Integrated optimization and control of an engine and aftertreatment system

Information

  • Patent Grant
  • 11619189
  • Patent Number
    11,619,189
  • Date Filed
    Thursday, September 23, 2021
    3 years ago
  • Date Issued
    Tuesday, April 4, 2023
    a year ago
Abstract
An engine and one or more aftertreatment subsystems integrated into one system for optimization and control. At least one controller may be connected to the engine and the one or more aftertreatment subsystems. The controller may contain and execute a program for the optimization and control of the one system. Controller may receive information pertinent to the engine and the one or more aftertreatment subsystems for the program. The controller may prescribe setpoints and constraints for measured variables and positions of actuators according to the program to aid in effecting the optimization and control of the one system.
Description
BACKGROUND

The present disclosure pertains to internal combustion engines and particularly to engines having aftertreatment mechanisms.


SUMMARY

The disclosure reveals an engine and one or more aftertreatment subsystems integrated into one system for optimization and control. At least one controller may be connected to the engine and the one or more aftertreatment subsystems. The controller may contain and execute a program for the optimization and control of the one system. Controller may receive information pertinent to the engine and the one or more aftertreatment subsystems for the program. The controller may prescribe setpoints and constraints for measured variables and positions of actuators according to the program to aid in effecting the optimization and control of the one system.





BRIEF DESCRIPTION OF THE DRAWING


FIG. 1 is a diagram of a basic scheme of the present system for integrated optimization and control of an engine with one or more aftertreatment subsystems;



FIG. 2 is a diagram of an illustrative example engine map;



FIG. 3 is a diagram of an illustrative example engine or aftertreatment subsystem interconnected with a controller;



FIG. 4 is a diagram of the example in FIG. 3 with an engine and a multiple of aftertreatment subsystems; and



FIG. 5 is a diagram of an illustrative example of an approach for the engine and aftertreatment system.





DESCRIPTION

The modern combustion engine appears to be a very complex system. The complexity growth may be driven namely by governmental legislation that restricts combustion engine emissions. Therefore, the original equipment manufacturers (OEMs) may be forced to add various equipment items, sensors and actuators to the engine to achieve the prescribed limits and to optimize engine operating costs, e.g., fuel economy, urea consumption, and so forth. Under these conditions, an engine operation optimization and design of an optimal control system may be a challenging task.


Some approaches may incorporate optimizing the engine and individual aftertreatment systems involving, e.g., selective catalytic reduction (SCR), diesel oxidation catalysts (DOC), diesel particulate filter (DPF), and so on, separately. These approaches do not necessarily provide a systematic way of optimization. They may involve time consuming and expensive tasks. Furthermore, it is not necessarily ensured that their results will be optimal. There might be a better solution.


Another approach may be to optimize the engine together with the aftertreatment subsystem (AFS) as a one system. Such an approach may enable one to find the global optimal behavior of the engine with an aftertreatment subsystem from an economical and technical point of view while satisfying virtually all of the prescribed emission limits. The engine and aftertreatment subsystem may have appropriate sensors and actuators as needed to effect an optimization program for the engine and aftertreatment subsystem or subsystems as one system. The engine may be seen as an exhaust gas source for the aftertreatment subsystem. The properties of the engine out exhaust gas as sensed may be influenced within certain range by manipulating available engine actuators such as those of a turbocharger waste gate (WG), variable geometry turbocharger (VGT), exhaust gas recirculation (EGR), start of injection (SOI), throttling valve (TV), and so on. Various degrees of freedom may be used to prepare or modify the exhaust gas properties for optimal operation of the aftertreatment subsystem at virtually all of the engine operating points. For example, if the actual state of the aftertreatment subsystem does not enable a reduction of emissions due to low temperature as sensed in some operating regimes, then the engine actuators may be controlled to increase temperature so that the engine exhaust gas out emissions do not violate prescribed limits. On the other hand, if the state of the aftertreatment subsystem enables a reduction of a significant amount of pollutants, the engine actuators may be controlled in a way to also achieve the best fuel economy.


An engine optimization and control design may be formulated as a rigorous mathematical optimization problem. The present approach may offer a modular and systematic solution to the problem. The approach may incorporate dividing the engine and aftertreatment optimization and control design into two stages: (i) an off-line part and (ii) an on-line part (real-time).


(i) The off-line part may be formulated as a mathematical optimization problem with constraints (known as mathematical programming) and the results may be various engine maps prescribing setpoints and constraints for different kinds of measured variables from sensors and positions of virtually all engine actuators for virtually all major operating points or conditions of the engine, e.g., over the engine speed and torque map. Virtually all of the maps may be parameterized by various variables of the engine and aftertreatment system but may be also parameterized by measured fuel and/or urea consumption and corresponding costs, by their ratio, or other relevant economically related quantities. Information about actual market prices of fuel and other fluids used by the engine and aftertreatment system may be incorporated to parameterize the control system and may be used as a tuning parameter during the engine's lifetime. This approach may enable a slight tuning of the controller behavior when the prices of the fluids used are changed, which can ensure economically optimal operation of the engine in view of such changes during its lifetime.


(ii) The on-line part may consist of one or more feedback single or multivariable real-time controllers. These controllers may be implemented, for example, as model based predictive controllers (MPCs). The feedback controllers may ensure realization of virtually all of the setpoints, but also satisfaction of virtually all of the constraints computed in the off-line part. The feedback controllers may also ensure disturbance rejection, a minimization of an impact of engine components production variability, and aging of the engine. Furthermore, the feedback controllers may also be designed to deliver needed performance during an engine transient operation.



FIG. 1 is a block diagram of a basic scheme of the present system for integrated optimization and control of an engine with one or more aftertreatment systems. The various blocks represent an engine 11, and several aftertreatment systems (AFSs) 12 and 13. Aftertreatment system 13 may be the last system and be denoted by an “N”. “N” may also indicate the total number of aftertreatment systems. An aftertreatment system 12 between engine 11 and aftertreatment system N may be denoted by an “i”. There may be any number of aftertreatment systems. If there is one aftertreatment system, then it may be represented as system N, wherein N=1.


“x0” within the symbol for engine 11 may indicate an internal state of the engine. “xi” and “xN” may indicate internal states of AFSi 12 and AFSN 13, respectively. “1V” may represent an external input 15 to engine 11. The external input may incorporate disturbance, fluid price, and so on. Similarly, “vi” and “vN” may represent external inputs 24 and 25 for AFSi 12 and AFSN 13, respectively. A “u0” input 16 may represent an actuator or actuators of engine 11, a “ui” input 26 may represent an actuator or actuators of AFSi 12, and a “uN” input 27 may represent an actuator or actuators of AFSN 13. Inputs 16, 26 and 27 may incorporate actuator inputs.


“J0(x0,v0i,u0)” on an output 17 may represent a subsystem cost function of x0, v0 and/or u0 for engine 11. “g(x0,v0,u0)≤0” also on output 17 may represent subsystem constraints of x0, v0 and/or u0 for engine 11. “y0” may represent an interconnection output 18 from engine 11 which may be an interconnection input “yi-119 to AFSi 12, assuming that AFSi 12 is the first AFS connected to engine 11, where i=1. However, there may be one or more AFSs connected between engine 11 and AFSi 12. “yi” may represent an interconnection output 21 from AFSi 12 which may be an interconnection input “yN-122 to AFSN 13, assuming that AFSN 13 is connected to AFSi 12. However, there may be one or more AFSs connected between AFSi 12 and AFSN 13. “yN” may represent an output 23 of the AFSN 13 and the preceding AFSs from “1” through “N−1”.


“Ji(xi,vi,ui,yi-1)” on an output 28 may represent a subsystem cost function of xi, vi, ui and/or yi-1 for AFSi 12. “Ji(.)” may be an abbreviated designation of the subsystem cost function. “g(xi,vi,ui,yi-1)≤0” also on output 28 may represent subsystem constraints of xi, vi, ui and/or yi-1 for AFSi 12. “g(.)” may be an abbreviated designation of the subsystem constraints. “JN(xN,vN,uN,yN-1)” on an output 29 may represent subsystem cost function of xN, vN, uN and/or yN-1 for AFSN 13. “g(xN,vN,uN,yN-1)≤0” also on output 29 may represent subsystem constraints of xN, vN, uN and/or yN-1 for AFSN 13. The similar designations may be made for additional AFSs, if any, between engine 11 and AFSi 12 and between AFSi 12 and AFSN 13, as done herein with the xs, vs, us and ys.



FIG. 2 may aid in illustrating off-line optimization. An objective may be to compute optimal steady-state engine maps for virtually all of the operating points, for example, in an engine speed-torque space as shown with a graph 31 of engine torque (nm) versus engine speed (rpm). Graph 31 illustrates an example k-th operating point 32 plotted at a specific torque and engine speed. The k-th operating point may represent any point at various locations on graph 31.


An optimization problem in each operating point may be indicated by:










min
U

J

=




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=
0

N



J
i

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i

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i

,

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U
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,

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s
.
t
.


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The resulting optimal steady-state maps may be indicated by:

uiss=fui(v0, . . . ,vN) and
yiss=fyi(v0, . . . ,vN).

Abbreviated designations of the steady-state map indications may be uiss=fui(.) and yiss=fyi(.), respectively. It may be noted that the maps may also be parameterized by xi under certain conditions.


An on-line part (real-time) for an i-th aftertreatment subsystem or an engine may be illustrated in FIG. 3. A controller may be integrated with an engine and an AFS. An engine or AFS_i 41 may have an internal state of x0 or xi, respectively. An actuator input u0 or ui 43 may go to the engine or AFS_i 41, respectively. The actuator input 43 may come from a controller_0 or a controller_i 42, respectively. Controller 42 may provide steady state maps as represented by symbols u0ss=fu0(.) and y0ss=fy0(.) or uiss=fui(.) and yiss=fyi(.), respectively. The controller_i 42 may be implemented by MPC controller. External inputs v0, . . . , vN 44 (i.e., disturbance, fluid price, and so forth) may be provided to controller 42. A specific input v0 or vi 45 may be input to engine or AFS_i 41, respectively. An interconnection output yi-1 47 from an engine or an AFS_i−1 may be an input 46 to AFS_i 41 and controller_i 42. There may be an interconnection output y0 or yi 47 from the engine or AFS_i 41 as an input 46 for an AFS_i+1 or an AFS_N. Interconnection output 47 may also go to controller_i 42. In general, 47 may contain also signals which are not interconnections but measurements of variables that can be useful for integrated optimization.


