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Development of an EKF Observer for an automotive SCR system
9th IFAC International Symposium on Advances in Automotive 9th IFAC International Symposium on Advances in Automotive Control Control at www.sciencedirect.com Orléans, June Symposium 23-27, 2019 onAvailable 9th IFAC France, International Advancesonline in Automotive 9th IFAC France, International Orléans, June Symposium 23-27, 2019 on Advances in Automotive Control Control Orléans, France, June 23-27, 2019 Orléans, France, June 23-27, 2019
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IFAC PapersOnLine 52-5 (2019) 538–543
Development of an EKF Observer for an automotive SCR system Development of an EKF Observer for an automotive SCR system Development of an Observer for automotive system Federica Arsie*, Cesare Pianese*, Matteo De SCR Cesare** Development ofD’Aniello*, an EKF EKFIvan Observer for an an automotive SCR system
Federica D’Aniello*, Ivan Arsie*, Cesare Pianese*, Matteo De Cesare** Federica D’Aniello*, Ivan Arsie*, Cesare Pianese*, Matteo De Cesare** Federica D’Aniello*, Ivan Arsie*, Cesare Pianese*, Matteo De Cesare** *University of Salerno, Fisciano (SA), Italy (e-mail: fdaniello, iarsie, pianese@unisa.it) (e-mail: fdaniello, iarsie, pianese@unisa.it) *University of Salerno, Fisciano (SA), Italy (e-mail: [email protected]) **Magneti Marelli Powertrain, Bologna, Italy University of Salerno, Fisciano (SA), Italy (e-mail: fdaniello, iarsie, pianese@unisa.it) ****Magneti Marelli Powertrain, Bologna, [email protected]) University of Salerno, Fisciano (SA), Italy (e-mail: fdaniello, iarsie, pianese@unisa.it) Magneti Marelli Bologna,isItaly (e-mail: [email protected]) In the** recent years, enginePowertrain, developer attention focused on the application of advanced control strategies ** Magneti Marelli Powertrain, Bologna, Italy (e-mail: [email protected]) In years, engine developer attention focused onfor thethe application advanced control strategies forthe therecent SCR system, considered as the leadingistechnology abatementofof NOX emissions in Diesel for the SCR system, considered as the leading technology for the abatement of NO in Diesel X emissions In the recent years, engine developer attention is focused on the application of advanced control strategies The years, most challenging task inattention SCR control is theonoptimization of the urea dosing strategy while engines. In the recent engine developer is focused the application of advanced control strategies engines. The most challenging task in SCR control is the optimization of the urea dosing strategy while for the SCR system, considered as the leading technology for the abatement of NO emissions in Diesel X reduction efficiency and low NH3 slip in the tailpipe, especially reaching the system, balance considered between high NOXleading for the SCR as the technology for the abatement of NO emissions in Diesel X engines. The most challenging task in SCR control is the optimization of urea dosing strategy while reaching the balance between high NO efficiency and low NH slip in the tailpipe, especially X reduction 3the during transients. order to task accomplish this task, an optimization Extended Kalman Filterdosing (EKF)strategy estimator is engines. The most In challenging in SCR control is the of the urea while reaching the balance between high NO reduction efficiency and low NH slip in the tailpipe, especially during transients. In order to accomplish this task, an Extended Kalman Filter (EKF) estimator is X loop control of SCR urea dosing. The 3 developed, tobalance be integrated in the closed EKF estimator is applied to reaching the between high NO X reduction efficiency and low NH3 slip in the tailpipe, especially during transients. In order to accomplish this task, an Extended Kalman Filter (EKF) estimator is developed, to be integrated in the closed loop control of SCR urea dosing. The EKF estimator is applied to aduring one-state model, in coverage ratioan θ isExtended the only dynamic taken into account. transients. In which order the to ammonia accomplish this task, Kalman process Filter (EKF) estimator is aToone-state model, in which the ammonia coverage ratio θ is the only dynamic process taken into account. developed, to be integrated in the closed loop control of SCR urea dosing. The EKF estimator is applied to improvetothe model accuracy, an additional dynamics, related todosing. total ammonia adsorption capacity Ω, developed, be integrated in the closed loop control of SCR urea The EKF estimator is applied to improvemodel, thewhile model accuracy, an dynamics, toalgorithm total ammonia adsorption capacity Ω, aToone-state in which the ammonia coverage ratio θ related is the only dynamic process taken into account. The recursive enhanced is considered conceiving theadditional EKF. ais one-state model, in which the ammonia coverage ratio θ is EKF the only dynamicallows processachieving taken intoenhanced account. considered while conceiving the EKF. The recursive EKF algorithm allows achieving To improve the model accuracy, an additional dynamics, related to total ammonia adsorption capacity Ω, estimation with a slight an increase of the computational burden is still adsorption suitable with on-board To improveaccuracy the model accuracy, additional dynamics, relatedburden to totalthat ammonia capacity Ω, estimation accuracy with a slight increase of the computational that is still suitable with on-board is considered while conceiving the EKF. The recursive EKF algorithm allows achieving enhanced application. is considered while conceiving the EKF. The recursive EKF algorithm allows achieving enhanced application.accuracy with a slight increase of the computational burden that is still suitable with on-board estimation estimation accuracy with increase of Model-based the computational burden is still suitable with on-board Keywords: Exhaust Gasa slight Aftertreatment, Plant Modelling and System © 2019, IFAC (International Federation of Automatic Control)Calibration, Hosting by that Elsevier Ltd. All rights reserved. application. Keywords: Exhaust Gas Aftertreatment, Model-based Calibration, Plant Modelling and System application. Identification, SCR modeling, NOX control, Model-based Diagnostics Keywords: Exhaust Gas Aftertreatment, Calibration, Plant Modelling and System Identification, SCR modeling, NOX control, Model-based Diagnostics Keywords: Exhaust Gas Aftertreatment, Model-based Calibration, Plant Modelling and System Identification, SCR modeling, NOX control, Model-based Diagnostics Identification, SCR modeling, NOX control, Model-based Diagnostics a NOX absorber which is able to capture 65% of NOX produced 1. INTRODUCTION absorber which is able to capture 65% of NO produced awhen NOX SCR temperature is less than 150°C, andX passively 1. INTRODUCTION when SCR temperature is less than 150°C, and passively a NO absorber which is able to capture 65% of NO produced X X in case of higher temperature. In65% the last decade, SCR One of the main issue to Diesel engines application in arelease NOX absorber which is able to captureIn of NO 1. related INTRODUCTION X produced in case of higher the last decade, One of the main issue to Dieselby engines application in release when temperature isfocused less than and passively 1. related INTRODUCTION systemSCR design has beentemperature. on 150°C, SCR coated on SCR DPF the automotive field is represented the high level of when SCR temperature is less than 150°C, and passively release case ofhas higher theinlast decade, design beentemperature. focused onIn SCR coated on SCR DPF the automotive field related is response represented the high level of One of the emissions. main issue to Diesel engines application in system system. in The combination of SCR and DPF one