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Control-Oriented Modeling and Model-Based Estimation and Control for Diesel Engine Aftertreatment Systems OPEN ACCESS

[+] Author Notes
Junmin Wang

Department of Mechanical and Aerospace Engineering The Ohio State University

Mechanical Engineering 137(12), S11-S14 (Dec 01, 2015) (4 pages) Paper No: ME-15-DEC-8; doi: 10.1115/1.2015-Dec-8

This article provides an overview of control-oriented modeling and model-based estimation and control for diesel engine aftertreatment systems. The chemical reactions and physical processes that occur in diesel engine after-treatment systems are quite complex. Computational models describing the chemical reaction kinetics, flow, and thermo-physical phenomena in engine exhaust aftertreatment systems have been coming forth since the 1960s when catalytic converters were introduced for vehicle applications {AQ: This word ‘catalystic’ is not found in standard dictionaries. Please check and correct if necessary.}. Such models can provide insightful understanding and mathematical descriptions on the chemical reactions, mass transfer, and heat transfer processes in one-dimensional and multi-dimensional fashions. The primary purpose of diesel engine aftertreatment system control-oriented models is to serve for the designs of real-time aftertreatment control and fault-diagnosis systems to reduce tailpipe emissions during real-world vehicle operations. Because such control-oriented models contain physically-meaningful parameters of the actual treatment systems, the model-based estimation and control algorithms can have excellent generalizability among different platforms.

Diesel engine emission aftertreatment systems have experienced a fast and inventive development phase in the past decade. As the primary means of converting the harmful engine-out emissions into environmentally-friendly species, aftertreatment systems have become necessary parts for diesel engines or diesel- hybrid powertrains in light-to-heavy-duty vehicles. Reducing tailpipe nitrogen oxide (NOx) and particulate matter (PM) emissions is the main task for diesel aftertreatment systems. While the three-way catalyst has been the dominant and effective aftertreatment system for gasoline engines, diesel engine aftertreatment systems are multifarious including diesel oxidation catalyst (DOC), diesel particulate filter (DPF), selective catalytic reduction (SCR) system, and lean NOx trap (LNT), etc. Figure 1 shows a typical configuration of diesel aftertreatment system where the DOC, DPF, and SCR are connected in series and Figure 2 is an example experimental setup. The complex physical processes and chemical reactions occurring within such catalysts naturally make them nonlinear and multivariable dynamic systems, and highlight the significance of model-based estimation and control systems. This article briefly describes some recent progress on control-oriented modeling and model-based estimation and control for diesel engine aftertreatment systems.

Figure 1 Diesel engine and aftertreatment system configuration.

Grahic Jump LocationFigure 1 Diesel engine and aftertreatment system configuration.

Figure 2 An example experimental setup of the diesel aftertreatment system.

Grahic Jump LocationFigure 2 An example experimental setup of the diesel aftertreatment system.

The chemical reactions and physical processes that occur in diesel engine aftertreatment systems are quite complex. Computational models describing the chemical reaction kinetics, flow, and thermo-physical phenomena in engine exhaust aftertreatment systems have been coming forth since the 1960s when catalytic converters were introduced for vehicle applications [1]. Such models can provide insightful understanding and mathematical descriptions on the chemical reactions, mass transfer, and heat transfer processes in one-dimensional and multidimensional fashions. From the real-time model-based control and estimation viewpoints, models that describe the main dynamics and characteristics of the catalysts in ordinary differential equations (ODE) are desirable and tractable for the designs of aftertreatment system control, estimation, and fault diagnosis algorithms. Such control-oriented models are typically generated based on the aftertreatment system operating principles using lumped-parameter modeling approaches.