The on-line part for an i-th subsystem of FIG. 3 may be illustrated as an engine and multiple AFSs connected in a diagram of FIG. 4. Two or more controllers 42 of FIG. 4 may be combined as one controller 42. The numerical labels are the same for similar components and lines as shown in FIG. 3.



FIG. 5 is diagram of a two-part engine and aftertreatment optimization control approach 50 with an off-line stage 51 and an on-line stage 52. At the off-line stage 51 may be a mathematical optimization of the engine and aftertreatment system at symbol 53, an engine map with setpoints and constraints at symbol 54, and a parameterization of engine maps at symbol 55. At the on-line stage 52 may be feedback real-time controller or controllers at symbol 56, a realization of setpoints at symbol 57, and a satisfaction of constraints at symbol 58.


Some of the items or activities of the disclosed system in FIGS. 1-5 not covered by one or more controllers may be performed by a processor/computer.


A recap of the disclosure is provided in the following. An engine and aftertreatment system may incorporate an engine, an aftertreatment mechanism connected to the engine, and a controller connected to the engine and the aftertreatment mechanism. The controller may have an optimization program. The optimization program may be for optimized performance of the engine and the aftertreatment mechanism integrated as one system. Optimized performance may incorporate reducing emissions and increasing fluid efficiency of the one system.


The optimization program may incorporate the aftertreatment mechanism for reducing emissions from an exhaust of the engine to a prescribed level, and increasing fluid efficiency of the engine and the aftertreatment mechanism while the emissions are reduced at least down to the prescribed level.


The engine may incorporate a control input to actuators on the engine, an interconnection output and an information output. The information output may indicate engine costs and/or engine constraints. The aftertreatment mechanism may incorporate an interconnection input connected to the interconnection output of the engine, a control input to actuators on the aftertreatment mechanism, an interconnection output, and an information output. The information output may indicate aftertreatment mechanism costs and/or aftertreatment mechanism constraints. The costs and constraints may be a basis incorporated in the optimization program for optimized performance of the engine and the aftertreatment mechanism integrated as one system.


The controller may further incorporate a first input connected to the interconnection output of the engine, a first output connected to the control input to actuators of the engine, a second input connected with the interconnection input of the aftertreatment mechanism, a third input connected to the interconnection output of the aftertreatment mechanism, and a second output connected to the control input to actuators of the aftertreatment mechanism.


The controller may further incorporate a feedback loop for disturbance rejection, minimizing an impact of variability of performance of the engine, and/or delivering predetermined performance of the aftertreatment mechanism during transient operation of the engine, and maps prescribing setpoints and constraints for measured variables and positions of engine actuators for one or more operating points of the engine. The maps may be parameterized by variables of the engine and the aftertreatment mechanism. The maps may be a basis incorporated in the optimization program for optimized performance of the engine and the aftertreatment mechanism integrated as one system.


An approach for engine and aftertreatment optimization and control may incorporate formulating an off-line part which involves mathematically optimizing an engine and aftertreatment system, providing engine maps prescribing setpoints and constraints for measured variables from sensors and positions of engine actuators for operating points and conditions of the engine, and parameterizing the engine maps with variables of the engine and the aftertreatment system.


The approach for engine and aftertreatment optimization and control may also incorporate formulating an on-line part providing one or more feedback real-time controllers realizing the setpoints of the engine and aftertreatment system, and satisfying computed constraints with the one or more controllers. The one or more controllers may be model predictive controllers.


The one or more controllers may ensure disturbance rejection, minimization of input of engine components production variability, and/or engine aging. The one or more controllers may deliver needed performance during an engine transient operation.


The approach may further incorporate parameterizing the engine and aftertreatment system by measured fuel, urea consumption and/or corresponding costs. The approach may also further incorporate parameterizing a control system with market price information of fuel and other fluids used by the engine and aftertreatment system. There may also be parameterizing the control system to tune the controller when there are changes of prices of fluids used by the engine and aftertreatment system to ensure economically optimal operation of the engine during the changes.


There may be a system of an engine and aftertreatment subsystem incorporating an engine, an aftertreatment subsystem connected to the engine, and a controller connected to the engine and the aftertreatment subsystem. The controller may receive signals from sensors of the engine and the aftertreatment subsystem, process the signals, and provide signals to actuators of the engine and the aftertreatment subsystem according to an optimization program for optimized performance of the engine and the aftertreatment subsystem as one system. The optimized performance may incorporate reducing emissions and increasing fluid efficiency of the one system.


The external inputs of the engine and the aftertreatment subsystem may be connected to the controller. The controller may incorporate engine maps for operating points of the engine. The maps may be a basis for optimized performance of the engine and the aftertreatment subsystem as one system. The maps may prescribe setpoints and constraints for measured variables from the sensors and for actuators.


The engine may incorporate an external input and an actuator input from the controller, and an interconnection output connected to the controller. The external input may have external information pertinent to the engine.


The aftertreatment subsystem may incorporate an interconnection input connected to the interconnection output of the engine and connected to the controller, an external input, an actuator input from the controller, and an interconnection output connected to the controller. The external input may have external information pertinent to the aftertreatment subsystem.


The engine may further incorporate an internal state and an information output. The information output may indicate engine costs as a function of the engine internal state, the external input and/or the actuator input.


The aftertreatment subsystem may further incorporate an internal state and an information output. The information output may indicate aftertreatment costs as a function of the aftertreatment subsystem internal state, the external input, actuator input, and/or the interconnection input.


The information output of the engine may indicate engine constraints as a function of the internal state, the external input and/or the actuator input of the engine. The information output of the aftertreatment subsystem may indicate aftertreatment constraints as a function of the internal state, the external input, the actuator input, and/or the interconnection input of the aftertreatment subsystem. The costs and constraints may be a basis for optimized performance of the engine and the aftertreatment subsystem as one system.


An approach for controlling a combined engine and aftertreatment system may incorporate providing an engine, adding one or more aftertreatment subsystems to result in a combined engine and aftertreatment system, connecting one of the one or more aftertreatment subsystems to an exhaust output of the engine, and manipulating actuators of the engine and the one or more aftertreatment subsystems with one or more controllers to change the properties of the exhaust for optimal operation of the combined engine and aftertreatment system. Optimal operation may incorporate reduction of emissions and improvement of fluid efficiency of the combined engine and aftertreatment system.


To change the properties of the exhaust may incorporate reducing an amount of pollutants in the exhaust to a magnitude equal to or less than a prescribed magnitude. Manipulating the actuators of the engine may increase fuel economy of the engine if the one or more aftertreatment subsystems reduce an amount of pollutants in the exhaust to a magnitude equal to or less than the prescribed magnitude.


The approach may further incorporate providing one or more engine maps as a basis for optimal operation of the combined engine and aftertreatment system, processing the one or more engine maps prescribing setpoints and/or constraints for measured variables and positions of the actuators on the engine for operating points and/or conditions of the engine, and parameterizing the engine maps by variables of the engine and of the one or more aftertreatment subsystems.


The approach may further incorporate parameterizing the engine maps by costs of fuel consumed by the engine and/or urea consumed by the one or more aftertreatment subsystems. The one or more engine maps may incorporate a speed and torque map of the engine. The one or more controllers may be connected to the engine and the one or more aftertreatment subsystems of the combined engine and aftertreatment system. The one or more controllers may ensure realization of the setpoints, and/or ensure satisfaction of the constraints.


In the present specification, some of the matter may be of a hypothetical or prophetic nature although stated in another manner or tense.


Although the present system and/or approach has been described with respect to at least one illustrative example, many variations and modifications will become apparent to those skilled in the art upon reading the specification. It is therefore the intention that the appended claims be interpreted as broadly as possible in view of the related art to include all such variations and modifications.