component pollutants In to by the numerous reports release in case of higher temperature. In the last decade, SCR One of the main issue related to Diesel engines application in system design has been focused on SCR coated on DPF system. The combination of SCR and DPF in one component the automotive field is represented by the high level of pollutants emissions. In response to the numerous reports allows getting the SCR closer to theonengine, faster conducted by International Agency for research cancer system getting design the hasSCR been focused SCR promoting coated on faster DPF the automotive field In is response represented by the highon of allows closer to the engine, pollutants emissions. theresearch numerous reports conducted by International Agencytofor onlevel cancer system. The combination of SCR and DPF inpromoting onedeNOX component light-off. The effect of combined SCR-DPF on and (IARC, 2010), European Commission is tightening the system. The combination of SCR and DPF in one component pollutants emissions. In response to the numerous reports light-off. The processes effect of closer combined SCR-DPF onquestion deNOXfaster and (IARC, amount 2010), European Commission isVItightening the allows getting the SCR to the engine, promoting conducted by International Agency for research on cancer regeneration is still an open and allowed of released emissions. Euro regulations in allows getting processes the SCR closer to thean engine, faster conducted by International Agency for research on cancer is still openpromoting allowed ensures amount released emissions. Euro regulations in regeneration light-off. Theaddressed effect ofby combined SCR-DPF onquestion deNOX and (IARC, 2010), of European Commission isVItightening the thoughtfully literature (Walker, 2012 Folic and Europe that emissions are contained for both typelight-off. Theaddressed effect ofby combined SCR-DPF on deNOX (IARC, 2010), that European Commission is tightening the thoughtfully literature (Walker, - Folic and Europe ensures emissions are contained for both typeprocesses is still an open2012 question and allowed amount of released emissions. Euro VIthis regulations in regeneration Johansen, 2012 Schrade et al., 2012). approval and real driving profiles. In direction, processes is al., still an open question and allowed amount released emissions. Euro regulations in regeneration 2012 - Schrade 2012). approvalensures andandof real driving profiles. In VIthis direction, thoughtfully addressed by et literature (Walker, 2012 - Folic and Europe that emissions are contained for both type- Johansen, manufactures car-makers propose complex configuration thoughtfully addressed by literature (Walker, 2012 - Folic and Europe ensures that emissions are contained for both typeUp to now, most studies focus on manufactures car-makers propose complex configuration Johansen, 2012 - Schrade et al., 2012).either engine or afterapproval andandreal driving profiles. In this direction, of exhaust after-treatment (ATs) and powertrain concepts with Up to now, most studies focus on either engine or afterJohansen, 2012 Schrade et al., 2012). approval and real driving profiles. In this direction, of exhaust after-treatment and powertrain with treatment control separately, with individual targets set for manufactures and car-makers propose complex concepts configuration the aim to satisfy the (ATs) restrictive upcoming legislation. treatment control separately, with individual targets for Up to now, most studies focus on either engine orset aftermanufactures and car-makers propose complex configuration emissions and catalysts performance. As the emission of after-treatment (ATs) and powertrain concepts with engine theexhaust aim to restrictive upcoming legislation. Up to emissions now, mostand studies focus on either engine or afterCurrently, thesatisfy Diesel the Oxidation Catalyst (DOC) is adopted engine catalysts performance. As the emission treatment control separately, with individual targets set for of exhaust after-treatment (ATs) and powertrain concepts with getcontrol lower,separately, especiallywith during real-life operating the aimfortohydrocarbons restrictive upcoming Currently, thesatisfy Diesel the Oxidation Catalyst (DOC) is adopted limits treatment individual targets set for mainly emissions control, Diesel legislation. Particulate limits get lower, especially during real-life operating engine emissions and catalysts performance. As the emission the aim to satisfy the restrictive upcoming legislation. conditions, the trade-off between catalyst performance and Currently, the Diesel Oxidation Catalyst (DOC) isthe adopted mainly(DPF) for hydrocarbons emissions control, Diesel engine emissions and catalysts performance. As the emission Filter is employed for particulate control andParticulate Urea- conditions, trade-off between performance and limits lower, during real-life Currently, the Diesel Oxidation Catalyst (DOC) isthe adopted system get coststhe can beespecially achieved bycatalyst exploiting theoperating synergy Filter (DPF) is employed for particulate control and Urealimits get lower, especially during real-life operating mainly for hydrocarbons emissions control, Diesel Particulate Selective Catalytic reduction (SCR) coupled withParticulate ammonia system conditions, the trade-off between catalyst performance and costs can be achieved by exploiting the synergy mainly for hydrocarbons emissions control, Diesel between engine and after-treatment system. This requires Selective Catalytic reduction (SCR) coupled ammonia conditions, thecan trade-off betweenbycatalyst performance and Filter (DPF) is employed for particulate controlwith and Urea- between oxidation (AMOX) are considered thethe costs beinteraction achieved exploiting theEGR-SCR synergy engine and after-treatment system. This requires Filter (DPF)catalyst is employed for particulate control and theleading Urea- system optimal engine-ATs by the so called oxidation catalyst (AMOX) are considered the leading system costs can be achieved by exploiting the synergy Selective Catalytic reduction (SCR) coupled with ammonia technology for the abatement NOX coupled emissionswith in traditional engine-ATs interaction by thesystem. so called between engine and2013). after-treatment ThisEGR-SCR requires Selective Catalytic reduction of (SCR) ammonia optimal balancing (Willems, emissions traditional technology for the abatement of NO between engine and2013). after-treatment system. This requires Xconsidered oxidation catalyst (AMOX) are the leading Diesel engines (Lambert, 2004 – Jiang, 2016).in Due to the balancing (Willems, optimal engine-ATs interaction by the so called EGR-SCR oxidation catalyst (AMOX) are considered the leading Diesel engines (Lambert, 2004 – Jiang, 2016). to the optimal engine-ATs interaction(NH by3)theassoreducing called EGR-SCR technology for the abatement of NO traditional X emissions increasing demand to meet the tightening cold startinDue and deNO SCR system uses ammonia agent to X balancing (Willems, 2013). technology for the abatement of NO X emissions in traditional Diesel engines (Lambert, 2004 – Jiang, 2016). Due to the increasing demand to meet the tightening cold start and deNO SCR system uses ammonia (NH balancing (Willems, 2013). X 3) as reducing agent to efficiency requirements under – low-load urban driving catalytically convert NOX in the exhaust to nitrogen molecules Diesel engines (Lambert, 2004 Jiang, 2016). Due to the increasing demand to meetto the tightening cold start andlow-cost deNOX SCR systemThe uses ammonia asto reducing agent to the(NH exhaust nitrogen molecules efficiency under low-load urban driving catalytically convert NOX incannot 3) directly conditions, itrequirements is desirable develop small size and water. ammonia carried in the increasing