 수 있다. 각 도면에서 동일한 구성은 동일한 참조부호로 도시한 경우가 있음을 유의하여야 한다. 본 발명의 요지를 불필요하게 흐릴 수 있다고 판단되는 공지 기능 및 구성에 대한 상세한 기술은 생략된다.In order to fully understand the present invention, a preferred embodiment of the present invention will be described with reference to the accompanying drawings. Embodiments of the present invention may be modified in various forms, and the scope of the present invention should not be construed as being limited to the examples described in detail below. This embodiment is provided to more completely explain the present invention to those skilled in the art. Therefore, the shapes of elements in the drawings may be exaggerated to emphasize a clearer explanation. It should be noted that in each drawing, the same configuration may be indicated by the same reference numerals. Detailed descriptions of well-known functions and configurations that may unnecessarily obscure the subject matter of the present invention are omitted.

디젤엔진에 장착된 DPF (Diesel Particulate Filter)는 엔진에서 나오는 soot 를 포집하고, 특정 기준량 이상 soot 포집 시 soot 연소 가능 온도 이상으로 온도를 제어하는 강제 재생 모드를 통해 태우는 과정을 반복한다. 이 때 연료, 오일 등에 섞인 불순물들에 의해 만들어진 ASH 성분은 연소되지 않고, DPF 내 그대로 남아있게 되며, 그로 인해 차량 마일리지가 증가할 수록 그 양은 점점 많아지며, DPF 내 차압 변화가 생기고, 포집된 soot 량 예측에 오류를 발생시킬 수 있다. 도 4는 DPF에 ASH가 퇴적되는 과정을 보여주는 그림이다.The DPF (Diesel Particulate Filter) installed in the diesel engine collects the soot from the engine and repeats the burning process through forced regeneration mode, which controls the temperature to a temperature higher than the combustible temperature of the soot when a certain amount of soot is collected. At this time, the ASH component made by impurities mixed with fuel, oil, etc. is not combusted and remains in the DPF, and as the vehicle mileage increases, the amount increases, and the differential pressure in the DPF changes, and the collected soot This can lead to errors in volume prediction. 4 is a diagram showing a process in which ASH is deposited on a DPF.

따라서, ASH 량을 정확하게 인식하여 그 양만큼 보정하여 정확한 soot 량을 인식하는 것이 중요하다. Therefore, it is important to accurately recognize the amount of ASH and correct it by the amount to recognize the correct amount of soot.

본 발명은 이와 같은 문제점을 해결하고자, DPF 내부에 퇴적된 ASH량을 학습하는 ASH 학습방법에 관한 것으로, ASH량 학습이 가능한 상태인지 판단하는 학습조건 판단단계, DPF를 통과하는 유량과 상기 DPF의 전후단의 압력 차이값을 이용하여 상기 ASH량을 학습하는 ASH 학습단계를 포함하며, 상기 ASH 학습단계를 완료한 신규 학습값이 기존 학습값 대비 예측범위에 포함되는지 판단하는 ASH 학습 종료조건 판단단계를 더 포함할 수 있다. In order to solve this problem, the present invention relates to an ASH learning method for learning the amount of ASH accumulated inside a DPF. An ASH learning step of learning the ASH amount using a pressure difference between the front and back ends, and determining whether the new learning value that has completed the ASH learning step is included in the predicted range compared to the existing learning value ASH learning end condition determination step may further include.

도 1은 본 발명에 따른 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법의 순서도의 일 실시예이며, 도 1을 참조하여 각 단계별 특징을 설명하면 다음과 같다.1 is an embodiment of a flowchart of an ASH learning method deposited inside a diesel particulate filter (DPF) according to the present invention, and the characteristics of each step are described with reference to FIG. 1 as follows.

상기 ASH 학습단계(S30)는 학습 전 차압곡선의 기울기(차압/체적유량)와 학습 후 차압곡선의 기울기(차압/체적유량)를 산출하는 제1계산단계(S31)와 상기 학습 전 차압곡선의 기울기(차압/체적유량)와 상기 학습 후 차압곡선의 기울기(차압/체적유량)의 비율로 상기 학습값을 계산하는 제2계산단계(S32)를 포함한다.The ASH learning step (S30) includes a first calculation step (S31) of calculating the slope of the differential pressure curve before learning (differential pressure/volume flow rate) and the slope of the differential pressure curve after learning (differential pressure/volume flow rate), and the differential pressure curve before learning and a second calculation step (S32) of calculating the learning value as a ratio of the slope (differential pressure/volume flow rate) and the slope (differential pressure/volume flow rate) of the differential pressure curve after the learning.