Claims
  • 1. An engine monitoring system comprising: an engine having engine actuators and including at least a variable geometry turbocharger (VGT) wherein at least one of the engine actuators is associated with the VGT, wherein the engine is configured to discharge exhaust gasses;an aftertreatment system configured to receive the exhaust gasses from the engine, reduce pollutants in the exhaust gasses below an emission limit, and emit a discharged gas;sensors configured to sense properties in the exhaust gasses; anda controller operatively coupled to the engine and the sensors and configured to: receive measurements of the sensed properties in the exhaust gasses from the sensors;control the VGT to raise a temperature of the exhaust gasses when a state of the aftertreatment system does not enable reduction of the pollutants to a level below the emission limit due to low temperature; andcontrol the engine actuators to maximize a fuel economy of the engine when the state of the aftertreatment system does enable reduction of the pollutants to a level below the emission limit.
  • 2. The engine monitoring system of claim 1, wherein the controller uses the measurements of the sensed properties in the exhaust gasses and a set of engine maps to determine setpoints and constraints for the engine actuators to maximize the fuel economy of the engine.
  • 3. The engine monitoring system of claim 2, wherein the set of engine maps includes at least one of an engine speed map and a torque map.
  • 4. The engine monitoring system of claim 1, wherein the controller further uses a consumption of fuel and a consumption of urea to maximize the fuel economy of the engine.
  • 5. The engine monitoring system of claim 1, wherein the controller further uses a market price of fuel to maximize the fuel economy of the engine.
  • 6. The engine monitoring system of claim 1, wherein the controller further uses an aging of the engine actuators to maximize the fuel economy of the engine.
  • 7. An engine monitoring system comprising: an engine having engine actuators and including at least a turbocharger having a turbine and a wastegate (WG) configured to controllably allow exhaust gasses to bypass the turbine, wherein at least one of the engine actuators controls a position of the WG, and wherein the engine is configured to discharge the exhaust gasses;an aftertreatment system configured to receive the exhaust gasses from the engine, reduce pollutants in the exhaust gasses below an emission limit, and emit a discharged gas;sensors configured to sense properties in the exhaust gasses; anda controller operatively coupled to the engine and the sensors and configured to: receive measurements of the sensed properties in the exhaust gasses from the sensors;control the WG to raise a temperature of the exhaust gasses when a state of the aftertreatment system does not enable reduction of the pollutants to a level below the emission limit due to low temperature; andcontrol the engine actuators to maximize a fuel economy of the engine when the state of the aftertreatment system does enable reduction of the pollutants to a level below the emission limit.
  • 8. The engine monitoring system of claim 7, wherein the controller uses the measurements of the sensed properties in the exhaust gasses and a set of engine maps to determine setpoints and constraints for the engine actuators to maximize the fuel economy of the engine.
  • 9. The engine monitoring system of claim 8, wherein the set of engine maps includes at least one of an engine speed map and a torque map.
  • 10. The engine monitoring system of claim 7, wherein the controller further uses a consumption of fuel and a consumption of urea to maximize the fuel economy of the engine.
  • 11. The engine monitoring system of claim 7, wherein the controller further uses a market price of fuel to maximize the fuel economy of the engine.
  • 12. The engine monitoring system of claim 7, wherein the controller further uses an aging of the engine actuators to maximize the fuel economy of the engine.
  • 13. A method for monitoring an engine system comprising an engine having engine actuators including at least one of a variable geometry turbocharger (VGT) or a turbocharger with a wastegate (WG), the engine system also including an aftertreatment system configured to receive exhaust gas from the engine, reduce a level of pollutants in the exhaust gas below an emission limit, and emit a discharge gas, the method comprising: receiving measurements of sensed properties in the exhaust gas;controlling at least one of the VGT or WG to raise a temperature when a state of the aftertreatment systems does not enable reduction of the level of the pollutants to a level below the emission limit due to low temperature; andcontrolling the engine actuators to maximize a fuel economy of the engine when the state of the aftertreatment system does enable reduction of the level of pollutants to a level below the emission limit.
  • 14. The method of claim 13, wherein the measurements of sensed properties in the exhaust gas and a set of engine maps are used to determine setpoints and constraints for the engine actuators to maximize the fuel economy of the engine.
  • 15. The method of claim 14, wherein the set of engine maps includes at least one of an engine speed map and a torque map.
  • 16. The method of claim 13, wherein a consumption of fuel and a consumption of urea are used to maximize the fuel economy of the engine.
  • 17. The method of claim 13, wherein a market price of fuel is used to maximize the fuel economy of the engine.
  • 18. The method of claim 13, wherein the engine includes the VGT and not the turbocharger with the wastegate, and the step of controlling at least one of the VGT or WG comprises controlling the VGT to raise the temperature.
  • 19. The method of claim 13, wherein the engine includes the turbocharger with the wastegate and not the VGT, and the step of controlling at least one of the VGT or WG comprises controlling the WG to raise the temperature.
  • 20. The method of claim 13 wherein the engine also includes a throttle, and the step of controlling at least one of the VGT or WG further includes controlling the throttle to raise the temperature.
Parent Case Info

This application is a continuation of U.S. patent application Ser. No. 16/424,362, filed May 28, 2019, which is a continuation of Ser. No. 13/290,025, filed Nov. 4, 2011 (now abandoned), both of which are hereby incorporated by reference.