demand to meetto thedevelop tightening cold start andlow-cost deNOX and SCR systemTheuses ammonia (NHbe 3) as reducing agent to and water. ammonia be directly carried in the conditions, it is desirable small size and X incannot efficiency requirements under low-load urban driving catalytically convert NO the exhaust to nitrogen molecules after-treatment systems under layout low-load to integrate multiple tail pipe for NO reason of toxicity (Shimizu, 2007), thus efficiency requirements urban multiple driving exhaust catalytically convert X in the exhaust to nitrogen molecules exhaust tail urea pipe for reason of toxicity (Shimizu, 2007), after-treatment systems to integrate conditions, it isand desirable tolayout develop small size and low-cost and water. The ammonia cannot directly carried in thus the functionalities exploit any possible synergies between an aqueous solution (which is be referred as Diesel Exhaust conditions, it isand desirable to any develop smallsynergies size and low-cost andaqueous water. urea The solution ammonia(which cannotis be directly carriedExhaust in the an referred as Diesel functionalities exploit possible between after-treatment systems layout to integrate multiple exhaust tail pipe for reason of toxicity (Shimizu, 2007), thus devices. To accomplish thislayout task, thetoproper systemsmultiple design fluid in the United States and by the brand name2007), AdBlue in after-treatment systemsthis integrate exhaust tail United pipe forStates reasonand of by toxicity (Shimizu, thus fluid in the the brand AdBlue in devices. To accomplish task, the proper systems design functionalities and exploit any possible synergies between an aqueous urea solution (which is referred asname Diesel Exhaust and optimal control of such advanced exhaust line Europe) is injected upstream the catalyst. The injected urea is functionalities and exploit possible synergies between an aqueous urea solution (which is referred as injected Diesel Exhaust is injected The urea in is and optimal control of any such advanced exhaust line Europe) devices. To accomplish this task, proper design fluid the United States andthe by the brand name configurations are required. Forthe light dutysystems applications, then in converted to upstream ammonia bycatalyst. pyrolysis and AdBlue hydrolysis devices. To accomplish this task, the proper systems design fluid in the United States and by the brand name AdBlue in converted to upstream ammonia pyrolysis and hydrolysis configurations are Kwee required. For advanced light duty exhaust applications, and optimal and control of (2009) such line then Europe) is injected thebycatalytic catalyst. The injected is Holderbaum evaluated the optimal reactions, then adsorbed into substrate ofurea SCR and optimal and control of (2009) such advanced exhaust line reactions, Europe) is injected upstream thebycatalytic catalyst. The injected urea is configurations are required. For light duty applications, then converted to ammonia pyrolysis and hydrolysis then adsorbed into substrate of SCR Holderbaum Kwee evaluated the optimal configuration ofare the required. SCR and DPF along the exhaust line by then converter (Hsieh,to2011). Since only the adsorbed ammonia is configurations For light duty applications, converted ammonia by pyrolysis and hydrolysis Holderbaum and (2009) evaluated the optimal reactions, thenwhich adsorbed intoonly catalytic substrate of (Hsieh, 2011). theNO adsorbed ammonia is configuration the Kwee SCR and exhaust line by converter considering theof fuel required toDPF heatalong up thethe SCR and the DPF the reductant canSince react with specie, the SCR key X Holderbaumthe and Kwee (2009) evaluated the optimal reactions, thenwhich adsorbed into catalytic substrate of SCR the key the reductant can react with NO considering fuel required to heat up the SCR and the DPF X specie, configuration of the SCR and DPF along the exhaust line by converter (Hsieh, 2011). Since only the adsorbed ammonia is regeneration. The authors recognise the SCR in frontline of the of NH3-SCR chemistry arethe theadsorbed ammoniaammonia adsorption configuration The of the SCR and DPF along the exhaust by processes converter (Hsieh, 2011).chemistry Since only is -SCR are the ammonia adsorption processes of NH regeneration. authors recognise the SCR in front of the X specie, 3 considering the fuel required to heat up the SCR and the DPF the reductant which can react with NO the key DPF as the best configuration due to up the the fuelSCR penalty incurs in and desorptionwhich from the catalyst surface (Colombo, 2012), considering the fuel required to heat and the DPF the reductant can react with NO X specie, the key desorption catalyst surface (Colombo, 2012), DPF as the configuration due to the fuel penalty incurs in and 3-SCRthe regeneration. The authors recognise the SCR in front of the processes NHfrom chemistry the ammonia adsorption case of SCRbest behind the DPF. Henry et al. (2011) investigate which areof highly affected by theare rapid variation of engine regeneration. The authors recognise the SCR in front of the which processes ofhighly NH3-SCR chemistry are the ammonia adsorption are affected by the rapid variation of engine case of SCR behind the DPF. Henry et al. (2011) investigate DPF as the best configuration due to the fuel penalty incurs in and desorption from the catalyst surface (Colombo, 2012), the performance characteristicsdue when an fuel SCRpenalty is coupled with working conditions. DPFperformance as the best configuration to the incurs in working and desorption from the catalyst surface (Colombo, 2012), conditions. the characteristics when anal. SCR is coupled with case of SCR behind the DPF. Henry et (2011) investigate which are highly affected by the rapid variation of engine case of SCR behind the DPF. Henry et al. (2011) investigate which are highly affected by the rapid variation of engine the performance characteristics when an SCRofisAutomatic coupled with 538Hosting working conditions. Copyright © 2019, 2019 IFAC 2405-8963 © IFAC (International Federation Elsevier Ltd. All rights reserved. the performance characteristics when an SCR is coupled Control) with workingbyconditions. Copyright 2019 responsibility IFAC 538Control. Peer review©under of International Federation of Automatic 10.1016/j.ifacol.2019.09.085 Copyright © 2019 IFAC 538 Copyright © 2019 IFAC 538
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The urea dosing and NOX conversion efficiency are strictly related to the engine out NOX concentration and flow rate, catalyst temperature and the amount of ammonia stored in the catalyst. The most common Cu-Zeolite SCR catalyst has very high ammonia storage capacity and ammonia adsorption and desorption rates appear slow at relatively low temperatures. These characteristics imply that the NOX conversion is not immediately controllable by the urea injection rate. Thus, the urea injection is controlled to keep a certain level of ammonia storage on the catalyst surface to assure high NOX conversion, while avoiding excessive ammonia slip in case of a sudden temperature increase (Cloudt, 2011). Since the urea injection rate cannot control the short-term NOX conversion, it is not possible to reach 100 % of NOX reduction during transient conditions due to the much slower dynamics of the catalyst compared to the engine (the catalyst typically requires several minutes before reaching chemical equilibrium). Instantaneous tailpipe NOX emissions are controlled by the engine, exhaust gas flow and temperature. Therefore, the objective of the SCR control system design is to find an appropriate control strategy in terms of rate and timing of injected urea which can prevent NH3 slip phenomena while optimizing NOX conversion efficiency with the minimum dosage of AdBlue injected.
