U.S. patent application number 14/262580 was filed with the patent office on 2015-10-29 for exhaust emission prediction system and method.
This patent application is currently assigned to Caterpillar Inc.. The applicant listed for this patent is Caterpillar Inc.. Invention is credited to Salim Aziz JALIWALA, Chad Palmer KOCI, Yanchai ZHANG.
Application Number | 20150308321 14/262580 |
Document ID | / |
Family ID | 54334304 |
Filed Date | 2015-10-29 |
United States Patent
Application |
20150308321 |
Kind Code |
A1 |
ZHANG; Yanchai ; et
al. |
October 29, 2015 |
EXHAUST EMISSION PREDICTION SYSTEM AND METHOD
Abstract
An exhaust emission prediction system includes an engine
configured to generate a flow of exhaust and a controller
configured to determine a first estimation of an amount of an
emissions constituent at a first location using an empirical model.
The first location is downstream of the engine. The controller is
also configured to determine a second estimation of the amount of
the emissions constituent at the first location using a
physics-based model and determine a third estimation of the amount
of the emissions constituent at the first location based on at
least one of the first estimation or the second estimation.
Inventors: |
ZHANG; Yanchai; (Dunlap,
IL) ; JALIWALA; Salim Aziz; (Peoria, IL) ;
KOCI; Chad Palmer; (Washington, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Caterpillar Inc. |
Peoria |
IL |
US |
|
|
Assignee: |
Caterpillar Inc.
Peoria
IL
|
Family ID: |
54334304 |
Appl. No.: |
14/262580 |
Filed: |
April 25, 2014 |
Current U.S.
Class: |
60/286 ;
701/101 |
Current CPC
Class: |
Y02T 10/24 20130101;
F01N 3/208 20130101; F01N 2900/12 20130101; F01N 9/005 20130101;
Y02T 10/12 20130101; F01N 2900/08 20130101; Y02T 10/40 20130101;
F01N 2560/026 20130101; F01N 2560/028 20130101; F01N 2900/0601
20130101; F01N 2900/1402 20130101; Y02T 10/47 20130101 |
International
Class: |
F01N 9/00 20060101
F01N009/00; F01N 3/20 20060101 F01N003/20 |
Claims
1. An exhaust emission prediction system comprising: an engine
configured to generate a flow of exhaust; and a controller
configured to: determine a first estimation of an amount of an
emissions constituent at a first location using an empirical model,
the first location being downstream of the engine; determine a
second estimation of the amount of the emissions constituent at the
first location using a physics-based model; and determine a third
estimation of the amount of the emissions constituent at the first
location based on at least one of the first estimation or the
second estimation.
2. The exhaust emission prediction system of claim 1, wherein: the
empirical model is trained using at least one trained operating
condition; and the controller is further configured to determine an
operating condition of the engine and determine the third
estimation based on whether the at least one trained operating
condition for the empirical model includes the operating condition
of the engine.
3. The exhaust emission prediction system of claim 2, wherein: when
the at least one trained operating condition for the empirical
model includes the operating condition of the engine, the
controller is configured to determine the third estimation based at
least in part on the first estimation; and when the at least one
trained operating condition for the empirical model does not
include the operating condition of the engine, the controller is
configured to determine the third estimation based at least in part
on the second estimation.
4. The exhaust emission prediction system of claim 3, wherein, when
the at least one trained operating condition for the empirical
model includes the operating condition of the engine, the
controller is configured to determine that the third estimation is
equal to the first estimation.
5. The exhaust emission prediction system of claim 3, wherein, when
the at least one trained operating condition for the empirical
model does not include the operating condition of the engine, the
controller is configured to determine that the third estimation is
equal to the second estimation.
6. The exhaust emission prediction system of claim 3, wherein, when
the at least one trained operating condition for the empirical
model does not include the operating condition of the engine, the
controller is configured to determine the third estimation based on
the first estimation and the second estimation.
7. The exhaust emission prediction system of claim 2, wherein the
operating condition includes at least one of an altitude, an
ambient temperature, an engine speed, an ambient pressure, an
application cycle, an engine configuration, or a fuel injection
system calibration.
8. The exhaust emission prediction system of claim 1, wherein the
empirical model includes a neural network model or a curve fitting
model.
9. The exhaust emission prediction system of claim 1, wherein, to
determine the first estimation, the controller is configured to
input into the empirical model at least one of a speed of the
engine, a fuel injection timing of the engine, an amount of fuel
injected into the engine, a pressure of the fuel injected into the
engine, a pressure in an intake manifold of the engine, a
temperature in the intake manifold, or an amount of exhaust
recirculated into the engine.
10. The exhaust emission prediction system of claim 1, wherein the
controller is further configured to determine at least one
in-cylinder characteristic of at least one cylinder of the engine
using the physics-based model and determine the second estimation
using the at least one in-cylinder characteristic.
11. The exhaust emission prediction system of claim 10, wherein the
at least one in-cylinder characteristic includes at least one of a
pressure, a bulk gas temperature, a bulk gas specific heat, a bulk
gas density, a bulk gas mass, or a total heat transfer to the flow
of exhaust.
12. The exhaust emission prediction system of claim 1, wherein, to
determine the second estimation, the controller is configured to
input into the physics-based model at least one of a speed of the
engine, a crank angle of the engine, an air flow rate into the
engine, a fuel flow rate into the engine, a pressure in the engine,
a temperature in the engine, or a volume percent of recirculated
exhaust gas in the engine.
13. The exhaust emission prediction system of claim 1, wherein the
emissions constituent includes NOx.
14. The exhaust emission prediction system of claim 13, further
comprising: at least one NOx sensor disposed downstream of the
engine and configured to output a measured amount of NOx; wherein
the controller is in communication with the at least one NOx sensor
and further configured to adjust the third estimation based on the
measured amount.