상기 ASH 학습단계(S30)는 하기의 <수학식 1>을 이용하여 상기 학습값을 산출하는 단계이다.The ASH learning step (S30) is a step of calculating the learning value using the following <Equation 1>.

Figure 112018039117374-pat00002
<수학식 1>
Figure 112018039117374-pat00002
<Equation 1>

여기서,

Figure 112018039117374-pat00003
는 학습값, an- 1는 학습 전 차압/체적유량(기울기), an는 학습 후 차압/체적유량(기울기), i는 샘플수이다.here,
Figure 112018039117374-pat00003
is the learning value, a n- 1 is the differential pressure/volume flow rate (slope) before learning, a n is the differential pressure/volume flow rate (slope) after learning, and i is the number of samples.

또한, 상기 ASH 학습방법은 상기 <수학식 1>을 이용하여 취득한 상기 학습값을 기존 학습값이 적용된 차압곡선에 곱하여 업데이트하는 차압곡선 업데이트 단계(S40)를 더 포함하며, 상기 차압곡선 업데이트 단계는 하기의 <수학식 2>를 이용하여 상기 차압곡선을 업데이트한다.In addition, the ASH learning method further includes a differential pressure curve updating step (S40) of updating the learning value acquired using <Equation 1> by multiplying the differential pressure curve to which the existing learning value is applied, and the differential pressure curve updating step is The differential pressure curve is updated using Equation 2 below.

Figure 112018039117374-pat00004
<수학식 2>
Figure 112018039117374-pat00004
<Equation 2>

여기서,

Figure 112018039117374-pat00005
는 n번째 차압값이고,
Figure 112018039117374-pat00006
은 n번째 학습값이며, f1는 최초 학습값이다.here,
Figure 112018039117374-pat00005
is the nth differential pressure value,
Figure 112018039117374-pat00006
is the nth learning value, and f 1 is is the initial learning value.

상기 최초 학습값

Figure 112018039117374-pat00007
은 하기의 <수학식 3>을 이용하여 산출한다.above initial learning value
Figure 112018039117374-pat00007
is calculated using the following <Equation 3>.

Figure 112018039117374-pat00008
<수학식 3>
Figure 112018039117374-pat00008
<Equation 3>

여기서, a0는 중앙품(Fresh DPF)의 차압/체적유량(기울기), a1은 최초 ASH 학습 후 차압/체적유량(기울기), i는 샘플수이다.Here, a 0 is the differential pressure/volume flow rate (slope) of the central product (Fresh DPF), a 1 is the differential pressure/volume flow rate (slope) after the first ASH learning, and i is the number of samples.

상기 학습조건 판단단계(S20)는 상기 DPF의 통과 유량, 차압센서 변동량, 재생온도를 판단요소로 하여 상기 판단요소가 각각의 기준범위를 만족하는지 지속적으로 확인하며, 만족하지 않을 때는 상기 ASH 학습 계산단계를 진행하지 않고 만족할 땔까지 기다린다.
여기서 상기 DPF의 통과 유량, 차압센서 변동량, 재생온도의 기준범위는 차량의 급가속이 없는 정속조건에서 ASH 학습값의 일관성을 유지할 수 있도록 반복시험을 통해 얻어질 수 있고, 차량 및 디젤엔진에 따라 상기 기준범위는 각각 달리 설정될 수 있다.The learning condition determination step (S20) continuously checks whether the determination elements satisfy each reference range using the flow rate of the DPF, the differential pressure sensor variation, and the regeneration temperature as determination factors, and if not satisfied, the ASH learning calculation Do not proceed with the steps and wait for firewood to be satisfied.
Here, the reference range of the flow rate of the DPF, the differential pressure sensor variation, and the regeneration temperature can be obtained through repeated tests to maintain the consistency of the ASH learning value under a constant speed condition without rapid acceleration of the vehicle, and depending on the vehicle and diesel engine The reference range may be set differently.

