U.S. patent application number 13/325677 was filed with the patent office on 2013-06-20 for diesel eission fluid quality detection system and method.
This patent application is currently assigned to Caterpillar Inc.. The applicant listed for this patent is Praveen S. Chavannavar. Invention is credited to Praveen S. Chavannavar.
Application Number | 20130152545 13/325677 |
Document ID | / |
Family ID | 48608723 |
Filed Date | 2013-06-20 |
United States Patent
Application |
20130152545 |
Kind Code |
A1 |
Chavannavar; Praveen S. |
June 20, 2013 |
DIESEL EISSION FLUID QUALITY DETECTION SYSTEM AND METHOD
Abstract
An exhaust treatment system is provided including: a selective
catalyst reduction (SCR) unit; a reducing agent dispensing unit
configured to introduce a reducing agent into the exhaust; a first
NO.sub.X sensor upstream of the SCR unit; a second NO.sub.X sensor
at a location downstream of the SCR unit; a first temperature
sensor at a location upstream of where the reducing agent is
introduced into the exhaust; a second temperature sensor at a
location downstream of where the reducing agent is introduced into
the exhaust and upstream of the SCR unit; and a controller
configured to determine a reductant quality indicator according to
a NO.sub.X differential between the first NO.sub.X sensor and the
second NO.sub.X sensor relative to a predicted NO.sub.X
differential and a temperature differential between the first
temperature sensor and the second temperature sensor relative to a
predicted temperature differential.
Inventors: |
Chavannavar; Praveen S.;
(Dunlap, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Chavannavar; Praveen S. |
Dunlap |
IL |
US |
|
|
Assignee: |
Caterpillar Inc.
Peoria
IL
|
Family ID: |
48608723 |
Appl. No.: |
13/325677 |
Filed: |
December 14, 2011 |
Current U.S.
Class: |
60/274 ;
60/286 |
Current CPC
Class: |
F01N 11/005 20130101;
Y02T 10/40 20130101; Y02T 10/47 20130101; F01N 2560/06 20130101;
F01N 2900/0416 20130101; F01N 2900/08 20130101; F01N 2560/14
20130101; F01N 3/035 20130101; F01N 2610/02 20130101; F01N
2900/1404 20130101; F02D 41/1461 20130101; F01N 2560/026 20130101;
F02D 41/1462 20130101; F02D 41/1465 20130101; F01N 9/00 20130101;
F01N 13/009 20140601; F02D 41/1463 20130101; F01N 2900/1402
20130101; F01N 2900/0601 20130101; F01N 2900/1818 20130101 |
Class at
Publication: |
60/274 ;
60/286 |
International
Class: |
F01N 3/18 20060101
F01N003/18; F01N 9/00 20060101 F01N009/00 |
Claims
1. An exhaust treatment system for treating a flow of exhaust
produced by an engine comprising: a selective catalyst reduction
(SCR) unit; a reducing agent dispensing unit configured to
introduce a reducing agent into the exhaust; a first NO.sub.X
sensor configured to indicate a NO.sub.X emission level of the
exhaust at a location upstream of the SCR unit; a second NO.sub.X
sensor configured to indicate a NO.sub.X emission level of the
exhaust at a location downstream of the SCR unit; a first
temperature sensor configured to indicate a temperature of the
exhaust at a location upstream of where the reducing agent is
introduced into the exhaust; a second temperature sensor configured
to indicate a temperature of the exhaust at a location downstream
of where the reducing agent is introduced into the exhaust and
upstream of the SCR unit; and a controller configured to
electronically communicate with the first NO.sub.X sensor, the
second NO.sub.X sensor, the first temperature sensor, and the
second temperature sensor, and to determine a reductant quality
indicator according to a NO.sub.X differential between the first
NO.sub.X sensor and the second NO.sub.X sensor relative to a
predicted NO.sub.X differential and a temperature differential
between the first temperature sensor and the second temperature
sensor relative to a predicted temperature differential.
2. The exhaust treatment system of claim 1, further comprising a
filter, and wherein the first NO.sub.X sensor is configured to
indicate a NO.sub.X level of the exhaust downstream of the
filter.
3. The exhaust treatment system of claim 2, wherein the filter is a
diesel particulate filter (DPF).
4. The exhaust treatment system of claim 2, further comprising
generating a signal when the reductant quality indicator is not
within a tolerance level.
5. The exhaust treatment system of claim 2, wherein the reducing
agent is urea.