The fundamental NOx reduction mechanism of urea-SCR systems is to supply ammonia (NH3) for catalytically converting the engine-out NOx into nitrogen (N2) and water (H2O). Diesel exhaust fluid containing 32.5% aqueous urea and 67.5% deionized water is used to provide ammonia to SCR catalysts. The injected urea solution upstream of SCR needs to go through a urea-to-ammonia conversion process that includes urea solution evaporation, thermal decomposition of solid urea, and hydrolysis of isocyanic acid [2]. Next, ammonia can be adsorbed to the SCR catalyst as NH*3 that can react with NOx and may desorb from the catalyst as well. The SCR catalytic NOx reduction reactions can be described by the following three reactions [3]:

4NH*3 + 4NO + O2 → 4N2 + 6H2O,

2NH*3 + NO+NO2 → 2N2 + 3H2O,

4NH*3 + 3NO2 → 3.5N2 + 6H2O.

The SCR ammonia coverage ratio (ACR), i.e. the percentage of the catalyst sites being occupied by NH*3, is a critical variable that affects both tailpipe NOx and NH3 emissions. Lowering tailpipe NOx emissions and lowering tailpipe NH3 emissions are naturally conflicting objectives. A high ACR can increase the NOx reduction efficiency but may cause high tailpipe NH3 slip. A low ACR may help to reduce the tailpipe NH3 slip but cannot sufficiently convert the NOx.

Several control-oriented models for urea-SCR systems have been developed in the past decade [4][5][6]. In order to yield the models as ODEs, a common assumption employed in the SCR system control-oriented modeling work is to treat the SCR as a continuous stirred tank reactor where all the states are homogeneous. Such models consider the aforementioned reactions and the urea-to-ammonia conversion process with the assumption that the injected urea is completely converted into ammonia upstream of the SCR. Arrhenius equations are typically used to model the reaction rates. Based on the mass conservation law, the model in [6] is expressed as a five-state nonlinear ODE model whose parameters were identified by minimizing the least-square errors of the measured and model-predicted NO, NO2, and NH3 concentrations in various SCR operations using a genetic algorithm.

DOC and DPF are two other indispensable components for diesel engines, with the DOC's main function being to oxidize CO, HC, and organic fraction of diesel particulates and the DPF's main function being to reduce tailpipe PM emissions. With excessive oxygen in diesel exhaust, the DOC CO and HC oxidation efficiencies are usually quite high as long as the temperature is above the light-off temperature. Because DOC and DPF are typically placed upstream of the NOx treatment devices, their dynamics on gas temperature, oxygen concentration, and NO/NO2 ratio are of particular interest for downstream system operation and tailpipe emissions. In [7]-[9], several control-oriented DOC and DPF models are generated to describe these dynamics in a tractable fashion. By utilizing the Eley-Rideal mechanism to describe the chemical reactions inside a DOC and treating it as an ideal combustion chamber, the temperature dynamics for DOC solid materials and exhaust gas passing through the DOC can be formulated as ODEs [7]. In [10], a simplified DPF model was developed to describe the thermal response and it was later modified by including the heat generation from the chemical reactions inside a DPF, yielding a control-oriented model of DPF solid and gas temperature dynamics [7] based on the energy conservation. The model parameter values can be acquired through parameter identification and optimization using the experimental data obtained under various engine operating conditions.

Another important variable for the diesel aftertreatment systems, particularly for DPF and SCR, is the ratio of NO2/NOx in the exhaust gas. While a majority of the NOx in diesel exhaust is NO, a higher NO2/NOx ratio is preferable for both NO2-assisted DPF regeneration and SCR NOx reduction. Due to the NO oxidation in the DOC and NO2 consumption in the DPF, the NO2/NOx ratio changes along the diesel aftertreatment components. Unfortunately, the current NOx sensors cannot differentiate NO or NO2 but only measure the lumped NOx concentration. With an empirical model characterizing the engine-out NO and NO2 concentrations, the NO and NO2 related chemical reactions in the DOC and DPF are considered in the control-oriented models developed in [8], which assumes that the exhaust gas NOx consists of only NO and NO2 and the total NOx concentration does not change through the DOC or DPF. The model parameters were identified based on experimental data.