US Referenced Citations (475)
Number Name Date Kind
3744461 Davis Jul 1973 A
4005578 McInerney Feb 1977 A
4055158 Marsee Oct 1977 A
4206606 Yamada Jun 1980 A
4252098 Tomczak et al. Feb 1981 A
4359991 Stumpp et al. Nov 1982 A
4383441 Willis et al. May 1983 A
4426982 Lehner et al. Jan 1984 A
4438497 Willis et al. Mar 1984 A
4440140 Kawagoe et al. Apr 1984 A
4456883 Bullis et al. Jun 1984 A
4485794 Kimberley et al. Dec 1984 A
4601270 Kimberley et al. Jul 1986 A
4616308 Morshedi et al. Oct 1986 A
4653449 Kamei et al. Mar 1987 A
4671235 Hosaka Jun 1987 A
4677559 Van Brück Jun 1987 A
4735181 Kaneko et al. Apr 1988 A
4947334 Massey et al. Aug 1990 A
4962570 Hosaka et al. Oct 1990 A
5044337 Williams Sep 1991 A
5076237 Hartman et al. Dec 1991 A
5089236 Clerc Feb 1992 A
5094213 Dudek et al. Mar 1992 A
5095874 Schnaibel et al. Mar 1992 A
5108716 Nishizawa Apr 1992 A
5123397 Richeson Jun 1992 A
5150289 Badavas Sep 1992 A
5186081 Richardson et al. Feb 1993 A
5233829 Komatsu Aug 1993 A
5270935 Dudek et al. Dec 1993 A
5273019 Matthews et al. Dec 1993 A
5282449 Takahashi et al. Feb 1994 A
5293553 Dudek et al. Mar 1994 A
5349816 Sanbayashi et al. Sep 1994 A
5365734 Takeshima Nov 1994 A
5394322 Hansen Feb 1995 A
5394331 Dudek et al. Feb 1995 A
5398502 Watanabe Mar 1995 A
5408406 Mathur et al. Apr 1995 A
5431139 Grutter et al. Jul 1995 A
5452576 Hamburg et al. Sep 1995 A
5477840 Neumann Dec 1995 A
5560208 Halimi et al. Oct 1996 A
5570574 Yamashita et al. Nov 1996 A
5598825 Neumann Feb 1997 A
5609139 Ueda et al. Mar 1997 A
5611198 Lane et al. Mar 1997 A
5682317 Keeler et al. Oct 1997 A
5690086 Kawano et al. Nov 1997 A
5692478 Nogi et al. Dec 1997 A
5697339 Esposito Dec 1997 A
5704011 Hansen et al. Dec 1997 A
5740033 Wassick et al. Apr 1998 A
5746183 Parke et al. May 1998 A
5765533 Nakajima Jun 1998 A
5771867 Amstutz et al. Jun 1998 A
5785030 Paas Jul 1998 A
5788004 Friedmann et al. Aug 1998 A
5842340 Bush et al. Dec 1998 A
5846157 Reinke et al. Dec 1998 A
5893092 Driscoll Apr 1999 A
5924280 Tarabulski Jul 1999 A
5942195 Lecea et al. Aug 1999 A
5964199 Atago et al. Oct 1999 A
5970075 Wasada Oct 1999 A
5974788 Hepburn et al. Nov 1999 A
5995895 Watt et al. Nov 1999 A
6029626 Bruestle Feb 2000 A
6035640 Kolmanovsky et al. Mar 2000 A
6048620 Zhong Apr 2000 A
6048628 Hillmann et al. Apr 2000 A
6055810 Borland et al. May 2000 A
6058700 Yamashita et al. May 2000 A
6067800 Kolmanovsky et al. May 2000 A
6076353 Freudenberg et al. Jun 2000 A
6105365 Deeba et al. Aug 2000 A
6122555 Lu Sep 2000 A
6134883 Kato et al. Oct 2000 A
6153159 Engeler et al. Nov 2000 A
6161528 Akao et al. Dec 2000 A
6170259 Boegner et al. Jan 2001 B1
6171556 Burk et al. Jan 2001 B1
6178743 Hirota et al. Jan 2001 B1
6178749 Kolmanovsky et al. Jan 2001 B1
6208914 Ward et al. Mar 2001 B1
6216083 Ulyanov et al. Apr 2001 B1
6233922 Maloney May 2001 B1
6236956 Mantooth et al. May 2001 B1
6237330 Takahashi et al. May 2001 B1
6242873 Drozdz et al. Jun 2001 B1
6263672 Roby et al. Jul 2001 B1
6273060 Cullen Aug 2001 B1
6279551 Iwano et al. Aug 2001 B1
6312538 Latypov et al. Nov 2001 B1
6314724 Kakuyama et al. Nov 2001 B1
6321538 Hasler Nov 2001 B2
6327361 Harshavardhana et al. Dec 2001 B1
6338245 Shimoda et al. Jan 2002 B1
6341487 Takahashi et al. Jan 2002 B1
6347619 Whiting et al. Feb 2002 B1
6360159 Miller et al. Mar 2002 B1
6360541 Waszkiewicz et al. Mar 2002 B2
6360732 Bailey et al. Mar 2002 B1
6363715 Bidner et al. Apr 2002 B1
6363907 Arai et al. Apr 2002 B1
6379281 Collins et al. Apr 2002 B1
6389803 Sumilla et al. May 2002 B1
6425371 Majima Jul 2002 B2
6427436 Allansson et al. Aug 2002 B1
6431160 Sugiyama et al. Aug 2002 B1
6445963 Blevins et al. Sep 2002 B1
6446430 Roth et al. Sep 2002 B1
6453308 Zhao et al. Sep 2002 B1
6463733 Asik et al. Oct 2002 B1
6463734 Tamura et al. Oct 2002 B1
6466893 Latwesen et al. Oct 2002 B1
6470682 Gray, Jr. Oct 2002 B2
6470862 Isobe et al. Oct 2002 B2
6470886 Jestrabek-Hart Oct 2002 B1
6481139 Weldle Nov 2002 B2
6494038 Kobayashi et al. Dec 2002 B2
6502391 Hirota et al. Jan 2003 B1
6502550 Kotwicki et al. Jan 2003 B1
6505465 Kanazawa et al. Jan 2003 B2
6510351 Blevins et al. Jan 2003 B1
6512974 Houston et al. Jan 2003 B2
6513495 Franke et al. Feb 2003 B1
6532433 Bharadwaj et al. Mar 2003 B2
6546329 Bellinger Apr 2003 B2
6550307 Zhang et al. Apr 2003 B1
6553754 Meyer et al. Apr 2003 B2
6560528 Gitlin et al. May 2003 B1
6560960 Nishimura et al. May 2003 B2
6571191 York et al. May 2003 B1
6579206 Liu et al. Jun 2003 B2
6591605 Lewis Jul 2003 B2
6594990 Kuenstler et al. Jul 2003 B2
6601387 Zurawski et al. Aug 2003 B2
6612293 Schweinzer et al. Sep 2003 B2
6615584 Ostertag Sep 2003 B2
6625978 Eriksson et al. Sep 2003 B1
6629408 Murakami et al. Oct 2003 B1
6637382 Brehob et al. Oct 2003 B1
6644017 Takahashi et al. Nov 2003 B2
6647710 Nishiyama et al. Nov 2003 B2
6647971 Vaughan et al. Nov 2003 B2
6651614 Flamig-Vetter et al. Nov 2003 B2
6662058 Sanchez Dec 2003 B1
6666198 Mitsutani Dec 2003 B2
6666410 Boelitz et al. Dec 2003 B2
6671603 Cari et al. Dec 2003 B2
6672052 Taga et al. Jan 2004 B2
6672060 Buckland et al. Jan 2004 B1
6679050 Takahashi et al. Jan 2004 B1
6687597 Sulatisky et al. Feb 2004 B2
6688283 Jaye Feb 2004 B2
6694244 Meyer et al. Feb 2004 B2
6694724 Tanaka et al. Feb 2004 B2
6705084 Allen et al. Mar 2004 B2
6718254 Hashimoto et al. Apr 2004 B2
6718753 Bromberg et al. Apr 2004 B2
6725208 Hartman et al. Apr 2004 B1
6736120 Sumilla May 2004 B2
6739122 Kitajima et al. May 2004 B2
6742330 Genderen Jun 2004 B2
6743352 Ando et al. Jun 2004 B2
6748936 Kinomura et al. Jun 2004 B2
6752131 Poola et al. Jun 2004 B2
6752135 McLaughlin et al. Jun 2004 B2
6757579 Pasadyn Jun 2004 B1
6758037 Terada et al. Jul 2004 B2
6760631 Berkowitz et al. Jul 2004 B1
6760657 Katoh Jul 2004 B2
6760658 Yasui et al. Jul 2004 B2
6770009 Badillo et al. Aug 2004 B2
6772585 Iihoshi et al. Aug 2004 B2
6775623 Ali et al. Aug 2004 B2
6779344 Hartman et al. Aug 2004 B2
6779512 Mitsutani Aug 2004 B2
6788072 Nagy et al. Sep 2004 B2
6789533 Hashimoto et al. Sep 2004 B1
6792927 Kobayashi Sep 2004 B2
6804618 Junk Oct 2004 B2
6814062 Esteghlal et al. Nov 2004 B2
6817171 Zhu Nov 2004 B2
6823667 Braun et al. Nov 2004 B2
6823675 Brunell et al. Nov 2004 B2
6826903 Yahata et al. Dec 2004 B2
6827060 Huh Dec 2004 B2
6827061 Nytomt et al. Dec 2004 B2
6827070 Fehl et al. Dec 2004 B2
6834497 Miyoshi et al. Dec 2004 B2
6839637 Moteki et al. Jan 2005 B2
6849030 Yamamoto et al. Feb 2005 B2
6873675 Kurady et al. Mar 2005 B2
6874467 Hunt et al. Apr 2005 B2
6879906 Makki et al. Apr 2005 B2
6882929 Liang et al. Apr 2005 B2
6904751 Makki et al. Jun 2005 B2
6911414 Kimura et al. Jun 2005 B2
6915779 Sriprakash Jul 2005 B2
6920865 Lyon Jul 2005 B2
6923902 Ando et al. Aug 2005 B2
6925372 Yasui Aug 2005 B2
6925796 Nieuwstadt et al. Aug 2005 B2
6928362 Meaney Aug 2005 B2
6928817 Ahmad Aug 2005 B2
6931840 Strayer et al. Aug 2005 B2
6934931 Plumer et al. Aug 2005 B2
6941744 Tanaka Sep 2005 B2
6945033 Sealy et al. Sep 2005 B2
6948310 Roberts, Jr. et al. Sep 2005 B2
6953024 Linna et al. Oct 2005 B2
6965826 Andres et al. Nov 2005 B2
6968677 Tamura Nov 2005 B2
6971258 Rhodes et al. Dec 2005 B2
6973382 Rodriguez et al. Dec 2005 B2
6978744 Yuasa et al. Dec 2005 B2
6988017 Pasadyn et al. Jan 2006 B2
6996975 Radhamohan et al. Feb 2006 B2
7000379 Makki et al. Feb 2006 B2
7013637 Yoshida Mar 2006 B2
7016779 Bowyer Mar 2006 B2
7028464 Rösel et al. Apr 2006 B2
7039475 Sayyarrodsari et al. May 2006 B2
7047938 Flynn et al. May 2006 B2
7052434 Makino et al. May 2006 B2
7055311 Beutel et al. Jun 2006 B2
7059112 Bidner et al. Jun 2006 B2
7063080 Kita et al. Jun 2006 B2
7069903 Sumilla et al. Jul 2006 B2
7082753 Dalla Betta et al. Aug 2006 B2
7085615 Persson et al. Aug 2006 B2
7106866 Astorino et al. Sep 2006 B2
7107978 Itoyama Sep 2006 B2
7111450 Sumilla Sep 2006 B2
7111455 Okugawa et al. Sep 2006 B2
7113835 Boyden et al. Sep 2006 B2
7117046 Boyden et al. Oct 2006 B2
7124013 Yasui Oct 2006 B2
7149590 Martin et al. Dec 2006 B2
7151976 Lin Dec 2006 B2
7152023 Das Dec 2006 B2
7155334 Stewart et al. Dec 2006 B1
7165393 Betta et al. Jan 2007 B2
7165399 Stewart Jan 2007 B2
7168239 Ingram et al. Jan 2007 B2
7182075 Shahed et al. Feb 2007 B2
7184845 Sayyarrodsari et al. Feb 2007 B2
7184992 Polyak et al. Feb 2007 B1
7188637 Dreyer et al. Mar 2007 B2
7194987 Mogi Mar 2007 B2
7197485 Fuller Mar 2007 B2
7200988 Yamashita Apr 2007 B2
7204079 Audoin Apr 2007 B2
7212908 Li et al. May 2007 B2
7275374 Stewart et al. Oct 2007 B2
7275415 Rhodes et al. Oct 2007 B2
7275518 Gartner et al. Oct 2007 B1
7281368 Miyake et al. Oct 2007 B2
7292926 Schmidt et al. Nov 2007 B2
7302937 Ma et al. Dec 2007 B2
7321834 Chu et al. Jan 2008 B2
7323036 Boyden et al. Jan 2008 B2
7328577 Stewart et al. Feb 2008 B2
7337022 Wojsznis et al. Feb 2008 B2
7349776 Spillane et al. Mar 2008 B2
7357125 Kolavennu Apr 2008 B2
7375374 Chen et al. May 2008 B2
7376471 Das et al. May 2008 B2
7380547 Ruiz Jun 2008 B1
7383118 Imai et al. Jun 2008 B2
7389773 Stewart et al. Jun 2008 B2
7392129 Hill et al. Jun 2008 B2
7398082 Schwinke et al. Jul 2008 B2
7398149 Ueno et al. Jul 2008 B2