15. A method of predicting an amount of NOx in a flow of exhaust
from an engine using a controller, the method comprising:
determining, using the controller, a first estimation of the amount
of NOx at a first location using an empirical model, the first
location being downstream of the engine; determining, using the
controller, a second estimation of the amount of NOx at the first
location using a physics-based model; and determining, using the
controller, a third estimation of the amount of NOx at the first
location based on at least one of the first estimation or the
second estimation.
16. The method of claim 15, further comprising: determining an
ambient humidity; wherein the first estimation is further
determined, using the controller, based on the ambient
humidity.
17. The method of claim 15, further comprising: determining, using
the controller, an operating condition of the engine; wherein the
empirical model is trained using at least one trained operating
condition; wherein the third estimation is further determined,
using the controller, based at least in part on the first
estimation when the at least one trained operating condition for
the empirical model includes the operating condition of the engine;
and wherein the third estimation is further determined, using the
controller, based at least in part on the second estimation when
the at least one trained operating condition for the empirical
model does not include the operating condition of the engine.
18. An engine system comprising: an engine configured to generate a
flow of exhaust; an injector configured to inject a reductant into
the flow of exhaust; a catalytic device configured to receive the
flow of exhaust after being injected with the reductant; a
processor; a memory module configured to store instructions that,
when executed, enable the processor to: determine a first
estimation of an amount of NOx at a first location using an
empirical model, the first location being downstream of the engine
and upstream of the catalytic device; determine a second estimation
of the amount of NOx at the first location using a physics-based
model; determine a third estimation of the amount of NOx at the
first location based on at least one of the first estimation or the
second estimation; and adjust an amount of the reductant injected
by the injector based on the determined third estimation.
19. The engine system of claim 18, further comprising a turbine
upstream of the injector and configured to receive the flow of
exhaust, wherein the first location is downstream of the
turbine.
20. The engine system of claim 18, wherein: the memory module is
further configured to store instructions that, when executed,
enable the processor to: determine an operating condition of the
engine; determine the third estimation based at least in part on
the first estimation when the at least one trained operating
condition for the empirical model includes the operating condition
of the engine; and determine the third estimation based at least in
part on the second estimation when the at least one trained
operating condition for the empirical model does not include the
operating condition of the engine.
Description
TECHNICAL FIELD
[0001] The present disclosure relates generally to an exhaust
system, and more particularly, to an exhaust emission prediction
system and method.
BACKGROUND
[0002] Internal combustion engines, including diesel engines,
gasoline engines, gaseous fuel-powered engines, and other engines
known in the art, may produce a flow of exhaust composed of gaseous
and solid compounds, including particulate matter, nitrogen oxides
(NOx), and sulfur compounds. Due to heightened environmental
concerns, exhaust emission standards have become increasingly
stringent. The amount of one or more constituents of the flow of
exhaust emitted from the engine may be regulated depending on the
type, size, and/or class of engine.
[0003] One method that has been implemented by engine manufacturers
to comply with the regulation of NOx exhausted to the environment
is a strategy called selective catalytic reduction (SCR). SCR is a
process by which gaseous or liquid reductant (e.g., a mixture of
urea and water) is injected into the flow of exhaust from the
engine. The combined flow may form ammonia (NH.sub.3), which may
then be absorbed onto an SCR catalyst. The ammonia may react with
NOx in the flow of exhaust to form H.sub.2O and N.sub.2, thereby
reducing the amount of NOx in the flow of exhaust.
[0004] The ability of the SCR catalyst to reduce NOx depends upon
many factors, such as catalyst formulation, the size of the SCR
catalyst, exhaust gas temperature, and urea dosing rate. With
regard to the dosing rate, the NOx reduction efficiency tends to
increase linearly until the dosing rate reaches a certain limit.
Above the limit, the NOx reduction efficiency may increase at a
slower rate because the ammonia may be supplied at a faster rate
than the NOx reduction process can consume. The excess ammonia,
known as ammonia slip, may be expelled from the SCR catalyst.
[0005] The urea dosing rate may be controlled using signals from a
NOx sensing device, such as a NOx sensor, placed in the exhaust
stream after the SCR catalyst. The NOx sensing device may measure
the level of NOx and provide signals to a SCR controller to adjust
the urea dosing rate. Although NOx reduction efficiency may be
increased using this process, the costs and maintenance associated
with NOx sensing devices may make implementing this process
unattractive to engine manufacturers.
[0006] To minimize the costs associated with physical sensors, some
conventional engine control systems may implement virtual sensors.
For example, U.S. Pat. No. 6,236,908 issued to Cheng et al. (the
'908 patent) describes an engine control module (ECM) including one
or more neural networks that act as virtual sensing devices to
replace or enhance traditional physical sensors. The ECM receives
values associated with various engine operating parameters from a
plurality of physical sensors and applies the values to the neural
network to produce values for one or more output parameters. For
example, the neural network may receive values, such as engine
speed, manifold pressure, exhaust gas recirculation, and air/flow
ratio values, from physical sensors. Based on the input values, the
neural network may determine values of other engine operating
parameters, including residual mass fraction, emissions, knock
index, peak pressure rise rate, exhaust gas temperature, and
exhaust gas oxygen content. The neural network is trained using
data produced by a simulation model calibrated with actual engine
test data.
[0007] Although the '908 patent suggests the ability to use a
neural network as a virtual sensing device to replace or enhance
traditional physical sensors, the available engine test data used
to train the neural network is limited. The '908 patent describes
that the simulation model interpolates or extrapolates a more
complete set of data. However, the interpolated or extrapolated
data may not be accurate, which may cause the neural network to
provide inaccurate outputs.
[0008] The disclosed system is directed to overcoming one or more
of the problems set forth above.