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the NH3 sensor can be considered as a potential candidate for SCR control to avoid ammonia cross sensitivity of NOx sensors. Although model based feedback control have demonstrated good accuracy and efficient feasibility in the urea dosing control, system vs. model mismatching conditions, measurements errors and system aging could negatively affect the SCR performance. Nonlinear closed loop estimator is motivated based on the rapid variation of Diesel engine operating conditions, which expose the SCR system to changing flow rate, composition and temperature of exhaust gas. For this task, Zhou and Jorgenses (2012) develop Model Predictive Control (MPC) of the SCR process based on Extended Kalman filter for state estimation in SCR process based on four state modelling approach. While Hsieh and Wang (2011) use an extended Kalman Filter for three state model and report good performance and robustness to measurements errors. Mora et al. (2018) propose an observer based on a five states Extended Kalman filter and the SCR downstream NOX and NH3 measurements; the observer is able to recognize the cause of a decrease in SCR performance, which can be due to SCR ageing, urea dilution or urea injector drift. In this paper a two state Extended Kalman Filter (EKF) estimator is developed to be integrated in the closed loop control of SCR urea dosing. To satisfy OBD requirements and industrial-scale application, the one state 0-D model, presented in previous works Arsie et al. (2017a, 2018), is adopted to estimate the states in the EKF. The application of EKF for SCR control is intended to provide robustness and adaptation features to SCR feedback control. The observer is useful and suitable to support the light physics-based models implemented and to run on state-of-the-art Euro VI production engines (Cloudt et al., 2010). The paper is organized as follows: section 2 briefly describes the main chemical phenomena and the overall structure of one state model; section 3 presents the development of EKF; in the latter section results and discussion are addressed.
In standard industrial application, there is a closed-loop feedback control using NOX and NH3 sensors downstream the SCR. the downstream NOX sensor is mandatory to perform SCR diagnostics. However, its cross-sensitivity with NH3 opens several possibilities, taking also into account that an AMOX catalyst is placed after the SCR. On the one hand, the NOx sensor could be placed after the AMOX if an NH3 sensor is used between the SCR and the AMOX. On the other hand, if the NH3 signal is to be measured isolating NH3 from the NOx sensor signal, the NOx sensor should be located between the SCR and the AMOX. The separation of the NOx upstream signal into NO and NO2, which can be obtained by an upstream NOx sensor, is already used in Euro VI production engines. SCR control is, actually, the great challenge due to its complicated dynamics and limited feedback (Hsieh, 2011). Due to the system complexity, to systematically assess ATs layout in terms of interactions, calibration effects, performance and critical states, a massive amount of data should be exploited, alternately control oriented models and simulation tools are effective in predicting systems behaviour and critical states. Several control strategies are developed and presented by Willems et al. (2007) by concluding that feedback control is necessary to achieve emissions consistent with upcoming legislation. Therefore, Devarakonda et al. (2009) developed a model based estimator for the relevant species (i.e. NO, NO 2 and NH3) and a control strategy of the urea-SCR plant based on a four states model. In Schar et al. (2006) a model-based feedforward controller (FFC) and a model-based feedback controller (FBC) are designed to achieve an effective control strategy for the urea solution injection. The feedback control is accomplished by means of a NOx sensor located downstream of the SCR. Nevertheless, due to NOX sensor slow time response and cross-sensitivity towards NH3, real sensors can be replaced by NOX virtual sensors based on suitable modelling and identification approaches (Arsie, 2017 b). In addition, as evidenced by Devarakonda et al. (2009a, 2009b),
2. CONTROL ORIENTED MODEL FOR SCR While the dynamics of chemical reactions occurring within the SCR catalyst are quite complex, lumped-approach 0-D mathematic models are preferred for real-time estimation of main variables and system control design. The accurate modelling of chemical reaction mechanism and fluid dynamics inside the catalyst requires the use of partial differential equations (PDE) to offer insightful investigation of spatialtemporal distribution of specie concentrations. Since PDE are computationally expensive and hard to be employed for real time control, the SCR catalyst is assumed to be a continuous stirred tank reactor (CSTR) for developing a 0-D model (Nova et Tronconi, 2014). The processes taking place within the catalyst can be summarized by means of three main steps. Firstly, the urea solution (AdBlue) is converted into ammonia; secondly, the ammonia in the catalyst is adsorbed on the catalytic substrate. In the latter, the adsorbed ammonia catalytically reacts with NOX and converts them to nitrogen and water. The adopted modelling approach is described in detail in previous works (Arsie et al., 2017a – Arsie et al.,2018) and for reason of brevity is not reported in the current paper. Particularly, the model is based on mass conservation law and 539