7400967 Ueno et al. Jul 2008 B2
7413583 Langer et al. Aug 2008 B2
7415389 Stewart et al. Aug 2008 B2
7418372 Nishira et al. Aug 2008 B2
7430854 Yasui et al. Oct 2008 B2
7433743 Pistikopoulos et al. Oct 2008 B2
7444191 Caldwell et al. Oct 2008 B2
7444193 Cutler Oct 2008 B2
7447554 Cutler Nov 2008 B2
7467614 Stewart et al. Dec 2008 B2
7469177 Samad et al. Dec 2008 B2
7474953 Hülser et al. Jan 2009 B2
7493236 Mock et al. Feb 2009 B1
7515975 Stewart Apr 2009 B2
7522963 Boyden et al. Apr 2009 B2
7536232 Boyden et al. May 2009 B2
7542842 Hill et al. Jun 2009 B2
7577483 Fan et al. Aug 2009 B2
7587253 Rawlings et al. Sep 2009 B2
7591135 Stewart Sep 2009 B2
7599749 Sayyarrodsari et al. Oct 2009 B2
7599750 Piche Oct 2009 B2
7603226 Henein Oct 2009 B2
7627843 Dozorets et al. Dec 2009 B2
7630868 Turner et al. Dec 2009 B2
7634323 Vermillion et al. Dec 2009 B2
7634417 Boyden et al. Dec 2009 B2
7650780 Hall Jan 2010 B2
7668704 Perchanok et al. Feb 2010 B2
7676318 Allain Mar 2010 B2
7698004 Boyden et al. Apr 2010 B2
7702519 Boyden et al. Apr 2010 B2
7725199 Brackney May 2010 B2
7734291 Mazzara, Jr. Jun 2010 B2
7743606 Havlena et al. Jun 2010 B2
7748217 Muller Jul 2010 B2
7752840 Stewart Jul 2010 B2
7765792 Rhodes et al. Aug 2010 B2
7779680 Sasaki et al. Aug 2010 B2
7793489 Wang et al. Sep 2010 B2
7798938 Matsubara et al. Sep 2010 B2
7826909 Attarwala Nov 2010 B2
7831318 Bartee et al. Nov 2010 B2
7840287 Wojsznis et al. Nov 2010 B2
7844351 Piche Nov 2010 B2
7844352 Vouzis et al. Nov 2010 B2
7846299 Backstrom et al. Dec 2010 B2
7850104 Havlena et al. Dec 2010 B2
7856966 Saitoh Dec 2010 B2
7860586 Boyden et al. Dec 2010 B2
7862771 Boyden et al. Jan 2011 B2
7877239 Grichnik et al. Jan 2011 B2
7878178 Stewart et al. Feb 2011 B2
7891669 Araujo et al. Feb 2011 B2
7904280 Wood Mar 2011 B2
7905103 Larsen et al. Mar 2011 B2
7907769 Sammak et al. Mar 2011 B2
7930044 Attarwala Apr 2011 B2
7933849 Bartee et al. Apr 2011 B2
7958730 Stewart Jun 2011 B2
7970482 Srinivasan et al. Jun 2011 B2
7987145 Baramov Jul 2011 B2
3001767 Kakuya et al. Aug 2011 A1
7996140 Stewart et al. Aug 2011 B2
3019911 Dressier et al. Sep 2011 A1
3025167 Schneider et al. Sep 2011 A1
8032235 Sayyar-Rodsari Oct 2011 B2
8060290 Stewart et al. Nov 2011 B2
8078291 Pekar et al. Dec 2011 B2
8109255 Stewart et al. Feb 2012 B2
8121818 Gorinevsky Feb 2012 B2
8209963 Kesse et al. Jul 2012 B2
8229163 Coleman et al. Jul 2012 B2
8265854 Stewart et al. Sep 2012 B2
8281572 Chi et al. Oct 2012 B2
8311653 Zhan et al. Nov 2012 B2
8312860 Yun et al. Nov 2012 B2
8360040 Stewart et al. Jan 2013 B2
8379267 Mestha et al. Feb 2013 B2
8396644 Kabashima et al. Mar 2013 B2
8453431 Wang et al. Jun 2013 B2
8478506 Grichnik et al. Jul 2013 B2
RE44452 Stewart et al. Aug 2013 E
8505278 Farrell et al. Aug 2013 B2
8543170 Mazzara, Jr. et al. Sep 2013 B2
8555613 Wang et al. Oct 2013 B2
8596045 Tuomivaara et al. Dec 2013 B2
8649884 MacArthur et al. Feb 2014 B2
8649961 Hawkins et al. Feb 2014 B2
8694197 Rajagopalan et al. Apr 2014 B2
8700291 Herrmann Apr 2014 B2
8751241 Oesterling et al. Jun 2014 B2
8762026 Wolfe et al. Jun 2014 B2
8763377 Yacoub Jul 2014 B2
8813690 Kumar et al. Aug 2014 B2
8892221 Kram et al. Nov 2014 B2
8904760 Mital Dec 2014 B2
9170573 Kihas Oct 2015 B2
9223301 Stewart et al. Dec 2015 B2
9243576 Yu et al. Jan 2016 B2
9253200 Schwarz et al. Feb 2016 B2
20020116104 Kawashima et al. Aug 2002 A1
20030089102 Colignon et al. May 2003 A1
20030150961 Boelitz et al. Aug 2003 A1
20040006973 Makki et al. Jan 2004 A1
20040034460 Folkerts et al. Feb 2004 A1
20040086185 Sun May 2004 A1
20040117766 Mehta et al. Jun 2004 A1
20040118107 Ament Jun 2004 A1
20040144082 Mianzo et al. Jul 2004 A1
20040165781 Sun Aug 2004 A1
20040199481 Hartman et al. Oct 2004 A1
20040221889 Dreyer et al. Nov 2004 A1
20040226287 Edgar et al. Nov 2004 A1
20050143952 Fomoyasu et al. Jun 2005 A1
20050171667 Morita Aug 2005 A1
20050187643 Sayyar-Rodsari et al. Aug 2005 A1
20050193739 Brunell et al. Sep 2005 A1
20050209714 Rawlings et al. Sep 2005 A1
20050210868 Funabashi Sep 2005 A1
20060047607 Boyden et al. Mar 2006 A1
20060111881 Jackson May 2006 A1
20060168945 Samad et al. Aug 2006 A1
20060265203 Jenny et al. Nov 2006 A1
20060282178 Das et al. Dec 2006 A1
20060287795 Samad et al. Dec 2006 A1
20070142936 Denison et al. Jun 2007 A1
20070144149 Kolavennu et al. Jun 2007 A1
20070156259 Baramov et al. Jul 2007 A1
20070163244 Federle Jul 2007 A1
20070235011 Easley et al. Oct 2007 A1
20070245714 Frazier Oct 2007 A1
20070261654 Butcher et al. Nov 2007 A1
20070275471 Coward Nov 2007 A1
20080010973 Gimbres Jan 2008 A1
20080071395 Pachner Mar 2008 A1
20080097625 Vouzis et al. Apr 2008 A1
20080103747 Macharia et al. May 2008 A1
20080103748 Axelrud et al. May 2008 A1
20080104003 Macharia et al. May 2008 A1
20080109100 Macharia et al. May 2008 A1
20080125875 Stewart et al. May 2008 A1
20080132178 Chatterjee et al. Jun 2008 A1
20080183311 MacArthur et al. Jul 2008 A1
20080208778 Sayyar-Rodsari et al. Aug 2008 A1
20080244449 Morrison et al. Oct 2008 A1
20080276914 Bleile Nov 2008 A1
20090005889 Sayyar-Rodsari Jan 2009 A1
20090008351 Schneider et al. Jan 2009 A1
20090043546 Srinivasan et al. Feb 2009 A1
20090131216 Matsubara et al. May 2009 A1
20090182518 Chu et al. Jul 2009 A1
20090198350 Thiele Aug 2009 A1
20090240480 Baramov Sep 2009 A1
20090254202 Pekar et al. Oct 2009 A1
20090287320 MacGregor et al. Nov 2009 A1
20090312998 Berckmans et al. Dec 2009 A1
20100017094 Stewart et al. Jan 2010 A1
20100038158 Whitney et al. Feb 2010 A1
20100050607 He et al. Mar 2010 A1
20100122523 Vosz May 2010 A1
20100126481 Willi et al. May 2010 A1
20100204808 Thiele Aug 2010 A1
20100268353 Crisalle et al. Oct 2010 A1
20100300069 Herrmann et al. Dec 2010 A1
20100300070 He et al. Dec 2010 A1
20100305719 Pekar et al. Dec 2010 A1
20100327090 Havlena et al. Dec 2010 A1
20110006025 Schneider et al. Jan 2011 A1
20110010073 Stewart et al. Jan 2011 A1
20110029235 Berry Feb 2011 A1
20110046752 Piche Feb 2011 A1
20110056265 Yacoub Mar 2011 A1
20110060424 Havlena Mar 2011 A1
20110066308 Yang et al. Mar 2011 A1
20110071653 Kihas Mar 2011 A1
20110087420 Stewart et al. Apr 2011 A1
20110104015 Boyden et al. May 2011 A1
20110125293 Havlena May 2011 A1
20110125295 Bednasch et al. May 2011 A1
20110131017 Cheng et al. Jun 2011 A1
20110167025 Danai et al. Jul 2011 A1
20110264353 Atkinson et al. Oct 2011 A1
20110270505 Chaturvedi et al. Nov 2011 A1
20110301723 Pekar et al. Dec 2011 A1
20120024089 Couey et al. Feb 2012 A1
20120109620 Gaikwad et al. May 2012 A1
20130111905 Pekar et al. May 2013 A1
20130131956 Thibault et al. May 2013 A1
20130204403 Zheng et al. Aug 2013 A1
20130338900 Ardanese et al. Dec 2013 A1
20140032189 Hehle et al. Jan 2014 A1
20140034460 Chou Feb 2014 A1
20140318216 Singh Oct 2014 A1
20140343713 Ziegler et al. Nov 2014 A1
20140358254 Chu et al. Dec 2014 A1
20150121071 Schwarz et al. Apr 2015 A1
20150354877 Burns et al. Dec 2015 A1
Foreign Referenced Citations (52)
Number Date Country
102063561 May 2011 CN
102331350 Jan 2012 CN
19628796 Oct 1997 DE
19858584 Jun 2000 DE
10219382 Nov 2002 DE
102009016509 Oct 2010 DE
102011103346 Aug 2012 DE
0301527 Feb 1989 EP
0877309 Jun 2000 EP
1134368 Sep 2001 EP
1180583 Feb 2002 EP
1221544 Jul 2002 EP
1225490 Jul 2002 EP
1245811 Oct 2002 EP
1273337 Jan 2003 EP
0950803 Sep 2003 EP
1420153 May 2004 EP
1447727 Aug 2004 EP
1498791 Jan 2005 EP
1425642 Nov 2005 EP
1686251 Aug 2006 EP
1399784 Oct 2007 EP
2107439 Oct 2009 EP
2146258 Jan 2010 EP
1794339 Jul 2011 EP
1529941 Nov 2011 EP
2543845 Jan 2013 EP
2551480 Jan 2013 EP
2589779 May 2013 EP
2617975 Jul 2013 EP
2267559 Jan 2014 EP
2919079 Sep 2015 EP
S59190443 Oct 1984 JP
2010282618 Dec 2010 JP
0144629 Jun 2001 WO
0232552 Apr 2002 WO
02097540 Dec 2002 WO
02101208 Dec 2002 WO
03023538 Mar 2003 WO
2003048533 Jun 2003 WO
03065135 Aug 2003 WO
03078816 Sep 2003 WO
2004027230 Apr 2004 WO
2006021437 Mar 2006 WO
2007078907 Jul 2007 WO
2008033800 Mar 2008 WO
2008115911 Sep 2008 WO
2011130832 Oct 2011 WO
2012076838 Jun 2012 WO
2013119665 Aug 2013 WO
2014165439 Oct 2014 WO
2016053194 Apr 2016 WO
Non-Patent Literature Citations (184)
Entry
“SCR, 400-csi Coated Catalyst,” Leading NOx Control Technologies Status Summary, 1 page prior to Feb. 2, 2005.
Advanced Petroleum-Based Fuels-Diesel Emissions Control (APBF-DEC) Project, “Quarterly Update,” No. 7, 6 pages, Fall 2002.
Allanson, et al; “Optimizing the Low Temperature Performance and Regeneration Efficiency of the Continuously Regenerating Diesel Particulate Filter System,” SAE Paper No. 2002-01-0428, 8 pages, Mar. 2002.
Amstutz, et al; “EGO Sensor Based Robust Output Control of EGR in Diesel Engines,” IEEE TCST, vol. 3, No. 1, 12 pages, Mar. 1995.
Bemporad et al; “Explicit Model Predictive Control,” 1 page, prior to Feb. 2, 2005.
Bertsekas, “On the Goldstein-Levitin-Polyak Gradient Projection Method,” IEEE Transactions on Automatic Control, vol. AC-21. No. 2, pp. 174-184, Apr. 1976.
Bertsekas, Projected Newton Methods for Optimization Problems with Simple Constraints*, SIAM J. Control and Optimization, vol. 20, No. 2, pp. 221-246, Mar. 1982.