6. The exhaust treatment system of claim 5, wherein the controller
is configured to generate a first signal indicating that the
reducing agent has a low urea concentration when the NO.sub.X
differential is lower than the predicted NO.sub.X differential and
the temperature differential is approximately equal to the
predicted temperature differential.
7. The exhaust treatment system of claim 5, wherein the controller
is configured to generate a second signal indicating a likelihood
of a clogged injector when the NO.sub.X differential is lower than
the predicted NO.sub.X differential and the temperature
differential is lower than the predicted temperature
differential.
8. The exhaust treatment system of claim 5, wherein the controller
is configured to generate a third signal indicating a likelihood of
an injection failure when the NO.sub.X differential is
approximately zero and the temperature differential is
approximately zero.
9. The exhaust treatment system of claim 5, wherein the controller
is configured to generate a fourth signal indicating that the
reducing agent is likely diesel when the NO.sub.X differential is
moderately lower than the predicted NO.sub.X differential and the
temperature differential is higher than the predicted temperature
differential.
10. The exhaust treatment system of claim 5, wherein the controller
is configured to: generate a first signal indicating that the
reducing agent has a low urea concentration when the NO.sub.X
differential is lower than the predicted NO.sub.X differential and
the temperature differential is approximately equal to the
predicted temperature differential; generate a second signal
indicating likely clogged injector when the NO.sub.X differential
is lower than the predicted NO.sub.X differential and the
temperature differential is lower than the predicted temperature
differential; generate a third signal indicating likely injection
failure when the NO.sub.X differential is approximately zero and
the temperature differential is approximately zero; and generate a
fourth signal indicating that the reducing agent is likely diesel
when the NO.sub.X differential is moderately lower than the
predicted NO.sub.X differential and the temperature differential is
higher than the predicted temperature differential.
11. A method for detecting a reducing agent quality comprising:
obtaining a first NO.sub.X value indicating a NO.sub.X level for an
engine exhaust upstream of a selective catalyst reduction (SCR)
unit; obtaining a second NO.sub.X value indicating a NO.sub.X
emission level for the engine exhaust downstream of the SCR unit;
obtaining a first temperature value indicating a temperature for
the engine exhaust upstream of an introduction of a reducing agent;
obtaining a second temperature value indicating a temperature for
the engine exhaust downstream of the introduction of the reducing
agent and upstream of the SCR unit; and computing a reductant
quality indicator according to a NO.sub.X differential between the
first NO.sub.X value and the second NO.sub.X value relative to a
predicted NO.sub.X differential and a temperature differential
between the first temperature value and the second temperature
value relative to a predicted temperature differential.
12. The method of claim 11, wherein the first NO.sub.X value
further indicates a NO.sub.X level for the engine exhaust after
treatment by a filter.
13. The method of claim 11, further comprising generating a signal
when the reductant quality indicator indicated is not within a
tolerance level.
14. The method of claim 11, further comprising adjusting a rate at
which the reducing agent is injected into the exhaust according to
the reductant quality indicator.
15. The method of claim 11, wherein the reducing agent is urea.
16. The method of claim 15, further comprising generating a first
signal indicating that the reducing agent has a low urea
concentration when the NO.sub.X differential is lower than the
predicted NO.sub.X differential and the temperature differential is
approximately equal to the predicted temperature differential.
17. The method of claim 15, further comprising generating a second
signal indicating likely clogged injector when the NO.sub.X
differential is lower than the predicted NO.sub.X differential and
the temperature differential is lower than the predicted
temperature differential.
18. The method of claim 15, further comprising generating a third
signal indicating likely injection failure when the NO.sub.X
differential is approximately zero and the temperature differential
is approximately zero.
19. The method of claim 15, further comprising generating a fourth
signal indicating that the reducing agent is likely diesel when the
NO.sub.X differential is moderately lower than the predicted
NO.sub.X differential and the temperature differential is higher
than the predicted temperature differential.
20. The method of claim 15, further comprising: generating a first
signal indicating that the reducing agent has a low urea
concentration when the NO.sub.X differential is lower than the
predicted NO.sub.X differential and the temperature differential is
approximately equal to the predicted temperature differential;
generating a second signal indicating likely clogged injector when
the NO.sub.X differential is lower than the predicted NO.sub.X
differential and the temperature differential is lower than the
predicted temperature differential; generating a third signal
indicating likely injection failure when the NO.sub.X differential
is approximately zero and the temperature differential is
approximately zero; and generating a fourth signal indicating that
the reducing agent is likely diesel when the NO.sub.X differential
is moderately lower than the predicted NO.sub.X differential and
the temperature differential is higher than the predicted
temperature differential.