도 2는 본 발명에 따른 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법의 적용 전(an-1)과 적용한 후

Figure 112018039117374-pat00009
의 기울기(1차곡선)와 종래의 차압곡선(2차 곡선)을 비교한 그래프이고, 도 3은 본 발명에 따른 DPF(Diesel Particulate Filter) 내부에 퇴적된 ASH 학습방법을 적용한 학습값이 오차범위 내에서 일정값을 갖는 것을 보여주는 그래프(a)와, 차압곡선이 실제 측정한 차압곡선과 근접하게 위치하는 것을 보여주는 그래프(b)로, 도 2와 도 3을 통해 본 발명의 효과를 확인할 수 있다.Figure 2 is before (a n-1 ) and after application of the ASH learning method deposited inside the DPF (Diesel Particulate Filter) according to the present invention
Figure 112018039117374-pat00009
It is a graph comparing the slope (first order curve) and the conventional differential pressure curve (second order curve), and FIG. 3 shows the learning value applied with the ASH learning method deposited inside the DPF (Diesel Particulate Filter) according to the present invention. The effect of the present invention can be confirmed through FIGS. 2 and 3 with a graph (a) showing that the differential pressure curve has a constant value within and a graph (b) showing that the differential pressure curve is located close to the differential pressure curve actually measured. .

이상에서 설명된 본 발명의 실시 예는 예시적인 것에 불과하며, 본 발명이 속한 기술분야의 통상의 지식을 가진 자라면 이로부터 다양한 변형 및 균등한 타 실시 예가 가능하다는 점을 잘 알 수 있을 것이다. 그러므로 본 발명은 상기의 상세한 설명에서 언급되는 형태로만 한정되는 것은 아님을 잘 이해할 수 있을 것이다. 따라서 본 발명의 진정한 기술적 보호 범위는 첨부된 특허청구범위의 기술적 사상에 의해 정해져야 할 것이다. 또한, 본 발명은 첨부된 청구범위에 의해 정의되는 본 발명의 정신과 그 범위 내에 있는 모든 변형물과 균등물 및 대체물을 포함하는 것으로 이해되어야 한다.The embodiments of the present invention described above are merely exemplary, and those skilled in the art will appreciate that various modifications and equivalent other embodiments are possible therefrom. Therefore, it will be well understood that the present invention is not limited to the forms mentioned in the detailed description above. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims. It is also to be understood that the present invention includes all modifications, equivalents and alternatives within the spirit and scope of the present invention as defined by the appended claims.

Figure 112018039117374-pat00010
: n번째 학습값
an-1 : n번째 학습 전 차압/체적유량(기울기)
an : n번째 학습 후 차압/체적유량(기울기)
Figure 112018039117374-pat00011
: n번째 차압값
a0 : 중앙품(Fresh DPF)의 차압/체적유량(기울기)
Figure 112018039117374-pat00010
: nth learning value
a n-1 : Differential pressure/volume flow rate (slope) before the nth learning
a n : Differential pressure/volume flow rate (slope) after the nth learning
Figure 112018039117374-pat00011
: nth differential pressure value
a 0 : Differential pressure/volume flow rate (slope) of the central product (Fresh DPF)

Claims (8)