Borrelli, “Constrained Optimal Control of Linear and Hybrid Systems,” Lecture Notes in Control and Information Sciences, vol. 290, 2003.
Catalytica Energy Systems, “Innovative NOx Reduction Solutions for Diesel Engines,” 13 pages, 3rd Quarter, 2003.
Chatterjee, et al; “Catalytic Emission Control for Heavy Duty Diesel Engines,” JM, 46 pages, prior to Feb. 2, 2005.
De Schutter et al; “Model Predictive Control for Max-Min-Plus-Scaling Systems,” Proceedings of the 2001 American Control Conference, Arlington, Va, pp. 319-324, Jun. 2001.
Delphi, Delphi Diesel NOx Trap (DNT), 3 pages, Feb. 2004.
Diehl et al; “Efficient Numerical Methods for Nonlinear MPC and Moving Horizon Estimation,” Int. Workshop on Assessment and Future Directions of NMPC, 24 pages, Pavia, Italy, Sep. 5-9, 2008.
GM “Advanced Diesel Technology and Emissions,” powertrain technologies—engines, 2 pages, prior to Feb. 2, 2005.
Guerreiro et al; “Trajectory Tracking Nonlinear Model Predictive Control for Autonomous Surface Craft,” Proceedings of the European Control Conference, Budapest, Hungary, 6 pages, Aug. 2009.
Guzzella, et al; “Control of Diesel Engines,” IEEE Control Systems Magazine, pp. 53-71, Oct. 1998.
Havelena, “Componentized Architecture for Advanced Process Management,” Honeywell International, 42 pages, 2004.
Hiranuma, et al; “Development of DPF System for Commercial Vehicle—Basic Characteristic and Active Regeneration Performance,” SAE Paper No. 2003-01-3182, Mar. 2003.
Honeywell, “Profit Optimizer A Distributed Quadratic Program (DPQ) Concepts Reference,” 48 pages, prior to Feb. 2, 205.
http://www.not2fast.wryday.com/turbo/glossary/turbo_glossary.shtml, “Not2Fast:Turbo Glossary,” 22 pages, printed Oct. 1, 2004.
http://www.tai-cmv.com/sb1106.0.html, “Technical Overview—Advanced Control Solutions,” 6 pages, printed Sep. 9, 2004.
Jonsson, “Fuel Optimized Predictive Following in Low Speed Conditions,” Master's Thesis, 46 pages, Jun. 28, 2003.
Kelly et al; “Reducing Soot Emissions from Diesel Engines Using One Atmosphere Uniform Glow Discharge Plasma,” SAE Paper No. 2003-01-1183, Mar. 2003.
Kolmanovsky, et al; “Issues in Modeling and Control of Intake Flow in Variable Geometry Turbocharged Engines”, 18th IFIP Conf. System Modeling and Optimization, pp. 436-445, Jul. 1997.
Kulhavy et al; “Emerging Technologies for Enterprise Optimization in the Process Industries,” Honeywell, 12 pages, Dec. 2000.
Locker, et al; “Diesel Particulate Filter Operational Characterization,” Corning Incorporated, 10 pages, prior to Feb. 2, 2005.
Lu, “Challenging Control Problems and Engineering Technologies in Enterprise Optimization,” Honeywell Hi-Spec Solutions, 30 pages, Jun. 4-6, 2001.
Mehta, “The Application of Model Predictive Control to Active Automotive Suspensions,” 56 pages, May 17, 1996.
Moore, “Living with Cooled-EGR Engines,” Prevention Illustrated, 3 pages, Oct. 3, 2004.
Murayama et al; “Speed Control of Vehicles with Variable Lift Engine by Nonlinear MPC,” ICROS-SICE International Joint Conference, pp. 4128-4133, 2009.
National Renewable Energy Laboratory (NREL), Diesel Emissions Control—Sulfur Effects Project (DECSE) Summary of Reports, U.S Department of Energy, 19 pages, Feb. 2022.
Salvat, et al; “Passenger Car Serial Application of a Particulate Filter System on a Common Rail Direct Injection Engine,” SAE Paper No. 2000-01-0473, 14 pages, Feb. 2000.
Shamma, et al. “Approximate Set-Valued Observers for Nonlinear Systems,” IEEE Transactions on Automatic Control, vol. 42, No. 5, May 1997.
Soltis, “Current Status of NOx Sensor Development,” Workshop on Sensor Needs and Requirements for PEM Fuel Dell and Direct-Injection Engines, 9 pages, Jan. 25-26, 2000.
Stefanopoulpou, et al; “Control of Variable Geometry Turbocharged Diesel Engines for Reduced Emissions,” IEEE Transactions on Control Systems Technology, vol. 8, No. 4, pp. 733-745, Jul. 2000.
Storset, et al; “Air Charge Estimation for Turbocharged Diesel Engines,” vol. 1, Proceedings of the American Control Conference, 8 pages, Jun. 28-30, 2000.
The Math Works, “Model-Based Calibration Toolbox 2.1 Calibrate complex powertrain Systems,” 4 pages, prior to Feb. 2, 2005.
The Math Works, “Model-Based Calibration Toolbox 2.1.2,” 2 pages, prior to Feb. 2, 2005.
Theiss, “Advanced Reciprocating Engine System (ARES) Activities at the Oak Ridge National Lab (ORNL), Oak Ridge National Laboratory,” U.S. Department of Energy, 13 pages, Apr. 14, 2004.
Van Basshuysen et al; “Lexicon Motorentechnik,” (Dictionary of Automotive Technology) published by Vieweg Verlag, Wiesbaden 039936, p. 518, 2004. (English Translation).
Van Den Boom, et al; “MPC for Max-Plus-Linear Systems: Closed Loop Behavior and Tuning,” Proceedings of the 2001 American Control Conference, Arlington, VA, pp. 325-330, Jun. 2001.
Van Keulen et al; “Predictive Cruise Control in Hybrid Electric Vehicles,” World Electric Vehicle Journal vol. 3, ISSN 2032-6653, pp. 1-11, 2009.
Wang et al; “Fast Model Predictive Control Using Online Optimization,” Proceedings of the 17th Worid Congress, the International Federation of Automatic Control, Seoul, Korea, pp. 6974-6979, Jul. 6-11, 2008.
Wang et al; “PSO-Based Model Predictive Control for Nonlinear Processes,” Advances in Natural Computation, Lecture Notes in Computer Science, vol. 3611/2005, 8 pages, 2005.
Zavala et al; “The Advance-Step NMPC Controller: Optimality, Stability, and Robustness,” Automatica, vol. 45, pp. 86-93, 2009.
Zeilinger et al; “Real-Time MPC—Stability Through Robust MPC Design,” Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, P.R. China, pp. 3980-3986, Dec. 16-18, 2009.
Zelenka, et al; “An Active Regeneration as a Key Element for Safe Particulate Trap Use,” SAE Paper No. 2001-0103199, 13 pages, Feb. 2001.
Zhu, “Constrained Nonlinear Model Predictive Control for Vehicle Regulation,” Dissertation, Graduate School of the Ohio State University, 125 pages, 2008.
“Aftertreatment Modeling of RCCI Engine During Transient Operation,” University of Wisconsin—Engine Research Center, 1 page, May 31, 2014.
“Chapter 14: Pollutant Formation,” Fluent Manual, Release 15.0, Chapter 14, pp. 313-345, prior to Jan. 29, 2016.
“Chapter 21, Modeling Pollutant Formation,” Fluent Manual, Release 12.0, Chapter 21, pp. 21-1-21-54, Jan. 30, 2009.
“J1979 E/E Diagnostic Test Modules,” Proposed Regulation, Vehicle E.E. System Diagnostic Standards Committee, 1 page, Sep. 28, 2010.
“MicroZed Zynq Evaluation and Development and System on Module, Hardware User Guide,” Avnet Electronics Marketing, Version 1.6, Jan. 22, 2015.
Actron, “Elite AutoScanner Kit—Enhanced Obd I & II Scan Tool, OBD 1300,” Downloaded from https://actron.com/content/elite-autoscanner-kit-enhanced-obd-i-and-obd-ii-scan-tool?utm_ . . . , 5 pages, printed Sep. 27, 2016.
Andersson et al., “A Predictive Real Time NOx Model for Conventional and Partially Premixed Diesel Combustion,” SAE International 2006-01-3329, 10 pages, 2006.
Andersson et al., “A Real Time NOx Model for Conventional and Partially Premixed Diesel Combustion,” SAE Technical Paper Series 2006-01-0195, 2006 SAE World Congress, 13 pages, Apr. 3-6, 2006.
Andersson et al., “Fast Physical NOx Prediction in Diesel Engines, The Diesel Engine: The Low CO2 and Emissions Reduction Challenge,” Conference Proceedings, Lyon, 2006. Unable to Obtain This Reference.
Arregle et al., “On Board NOx Prediction in Diesel Engines: A Physical Approach,” Automotive Model Predictive Control, Models Methods and Applications, Chapter 2, 14 pages, 2010.
Asprion, “Optimal Control of Diesel Engines,” PHD Thesis, Diss ETH No. 21593, 436 pages, 2013.
Assanis et al., “A Predictive Ignition Delay Correlation Under Steady-State and Transient Operation of a Direct Injection Diesel Engine,” ASME, Journal of Engineering for Gas Turbines and Power, vol. 125, pp. 450-457, Apr. 2003.
Bako et al., “A Recursive Identification Algorithm for Switched Linear/Affine Models,” Nonlinear Analysis: Hybrid Systems, vol. 5, pp. 242-253, 2011.
Barba et al., “A Phenomenological Combustion Model for Heat Release Rate Prediction in High-Speed DI Diesel Engines with Common Rail Injection,” SAE Technical Paper Series 2000-01-2933, International Fall Fuels and Lubricants Meeting Exposition, 15 pages, Oct. 16-19, 2000.
Blanco-Rodriguez, “Modelling and Observation of Exhaust Gas Concentrations for Diesel Engine Control,” Phd Dissertation, 242 pages, Sep. 2013.
Blue Streak Electronics Inc., “Ford Modules,” 1 page, May 12, 2010.
Bourn et al., “Advanced Compressor Engine Controls to Enhance Operation, Reliability and Integrity,” Southwest Research Institute, DOE Award No. DE-FC26-03NT41859, SwRI Project No. 03.10198, 60 pages, Mar. 2004.
Charalampidis et al., “Computationally Efficient Kalman Filtering for a Class of Nonlinear Systems,” IEEE Transactions on Automatic Control, vol. 56, No. 3, pp. 483-491, Mar. 2011.
Chew, “Sensor Validation Scheme with Virtual NOx Sensing for Heavy Duty Diesel Engines,” Master's Thesis, 144 pages, 2007.
European Search Report for EP Application No. EP 10175270.7-2302419 dated Jan. 16, 2013.