SUMMARY
[0009] In one aspect, the present disclosure is directed to an
exhaust emission prediction system. The exhaust emission prediction
system includes an engine configured to generate a flow of exhaust
and a controller configured to determine a first estimation of an
amount of an emissions constituent at a first location using an
empirical model. The first location is downstream of the engine.
The controller is also configured to determine a second estimation
of the amount of the emissions constituent at the first location
using a physics-based model and determine a third estimation of the
amount of the emissions constituent at the first location based on
at least one of the first estimation or the second estimation.
[0010] In another aspect, the present disclosure is directed to a
method of predicting an amount of NOx in a flow of exhaust from an
engine using a controller. The method includes determining, using
the controller, a first estimation of the amount of NOx at a first
location using an empirical model, the first location being
downstream of the engine. The method also includes determining,
using the controller, a second estimation of the amount of NOx at
the first location using a physics-based model, and determining,
using the controller, a third estimation of the amount of NOx at
the first location based on at least one of the first estimation or
the second estimation.
[0011] In another aspect, the present disclosure is directed to an
engine system including an engine configured to generate a flow of
exhaust, an injector configured to inject a reductant into the flow
of exhaust, and a catalytic device configured to receive the flow
of exhaust after being injected with the reductant. The engine
system also includes a processor and a memory module configured to
store instructions that, when executed, enable the processor to
determine a first estimation of an amount of NOx at a first
location using an empirical model. The first location is downstream
of the engine and upstream of the catalytic device. The memory
module is also configured to store instructions that, when
executed, enable the processor to determine a second estimation of
the amount of NOx at the first location using a physics-based
model, determine a third estimation of the amount of NOx at the
first location based on at least one of the first estimation or the
second estimation, and adjust an amount of the reductant injected
by the injector based on the determined third estimation.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 is a diagrammatic illustration of an engine and an
exhaust emission prediction system, according to an exemplary
embodiment;
[0013] FIG. 2 is a diagrammatic illustration of a controller for
the exhaust emission prediction system of FIG. 1;
[0014] FIG. 3 is a diagrammatic illustration of an empirical NOx
model for the controller of FIG. 2;
[0015] FIG. 4 is a diagrammatic illustration of a physics-based NOx
model for the controller of FIG. 2; and
[0016] FIG. 5 is a flow chart illustrating an exemplary disclosed
method of estimating an amount of an emissions constituent in a
flow of exhaust, according to an exemplary embodiment.
DETAILED DESCRIPTION
[0017] Reference will now be made in detail to exemplary
embodiments, which are illustrated in the accompanying drawings.
Wherever possible, the same reference numbers will be used
throughout the drawings to refer to the same or like parts.
[0018] FIG. 1 is a diagrammatic illustration of a power source,
such as an engine 10, of a machine and an exhaust emission
prediction system, according to an exemplary embodiment. The
disclosed embodiment may be applicable to various types of machines
such as, for example, a fixed or mobile machine that performs some
type of operation associated with an industry such as mining,
construction, farming, transportation, power generation, tree
harvesting, forestry, or any other industry known in the art. The
engine 10 may be an internal combustion engine, such as, for
example, a diesel engine, a gasoline engine, a gaseous fuel-powered
engine, or any other engine apparent to one skilled in the art. The
engine 10 may alternatively be another source of power such as a
furnace or any other suitable source of power for a powered system
such as a factory or power plant.
[0019] Operation of the engine 10 may produce power and a flow of
exhaust. For example, the engine 10 may include a plurality of
cylinders 12. Each cylinder 12 may include a combustion chamber
that may mix fuel with air and/or recirculated exhaust gas, as
described below, and burn the mixture therein to produce the flow
of exhaust. The flow of exhaust may contain carbon monoxide, NOx,
carbon dioxide, aldehydes, soot, oxygen, nitrogen, water vapor,
and/or hydrocarbons.
[0020] An exhaust system 14 is provided with the engine 10 such
that the flow of exhaust may be fluidly communicated from the
engine 10 to the exhaust system 14. The flow of exhaust produced by
the engine 10 may be directed from the engine 10 to components of
the exhaust system 14 by flow lines. For example, as shown in FIG.
1, the flow lines may include pipes, tubing, conduits, and/or other
exhaust-carrying structures known in the art through which the flow
of exhaust may be directed through the exhaust system 14 to one or
more of a turbine 16 of a turbocharger 18, one or more
aftertreatment devices 20, an injector 22, and a catalytic device
(e.g., an SCR catalyst 24) in the exhaust system 14. The exhaust
system 14 may also include additional components for directing the
flow of exhaust out of the engine 10 that are known in the art.
[0021] The turbine 16 may be disposed between an exhaust passageway
of the engine 10 and the inlet of the exhaust system 14. The
turbine 16 may be configured to drive a connected compressor 26 of
the turbocharger 18. For example, as the hot exhaust gases exiting
the engine 10 expand against blades (not shown) of the turbine 16,
the turbine 16 may rotate and drive the compressor 26. The
compressor 26 may be located in an air induction system of the
engine 10 and may be configured to compress the atmospheric air
received by the air induction system to a predetermined pressure
level. The air induction system may also include additional
components for introducing the compressed air into the cylinders 12
of the engine 10, such as, for example, a filter, a valve, air
cleaner, air cooler, waste gate, a venturi, etc., as known in the
art.
[0022] The aftertreatment device(s) 20 may be configured to remove
particulates and other constituents from the flow of exhaust, e.g.,
a filter for capturing particulates, ash, or other materials from
the exhaust gas to prevent their discharge into the surrounding
environment, such as a diesel particulate filter (DPF), a system
for regenerating the filter by removing the particulate matter
trapped by the filter, other catalytic devices, and/or other
exhaust gas treatment devices. For example, a diesel oxidation
catalyst (DOC) may raise the NO.sub.2/NOx ratio, which may improve
the NOx conversion efficiency of the SCR catalyst 24. One or more
aftertreatment device(s) may also be located downstream of the SCR
catalyst 24, such as an ammonia oxidation (AMOX) catalyst that may
oxidize ammonia that slips from the SCR catalyst 24 to form N.sub.2
and H.sub.2O.