Description
TECHNICAL FIELD
[0001] Embodiments of the present disclosure pertain to a diesel
emission fluid quality detection system and method.
BACKGROUND
[0002] Increasingly stringent government standards associated with
combustion engine emissions have increased the burden on
manufacturers to reduce the amount of nitrogen oxides (NO.sub.X)
and particulates that may be enitted from their developed engines.
Along with this burden is the manufacturer's commitment to its
customers to produce fuel efficient engines.
[0003] One known type of NO.sub.X reduction technique is selective
catalytic reduction (SCR). This technique of reducing NO.sub.X in a
combustion engine generally includes the use of reductants, such as
ammonia, aqueous urea, and other compounds, in conjunction with an
appropriate catalyst material.
[0004] In a conventional open loop control urea based SCR system, a
urea pump may provide a pressurized supply of urea to an atomizer
or injector, which then injects the a urea solution into the
exhaust stream of a combustion engine. An SCR controller may
control the rate of urea that is being applied to the atomizer.
Within the exhaust stream, the urea solution may decompose into
ammonia (NH.sub.3) and water vapor above certain temperatures, such
as 160 degrees C. When the exhaust gas mixture is passed over an
SCR catalyst, the NO.sub.X and NH.sub.3 molecules react with the
catalyst and generally produce diatomic nitrogen (N.sub.2) and
water (H.sub.2O.
[0005] The ability of an SCR catalyst to reduce NO.sub.X depends
upon many factors, such as catalyst formulation, the size of the
catalyst, exhaust gas temperature, and urea dosing rate. With
regard to the dosing rate, the NO.sub.X reduction efficiency tends
to increase linearly until the dosing rate reaches a certain limit.
Above the limit, the efficiency of the NO.sub.X reduction may start
to increase at a slower rate. One reason for the decline in the
NO.sub.X reduction efficiency is than the ammonia may be supplied
at a faster rate than the NO.sub.X reduction process can consume.
The excess ammonia, known as ammonia slip, may be expelled from the
SCR catalyst.
[0006] In order for an optimal NO.sub.X reduction to take place,
the integrity of the reductant (e.g., urea) must be maintained. For
instance, if the reductant is diluted (e.g., in water) or overly
concentrated, an ideal reaction in the SCR system will not occur.
Thus, to promote an optimal reaction, it is beneficial to ensure
the quality of the reductant.
[0007] Physical sensors are widely used in many products to measure
and monitor physical phenomena, such as temperature, speed, and
emissions from motor vehicles. Physical sensors often take direct
measurements of the physical phenomena and convert these
measurements into measurement data to be further processed by
control systems. For example U.S. Pat. No. 7,216,478 describes a
method of monitoring a dosing system.
[0008] Although physical sensors take direct measurements of the
physical phenomena, physical sensors and their associated hardware
are often costly and, sometimes, unreliable. For instance, directly
measuring the quality of a reductant, such as urea, with physical
sensors in a field environment is difficult and may be
unreliable.
[0009] Instead of direct measurements, virtual sensors have been
developed to process other various physically measured values and
to produce values that were previously measured directly by
physical sensors. For example, U.S. Pat. No. 5,386,373 (the '373
patent) issued to Keeler et al. on Jan. 31, 1995, discloses a
virtual continuous emission monitoring system with sensor
validation. The '373 patent uses a back propagation-to-activation
model and a Monte Carlo search technique to establish and optimize
a computational model used for the virtual sensing system to derive
sensing parameters from other measured parameters.
SUMMARY
[0010] According to aspects disclosed herein, a system and method
are provided to detect the quality of a reductant according to
sensor differentials.
[0011] According to an aspect of an embodiment herein, an exhaust
treatment system for treating a flow of exhaust produced by an
engine is disclosed. The exhaust treatment system for treating a
flow of exhaust produced by an engine includes: a selective
catalyst reduction (SCR) unit; a reducing agent dispensing unit
configured to introduce a reducing agent into the exhaust; a first
NO.sub.X sensor configured to indicate a NO.sub.X emission level of
the exhaust at a location upstream of the SCR unit; a second
NO.sub.X sensor configured to indicate a NO.sub.X emission level of
the exhaust at a location downstream of the SCR unit; a first
temperature sensor configured to indicate a temperature of the
exhaust at a location upstream of where the reducing agent is
introduced into the exhaust; a second temperature sensor configured
to indicate a temperature of the exhaust at a location downstream
of where the reducing agent is introduced into the exhaust and
upstream of the SCR unit; and a controller configured to
electronically communicate with the first NO.sub.X sensor, the
second NO.sub.X sensor, the first temperature sensor, and the
second temperature sensor, and to determine a reductant quality
indicator according to a NO.sub.X differential between the first
NO.sub.X sensor and the second NO.sub.X sensor relative to a
predicted NO.sub.X differential and a temperature differential
between the first temperature sensor and the second temperature
sensor relative to a predicted temperature differential.