European Search Report for EP Application No. EP 15152957.5-1807 dated Feb. 10, 2015.
The Extended European Search Report for EP Application No. 15155295.7-1606, dated Aug. 4, 2015.
The Extended European Search Report for EP Application No. 15179435.1, dated Apr. 1, 2016.
U.S. Appl. No. 15/005,406, filed Jan. 25, 2016.
U.S. Appl. No. 15/011,445, filed Jan. 29, 2016.
Desantes et al., “Development of NOx Fast Estimate Using NOx Sensor,” EAEC 2011 Congress, 2011. Unable to Obtain This Reference.
Ding, “Characterising Combustion in Diesel Engines, Using Parameterised Finite Stage Cylinder Process Models,” 281 pages, Dec. 21, 2011.
Docquier et al., “Combustion Control and Sensors: a Review,” Progress in Energy and Combustion Science, vol. 28, pp. 107-150, 2002.
Egnell, “Combustion Diagnostics by Means of Multizone Heat Release Analysis and NO Calculation,” SAE Technical Paper Series 981424, International Spring Fuels and Lubricants Meeting and Exposition, 22 pages, May 4-6, 1998.
Ericson, “NOx Modelling of a Complete Diesel Engine/SCR System,” Licentiate Thesis, 57 pages, 2007.
Finesso et al., “Estimation of the Engine-Out NO2/NOx Ration in a Euro VI Diesel Engine,” SAE International 2013-01-0317, 15 pages, Apr. 8, 2013.
Fleming, “Overview of Automotive Sensors,” IEEE Sensors Journal, vol. 1, No. 4, pp. 296-308, Dec. 2001.
Ford Motor Company, “2012 My OBD System Operation Summary for 6.7L Diesel Engines,” 149 pages, Apr. 21, 2011.
Formentin et al., “NOx Estimation in Diesel Engines Via In-Cylinder Pressure Measurement,” IEEE Transactions on Control Systems Technology, vol. 22, No. 1, pp. 396-403, Jan. 2014.
Galindo, “An On-Engine Method for Dynamic Characterisation of NOx Concentration Sensors,” Experimental Thermal and Fluid Science, vol. 35, pp. 470-476, 2011.
Gamma Technologies, “Exhaust Aftertreatment with GT-Suite,” 2 pages, Jul. 17, 2014.
Goodwin, “Researchers Hack A Corvette's Brakes Via Insurance Black Box,” Downloaded from http://www.cnet.com/roadshow/news/researchers-hack-a-corvettes-brakes-via-insurance-black-box/, 2 pages, Aug. 2015.
Geenberg, “Hackers Remotely Kill A Jeep On The Highway—With Me In It,” Downloaded from http://www.wired.com/2015/07/hackers-remotely-kill-jeep-highway/, 24 pages, Jul. 21, 2015.
Guardiola et al., “A Bias Correction Method for Fast Fuel-to-Air Ratio Estimation in Diesel Engines,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 227, No. 8, pp. 1099-1111, 2013.
Guardiola et al., “A Computationally Efficient Kalman Filter Based Estimator for Updating Look-Up Tables Applied to NOx Estimation in Diesel Engines,” Control Engineering Practice, vol. 21, pp. 1455-1468.
Guzzella et al., “Introduction to Modeling and Control of Internal Combustion Engine Systems,” 303 pages, 2004.
Hahlin, “Single Cylinder ICE Exhaust Optimization,” Master's Thesis, retrieved from https://pure.ltu.se/portal/tiles/44015424/LTU-EX-2013-43970821.pdf, 50 pages, Feb. 1, 2014.
Hammacher Schlemmer, “The Windshield Heads Up Display,” Catalog, p. 47, prior to Apr. 26, 2016.
Heywood, “Pollutant Formation and Control,” Internal Combustion Engine Fundamentals, pp. 567-667, 1988.
Hirsch et al., “Dynamic Engine Emission Models,” Automotive Model Predictive Control, Chapter 5, 18 pages, LNCIS 402, 2012.
Hirsch et al., “Grey-Box Control Oriented Emissions Models,” The International Federation of Automatic Control (IFAC), Proceedings of the 17th World Congress, pp. 8514-8519, Jul. 6-11, 2008.
Hockerdal, “EKF-based Adaptation of Look-Up Tables with an Air Mass-Flow Sensor Application,” Control Engineering Practice, vol. 19, 12 pages, 2011.
http://nexceris.com/news/nextech-materials/, “NEXTECH Materials is Now NEXCERIS,” 7 pages, printed Oct. 4, 2016.
http://www.arb.ca.gov/msprog/obdprog/hdobdreg.htm, “Heavy-Duty OBD Regulations and Rulemaking,” 8 pages, printed Oct. 4, 2016.
https://www.dieselnet.com/standards/us/obd.php, “Emission Standards: USA: On-Board Diagnostics,” 6 pages, printed Oct. 3, 2016.
International Search Report for Corresponding Application No. EP12191156 dated Feb. 2, 2015.
https://www.en.wikipedia.org/wiki/Public-key_cryptography, “Public-Key Cryptography,” 14 pages, printed Feb. 26, 2016.
Ishida et al., “An Analysis of the Added Water Effect on NO Formation in D.I. Diesel Engines,” SAE Technical Paper Series 941691, International Off-Highway and Power-Plant Congress and Exposition, 13 pages, Sep. 12-14, 1994.
Ishida et al., “Prediction of NOx Reduction Rate Due to Port Water Injection in a DI Diesel Engine,” SAE Technical Paper Series 972961, International Fall Fuels and Lubricants Meeting and Exposition, 13 pages, Oct. 13-16, 1997.
Jensen, “The 13 Monitors of an OBD System,” http://www.oemoffhighway.com/article/1 0855512/the-13-monito . . . , 3 pages, printed Oct. 3, 2016.
Khair et al., “Emission Formation in Diesel Engines,” Downloaded from https://www.dieselnet.com/tech/diesel_emiform.php, 33 pages, printed Oct. 14, 2016.
Kihas et al., “Chapter 14, Diesel Engine SCR Systems: Modeling Measurements and Control,” Catalytic Reduction Technology (book), Part 1, Chapter 14, prior to Jan. 29, 2016.
Krause et al., “Effect of Inlet Air Humidity and Temperature on Diesel Exhaust Emissions,” SAE International Automotive Engineering Congress, 8 pages, Jan. 8-12, 1973.
Lavoie et al., “Experimental and Theoretical Study of Nitric Oxide Formation in Internal Combustion Engines,” Combustion Science and Technology, vol. 1, pp. 313-326, 1970.
Manchur et al., “Time Resolution Effects on Accuracy of Real-Time NOx Emissions Measurements,” SAE Technical Paper Series 2005-01-0674,2005 SAE World Congress, 19 pages, Apr. 11-14, 2005.
Mohammadpour et al., “A Survey on Diagnostics Methods for Automotive Engines,” 2011 American Control Conference, pp. 985-990, Jun. 29-Jul. 1, 2011.
Moos, “Catalysts as Sensors—A Promising Novel Approach in Automotive Exhaust Gas Aftertreatment,” http://www.mdpi.com/1424-8220/10/7/6773htm, 10 pages, Jul. 13, 2010.
Olsen, “Analysis and Simulation of the Rate of Heat Release (ROHR) in Diesel Engines,” MSc-Assignment, 105 pages, Jun. 2013.
Pipho et al., “NO2 Formation in a Diesel Engine,” SAE Technical Paper Series 910231, International Congress and Exposition, 15 pages, Feb. 25-Mar. 1, 1991.
Querel et al., “Control of an SCR System Using a Virtual NOx Sensor,” 7th IFAC Symposium on Advances in Automotive Control, The International Federation of Automotive Control, pp. 9-14, Sep. 4-7, 2013.
Ricardo Software, “Powertrain Design at Your Fingertips,” retrieved from http://www.ricardo.com/PageFiles/864/WaveFlyerA4_4PP.pdf, 2 pages, downloaded Jul. 27, 2015.
Santin et al., “Combined Gradient/Newton Projection Semi-Explicit QP Solver for Problems with Bound Constraints,” 2 pages, prior to Jan. 29, 2016.
Schilling et al., “A Real-Time Model for the Prediction of the NOx Emissions in DI Diesel Engines,” Proceedings of the 2006 IEEE International Conference on Control Applications, pp. 2042-2047, Oct. 4-7, 2006.
Schilling, “Model-Based Detection and Isolation of Faults in the Air and Fuel Paths of Common-Rail DI Diesel Engines Equipped with a Lambda and a Nitrogen Oxides Sensor,” Doctor of Sciences Dissertation, 210 pages, 2008.
Shahzad et al., “Preconditioners for Inexact Interior Point Methods for Predictive Control,” 2010 American Control Conference, pp. 5714-5719, Jun. 30-Jul. 2010.
Signer et al., “European Programme on Emissions, Fuels and Engine Technologies (EPEFE)—Heavy Duty Diesel Study,” International Spring Fuels and Lubricants Meeting, SAE 961074, May 6-8, 1996.
Smith, “Demonstration of a Fast Response On-Board NOx Sensor for Heavy-Duty Diesel Vehicles,” Technical report, Southwest Research Institute Engine and Vehicle Research Division SwRI Project No. 03-02256 Contract No. 98-302, 2000. Unable to Obtain This Reference.
Stradling et al., “The Influene of Fuel Properties and Injection Timing on the Exhaust Emissions and Fuel Consumption of an Iveco Heavy-Duty Diesel Engine,” International Spring Fuels and Lubricants Meeting, SAE 971635, May 5-8, 1997.
Traver et al., “A Neural Network-Based Virtual NOx Sensor for Diesel Engines,” 7 pages, prior to Jan. 29, 2016.
Tschanz et al., “Cascaded Multivariable Control of the Combustion in Diesel Engines,” The International Federation of Automatic Control (IFAC), 2012 Workshop on Engine and Powertrain Control, Simulation and Modeling, pp. 25-32, Oct. 23-25, 2012.
Tschanz et al., “Control of Diesel Engines Using NOx-Emission Feedback,” International Journal of Engine Research, vol. 14, No. 1, pp. 45-56, 2013.
Tschanz et al., “Feedback Control of Particulate Matter and Nitrogen Oxide Emissions in Diesel Engines,” Control Engineering Practice, vol. 21, pp. 1809-1820, 2013.
Turner, “Automotive Sensors, Sensor Technology Series,” Momentum Press, Unable to Obtain the Entire Book, the Front and Back Covers and Table of Contents are Provided, 2009.
Van Helden et al., “Optimization of Urea SCR deNOx Systems for HD Diesel Engines,” SAE International 2004-01-0154, 13 pages, 2004.
Vdo, “UniNOx-Sensor Specification,” Continental Trading GmbH, 2 pages, Aug. 2007.