[0023] The injector 22 may be connected to a reductant supply (not
shown) and may inject reductant, such as urea, urea and water,
ammonia, and/or other elements or compounds capable of chemically
reducing compounds, e.g., NOx, contained within the flow of exhaust
in the presence of, for example, catalyst materials. The injector
22 may include a nozzle (not shown) or other flow control device
configured to assist in controllably releasing a flow of the
reductant into the flow of exhaust from the engine 10. The nozzle
may be any type of injector known in the art and may include any
device capable of injecting and/or atomizing an injected fluid.
[0024] The SCR catalyst 24 may chemically reduce the amount of NOx
in the flow of exhaust. The reductant injected into the flow of
exhaust by the injector 22 upstream from the SCR catalyst 24 may be
absorbed onto the SCR catalyst 24 so that the reductant may react
with NOx in the flow of exhaust to form H.sub.2O (water vapor) and
N.sub.2 (nitrogen gas). For example, a mixture of urea and water
injected by the injector 22 may decompose to ammonia, and the SCR
catalyst 24 may facilitate a reaction between the ammonia and NOx
in the flow of exhaust to produce water and nitrogen gas, thereby
removing NOx from the flow of exhaust. The SCR catalyst 24 may
include catalyst materials such as, but not limited to, zeolites
(e.g., iron zeolite or copper zeolite) or vanadia.
[0025] A portion of the flow of exhaust exiting the SCR catalyst 24
may enter an exhaust gas recirculation (EGR) passageway 28, which
may direct a flow of recirculated exhaust to the compressor 26 for
subsequent combustion while the remaining portion of the flow of
exhaust exiting the SCR catalyst 24 may be output from the exhaust
system 14, e.g., released into the surrounding atmosphere, such as
through a tail pipe. Alternatively, the EGR passageway 28 may be
configured to direct a flow of recirculated exhaust exiting at
least one of the aftertreatment device(s) 20 upstream from the
injector 22 to the compressor 26 while the remaining portion of the
flow of exhaust exiting at least one of the aftertreatment
device(s) 20 may be directed to the injector 22 and the SCR
catalyst 24 before being output from the exhaust system 14.
[0026] The engine 10 may also be provided with an intake manifold
11 and/or an exhaust manifold. The intake manifold 11 may receive
the compressed air and/or recirculated exhaust, and allow the
compressed air and/or recirculated exhaust to flow to the cylinders
12. The exhaust manifold may receive the flow of exhaust from the
cylinders 12 and direct the flow of exhaust to the turbine 16,
e.g., via an exhaust passageway.
[0027] The exhaust emission prediction system may include a
controller 30 connected via communication lines 32 to one or more
of the components of the engine 10 and the exhaust system 14. For
example, the controller 30 may receive input via the communication
lines 32 from a variety of sources including, for example, a timer
and/or one or more sensors configured to measure temperature,
speed, pressure, fuel quantity consumed, flow rate, amount of
reductant injected, and/or other operating characteristics of the
engine 10 and/or exhaust system 14. As shown in FIG. 1, the
controller 30 may be connected by the communication lines 32 to a
NOx sensor 34 and a humidity sensor 36. The sensors 34, 36 may be
physical (hardware) sensors. The NOx sensor 34 may be located
downstream of the engine 10 and the turbine 16, and upstream of the
aftertreatment device(s) 20, the injector 22 and the SCR catalyst
24. Alternatively, the NOx sensor 34 may be disposed in other
locations in the exhaust system 14, e.g., downstream from the
aftertreatment device(s) 20 and/or the SCR catalyst 24. The
humidity sensor 36 may be located in the air induction system,
e.g., at a location upstream of an inlet of the compressor 26, the
connection of the EGR passageway 28 to the air induction system,
and/or one or more filters in the air induction system.
Alternatively, the humidity sensor 36 may be disposed at another
location that allows the humidity sensor 36 to measure an ambient
humidity of the atmospheric air.
[0028] The controller 30 may include components required to run an
application such as, for example, a computer, a memory module, a
secondary storage device (e.g., a database), and a processor or
microprocessor, such as a central processing unit, as known in the
art. The memory module may be configured to store information used
by the processor, e.g., computer programs or code used by the
processor to enable the processor to perform functions consistent
with disclosed embodiments, e.g., the processes described in detail
below. The controller 30 may be communicatively coupled with one or
more components of the engine 10 and/or the exhaust system 14 to
change the operation thereof. Optionally, the controller 30 may be
integrated into the engine 10, e.g., as part of an engine control
module (ECM). The controller 30 may use the inputs to form a
control signal based on a pre-set control algorithm. The control
signal may be transmitted from the controller 30 via the
communication lines 32 to various actuation devices, such as one or
more components of the engine 10 and/or the exhaust system 14,
e.g., the injector 22 to control the timing and amount of
injections.
[0029] FIG. 2 is a diagrammatic illustration of the controller 30,
according to an exemplary embodiment. The controller 30 may include
an empirical model and a physics-based model for determining
respective estimations of an amount of an emissions constituent at
a location in the exhaust system 14. In the exemplary embodiment,
the controller 30 includes an empirical NOx model 40 and a
physics-based NOx model 60. The empirical NOx model 40 and the
physics-based NOx model 60 may each include one or more models, and
may be configured to determine respective first and second NOx
estimations 42 and 62 of an amount of NOx in the flow of exhaust
output from the engine 10. In the exemplary embodiment, the
empirical NOx model 40 and the physics-based NOx model 60 are
configured to determine the respective first and second NOx
estimations 42 and 62 at a location downstream from the turbine 16
and upstream of the aftertreatment device(s) 20. Alternatively, the
empirical NOx model 40 and the physics-based NOx model 60 may
determine the respective first and second NOx estimations 42 and 62
at other locations downstream from the engine 10.