The primary purpose of the abovementioned engine aftertreatment system control-oriented models is to serve for the designs of real-time aftertreatment control and fault-diagnosis systems to reduce tailpipe emissions during real-world vehicle operations. Because such control-oriented models contain physically-meaningful parameters of the actual aftertreatment systems, the model-based estimation and control algorithms can have excellent generalizability among different platforms. In this section, some observer and controller design examples using the control-oriented models are briefly described.

Due to the complexities associated with the chemical reactions, physical processes, and structural characteristics of diesel engine aftertreatment systems, many system signals are not directly measurable or are too expensive to measure in production vehicles. Model-based observers thus are instrumental for providing the necessary information for real-time control and diagnosis systems. As the aftertreatment system dynamics generally feature time-varying nonlinearities, synergistic combinations of estimation theory with insight into the aftertreatment system characteristics may offer effective approaches.

In [8], an observer was designed to estimate the DOC-out and DPF-out NO and NO2 concentrations with the total NOx concentration being the measurement. The estimation error convergence was proved by a Lyapunov analysis studying the time-varying parameter characteristics of DOC and DPF models. Following a similar thought process, in [7] and [9], two observers were designed and experimentally validated for estimating the DOC and DPF gas and solid temperatures as well as the DOC-out and DPF-out oxygen concentrations based on the control- oriented models mentioned earlier. The convergences of the estimation errors are also guaranteed by analyses using Lyapunov method incorporating the characteristics of the model structures and time-varying parameters.

For the urea-SCR systems, there are two important variables that certainly require accurate real-time estimations. One is the SCR ACR which has a pivotal influence on both the SCR NOx reduction and ammonia slip, but cannot be measured by any sensors. The other variable is the actual NOx concentration in the presence of NH3 due to the NOx sensor ammonia cross-sensitivity. In [11]-[13], sliding-mode observers and a gain-scheduled observer were developed based on the SCR control-oriented models to estimate the ACR in real time. Simulations and indirect experimental measurements were used to demonstrate the effectiveness of such observers. Different methods for estimating the actual NOx concentrations in the presence of NH3 have been devised. In [14], an extended Kalman filter was used to produce the correct NOx concentration reading from both NOx sensor and NH3 sensor signals. Lately, an adaptive-network-based fuzzy inference system based method was developed to describe the relationship between the NOx sensor ammonia cross-sensitivity factor and temperature to correct the NOx sensor readings [15]. Experimental results show that such methods can well correct the NOx sensor outputs in the presence of both NOx and NH3.

Real-time controls of the diesel aftertreatment systems, particularly the NOx treatment systems, are crucial because of the transient engine operations and the increasingly stringent tailpipe emission regulations [16][17][18][19]. The naturally conflicting requirements on simultaneously reducing both tailpipe NOx and NH3 emissions for SCR systems make the urea dosing control quite challenging. One of the fundamental difficulties for SCR control is how to control the ammonia coverage ratio distribution profile along the SCR longitudinal axial direction, which can significantly affect the SCR NOx reduction and NH3 slip performance. It would be ideal to have the ACR high upstream of the SCR and low downstream of the SCR for achieving high NOx conversion efficiency and low tailpipe ammonia slip. However, in all the control-oriented SCR models, the states, including the ACR, are assumed homogeneous inside an SCR without differentiating the actual state variations along the SCR axial direction. This modeling deficiency inherently limits the performance of SCR control systems.

In [16][19], control and optimization methods were developed to achieve approximated SCR ACR distribution profile control by using two SCR cans (or bricks) connected in series. Figure 3 exemplifies the control structure of such SCR ammonia storage distribution control strategy. By inserting sensors in the SCR catalyst or splitting the catalyst into two cans / bricks, such SCR control approaches control urea dosing rate so that the ACR of the upstream SCR can be high for high NOx conversion efficiency while keeping the ACR of the downstream SCR can below a low upper bound to constrain the tailpipe ammonia slip. Figure 4 shows the experimental results of such a two-can SCR control strategy under the USO6 test cycle where both the tailpipe NOx and NH3 emissions can be effectively reduced [16]. Further efforts have led to methods to reduce the total number of sensors for the system [20] and optimal sizing between the upstream and downstream SCR cans [21].