Vereschaga et al., “Piecewise Affine Modeling of NOx Emission Produced by a Diesel Engine,” 2013 European Control Conference (ECC), pp. 2000-2005, Jul. 17-19, 2013.
Wahlstrom et al., “Modelling Diesel Engines with a Variable-Geometry Turbocharger and Exhaust Gas Recirculation by Optimization of Model Parameters for Capturing Non-Linear System Dynamics,” (Original Publication) Proceedings of the Institution of Mechanical Engineers, Part D, Journal of Automobile Engineering, vol. 225, No. 7, 28 pages, 2011.
Wang et al., “Sensing Exhaust NO2 Emissions Using the Mixed Potential Principal,” SAE 2014-01-1487, 7 pages, Apr. 1, 2014.
Wilhemsson et al., “A Fast Physical NOx Model Implemented on an Embedded System,” Proceedings of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling, pp. 207-215, Nov. 30-Dec. 2, 2009.
Wilhemsson et al., “A Physical Two-Zone NOx Model Intended for Embedded Implementation,” SAE 2009-01-1509, 11 pages, 2009.
Winkler et al., “Incorporating Physical Knowledge About the Formation of Nitric Oxides into Evolutionary System Identification,” Proceedings of the 20th European Modeling and Simulation Symposium (EMSS), 6 pages, 2008.
Winkler et al., “On-Line Modeling Based On Genetic Programming,” 12 pages, International Journal on Intelligent Systems Technologies and Applications 2, 2007.
Winkler et al., “Using Genetic Programming in Nonlinear Model Identification,” 99 pages, prior to Jan. 29, 2016.
Winkler et al., “Virtual Sensors for Emissions of a Diesel Engine Produced by Evolutionary System Identification,” LNCS, vol. 5717, 8 pages, 2009.
Winkler “Evolutionary System Identification - Modern Approaches and Practical Applications,” Kepler Universitat Linz, Reihe C: Technik und Naturwissenschaften, Universitatsveriag Rudolf Trauner, 2009. Unable to Obtain This Reference.
Wong, “Carb Heavy-Duty OBD Update,” California Air Resources Board, SAE OBD TOPTEC, Downloaded from http://www.arb.ca.gov/msprog/obdprog/hdobdreg.htm, 72 pages, Sep. 15, 2005.
Yao et al., “The Use of Tunnel Concentration Profile Data to Determine the Ratio of NO2/NOx Directly Emitted from Vehicles,” HAL Archives, 19 pages, 2005.
Zaman, “Lincoln Motor Company: Case study 2015 Lincoln MKC,” Automotive Electronic Design Fundamentals, Chapter 6, 2015.
Zeldovich, “The Oxidation of Nitrogen in Combustion and Explosions,” ACTA Physiochimica U.R.S.S., vol. XX1, No. 4, 53 pages, 1946.
Zhuiykov et al., “Development of Zirconia-Based Potentiometric NOx Sensors for Automotive and Energy Industries in the Early 21st Century: What Are the Prospects for Sensors?”, Sensors and Actuators B, vol. 121, pp. 639-651, 2007.
Examination Report for EP Application Serial No. 12191156.4 dated Nov. 25, 2016.
Von et al., “Beitrag Zur Automatisierten Steuerkennfeld-Applikation bei Fahrzeug-Dieselmotoren,” 178 pages, dated Aug. 28, 2008, retrieved from the internet at: http://opus.kobv.de/tuberiin/volltexte/2008/1972/pdf/jankov_kristian.pdf on Aug. 29, 2012.
“Model Predictive Control,” Wikipedia, pp. 1-5, Jan. 22, 2009. http://en.wikipedia.org/w/index.php/title=Special:Book&bookcmd=download & collection id=641cdlb5da77cc22&writer=rl&return_to= Model predictive control, retrieved Nov. 20, 2012.
Axehill et al; “A Dual Gradient Projection Quadratic Programming Algorithm Tailored for Model Predictive Control,” Proceedings of the 47th IEEE Conference on Decision and Control, Cancun Mexico, pp. 3057-3064, Dec. 9-11, 2008.
Axehill et al; “A Dual Gradient Projection Quadratic Programming Algorithm Tailored for Mixed Integer Predictive Control,” Technical Report from Linkopings Universitet, Report No. Li-Th-ISY-R-2833, 58 pages, Jan. 31, 2008.
Baffi et al; “Non-Linear Model Based Predictive Control through Dynamic Non-Linear Partial Least Squares,” Trans IChemE, vol. 80, Part A, pp. 75-86, Jan. 2002.
Search Report for Corresponding, Application No. 11167549.2 dated Nov. 27, 2012.
De Oliveira, “Constraint Handling and Stability Properties of Model Predictive Control,” Carnegie Institute of Technology, Department of Chemical Engineering, Paper 197, 64 pages, Jan. 1, 1993.
Dunbar, “Model Predictive Control: Extension to Coordinated Multi-Vehicle Formations and Real-Time Implementation,” CDS Technical Report 01-016, 64 pages, Dec. 7, 2001.
Patrinos et al; “A Global Piecewise Smooth Newton Method for Fast Large Large-Scale Model Predictive Control,” Tech Report TR 2010-02, National Technical University of Athens, 23 pages, 2010.
Rajamani, “Data-based Techniques to Improve State Estimation in Model Predictive Control,” Ph.D. Dissertation, 257 pages, 2007.
Takacs et al; “Newton-Raphson Based Efficient Model Predictive Control Applied on Active Vibrating Structures,” Proceeding of the European Control Conference 2009, Budapest, Hungary, pp. 2845-2850, Aug. 23-26, 2009.
Wright, “Applying New Optimization Algorithms to Model Predictive Control,” 5th International Conference on Chemical Process Control, 10 pages, 1997.
“Model Predictive Control Toolbox Release Notes,” The Mathworks, 24 pages, Oct. 2008.
“MPC Implementation Methods for the Optimization of the Response of Control Valves to Reduce Variability,” Advanced Application Note 002, Rev. A, 10 pages, 2007.
Bemporad et al; “Model Predictive Control Toolbox 3 User's Guide,” Matlab Mathworks, 282 pages, 2008.
Bemporad et al; “The Explicit Linear Quadratic Regulator for Constrained Systems,” Automatica, 38, pp. 3-20, 2002.
Bemporad, “Model Predictive Control Based on Linear Programming—The Explicit Solution,” IEEE Transactions on Automatic Control, vol. 47, No. 12, pp. 1974-1984, Dec. 2002.
Bemporad, Model Predictive Control Design: New Trends and Tools, Proceedings of the 45th IEEE Conference on Decision & Control, pp. 6678-6683, Dec. 13-15, 2006.
Borrelli et al; “An MPC/Hybrid System Approach to Traction Control,” IEEE Transactions on Control Systems Technology, vol. 14, No. 3, pp. 541-553, May 2006.
Borrelli, “Discrete Time Constrained Optimal Control,” A Dissertation Submitted to the Swiss Federal Institute of Technology (ETH) Zurich, Diss. ETH No. 14666, 232 pages, Oct. 9, 2002.
Bunting, “Increased Urea Dosing Could Cut SCR Truck Running Costs,” http://www.automotiveworid.com/article/85897-increased-urea-dosing-could-cut-scr-truck-running-costs. Automotive World, 3 pages, Feb. 24, 2011, printed Mar. 2, 2011.
International Application Status Report for WO2008/033800.
U.S. Appl. No. 13/236,217.
U.S. Appl. No. 13/290,012.
Johansen et al; “Hardware Architecture Design for Explicit Model Predictive Control,” Proceedings of the ACC, 6 pages, 2006.
Johansen et al; “Hardware Synthesis of Explicit Model Predictive Controllers,” IEEE Transactions on Control Systems Technology, vol. 15, No. 1, Jan. 2007.
Keulen et al; “Predictive Cruise Control in Hybrid Electric Vehicles,” May 2009, World Electric Journal, vol. 3, ISSN 2032-6653.
Maciejowski, “Predictive Control with Constraints,” Prentice Hall, Pearson Education Limited, 4 pages, 2002.
Mariethoz et al; “Sensorless Explicit Model Predictive Control of the DC-DC Buck Converter with Inductor Current Limitation,” IEEE Applied Power Electronics Conference and Exposition, pp. 1710-1715, 2008.
Marjanovic, “Towards a Simplified Infinite Horizon Model Predictive Controller,” 6 pages, Proceedings of the 5th Asian Control Conference, 6 pages, Jul. 20-23, 2004.
Mayne et al.; “Constrained Model Predictive Control: Stability and Optimality,” Automatica, vol. 36, pp. 789-814, 2000.
Ortner et al; “MPC for a Diesel Engine Air Path Using an Explicit Approach for Constraint Systems,” Proceedings of the 2006 IEEE Conference on Control Applications, Munich Germany, pp. 2760-2765, Oct. 4-6, 2006.
Ortner et al.; “Predictive Control of a Diesel Engine Air Path,” IEEE Transactions on Control Systems Technology, vol. 15, No. 3, pp. 449-456, May 2007.
Pannocchia et al.; “Combined Design of Disturbance Model and Observer for Offset-Free Model Predictive Control,” EEE Transactions on Automatic Control, vol. 52, No. 6, 6 pages, 2007.
Qin et al.; “A Survey of Industrial Model Predictive Control Technology,” Control Engineering Practice, 11, pp. 733-764, 2003.
Rawlings, “Tutorial Overview of Model Predictive Control,” IEEE Control Systems Magazine, pp. 38-52, Jun. 2000.
Schauffele et al.; “Automotive Software Engineering Principles, Processes, Methods, and Tools,” SAE International, 10 pages, 2005.
Stewart et al.; “A Model Predictive Control Framework for Industrial Turbodiesel Engine Control,” Proceedings of the 17th IEEE Conference on Decision and Control, 8 pages, 2008.
Stewart et al.; “A Modular Model Predictive Controller for Turbodiesel Problems,” First Workshop on Automotive Model Predictive Control, Schloss Muhldorf, Feldkirchen, Johannes Kepler University, Linz, 3 pages, 2009.
Tondel et al; “An Algorithm for Multi-Parametric Quadratic Programming and Explicit MPC Solutions,” Automatica, 39, pp. 489-497, 2003.
Related Publications (1)
Number Date Country
20220010745 A1 Jan 2022 US
Continuations (2)
Number Date Country
Parent 16424362 May 2019 US
Child 17483512 US
Parent 13290025 Nov 2011 US
Child 16424362 US