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chemical kinetics for NO, NO2 and NH3 species while the interaction of ammonia with catalytic substrate is summarized in ammonia coverage ratio (𝜗𝜗 - defined in the equation (1)). The formation processes for NO, NO2 and NH3 species are assumed to be in chemical equilibrium due to their faster dynamics than ammonia storage one, thus the ammonia storage is the only relevant dynamic process to be modelled and 𝜗𝜗 (defined below) is the only state variable of the model. 𝜗𝜗 =
∗ 𝐶𝐶𝑁𝑁𝑁𝑁 3 Ω
(1)
Ω is the total adsorption capacity of catalyst evaluated at the minimum temperature and kept constant. The model state equations are reported in Eq. 2 to 5. 𝜗𝜗̇ = −(𝐾𝐾𝑑𝑑𝑑𝑑𝑑𝑑 + 𝐾𝐾𝑜𝑜𝑜𝑜𝑜𝑜 )𝜗𝜗 + 𝐾𝐾𝑎𝑎𝑎𝑎𝑎𝑎 𝐶𝐶𝑁𝑁𝑁𝑁 3 (1 − 𝜗𝜗) − 𝐾𝐾𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝜗𝜗𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑁𝑁𝑁𝑁 2 − 𝐾𝐾𝑠𝑠𝑠𝑠𝑠𝑠 𝜗𝜗𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑂𝑂 2 − 𝐾𝐾𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜗𝜗𝐶𝐶𝑁𝑁𝑁𝑁 2 −𝑄𝑄̅ 𝐶𝐶𝑁𝑁𝑁𝑁 − 𝐾𝐾𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝜗𝜗𝛺𝛺𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑁𝑁𝑁𝑁 2 − 𝐾𝐾𝑠𝑠𝑠𝑠𝑠𝑠 𝜗𝜗𝛺𝛺𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑂𝑂 2 + 𝑄𝑄̅ 𝐶𝐶𝑁𝑁𝑂𝑂𝑖𝑖𝑖𝑖 = 0 𝐾𝐾𝑑𝑑𝑑𝑑𝑑𝑑 𝛺𝛺𝜗𝜗 − [𝐾𝐾𝑎𝑎𝑎𝑎𝑎𝑎 Ω(1 − 𝜗𝜗) + 𝑄𝑄]𝐶𝐶𝑁𝑁𝑁𝑁 3 + 𝑄𝑄̅𝐶𝐶𝑁𝑁𝐻𝐻3𝑖𝑖𝑖𝑖 = 0 −𝑄𝑄̅ 𝐶𝐶𝑁𝑁𝑂𝑂2 − 𝐾𝐾𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝜗𝜗𝛺𝛺𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑁𝑁𝑁𝑁 2 − 𝐾𝐾𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜗𝜗𝛺𝛺𝐶𝐶𝑁𝑁𝑁𝑁 2 + 𝑄𝑄̅ 𝐶𝐶𝑁𝑁𝑂𝑂2 𝑖𝑖𝑖𝑖 = 0
(2)
(3)
coverage ratio/storage variations, which require complex formulation despite having limited measurable feedback. In addition, measurements uncertainties related to engine-out gas temperature, flow and composition, cross-sensitivity of NOX sensors toward NH3 and sensors time response has to be considered. The set of trouble described above, made SCR map-based control unsuitable to optimize NOX conversion efficiency whilst prevent NH3 slip during transients. Thus feedback non-linear model-based control is adopted to infer adaptive features vs. systems aging and plant-model mismatch. In the perspective of supporting this need, an Extended Kalman Filter (EKF) is established. The EKF is a recursive algorithm able to update model prediction with onboard measurements by considering both system and measurement noises for developing robust and reliable online estimator, directly embeddable on-board the vehicle with the aim of operating jointly with the physical sensors. The integration of measurements, model prediction and EKF estimation in the closed-loop control scheme is shown in Figure 1.
(4) (5)
In order to reach the most suited trade-off between model accuracy and computation efficiency, the lumped (nondimensional) approach is adopted. The inaccuracies due to the approximation of averaged vs. non-uniform distribution of ammonia adsorption capacity and thermo-fluid dynamic variables (spatial velocity, gas temperature and specie concentrations) cannot be overcome. Nevertheless, by partitioning the SCR catalyst in several slices the approximation error can be reduced. For this goal, the SCR model equations (Eqs. 1-5) are replied into four sections along axial direction in accordance with Willems et al. (2007) approach. Continuity conditions on spatial velocity and inlet concentrations are applied at the interface, while pressure drop and temperature dynamic are neglected, particularly temperature is considered homogeneous and constant for overall catalyst. The resulting model can be expressed by a set of ordinary differential equations (ODE) with a much lower computational burden.
Figure 1: EKF integration on model-based control. The EKF allows relating the actual state, not only to the previous state, but also to the stochastic relationship between model prediction and on-board measurements (Gelb, 1974 – Ljung, 1987). Generally, the EKF includes two steps, as referred in Fig 2: prediction and update.
3. DEVELOPMENT OF SCR ESTIMATOR BASED ON EXTENDED KALMAN FILTER
Figure 2: time updating and measurement updating in the EKF algorithm.
The optimization of urea dosing strategy in SCRs, aimed reaching the balance between high NOX reduction efficiency and low NH3 slip in the tailpipe, especially during transients, is the most challenging task in SCR control. The NOX reduction processes occurring in SCR system are represented by very complicated thermo-fluid dynamic/chemical phenomena and secondary effects (such as urea/NH 3 deposits upstream the catalyst and mass transfer phenomena) coupled with aging of SCRs could negatively affect the correct estimation of main states. The system representation requires careful and accurate modelling approach because of i) the nonlinearity of the system due to reactions kinetic; ii) the close link between system dynamics and catalyst temperature (highly variable during real driving conditions) and iii) NH3
In the prediction phase, the control oriented model (refer to Eqs. 1 to 5, section 2) described by coupled Differential and Algebraic Equations (DAEs), is adopted to provide a prior prediction of the main states. The EKF has been traditionally applied to models described by ordinary differential equations, while the design of observer for nonlinear DAE systems has been addressed by Becerra et al. (2001), who transform the DAE system into a corresponding explicit ordinary differential equation, thus allowing the use of standard observer design techniques. The one state model is then linearized following the first order Taylor expansion, to use it in state-space formulation. The system schematization is shown in Fig.3.