[0012] According to an aspect of an embodiment herein, method for
detecting a reducing agent quality is disclosed. The method for
detecting a reducing agent quality includes: obtaining a first
NO.sub.X value indicating a NO.sub.X level for an engine exhaust
upstream of a selective catalyst reduction (SCR) unit; obtaining a
second NO.sub.X value indicating a NO.sub.X emission level for the
engine exhaust downstream of the SCR unit; obtaining a first
temperature value indicating a temperature for the engine exhaust
upstream of an introduction of a reducing agent; obtaining a second
temperature value indicating a temperature for the engine exhaust
downstream of the introduction of the reducing agent and upstream
of the SCR unit; and computing a reductant quality indicator
according to a NO.sub.X differential between the first NO.sub.X
value and the second NO.sub.X value relative to a predicted
NO.sub.X differential and a temperature differential between the
first temperature value and the second temperature value relative
to a predicted temperature differential.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 illustrates an exemplary machine according to a
embodiment described herein;
[0014] FIG. 2 is a block diagram of a reductant quality detection
system in an after-treatment system according to an embodiment
herein;
[0015] FIG. 3 is a block diagram of a method of detecting reductant
quality according to an embodiment herein.
DETAILED DESCRIPTION
[0016] Exemplary embodiments of the present invention are presented
herein with reference to the accompanying drawings. Herein, like
numerals designate like parts throughout.
[0017] FIG. 1 illustrates an exemplary machine 100 according to a
embodiment described herein. The machine 100 may refer to any type
of stationary or mobile machine that performs some type of
operation associated with a particular industry. The machine 100
may also include any type of commercial vehicle, such as cars,
trucks, vans, boats, ships, and other vehicles or machines, such as
power generators and stationary gas compressors.
[0018] FIG. 2 is a block diagram of a reductant quality detection
system in an after-treatment system according to an embodiment
herein. According to FIGS. 1 and 2, a machine 100 may include an
exhaust treatment system 200. The exhaust treatment system 200 may
include: an engine 102, a selective catalytic reduction (SCR) unit
108, a reductant system 106 (e.g., a urea reservoir/tank, a pump,
and injection components), and sensor network 104.
[0019] The engine 102 generates an exhaust stream that is
transmitted to the SCR unit 108. Before reaching the SCR the
exhaust may be optionally routed through one or more after
treatment elements, e.g., a diesel particulate filter (DPF)
configured to reduce the amount of particulates in the exhaust. By
passing the exhaust through a DPF, prior to the SCR unit 108,
particulates in the exhaust may be removed. Removing particulates
from exhaust prior to use of a physical NO.sub.X sensor may
increase the operational life of the sensor.
[0020] The reductant system 106 is for holding and injecting a
reductant, such as urea, ammonia, or any other reductant according
to the specific SCR system.
[0021] According to an embodiment herein, the reductant system 106
is configured to supply a urea reductant to the SCR unit 108 for
reducing the exhaust NO.sub.X. For instance, the urea from the
reductant system 106 may be combined with the exhaust from the
engine 102 upstream of the SCR unit 108 in order to mix with the
exhaust prior to entering the SCR unit 108.
[0022] The SCR unit 108 receives the exhaust from the engine 102,
and receives a reducing agent (also referred to as reductant) from
the reductant system 106. The SCR unit 108 and reductant unit 106
are configured to reduce the NO.sub.X emission of the engine
exhaust by using SCR unit 108.
[0023] The sensor network 104 may include a first NO.sub.X sensor
202 for indicating a NO.sub.X level of the exhaust prior to the SCR
unit 108; a second NO.sub.X sensor 206 for indicating a NO.sub.X
level of the exhaust after the SCR unit 108; a first temperature
sensor 204 for indicating a temperature (e.g., a pre-urea injection
temperature) of the exhaust prior to the SCR unit 108; a second
temperature sensor 208 for indicating a temperature (e.g., a
post-urea injection temperature) of the exhaust prior to the SCR
unit 108, but after the reducing agent has mixed with the exhaust
from the engine 102; and a controller 210 configured to
electronically communicate with the first NO.sub.X sensor 202, the
second NO.sub.X sensor 206, the first temperature sensor 204, and
the second temperature sensor 208, and to determine a quality
indicator according to the differential between the first NO.sub.X
sensor 202 and the second NO.sub.X sensor 206 and between the first
temperature sensor 204 and the second temperature sensor 208.