DPF 내부에 퇴적된 ASH량을 학습하는 ASH 학습방법에 있어서,
ASH량 학습이 가능한 상태인지 판단하는 학습조건 판단단계;
DPF를 통과하는 유량과 상기 DPF의 전후단의 압력 차이값을 이용하여 상기 ASH량을 학습하는 ASH 학습단계; 를 포함하고,
상기 ASH 학습단계는
학습 전 차압곡선의 기울기와 학습 후 차압곡선의 기울기를 산출하는
제1계산단계;
상기 학습 전 차압곡선의 기울기와 상기 학습 후 차압곡선의 기울기의
비율로 학습값을 계산하는 제2계산단계; 를 포함하는 ASH 학습방법.In the ASH learning method for learning the amount of ASH accumulated in the DPF,
Learning condition determination step of determining whether ASH amount learning is possible;
an ASH learning step of learning the ASH amount by using a flow rate passing through the DPF and a pressure difference value between the front and rear ends of the DPF; including,
The ASH learning step is
Calculating the slope of the differential pressure curve before learning and the slope of the differential pressure curve after learning
a first calculation step;
The slope of the differential pressure curve before learning and the slope of the differential pressure curve after learning
a second calculation step of calculating a learning value with a ratio; ASH learning method comprising a. 삭제delete 제1항에 있어서,
상기 ASH 학습단계는
하기의 <수학식 1>을 이용하여 상기 학습값을 산출하는 단계인 것을 특징으로 하는 ASH 학습방법.
Figure 112022118475060-pat00012
<수학식 1>
(여기서,
Figure 112022118475060-pat00013
는 학습값, an-1는 학습 전 차압/체적유량(기울기), an는 학습 후 차압/체적유량(기울기), i는 샘플수임)According to claim 1,
The ASH learning step is
ASH learning method characterized in that the step of calculating the learning value using the following <Equation 1>.
Figure 112022118475060-pat00012
<Equation 1>
(here,
Figure 112022118475060-pat00013
is the learning value, a n-1 is the differential pressure/volume flow rate (slope) before learning, a n is the differential pressure/volume flow rate (slope) after learning, i is the number of samples) 제3항에 있어서,
상기 <수학식 1>을 이용하여 취득한 상기 학습값을 기존 학습값이 적용된 차압곡선에 곱하여 업데이트하는 차압곡선 업데이트 단계; 를 포함하는 것을 특징으로 하는 ASH 학습방법.According to claim 3,
a differential pressure curve updating step of multiplying the learning value acquired using Equation 1 by a differential pressure curve to which an existing learning value is applied and updating the result; ASH learning method comprising a. 제4항에 있어서,
상기 차압곡선 업데이트 단계는
하기의 <수학식 2>를 이용하여 상기 차압곡선을 업데이트하는 것을 특징으로 하는 ASH 학습방법.
Figure 112018039117374-pat00014
<수학식 2>
(여기서,
Figure 112018039117374-pat00015
는 n번째 차압값이고,
Figure 112018039117374-pat00016
은 n번째 학습값이며,
Figure 112018039117374-pat00017
는 최초 학습값 임)According to claim 4,
The step of updating the differential pressure curve
An ASH learning method characterized in that the differential pressure curve is updated using Equation 2 below.
Figure 112018039117374-pat00014
<Equation 2>
(here,
Figure 112018039117374-pat00015
is the nth differential pressure value,
Figure 112018039117374-pat00016
is the nth learning value,
Figure 112018039117374-pat00017
Is initial learning value) 제5항에 있어서,
상기 최초 학습값은
하기의 <수학식 3>을 이용하여 산출하는 단계인 것을 특징으로 하는 ASH 학습방법.
Figure 112018039117374-pat00018
<수학식 3>
(여기서,
Figure 112018039117374-pat00019
는 최초 학습값, a0는 중앙품(Fresh DPF)의 차압/체적유량(기울기), a1은 최초 ASH 학습 후 차압/체적유량(기울기), i는 샘플수임)According to claim 5,
The initial learning value is
ASH learning method, characterized in that the step of calculating using the following <Equation 3>.
Figure 112018039117374-pat00018
<Equation 3>
(here,
Figure 112018039117374-pat00019
is the initial learning value, a 0 is the differential pressure/volume flow rate (slope) of the central product (Fresh DPF), a 1 is the differential pressure/volume flow rate (slope) after the first ASH learning, i is the number of samples) 제1항에 있어서,
상기 학습조건 판단단계는
상기 DPF의 통과 유량, 차압센서 변동량, 재생온도를 판단요소로 하여
상기 판단요소가 각각의 기준범위를 만족하는지 지속적으로 확인하는 것을 특징으로 하는 ASH 학습방법.According to claim 1,
The learning condition determination step is
The flow rate of the DPF, the amount of change in the differential pressure sensor, and the regeneration temperature are used as determining factors.
ASH learning method characterized in that for continuously checking whether the determination element satisfies each reference range. 제1항에 있어서,
상기 ASH 학습단계를 완료한 학습값이 기존 학습값 대비 예측범위에 포함되는지 판단하는 ASH 학습 종료조건 판단단계; 를 포함하는 것을 특징으로 하는 ASH 학습방법.According to claim 1,
an ASH learning end condition determination step of determining whether the learning value that has completed the ASH learning step is included in a prediction range compared to the previous learning value; ASH learning method comprising a.
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