[0030] FIG. 3 is a diagrammatic illustration of the empirical NOx
model 40 for determining the first NOx estimation 42 in the flow of
exhaust output from the engine 10, according to an exemplary
embodiment. The empirical NOx model 40 may include one or more
models or maps (e.g., a neural network model and/or map, a curve
fitting model and/or map, etc.) that are data-driven, such as a
first NOx model 44 and a first map 46. The first NOx model 44
and/or the first map 46 may be created using experimental data from
one or more test engines under various operating conditions. For
example, the first NOx model 44 and/or the first map 46 may be
trained using data for one or more test engines operating under
various operating conditions, such as, but not limited to, one or
more altitudes (e.g., sea level or a range of altitudes), one or
more ambient temperatures, one or more engine speeds, one or more
ambient pressures, and/or one or more ambient humidity levels.
Other operating conditions that the test engine(s) may be trained
under may include one or more application cycles, one or more
tasks, one or more aggressive application cycles, one or more
non-aggressive application cycles, one or more steady-state
operating conditions, one or more transient operating conditions,
one or more engine configurations (e.g., engines including certain
hardware), one or more fuel injection system calibrations (e.g.,
fuel injection timing, amount, or pressure), etc. For operating
conditions for which data does not exist (e.g., operating
conditions for which the first NOx model 44 and/or the first map 46
were not trained), the first NOx model 44 and/or the first map 46
may be used to predict the performance of the engine 10, for
example, by interpolation and extrapolation.
[0031] In the exemplary embodiment, the first NOx model 44 may
receive one or more inputs 48 and determine the first NOx
estimation 42 of an amount of NOx in the flow of exhaust output
from the engine 10. The inputs 48 may include one or more of a
speed 50 of the engine 10 (e.g., in revolutions per minute or RPM),
a fuel injection timing 51, a fuel injection amount 52, a fuel
injection pressure 53, a pressure of the intake to the engine 10
(e.g., a pressure 54 in the intake manifold 11), a temperature of
the intake to the engine 10 (e.g., a temperature 55 in the intake
manifold 11), and an EGR amount 56. The inputs 48 may be estimated
(e.g., using the controller 30 and/or virtual sensors) or measured
(e.g., using physical sensors), as known in the art. Thus, the
first NOx model 44 may be trained to determine the first NOx
estimation 42 based on the inputs 48 at one or more of the
operating conditions described above.
[0032] The first NOx model 44 may output the first NOx estimation
42, which may be input into the first map 46. The first map 46 may
also receive one or more other inputs, such as an ambient humidity
57 measured by the humidity sensor 36 (FIG. 1). The first map 46
may be used to adjust the first NOx estimation 42 based on the
ambient humidity 57. For example, the first map 46 may correlate
the ambient humidity 57 and a correction factor for multiplying
with the first NOx estimation 42, and the first map 46 may be used
to determine the correction factor based on the measured ambient
humidity 57. The controller 30 may multiply the first NOx
estimation 42 by the correction factor to adjust the first NOx
estimation 42. Then, the first NOx estimation 42 may be output from
the empirical NOx model 40.
[0033] FIG. 4 is a diagrammatic illustration of the physics-based
NOx model 60 for determining the second NOx estimation 62 in the
flow of exhaust output from the engine 10, according to an
exemplary embodiment. The physics-based NOx model 60 may include
one or more models, such as a second NOx model 64 and an
in-cylinder engine model 66, that may be based on one or more
physical and/or chemical equations for determining the performance
of the engine 10 (e.g., the second NOx estimation 62 or an
estimation of another emissions constituent in the flow of
exhaust). For example, the second NOx model 64 and/or the
in-cylinder engine model 66 may be created using one or more
equations governing the functioning of the engine 10, such as the
chemical and physical reactions occurring in the cylinders 12,
e.g., the reactions governing the combustion of the mixture of fuel
and the compressed air and/or recirculated exhaust, and other
reactions relating to the formation of NOx and/or other emissions
constituents, etc. In the exemplary embodiment, the second NOx
model 64 and/or the in-cylinder engine model 66 are not data-driven
and therefore are not trained using experimental data from one or
more test engines.
[0034] The physics-based NOx model 60 may receive one or more
inputs 68. The inputs 68 may include one or more of the inputs 48
to the empirical NOx model 40, such as the engine speed 50, the
intake manifold pressure 54, and/or the intake manifold temperature
55. In addition, the inputs 68 may include a crank angle 71 of the
engine 10 (e.g., in crank angle degrees or CAD), an air flow rate
72 of air into one or more of the cylinders 12, a fuel flow rate 73
of fuel into one or more of the cylinders 12, and/or a volume
percent of EGR (EGR_IVC) 74 in one or more of the cylinders 12 at
the closing of the respective intake valve(s). The inputs 68 may be
estimated (e.g., using the controller 30 and/or virtual sensors) or
measured (e.g., using physical sensors), as known in the art. Based
on the inputs 68, the physics-based NOx model 60 may determine the
second NOx estimation 62 for the flow of exhaust output from the
engine 10.