Sensors 202-208 are electronically coupled to controller 210 and
may be physical or virtual sensors.
[0024] The controller 210 is configured to send or receive
information to or from the sensors (202-208), and may be configured
to send or receive information to or from other additional sensors.
For instance the controller 210 may receive information from
physical sensors (e.g., exhaust and/or reductant flow rate sensors,
NO.sub.X sensors, engine sensors, ambient condition sensors, etc.),
or may generate or utilize preconfigured virtual sensors (e.g., a
virtual NO.sub.X sensor, a virtual urea sensor, etc.) at various
points in the system.
[0025] The controller 210 may be a processing system that monitors
and controls operation of the machine 100. Controller 210 may be
configured to collect information from various sensors operating
within the machine 100 and to provide control signals that affect
the operations of devices within the machine 100. In one embodiment
of the present invention, the controller 210 may be part of an
engine control module (ECM) that monitors and controls the
operation of an engine 102 associated with machine 100. For
example, the controller 210 may be a module programmed or hardwired
within an ECM that performs functions dedicated to certain
embodiments described herein. For example, the controller 210 may
be implemented in software that is stored as instructions and data
within a memory device of an ECM and is executed by a processor
operating within the ECM. Alternatively, the controller 210 may be
a module that is separate from other components of the system, and
may be in electronic communication with other components of the
system.
[0026] Controller 210 may include a processor, memory, and an
interface. The processor may be a processing device, such as a
microcontroller, that may exchange data with the memory and
interface to perform certain processes consistent with features
described herein. One skilled in the art would recognize that the
controller 210 may include a plurality of processors that may
operate collectively to perform functions consistent with certain
embodiments presented herein.
[0027] The controller may also be configured to interact with a
plurality of sensors in addition to those shown in FIGS. 1 and 2.
These sensors may include a combination of one or more physical
and/or virtual sensors. For example, the sensors may include one or
more physical sensors provided for measuring certain parameters of
machine operating environment, such as physical sensors for
measuring emissions of machine 100, such as Nitrogen Oxides
(NO.sub.X), Sulfur Dioxide (SO.sub.2), Carbon Monoxide (CO), total
reduced Sulfur (TRS), etc. Physical sensors may include any
appropriate sensors that are used with engine 102 or other machine
components to provide various measured parameters about engine 102
or other components, such as temperature, speed, acceleration rate,
fuel pressure, power output, etc.
[0028] According to one embodiment, NO.sub.X sensor 202 is a
physical sensor which may be used by the controller 210 to predict
a NO.sub.X emission value. The sensor 202 may be a single sensor or
may reflect a combination of sensors for detecting parameters such
as ambient humidity, manifold pressure, manifold temperature, fuel
rate, and engine speed associated with the engine. Additionally, a
first NO.sub.X sensor 202 may be a physical NO.sub.X sensor located
upstream of the SCR unit 108 or may be a virtual NO.sub.X sensor
generated by the controller 210 based on variables such as those
provided by the other sensors. A second NO.sub.X sensor 206 may be
a physical NO.sub.X sensor located downstream of the SCR unit 108
or may be a virtual NO.sub.X sensor generated by the controller 210
based on variables such as those provided by the other sensors.
[0029] The controller 210 may register variables such as
temperature or time-of-last-fill of the reductant system 106 to
help determine a cause of the deviation from the anticipated
NO.sub.X values. One or both of the first and second NO.sub.X
sensors 204, 206 may be a virtual sensor.
[0030] Furthermore, during a steady state operation of the engine,
the temperature sensor measurement may vary according to the diesel
emission fluid (DEF) injection amount. Therefore, by comparing the
differential between the first temperature sensor 204 and the
second temperature sensor 208 to a predicted value, the quality of
the DEF fluid and/or the presence of significant deposits in the
system may be determined. For instance, a significant deviation
from the predicted temperature differential is indicative that a
non-standard DEF fluid (e.g., diluted DEF fluid, diesel, or water,
etc.) is being used or that high levels of urea deposits have
formed in the system.
[0031] The controller 210 may be further configured to generate a
signal when the quality index of the reducing agent indicated by
the controller 210 is not within a tolerance level (e.g., a
predefined tolerance level). For example, the controller 210 may be
configured to trigger a warning light or adjust the flow rate of
the reductant. For instance, if the NO.sub.X reduction is less than
expected, the controller 210 may generate a signal to increase the
amount of reductant to send to the SCR unit 108.