Figure 3 Overall control structure of the two-can SCR ammonia storage distribution control system.

Grahic Jump LocationFigure 3 Overall control structure of the two-can SCR ammonia storage distribution control system.

Figure 4 Experimental results of a two-can (brick) SCR system under US06 cycle.

Grahic Jump LocationFigure 4 Experimental results of a two-can (brick) SCR system under US06 cycle.

I. Nova and E. Tronconi (eds.), Urea-SCR Technology for DeNOx After Treatment of Diesel Exhausts, Springer, New York, 2014. [CrossRef]
S. D. Yim, S. J. Kim, J. H. Baik, I. Nam, Y. S. Mok, J. Lee, B. K. Cho and S. H. Oh, “Decomposition of Urea into NH3 for the SCR Process,” Ind Eng Chem Res, Vol. 43, pp. 4856– 4863, 2004. [CrossRef]
M. Koebel, M. Elsener, and M. Kleemann, “Urea-SCR: a promising technique to reduce NOx emissions from automotive diesel engines,” Catalysis Today, vol. 59, pp. 335– 345, 2000. [CrossRef]
C. M. Schär, C. H. Onder, and H. P. Geering, “Control- oriented model of an SCR catalytic converter system,” SAE Paper No. 2004-01-0153, 2004.
M. Devarakonda, G. Parker, J. H. Johnson, V. Strots, and S. Santhanam, “Model-Based Estimation and Control System Development in a Urea-SCR Aftertreatment System,” SAE Int. J. Fuels Lubr. Vol. 1, Issue. 1, pp. 646– 661, 2008. [CrossRef]
M.-F. Hsieh and J. Wang, “Development and Experimental Studies of a Control-Oriented SCR Model for a Two-Catalyst Urea-SCR System,” Control Engineering Practice, Vol. 19, Issue 4, pp. 409– 422, 2011. [CrossRef]
P. Chen and J. Wang, “Control-Oriented Modeling and Observer-based Estimation of Solid and Gas Temperatures for a Diesel Engine Aftertreatment System,” ASME Journal of Dynamic Systems, Measurement and Control, Vol. 134, Issue 6, 061011 (12 pages), 2012. [CrossRef]
M.-F. Hsieh and J. Wang, “NO and NO2 Concentration Modeling and Observer-Based Estimation across a Diesel Engine Aftertreatment System,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 133, Issue 4, 041005 (13 pages), 2011. [CrossRef]
P. Chen and J. Wang, “Oxygen Concentration Dynamic Model and Observer-Based Estimation through a Diesel Engine Aftertreatment System,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 134, Issue 3, 031008 (10 pages), 2012. [CrossRef]
D. Rumminger, X. Zhou, K. Balakrishnan, L. Edgar, and A. Ezekoye, “Regeneration Behavior and Transient Thermal Response of Diesel Particulate Filters,” Proceedings of the SAE 2001 World Congress, SAE paper No. 2001-01-1342, 2001.