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𝑘𝑘𝑘𝑘 = Pk− HkT [𝐻𝐻𝑘𝑘 Pk− 𝐻𝐻𝑘𝑘𝑇𝑇 + 𝑅𝑅𝑘𝑘 ]−1
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(12)
In Eqs. 11 and 12, 𝐻𝐻𝑘𝑘 is the Jacobian matrix of the observation function ℎ(𝑥𝑥̂𝑘𝑘 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘). 4. RESULTS AND DISCUSSION Simulation results and EKF vs. open loop model improvements are proved in this section. In-depth analysis of experimental data is presented in (Arsie, 2018). The experimental results were collected on a commercial Cuzeolite catalyst (Volume: 2.37 l) with honeycomb monolith (400 cpsi) washcoated. The catalyst was placed at the exhaust of a light-duty Diesel engine and the experimental data were measured at the engine test bench during the typical engine transients performed for tuning the map based SCR control. NO, NO2 and NH3 concentrations were measured downstream of the converter by a FTIR Analyser while direct measurements of the inlet concentrations were not available. Nevertheless, in order to exploit the one state model, the inlet concentrations of relevant species are needed and their values were assumed to equal the available outlet concentrations before the first AdBlue injection occurs. The test was carried out by setting engine speed equal to 2500 rpm and exhaust gas temperature is 350°C. In figure 4 the inlet concentration of relevant species (i.e. NO, NO 2 and NH3) and the temperature evolution during the test are depicted.
Figure 3: state space SCR model 𝑢𝑢𝑘𝑘 represents the model inputs: 𝐶𝐶𝑁𝑁𝑂𝑂𝑖𝑖𝑖𝑖 , 𝐶𝐶𝑁𝑁𝑂𝑂2 𝑖𝑖𝑖𝑖 and 𝐶𝐶𝑁𝑁𝐻𝐻3𝑖𝑖𝑖𝑖 inlet concentrations, evaluated at time tk ; 𝜗𝜗 𝑥𝑥̂𝑘𝑘 are the observer states, 𝑥𝑥̂𝑘𝑘 = [ ] ; 𝛺𝛺 𝐶𝐶𝑁𝑁𝑁𝑁 𝑧𝑧𝑘𝑘 represents the algebraic states vector, 𝑧𝑧𝑘𝑘 = [𝐶𝐶 ]; 𝑁𝑁𝑂𝑂2 𝑦𝑦̂𝑘𝑘 is the model output, 𝑦𝑦̂𝑘𝑘 = 𝐶𝐶𝑁𝑁𝐻𝐻3. As previously discussed, to satisfy OBD requirements, the ammonia coverage ratio θ was the only modelled dynamic process in the control oriented model development (refer to section 2); while the total adsorption capacity (Ω), whose value has been identified at minimum temperature, was kept as constant. Thus, to improve the observer accuracy, an additional dynamic related to Ω is considered while conceiving EKF. The set of equations below (refer to Eqs. 6 to 9) defines the observer during prediction phase. The state variable 𝑥𝑥̂ and the system error covariance matrix 𝑃𝑃 are time updated by following the Eqs. 6 and 9. Time updating phase consists of propagating the state estimates and error covariance through the system dynamics from time step tk-1+ to tk- before on-board measurement is taken: + (6) 𝑥𝑥̂𝑘𝑘− = 𝑓𝑓(𝑥𝑥̂𝑘𝑘−1 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘) 𝑔𝑔(𝑥𝑥̂𝑘𝑘 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘) = 0
(7)
𝑦𝑦̂(𝑘𝑘) = ℎ(𝑥𝑥̂𝑘𝑘 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘)
(8)
+ 𝑃𝑃𝑘𝑘− = ∅𝑘𝑘−1 𝑃𝑃𝑘𝑘−1 ∅𝑇𝑇𝑘𝑘−1 + 𝑄𝑄𝑘𝑘−1
(9)
The 𝑓𝑓 function of Eq. 6 is the linear model explained in Eq.2. The 𝑔𝑔 function proposed in equation 7 is the state-space formulation for NO and NO2 algebraic balances (refer to Eqs. 3 and 5). The ℎ function reported in Eq. 8 represent the state space representation for the output state (i.e. 𝐶𝐶𝑁𝑁𝐻𝐻3. )whose explicit formulation is proposed in equation 4. As widely referred by literature (Gelb, 1974 – Ljung, 1987), in the Kalman filter, In addition to the control input 𝑢𝑢(𝑘𝑘), the system is driven by two zero-mean, Gaussian and independent white noise sources: the state and measurement noises with covariance matrices Q and R respectively, while ∅𝑘𝑘−1 is the Jacobian matrix of the 𝑓𝑓 function. After the time update, taking the sensor measurements 𝑦𝑦𝑘𝑘 at the time tk, the measurements update is performed by achieving the new time step tk+. The state and error covariance update, refer to Eqs. 10 and 11, is achieved by means an optimal Kalman gain 𝑘𝑘𝑘𝑘 whose formulation is based on solution of Riccati covariance equation (refer to Eq.12). (10) 𝑥𝑥̂𝑘𝑘+ = 𝑥𝑥̂𝑘𝑘− + 𝑘𝑘𝑘𝑘 [𝑦𝑦𝑘𝑘 − ℎ(𝑥𝑥̂𝑘𝑘 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘)] 𝑃𝑃𝑘𝑘+ = [𝐼𝐼 − 𝑘𝑘𝑘𝑘 𝐻𝐻𝑘𝑘 ]𝑃𝑃𝑘𝑘−
Figure 4: Inlet concentration of NO, NO2 and NH3 and exhaust gas temperature vs. time during validation test transient. During the validation transient, both exhaust temperature and NO/NO2 inlet concentrations are assumed to be constant due to the steady state engine operating conditions. Four set-points of AdBlue injection are performed during the test, corresponding to different ratio between injected and stoichiometric amount of AdBlue (respectively 50%, 80%, 100% and 120%). The rapid increase of NH3 inlet concentration allows exciting the dynamics of adsorption/desorption phenomena; in addition, in the last part of the maneuver, the urea injection prolonged over time, allows reaching the ammonia slip convergence. During the test, the inlet oxygen concentration is measured by a UEGO sensor, thus the ammonia oxidation is taken into account along the transient. Figures 5, 6 and 7 compare the experimental data provided by the FTIR gas analyzer with the corresponding open loop one-state model and EKF observer predictions.