[0035] In the exemplary embodiment, the in-cylinder engine model 66
may receive one or more of the inputs 68 to determine one or more
outputs 70, which may include one or more in-cylinder
characteristics of one or more of the cylinders 12. The outputs 70
may be determined by the in-cylinder engine model 66 based on one
or more equations governing the chemical and physical reactions
occurring in one or more of the cylinders 12. For example, the
in-cylinder engine model 66 may receive the intake manifold
pressure 54, the intake manifold temperature 55, the air flow rate
72, and/or the fuel flow rate 73. The in-cylinder engine model 66
may include the reactions governing the combustion of the mixture
of fuel and the compressed air and/or recirculated exhaust. The
outputs 70 of the in-cylinder engine model 66 may include one or
more in-cylinder characteristics, such as one or more of a gross
heat release rate (CylGHRR) 75 of one or more of the cylinders 12
(e.g., in joules per crank angle degrees or J/CAD), a pressure
(CylP) 76 of one or more of the cylinders 12 (e.g., one or more
values of pressure as a function of the crank angle for a
720-degree CAD cycle of one of the cylinders 12), a bulk gas
temperature (CylT) 77 of one or more of the cylinders 12 (e.g., one
or more values of bulk gas temperature as a function of the crank
angle for a 720-degree CAD cycle of one of the cylinders 12), a
bulk gas specific heat (CylCP) 78 of one or more of the cylinders
12 (e.g., one or more values of bulk gas specific heat as a
function of the crank angle for a 720-degree CAD cycle of one of
the cylinders 12), a bulk gas density (CylRho) 79 of one or more of
the cylinders 12 (e.g., one or more values of bulk gas density as a
function of the crank angle for a 720-degree CAD cycle of one of
the cylinders 12), a bulk gas mass (CylMass) 80 of one or more of
the cylinders 12 (e.g., one or more values of bulk gas mass as a
function of the crank angle for a 720-degree CAD cycle of one of
the cylinders 12), and a total heat transfer (CylQ) 81 to the flow
of exhaust out of one or more of the cylinders 12 (e.g., one or
more values of total heat transfer to the flow of exhaust out of
one of the cylinders 12 as a function of the crank angle for a
720-degree CAD cycle). The outputs 70 from the in-cylinder engine
model 66 may be input into the second NOx model 64.
[0036] Alternatively, the in-cylinder engine model 66 may be
omitted, and one or more of the outputs 70 of the in-cylinder
engine model 66 may be measured using one or more physical sensors,
such as one or more pressure sensors configured to sense the
pressure (e.g., the pressure 76) in one or more of the cylinders 12
and/or one or more temperature sensors configured to sense the
temperature (e.g., the bulk gas temperature 77) in one or more of
the cylinders 12, to be input into the second NOx model 64. The
second NOx model 64 and/or one or more virtual sensors may estimate
the gross heat release rate 75, the bulk gas specific heat 78, the
bulk gas density 79, the bulk gas mass 80, and/or the total heat
transfer 81 using the inputs received from the physical sensors.
When the in-cylinder engine model 66 is omitted, the second NOx
model 64 may include the reactions governing the combustion of the
mixture of fuel and the compressed air and/or recirculated
exhaust.
[0037] Using either the outputs 70 from the in-cylinder engine
model 66 or the measurements from the physical sensors, in addition
to one or more of the inputs 68 to the physics-based NOx model 60,
the second NOx model 64 may determine the second NOx estimation 62
for the flow of exhaust output from the engine 10. For example, as
shown in FIG. 4, the second NOx model 64 may receive the engine
speed 50, the crank angle 71, the air flow rate 72, the fuel flow
rate 73, and/or the volume percent of EGR 74. The second NOx model
64 may include reactions relating to the formation of NOx and/or
other emissions constituents. To determine the second NOx
estimation 62, the second NOx model 64 may determine other
in-cylinder characteristics, such as an adiabatic flame temperature
of a stoichiometric mixture of fuel and air along a burn duration
from the start of combustion to the end of combustion and a brake
specific fuel consumption (e.g., in grams per kilowatt-hour or
g/kW-hr). The brake specific fuel consumption may be determined,
for example, based on the air flow rate 72 and the fuel flow rate
73. To determine the second NOx estimation 62, the second NOx model
64 may also include other characteristics (e.g., characteristics of
the fuel), such as a stoichiometric air-to-fuel mass ratio of the
fuel, a hydrogen-to-carbon ratio of the fuel, or a lower heating
value (LHV) of the fuel (e.g., in megajoules per kilogram or
MJ/kg).
[0038] Referring back to FIG. 2, the empirical NOx model 40 and the
physics-based NOx model 60 may output the respective first and
second NOx estimations 42 and 62 to a NOx determination module 90
in the controller 30. The NOx determination module 90 may determine
a third NOx estimation 92 of the amount of NOx in the flow of
exhaust output from the engine 10 (e.g., at a location downstream
from the turbine 16 and upstream of the aftertreatment device(s)
20) based on the first NOx estimation 42, the second NOx estimation
62, and the operating condition of the engine 10, as described in
further detail below.
[0039] The NOx determination module 90 may output the third NOx
estimation 92, which may be input into a second map 94. In the
exemplary embodiment, the second map 94 may also receive one or
more other inputs, such as a measured NOx 95 measured by the NOx
sensor 34 (FIG. 1). The second map 94 may be used to adjust the
third NOx estimation 92 based on the measured NOx 95 to output a
final NOx estimation 96, as described below. Alternatively, the
second map 94 and the NOx sensor 34 may be omitted, e.g., if there
is no NOx sensor 34 present in the exhaust system 14, and the NOx
determination module 90 may determine the third NOx estimation 92,
which may be the final NOx estimation, without using input from the
NOx sensor 34. The controller 30 may be configured to use the final
NOx estimation 96 as feedback for controlling the dosing of the
reductant using the injector 22, which may improve the performance
of the SCR catalyst 24.
INDUSTRIAL APPLICABILITY
[0040] The disclosed exhaust emission prediction system may be
applicable to any exhaust system. The exhaust emission prediction
system may incorporate both the empirical NOx model 40 and the
physics-based NOx model 60 to provide more accurate predictions for
emissions characteristics, such as the amount of NOx in the flow of
exhaust output from the engine 10.