[0032] If the temperature drop is low and the NO.sub.X reduction is
also low, the controller 210 may generate a signal indicating that
a clogged injector is likely or that deposits are being formed. And
if there is no temperature drop and no NO.sub.X reduction then the
controller 210 may generate a signal indicating that injector
failure is likely.
[0033] Additionally, if the temperature differential is zero or
and/or slightly increasing and there is a moderate, but less than
anticipated NO.sub.X reduction, than the controller 210 may
generate a signal indicating that the DEF tank may be filled with
diesel fluid or another fluid (e.g., a non-urea fluid).
[0034] Additionally, the exhaust treatment system 200, the first
NO.sub.X sensor 202 may further indicates a NO.sub.X level for the
engine exhaust after treatment by a filter. The filter may be a
diesel particulate filter (DPF).
[0035] FIG. 3 is a block diagram of a method of detecting reductant
quality according to an embodiment herein. According to FIG. 3, a
method for detecting a reducing agent (e.g., urea or urea mixture)
quality 300 includes an obtaining a pre-injection (e.g., pre-urea
injection) temperature step 302, an obtaining a pre-SCR NO.sub.X
value step 304, an obtaining a post-injection (e.g., post-urea
injection) temperature step 306, an obtaining a post-SCR NO.sub.X
value step 308, a computing a change in temperature step 310, a
computing a change in NO.sub.X step 312, an evaluating change in
temperature and NO.sub.X step 314, a computing the reductant
quality step 316 (also referred to as a predicting DEF status step
316). Optionally, the method 300 may also include a generating a
warning step 318. The warning step 318 may further include, but is
not limited to, triggering a warning light or adjusting the flow
rate of the reductant. For instance, if the NO.sub.X reduction is
less than expected, the controller may generate a signal to
increase the amount of reductant to send to the SCR unit 108.
[0036] During the evaluating change in temperature and NO.sub.X
step 314, a differential between the pre-SCR NO.sub.X value
obtained in step 304 and the post-SCR NO.sub.X obtained in step 308
is compared against a predicted NO.sub.X differential.
Additionally, during step 314a differential between the
pre-injection temperature value obtained in step 302 and the
post-injection temperature value obtained in step 306 is compared
against a predicted temperature differential.
[0037] The obtaining a pre-SCR NO.sub.X value step 304 includes
determining the first NO.sub.X value according to a first NO.sub.X
sensor indicating a NO.sub.X level for engine exhaust prior to
treatment by the SCR unit 108. The obtaining a second NO.sub.X
value (a post-SCR NO.sub.X value) step 308 includes determining a
value according to a second NO.sub.X sensor indicating a NO.sub.X
level for engine exhaust after treatment by the SCR unit 108.
[0038] The computing the reductant quality step 316 includes
generating a quality indicator signal, and may also include
generating a virtual urea quality sensor according to the NO.sub.X
and temperature values.
[0039] According to an embodiment herein, the first NO.sub.X sensor
202 may further indicate a NO.sub.X level for the engine exhaust
after treatment by a filter. Additionally, the method for detecting
a reducing agent quality 300 may further include generating a
signal when the reductant quality indicator indicated is not within
a tolerance range.
[0040] The controller 210 may be configured to generate a first
signal indicating that the reducing agent low has a low urea
concentration when the NO.sub.X differential is lower than the
predicted NO.sub.X differential and the temperature differential is
approximate to the predicted temperature differential; generate a
second signal indicating likely clogged injector when the NO.sub.X
differential is lower than the predicted NO.sub.X differential and
the temperature differential is lower than the predicted
temperature differential; generate a third signal indicating likely
injection failure when the NO.sub.X differential is approximately
zero and the temperature differential is approximately zero; and
generate a fourth signal indicating that the reducing agent is
likely diesel when the NO.sub.X differential is moderately lower
than the predicted NO.sub.X differential and the temperature
differential is higher than the predicted temperature differential
(e.g., the increase in temperature between the first and second
temperature sensors is greater than the expected change in
temperature.)
[0041] A virtual sensor network (also referred to as a virtual
sensor network system), as used herein, may refer to a collection
of virtual sensors integrated and working together using certain
control algorithms such that the collection of virtual sensors may
provide more desired or more reliable sensor output parameters than
discrete individual virtual sensors. A virtual sensor network
system may include a plurality of virtual sensors configured or
established according to certain criteria based on a particular
application. Additional sensors may provide information about the
ambient environmental conditions, such as humidity, air
temperature, and elevation.