(11)
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Figure 5: Simulated and measured NH3 concentration downstream of SCR converter.
Figure 7: Simulated and measured NO2 concentration downstream of SCR converter.
The NH3 outlet concentration depicted in Fig.5 proves that the one state model (red dashed line) captures the essential behavior of ammonia slip with a computational demand suitable for real-time application. In the first part of the transient (i.e. before 1000 s) inaccuracies are visible during the absorption and desorption phases mainly due to total ammonia capacity and the ammonia adsorption kinetic phenomena which cannot be optimally assessed during the identification process. Since the EKF application, considers an additional dynamic related to total ammonia adsorption capacity Ω allow improving overall level of ammonia downstream of the catalyst (refer to green dashed line in fig. 5). The recursive EKF algorithm allows achieving a gradually increasing estimation accuracy, thus the second part of the test evidences very good match. Figures 6 and 7 show the outlet concentration of NO and NO2 during the test transient. The comparison between predicted (open loop and EKF-2state models) and experimental concentrations evidence the overall good accuracy. A mismatch is observable mainly for NO-NO2 concentrations during under-stoichiometry and stoichiometry ammonia injection (refer to simulation results before 1000 s in Figs. 6 and 7) in which catalyst is empty, thus adsorption and desorption phenomena show great effects on modelled system dynamics while secondary effects inhibit the NOX reduction process in real system (mass transfer phenomena). More accurate results are obtained in over-stoichiometric conditions (refer to simulation results after 1000 s in Fig. 6 and 7) in which both adsorption/desorption dynamic and mass transfer phenomena can be neglected.
The EKF update, based on NH3 sensor feedback, improve prediction of NH3 slip mainly thanks 𝛺𝛺 additional dynamic, without compromising NO/NO2 concentrations. It is worth remarking that, in the current EKF application it is assumed constant values of covariance matrices Q and R; by considering that Kalman gain ki is the ratio between state estimate and measurements uncertainties the main differences in SCR phenomena under variable stoichiometric condition has to be taken into account. Q and R matrices could be a function of under/over stoichiometric conditions. 5. CONCLUSIONS The development of an EKF observer of ammonia storage for an automotive SCR has been presented. Besides the ammonia storage, the observer accounts for an additional dynamics related to the total ammonia adsorption capacity Ω. The validation results show that the EKF observer allows improving the prediction accuracy of NH3 downstream of the catalyst with respect to the feed-forward model. The improvement of NO/NO2 prediction is mainly due to the Ω additional dynamics and further improvements could be achieved by assuming Q and R matrices as function of under/over stoichiometric conditions. The application of the EKF for SCR control is intended to provide robustness and adaption features to feedback control. Further benefits include the extension of SCR operating range prior to malfunction indicator light appearance, as well as SCR monitoring for condition based maintenance. The observer is useful and suitable for real-time industrial-scale applications to support the light physics-based models implemented. REFERENCES Arsie, I., Cialeo, G., D'Aniello, F., Pianese, C., De Cesare, M., & Paiano, L. (2017). Control Oriented Modeling of SCR Systems for Automotive Application (No. 2017-24-0121). SAE Technical Paper. Arsie, I., Cricchio, A., De Cesare, M., Lazzarini, F., Pianese, C., & Sorrentino, M. (2017). Neural network models for virtual sensing of NOx emissions in automotive diesel engines with least square-based adaptation. Control Engineering Practice, 61, 11-20.
Figure 6: Simulated and measured NO concentration downstream of SCR converter.
Arsie, I., D'Aniello, F., Pianese, C., De Cesare, M., & Paiano, L. (2018). Development and Experimental Validation of a Control Oriented Model of SCR for Automotive Application (No. 2018-01-1263). SAE Technical Paper. 542
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Becerra, V. M., Roberts, P. D., & Griffiths, G. W. (2001). Applying the extended Kalman filter to systems described by nonlinear differential-algebraic equations. Control Engineering Practice, 9(3), 267-281.
543
Jiang, K., Geng, P., Meng, F., & Zhang, H. (2016). An extended Kalman filter for input estimations in diesel-engine selective catalytic reduction applications. Neurocomputing, 171, 569575.
Chi, J. N., & DaCosta, H. F. (2005). Modeling and control of a Lambert, C., Hammerle, R., McGill, R., Khair, M., & Sharp, C. urea-SCR aftertreatment system (No. 2005-01-0966). SAE (2004). Technical advantages of urea SCR for light-duty and Technical Paper. heavy-duty diesel vehicle applications (No. 2004-01-1292). SAE Technical Paper. Cloudt, R., & Willems, F. (2011). Integrated Emission Management strategy for cost-optimal engine-aftertreatment Ljung, L. (1987). System identification: theory for the user. operation. SAE International Journal of Engines, 4(1), 1784- Prentice-hall. 1797. Nova, I., & Tronconi, E. (Eds.). (2014). Urea-SCR technology Cloudt, R., Saenen, J., Eijnden, E. v. d., Rojer, C., apr 2010. for deNOx after treatment of diesel exhausts (pp. 6-25). New Virtual exhaust line for model-based diesel aftertreatment York: Springer. development. In: SAE 2010 World Congress & Exhibition. SAE Schar, C. M., Onder, C. H., & Geering, H. P. (2006). Control of International. an SCR catalytic converter system for a mobile heavy-duty Colombo, M., Koltsakis, G., Nova, I., & Tronconi, E. (2012). application. IEEE Transactions on Control Systems Modelling the ammonia adsorption–desorption process over an Technology, 14(4), 641-653. Fe–zeolite catalyst for SCR automotive applications. Catalysis Schrade, F., Brammer, M., Schaeffner, J., Langeheinecke, K., & today, 188(1), 42-52. Kraemer, L. (2012). Physico-chemical modeling of an Devarakonda, M., Parker, G., Johnson, J. H., & Strots, V. integrated SCR on DPF (SCR/DPF) system. SAE International (2009). Model-based control system design in a urea-SCR Journal of Engines, 5(3), 958-974. aftertreatment system based on NH 3 sensor feedback. Shimizu, K. I., & Satsuma, A. (2007). Hydrogen assisted ureaInternational Journal of Automotive Technology, 10(6), 653. SCR and NH3-SCR with silver–alumina as highly active and Devarakonda, M., Parker, G., Johnson, J. H., Strots, V., & SO2-tolerant