[0041] FIG. 5 shows a flow chart depicting an exemplary embodiment
of an algorithm of the software control used in connection with the
controller 30. The steps described below may be repeated by the
controller 30 periodically.
[0042] The controller 30 may determine the first NOx estimation 42
using the empirical NOx model 40 (step 100). As described above,
the empirical NOx model 40 includes the first NOx model 44, which
may receive one or more of the inputs 48 and may output the first
NOx estimation 42. The controller 30 may adjust the first NOx
estimation 42 based on the ambient humidity 57 (step 102). As
described above, the ambient humidity 57 may be measured using the
humidity sensor 36. The controller 30 may use the first map 46 to
determine a correction factor based on the ambient humidity 57 and
may multiply the first NOx estimation 42 by the correction factor.
Because the ambient humidity 57 may affect the rate at which NOx is
formed, the controller 30 may provide a more accurate estimate of
the amount of NOx in the flow of exhaust output from the engine 10
by taking into account the ambient humidity 57.
[0043] The controller 30 may also determine the second NOx
estimation 62 using the physics-based NOx model 60 (step 104). As
described above, the in-cylinder engine model 66 may receive one or
more of the inputs 68, and may determine one or more in-cylinder
characteristics (e.g., the gross heat release rate 75, the pressure
76, the bulk gas temperature 77, the bulk gas specific heat 78, the
bulk gas density 79, the bulk gas mass 80, and/or the total heat
transfer 81), which may be input into the second NOx model 64. The
second NOx model 64 may receive the in-cylinder characteristics
determined by the in-cylinder engine model 66 and one or more of
the inputs 68, and may output the second NOx estimation 62.
Alternatively, as described above, the in-cylinder engine model 66
may be omitted, and one or more of the in-cylinder characteristics
may be measured using physical sensors (e.g., pressure and
temperature in one or more of the cylinders 12) and input into the
second NOx model 64. The first and second NOx estimations 42 and 62
may be input into the NOx determination module 90.
[0044] The controller 30 (e.g., the NOx determination module 90)
may determine whether the engine 10 is operating under the
operating conditions for which the empirical NOx model 40 is
trained (the trained operating conditions) (step 106). The trained
operating conditions may include, e.g., one or a range of
altitudes, one or a range of ambient temperatures, one or a range
of engine speeds, one or a range of ambient pressures, one or more
application cycles, one or more tasks, one or more aggressive
application cycles, one or more non-aggressive application cycles,
one or more steady-state operating conditions, one or more
transient operating conditions, one or more engine configurations
(e.g., engines including certain hardware), one or more fuel
injection system calibrations (e.g., fuel injection timing, amount,
or pressure), etc.
[0045] For example, in an exemplary embodiment, the empirical NOx
model 40 may be trained at approximately sea level and at engine
speeds between approximately 800 RPM and approximately 1,800 RPM.
It is understood that the empirical NOx model 40 may be determined
to be trained for a range of operating conditions (e.g., engine
speeds between approximately 800 RPM and approximately 1,800 RPM)
even though the data used to train the empirical NOx model 40 may
include a subset of operating conditions within the range.
[0046] The NOx determination module 90 may determine that the
engine 10 is operating under the trained operating conditions (step
106; yes). In the exemplary embodiment described above, for
example, the NOx determination module 90 may determine whether the
engine 10 is operating at approximately sea level and at an engine
speed between approximately 800 RPM and approximately 1,800 RPM. If
so, the NOx determination module 90 may determine the third NOx
estimation 92 based at least in part on the first NOx estimation 42
determined by the empirical NOx model 40 (step 108). For example,
the NOx determination module 90 may determine that the third NOx
estimation 92 equals the first NOx estimation 42.
[0047] The controller 30 may receive the measured NOx 95 from the
NOx sensor 34 (step 110) and may adjust the third NOx estimation 92
using the measured NOx 95 (step 112). For example, the second map
94 may be used to determine a correction factor based on the
measured NOx 95. The correction factor may also depend on the
operating condition(s) under which the engine 10 is performing. For
example, the correction factor may adjust the third NOx estimation
92 to be closer to the measured NOx 95 when the engine 10 is
operating under one or more operation conditions for which there
may be relatively more confidence in the accuracy of the NOx sensor
34, e.g., below a certain period of time of use of the NOx sensor
34. The controller 30 may multiply the third NOx estimation 92 by
the correction factor to output the final NOx estimation 96.
Alternatively, the correction factor may be an offset that is added
to (or subtracted from) the third NOx estimation 92 to output the
final NOx estimation 96. Thus, the NOx sensor 34 may be used to
adjust the third NOx estimation 92 to determine the final NOx
estimation 96. Thus, the NOx sensor 34 may measure the amount of
NOx using the NOx sensor 34 disposed at or near the same location
at which the first, second, and third NOx estimations 42, 62, and
92 are estimating the amount of NOx. Alternatively, if the NOx
sensor 34 is located at another location in the exhaust system 14
(e.g., downstream of the aftertreatment device(s) 20), the second
map 94 may be configured to take into account any differences in
the amount of NOx between the location of the NOx sensor 34 and the
location at which the first, second, and third NOx estimations 42,
62, and 92 are estimating the amount of NOx (e.g., due to the
aftertreatment device(s) 20).
[0048] Optionally, steps 110 and 112 may be omitted, e.g., if there
is no NOx sensor 34 present in the exhaust system 14. As another
alternative, or in addition, the controller 30 may use the third
NOx estimation 92 to diagnose the NOx sensor 34 (e.g., to determine
when the NOx sensor 34 fails) or act as a backup virtual NOx sensor
if the NOx sensor 34 fails.