H. Zhang, J. Wang, and Y.Y. Wang, “Nonlinear Observer Design of Diesel Engine Selective Catalytic Reduction Systems with NOx Sensor Measurements,” IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 4, pp. 1585– 1594, 2015. [CrossRef]
H. Zhang, J. Wang, and Y.Y. Wang, “Robust Filtering for Ammonia Coverage Estimation in Diesel Engine Selective Catalytic Reduction (SCR) Systems,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 135, Issue 6, 064504 (7 pages), 2013.
M.-F. Hsieh and J. Wang, “Observer-Based Estimation of Selective Catalytic Reduction (SCR) Catalyst Ammonia Storage,” Journal of Automobile Engineering, Proceedings of the Institution of Mechanical Engineers, Part D, Vol. 224, No. 9, pp. 1199– 1211, 2010. [CrossRef]
M.-F. Hsieh and J. Wang, “Design and Experimental Validation of an Extended Kalman Filter-based NOx Concentration Estimator in Selective Catalytic Reduction System Applications,” Control Engineering Practice, Vol. 19, Issue 4, pp. 346– 353, 2011. [CrossRef]
Y.Y. Wang, H. Zhang, and J. Wang, “NOx Sensor Reading Correction in Diesel Engine Selective Catalytic Reduction System Applications,” IEEE/ASME Transactions on Mechatronics (in press) 2015 (DOI: 10.1109/TMECH.2015.2434846)
M.-F. Hsieh and J. Wang, “Adaptive and efficient ammonia storage distribution control for a two- catalyst selective catalytic reduction system,” ASME Journal of Dynamic Systems, Measurements, and Control, Vol. 134, No. 1, 011012, 2012.
T. McKinley and A. Alleyne, “Adaptive model predictive control of an SCR catalytic converter system for automotive applications,” IEEE Transactions on Control Systems Technology, 20 (6): 1533– 1547, 2012. [CrossRef]
Q. Song and G. Zhu, “Model-based Closed-loop Control of Urea SCR Exhaust Aftertreatment System for Diesel Engine”, Proceedings of the SAE 2002 World Congress, SAE 2002-01-0287, 2002.
M.-F. Hsieh and J. Wang, “A Two-Cell Backstepping Based Control Strategy for Diesel Engine Selective Catalytic Reduction Systems,” IEEE Transactions on Control Systems Technology, Vol. 19, No. 6, pp. 1504– 1515, 2011. [CrossRef]
H. Zhang, J. Wang, and Y.Y. Wang, “Sensor Reduction in Diesel Engine Two-Cell Selective Catalytic Reduction (SCR) Systems for Automotive Applications,” IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 5, pp. 2222– 2233, 2015. [CrossRef]
H. Zhang, J. Wang, and Y.Y. Wang, “Optimal Dosing and Sizing Optimization for a Ground Vehicle Diesel Engine Two-cell Selective Catalytic Reduction System,” IEEE Transactions on Vehicular Technology (in press), 2015 (DOI: 10.1109/TVT.2015.2476760).
Copyright © 2015 by ASME
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References