[0042] A virtual sensor, as used herein, may refer to a
mathematical algorithm or model that produces output measures
comparable to a physical sensor based on inputs from other systems.
For example, a physical NO.sub.X sensor may measure the level of
NO.sub.X present in the exhaust stream of the engine 102 and
provide values of the NO.sub.X level to other components, such a
controller 210; while a virtual NO.sub.X sensor may provide
calculated values of the NO.sub.X level to a controller 210 based
on other measured or calculated parameters, such as such as
compression ratios, turbocharger efficiency, after cooler
characteristics, temperature values, pressure values, ambient
conditions, fuel rates, and engine speeds, etc. The term "virtual
sensor" may be used interchangeably with "virtual sensor
model."
[0043] The virtual sensor network system may also facilitate or
control operations of the virtual sensors. The virtual sensors may
include any appropriate virtual sensor providing sensor output
parameters corresponding to one or more physical sensors in machine
100.
[0044] Further, the virtual sensor network system may be configured
as a separate control system or, alternatively, may coincide with
other control systems such as an ECM. The virtual sensor network
system may also operate in series with or in parallel to an ECM.
Virtual sensor network system and/or ECM may be implemented by any
appropriate computer system. Thus, the virtual sensor network
system may be implemented on the controller 210, or e.g., may be
implemenedt elsewhere and communications therewith may be relayed
through the controller 210. Additionally, a computer system may
also be configured to design, train, and validate virtual sensors
in virtual sensor network and other components of machine 100.
[0045] A virtual sensor process model may be established to build
interrelationships between physical and virtual sensors. After the
virtual sensor process model is established, values of input
parameters may be provided to the virtual sensor process model
(e.g., the controller 210) to generate values of output parameters
based on the given values of input parameters and the
interrelationships between input parameters and output parameters
established by the virtual sensor process model.
[0046] In certain embodiments, the virtual sensor system may
include a NO.sub.X virtual sensor to provide levels of NO.sub.X
emitted from an engine 102, and a virtual reductant sensor to
provide a quality level (or quality index) of the reductant stored
in the reductant system 106 and transmitted to the SCR unit 108.
Input parameters may include any appropriate type of data
associated with NO.sub.X levels. For example, input parameters may
include parameters that control operations of various response
characteristics of engine 102 and/or parameters that are associated
with conditions corresponding to the operations of engine 102. For
instance, input parameters may include fuel injection timing,
compression ratios, turbocharger efficiency, after cooler
characteristics, temperature values (e.g., intake manifold
temperature), pressure values (e.g., intake manifold pressure),
ambient conditions (e.g., ambient humidity), fuel rates, and engine
speeds, etc. Other parameters, however, may also be included. For
example, parameters originated from other vehicle systems, such as
chosen transmission gear, axle ratio, elevation and/or inclination
of the vehicle, etc., may also be included. Further, input
parameters may be measured by certain physical sensors, or created
by other control systems such as an ECM.
[0047] A virtual sensor process model may include any appropriate
type of mathematical or physical model indicating
interrelationships between input parameters and output parameters.
For example, the virtual sensor process model may be a neural
network based mathematical model that is trained to capture
interrelationships between input parameters and output parameters.
Other types of mathematic models, such as fuzzy logic models,
linear system models, and/or non-linear system models, etc., may
also be used. Virtual sensor process model may be trained and
validated using data records collected from a particular engine
application for which virtual sensor process model is established.
That is, the virtual sensor process model may be established
according to particular rules corresponding to a particular type of
model using the data records, and the interrelationships of virtual
sensor process model may be verified by using part of the data
records.
[0048] After the virtual sensor process model is trained and
validated, virtual sensor process model may be optimized to define
a desired input space of input parameters and/or a desired
distribution of output parameters. The validated or optimized
virtual sensor process model may be used to produce corresponding
values of output parameters when provided with a set of values of
input parameters.
[0049] Thus, a controller 210 may be configured to generate or to
utilize a preconfigured virtual sensor model to determine predicted
NO.sub.X values based on a model reflecting a predetermined
relationship between control parameters and NO.sub.X emissions,
wherein the control parameters include ambient humidity, manifold
pressure, manifold temperature, fuel rate, and engine speed
associated with the engine. Additional sensors may provide
information about the ambient environmental conditions, such as
humidity, air temperature, and elevation. Additionally, the virtual
sensor network can utilize additional sensors for detecting the
flow rate of the exhaust through the SCR and the flow rate of the
reductant through the SCR.