[0049] Referring back to step 106, the NOx determination module 90
may determine that the engine 10 is not operating under the trained
operating conditions (step 106; no). In the exemplary embodiment
described above, for example, the NOx determination module 90 may
determine that the engine 10 is operating at a relatively high
altitude and/or at an engine speed outside the range of
approximately 800 RPM and approximately 1,800 RPM. If so, then the
NOx determination module 90 may determine the third NOx estimation
92 based at least in part on the second NOx estimation 62
determined by the physics-based NOx model 60 (step 114). For
example, the NOx determination module 90 may determine that the
third NOx estimation 92 equals the second NOx estimation 62.
[0050] Alternatively, the NOx determination module 90 may determine
the third NOx estimation 92 based on both the first NOx estimation
42 and the second NOx estimation 62. According to an exemplary
embodiment, the NOx determination module 90 may determine a
weighing factor that may indicate a relative weight of the
empirical NOx model 40 compared to the physics-based NOx model 60.
The weighing factor may be determined based on the operating
condition(s) under which the engine 10 is performing, and may range
from 0 (indicating that the engine 10 is performing under operating
condition(s) for which there is more confidence in the accuracy of
the empirical NOx model 40) to 1 (indicating that the engine 10 is
performing under operating condition(s) for which there is more
confidence in the accuracy of the physics-based NOx model 60, e.g.,
if the engine 10 is operating in the domain to which the
physics-based NOx model 60 applies). The third NOx estimation 92
may be determined by applying the weighing factor to the first NOx
estimation 42 and the second NOx estimation 62. If the weighing
factor is equal to or closer to 0, then the third NOx estimation 92
may be equal to or closer to the first NOx estimation 42 than the
second NOx estimation 62. On the other hand, if the weighing factor
is equal to or closer to 1, then the third NOx estimation 92 may be
equal to or closer to the second NOx estimation 62 than the first
NOx estimation 42. The weighing factor may vary from 0 to 1 in
order to correspondingly vary the third NOx estimation 92 from the
first NOx estimation 42 to the second NOx estimation 62. After
determining the third NOx estimation 92, the controller 30 may
receive the measured NOx 95 from the NOx sensor 34 (step 110) and
may adjust the third NOx estimation 92 using the measured NOx 95 to
output the final NOx estimation 96 (step 112), as described
above.
[0051] The controller 30 may use the final NOx estimation 96 as
feedback for controlling the dosing of the reductant using the
injector 22. The controller 30 may determine the amount of NOx
entering the SCR catalyst 24 based on the final NOx estimation 96
and by taking into account the effect of the other exhaust
treatment components (e.g., the aftertreatment device(s) 20)
located between the turbine 16 and the SCR catalyst 24 on the
composition of the flow of exhaust. The controller 30 may adjust an
amount of the reductant injected by the injector 22 based on the
determined amount of NOx entering the SCR catalyst 24 to anticipate
and mitigate the release of NOx and/or ammonia downstream of the
SCR catalyst 24.
[0052] The flow chart described above in connection with FIG. 5
depicts an exemplary embodiment of the algorithm and software
control. Those skilled in the art will recognize that similar
algorithms and software control may be used without deviating from
the scope of the present disclosure.
[0053] Several advantages over the prior art may be associated with
the exhaust emission prediction system. The exhaust emission
prediction system may provide a hybrid virtual NOx sensing device
that includes both the empirical NOx model 40 and the physics-based
NOx model 60. Therefore, the exhaust emission prediction system may
provide more accurate and reliable estimations of the amount of NOx
in the flow of exhaust output from the engine 10. Optionally, the
NOx sensor 34 may be omitted, which may reduce costs, or the NOx
sensor 34 may be diagnosed or corrected using the estimations
determined by the exhaust emission prediction system.
[0054] The exhaust emission prediction system includes both the
empirical NOx model 40 and the physics-based NOx model 60, and
therefore may provide advantages from both models 40 and 60. The
exhaust emission prediction system may determine the final NOx
estimation 96 based on whether the engine 10 is operating under
trained operating conditions. The empirical NOx model 40 may have
higher accuracy than the physics-based NOx model 60 when the engine
10 is operating under trained operating conditions. If the engine
10 is operating under the trained operating conditions, then the
final NOx estimation 96 may be determined based at least in part on
the output (e.g., the first NOx estimation 42) from the empirical
NOx model 40. Therefore, the exhaust emission prediction system may
take advantage of the relative accuracy of the empirical NOx model
40 under the trained operating conditions.
[0055] Because the empirical NOx model 40 may not be as accurate
when the engine 10 is not operating under trained operating
conditions, the NOx determination module 90 may switch to the
output (e.g., the second NOx estimation 62) from the physics-based
NOx model 60 to determine the final NOx estimation 96 or may
calibrate the output from the empirical NOx model 40 using the
output from the physics-based NOx model 60 to determine the final
NOx estimation 96. For example, to calibrate the output from the
empirical NOx model 40, the NOx determination module 90 may use the
weighing factor to determine how much to weigh the outputs from the
empirical NOx model 40 and the physics-based NOx model 60. The
weighing factor may favor the empirical NOx model 40 if there is
higher confidence in the empirical NOx model 40 (e.g., based on the
operating conditions of the engine 10), may favor the physics-based
NOx model 60 if there is higher confidence in the physics-based NOx
model 60, or may average the two outputs if neither model 40 or 60
is favored. Therefore, when the engine 10 is not operating under
trained operating conditions, the exhaust emission prediction
system may take advantage of the relative accuracy of the empirical
NOx model 40 and/or the physics-based NOx model 60, depending on
the operating conditions under which the engine 10 is
operating.
[0056] It will be apparent to those skilled in the art that various
modifications and variations can be made to the disclosed exhaust
emission prediction system. Other embodiments will be apparent to
those skilled in the art from consideration of the specification
and practice of the disclosed exhaust emission prediction system.
It is intended that the specification and examples be considered as
exemplary only, with a true scope being indicated by the following
claims and their equivalents.
* * * * *