I. Nova and E. Tronconi (eds.), Urea-SCR Technology for DeNOx After Treatment of Diesel Exhausts, Springer, New York, 2014. [CrossRef]
S. D. Yim, S. J. Kim, J. H. Baik, I. Nam, Y. S. Mok, J. Lee, B. K. Cho and S. H. Oh, “Decomposition of Urea into NH3 for the SCR Process,” Ind Eng Chem Res, Vol. 43, pp. 4856– 4863, 2004. [CrossRef]
M. Koebel, M. Elsener, and M. Kleemann, “Urea-SCR: a promising technique to reduce NOx emissions from automotive diesel engines,” Catalysis Today, vol. 59, pp. 335– 345, 2000. [CrossRef]
C. M. Schär, C. H. Onder, and H. P. Geering, “Control- oriented model of an SCR catalytic converter system,” SAE Paper No. 2004-01-0153, 2004.
M. Devarakonda, G. Parker, J. H. Johnson, V. Strots, and S. Santhanam, “Model-Based Estimation and Control System Development in a Urea-SCR Aftertreatment System,” SAE Int. J. Fuels Lubr. Vol. 1, Issue. 1, pp. 646– 661, 2008. [CrossRef]
M.-F. Hsieh and J. Wang, “Development and Experimental Studies of a Control-Oriented SCR Model for a Two-Catalyst Urea-SCR System,” Control Engineering Practice, Vol. 19, Issue 4, pp. 409– 422, 2011. [CrossRef]
P. Chen and J. Wang, “Control-Oriented Modeling and Observer-based Estimation of Solid and Gas Temperatures for a Diesel Engine Aftertreatment System,” ASME Journal of Dynamic Systems, Measurement and Control, Vol. 134, Issue 6, 061011 (12 pages), 2012. [CrossRef]
M.-F. Hsieh and J. Wang, “NO and NO2 Concentration Modeling and Observer-Based Estimation across a Diesel Engine Aftertreatment System,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 133, Issue 4, 041005 (13 pages), 2011. [CrossRef]
P. Chen and J. Wang, “Oxygen Concentration Dynamic Model and Observer-Based Estimation through a Diesel Engine Aftertreatment System,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 134, Issue 3, 031008 (10 pages), 2012. [CrossRef]
D. Rumminger, X. Zhou, K. Balakrishnan, L. Edgar, and A. Ezekoye, “Regeneration Behavior and Transient Thermal Response of Diesel Particulate Filters,” Proceedings of the SAE 2001 World Congress, SAE paper No. 2001-01-1342, 2001.
H. Zhang, J. Wang, and Y.Y. Wang, “Nonlinear Observer Design of Diesel Engine Selective Catalytic Reduction Systems with NOx Sensor Measurements,” IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 4, pp. 1585– 1594, 2015. [CrossRef]
H. Zhang, J. Wang, and Y.Y. Wang, “Robust Filtering for Ammonia Coverage Estimation in Diesel Engine Selective Catalytic Reduction (SCR) Systems,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 135, Issue 6, 064504 (7 pages), 2013.
M.-F. Hsieh and J. Wang, “Observer-Based Estimation of Selective Catalytic Reduction (SCR) Catalyst Ammonia Storage,” Journal of Automobile Engineering, Proceedings of the Institution of Mechanical Engineers, Part D, Vol. 224, No. 9, pp. 1199– 1211, 2010. [CrossRef]
M.-F. Hsieh and J. Wang, “Design and Experimental Validation of an Extended Kalman Filter-based NOx Concentration Estimator in Selective Catalytic Reduction System Applications,” Control Engineering Practice, Vol. 19, Issue 4, pp. 346– 353, 2011. [CrossRef]
Y.Y. Wang, H. Zhang, and J. Wang, “NOx Sensor Reading Correction in Diesel Engine Selective Catalytic Reduction System Applications,” IEEE/ASME Transactions on Mechatronics (in press) 2015 (DOI: 10.1109/TMECH.2015.2434846)
M.-F. Hsieh and J. Wang, “Adaptive and efficient ammonia storage distribution control for a two- catalyst selective catalytic reduction system,” ASME Journal of Dynamic Systems, Measurements, and Control, Vol. 134, No. 1, 011012, 2012.
T. McKinley and A. Alleyne, “Adaptive model predictive control of an SCR catalytic converter system for automotive applications,” IEEE Transactions on Control Systems Technology, 20 (6): 1533– 1547, 2012. [CrossRef]
Q. Song and G. Zhu, “Model-based Closed-loop Control of Urea SCR Exhaust Aftertreatment System for Diesel Engine”, Proceedings of the SAE 2002 World Congress, SAE 2002-01-0287, 2002.
M.-F. Hsieh and J. Wang, “A Two-Cell Backstepping Based Control Strategy for Diesel Engine Selective Catalytic Reduction Systems,” IEEE Transactions on Control Systems Technology, Vol. 19, No. 6, pp. 1504– 1515, 2011. [CrossRef]
H. Zhang, J. Wang, and Y.Y. Wang, “Sensor Reduction in Diesel Engine Two-Cell Selective Catalytic Reduction (SCR) Systems for Automotive Applications,” IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 5, pp. 2222– 2233, 2015. [CrossRef]
H. Zhang, J. Wang, and Y.Y. Wang, “Optimal Dosing and Sizing Optimization for a Ground Vehicle Diesel Engine Two-cell Selective Catalytic Reduction System,” IEEE Transactions on Vehicular Technology (in press), 2015 (DOI: 10.1109/TVT.2015.2476760).

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