[0050] If the controller 210 (or the ECM or processor operating the
virtual network) determines that any individual input parameter or
output parameter is out of the respective range of the input space
or output space, the controller may send out a notification to
other computer programs, control systems, or a user of machine
100.
[0051] Optionally, controller 210 (or the ECM or processor
operating the virtual network) may also apply any appropriate
algorithm to maintain the values of input parameters or output
parameters in the valid range to maintain operation with a reduced
capacity. For instance, reducing the engine speed to reduce the
flow rate of the exhaust, or increase the flow rate of the
reductant in order to increase the reduction of NO.sub.X.
[0052] The controller 210 (or the ECM or processor operating the
virtual network) may also determine collectively whether the values
of input parameters are within a valid range. For example, a
processor may use a Mahalanobis distance to determine normal
operational condition of collections of input values.
[0053] During training and optimizing of virtual sensor models, a
valid Mahalanobis distance range for the input space may be
calculated and stored as calibration data associated with
individual virtual sensor models. In operation, a processor may
calculate a Mahalanobis distance for input parameters of a
particular virtual sensor model as a validity metric of the valid
range of the particular virtual sensor model. If the calculated
Mahalanobis distance exceeds the range of the valid Mahalanobis
distance range stored in the virtual sensor network, the controller
210 may send out a notification to other computer programs, control
systems, or a user of machine 100 to indicate that the particular
virtual sensor may be unfit to provide predicted values.
[0054] Other validity metrics may also be used. For example, a
processor may evaluate each input parameter against an established
upper and lower bounds of acceptable input parameter values and may
perform a logical AND operation on a collection of evaluated input
parameters to obtain an overall validity metric of the virtual
sensor model.
[0055] After monitoring and controlling individual virtual sensors,
the controller 210 (e.g., virtual sensor network processor) may
also monitor and control collectively a plurality of virtual sensor
models. That is, the controller 210 may determine and control
operational fitness of the virtual sensor network. A processor may
monitor any operational virtual sensor model. The processor may
also determine whether there is any interdependency among any
operational virtual sensor models including the virtual sensor
models becoming operational. If the controller 210 determines there
is interdependency between any virtual sensor models, the
controller 210 may determine that the interdependency between the
virtual sensors may have created a closed loop to connect two or
more virtual sensor models together, which may be neither intended
nor tested.
[0056] The controller 210 may then determine that the virtual
sensor network may be unfit to make predictions, and may send a
notification or report to control systems, such as ECM, or users of
the machine 100. That is, the controller (e.g., a processor) may
present other control systems or users with the undesired condition
via a sensor output interface. Alternatively, the controller may
indicate as unfit only the interdependent virtual sensors, while
keeping the remaining virtual sensors in operation.
[0057] As used herein, a decision that a virtual sensor or a
virtual sensor network is unfit is intended to include any instance
in which any input parameter to or any output parameter from the
virtual sensor or the virtual sensor network is beyond a valid
range or is uncertain, or any operational condition that affects
the predictability and/or stability of the virtual sensor or the
virtual sensor network. An unfit virtual sensor network may
continue to provide sensing data to other control systems using
virtual sensors not affected by the unfit condition, such as
interdependency, etc.
[0058] The controller 210 may also resolve unfit conditions
resulting from unwanted interdependencies between active virtual
sensor models by deactivating one or more models of lower priority
than those remaining active virtual sensor models.
[0059] For instance, if a first active virtual sensor model has a
high priority for operation of machine 100 but has an unresolved
interdependency with a second active virtual sensor having a low
priority for operation of machine 100, the second virtual sensor
model may be deactivated to preserve the integrity of the first
active virtual sensor model.
INDUSTRIAL APPLICABILITY
[0060] The disclosed reductant quality sensing system may be
implemented in an exhaust after-treatment system in various
machines. A reductant quality sensing system provides for enhanced
reliability of the NO.sub.X reduction process by verifying the
integrity of the reductant and/or an indication as to the proper
operation of the system that adds reductant to the exhaust stream
(e.g., a prediction of the status of urea injectors).
[0061] Although certain embodiments have been illustrated and
described herein for purposes of description, it will be
appreciated by those of ordinary skill in the art that a wide
variety of alternate and/or equivalent embodiments or
implementations calculated to achieve the same purposes may be
substituted for the embodiments shown and described without
departing from the scope of the present disclosure. Those with
skill in the art will readily appreciate that embodiments in
accordance with the present invention may be implemented in a very
wide variety of ways. This application is intended to cover any
adaptations or variations of the embodiments discussed herein.
Therefore, it is intended that embodiments in accordance with the
present invention be limited only by the claims and the equivalents
thereof.
* * * * *