de-NOx catalysis. Applied Catalysis B: Santhanam, S. (2009). Model-based estimation and control Environmental, 77(1-2), 202-205. system development in a urea-SCR aftertreatment system. SAE Walker, A. (2012, September). Current and future trends in International Journal of Fuels and Lubricants, 1(1), 646-661. catalyst-based emission control system design. In presentation Folić, M., & Johansen, K. (2012). SCR-DPF Integrations for at the SAE Heavy-Duty Diesel Emission Control Symposium. Diesel Exhaust Performance and Perspectives for High SCR Willems, F., Cloudt, R., Van Den Eijnden, E., Van Genderen, Loadings, presentation at the US Department of Energy M., Verbeek, R., de Jager, B., ... & van den Heuvel, I. (2007). Is Directions in Engine Efficiency and Emissions Research closed-loop SCR control required to meet future emission (DEER). Dearborn, MI, October. targets? (No. 2007-01-1574). SAE Technical Paper. Gelb, A. (Ed.). (1974). Applied optimal estimation. MIT press. Willems, F., Mentink, P., Kupper, F., & Van den Eijnden, E. Henry, C., Langenderfer, D., Yezerets, A., Ruth, M., Chen, H. (2013, September). Integrated emission management for cost Y., Hess, H., & Naseri, M. (2011). Passive catalytic approach to optimal EGR-SCR balancing in diesels. In Proc. IFAC Symp. low temperature NOx emission abatement. city, 15(570), 21-8. Adv. Automotive Contr (pp. 711-716). Holderbaum, B. (2009, April). Kwee,“. In Integration of DPF Yao, S., Shost, M., Yoo, J. H., Cabush, D., Racine, D., Cloudt, and SCR-Interfaces and Interactions”, presentation at the 5th R., & Willems, F. (2008). Ammonia sensor for closed-loop SCR International CTI Forum, SCR Systems, Fellbach, Germany. control. SAE international journal of passenger cars-electronic and electrical systems, 1(2008-01-0919), 323-333. Hsieh, M. F., & Wang, J. (2011). A two-cell backstepping-based control strategy for diesel engine selective catalytic reduction Zhou, G., Jørgensen, J. B., Duwig, C., & Huusom, J. K. (2012). systems. IEEE Transactions on Control Systems Technology, State estimation in the automotive SCR deNOx process. IFAC 19(6), 1504-1515. proceedings volumes, 45(15), 501-506. Hsieh, M. F., & Wang, J. (2011). Development and experimental studies of a control-oriented SCR model for a twocatalyst urea-SCR system. Control Engineering Practice, 19(4), 409-422. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans (2010). IARC monographs on the evaluation of carcinogenic risks to humans. Ingested nitrate and nitrite, and cyanobacterial peptide toxins. IARC monographs on the evaluation of carcinogenic risks to humans, 94, v-vii, 1-412.
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Development of an EKF Observer for an automotive SCR system Development of an EKF Observer for an automotive SCR system Development of an Observer for automotive system Federica Arsie*, Cesare Pianese*, Matteo De SCR Cesare** Development ofD’Aniello*, an EKF EKFIvan Observer for an an automotive SCR system
Federica D’Aniello*, Ivan Arsie*, Cesare Pianese*, Matteo De Cesare** Federica D’Aniello*, Ivan Arsie*, Cesare Pianese*, Matteo De Cesare** Federica D’Aniello*, Ivan Arsie*, Cesare Pianese*, Matteo De Cesare** *University of Salerno, Fisciano (SA), Italy (e-mail: fdaniello, iarsie, pianese@unisa.it) (e-mail: fdaniello, iarsie, pianese@unisa.it) *University of Salerno, Fisciano (SA), Italy (e-mail: [email protected]) **Magneti Marelli Powertrain, Bologna, Italy University of Salerno, Fisciano (SA), Italy (e-mail: fdaniello, iarsie, pianese@unisa.it) ****Magneti Marelli Powertrain, Bologna, [email protected]) University of Salerno, Fisciano (SA), Italy (e-mail: fdaniello, iarsie, pianese@unisa.it) Magneti Marelli Bologna,isItaly (e-mail: [email protected]) In the** recent years, enginePowertrain, developer attention focused on the application of advanced control strategies ** Magneti Marelli Powertrain, Bologna, Italy (e-mail: [email protected]) In years, engine developer attention focused onfor thethe application advanced control strategies forthe therecent SCR system, considered as the leadingistechnology abatementofof NOX emissions in Diesel for the SCR system, considered as the leading technology for the abatement of NO in Diesel X emissions In the recent years, engine developer attention is focused on the application of advanced control strategies The years, most challenging task inattention SCR control is theonoptimization of the urea dosing strategy while engines. In the recent engine developer is focused the application of advanced control strategies engines. The most challenging task in SCR control is the optimization of the urea dosing strategy while for the SCR system, considered as the leading technology for the abatement of NO emissions in Diesel X reduction efficiency and low NH3 slip in the tailpipe, especially reaching the system, balance considered between high NOXleading for the SCR as the technology for the abatement of NO emissions in Diesel X engines. The most challenging task in SCR control is the optimization of urea dosing strategy while reaching the balance between high NO efficiency and low NH slip in the tailpipe, especially X reduction 3the during transients. order to task accomplish this task, an optimization Extended Kalman Filterdosing (EKF)strategy estimator is engines. The most In challenging in SCR control is the of the urea while reaching the balance between high NO reduction efficiency and low NH slip in the tailpipe, especially during transients. In order to accomplish this task, an Extended Kalman Filter (EKF) estimator is X loop control of SCR urea dosing. The 3 developed, tobalance be integrated in the closed EKF estimator is applied to reaching the between high NO X reduction efficiency and low NH3 slip in the tailpipe, especially during transients. In order to accomplish this task, an Extended Kalman Filter (EKF) estimator is developed, to be integrated in the closed loop control of SCR urea dosing. The EKF estimator is applied to aduring one-state model, in coverage ratioan θ isExtended the only dynamic taken into account. transients. In which order the to ammonia accomplish this task, Kalman process Filter (EKF) estimator is aToone-state model, in which the ammonia coverage ratio θ is the only dynamic process taken into account. developed, to be integrated in the closed loop control of SCR urea dosing. The EKF estimator is applied to improvetothe model accuracy, an additional dynamics, related todosing. total ammonia adsorption capacity Ω, developed, be integrated in the closed loop control of SCR urea The EKF estimator is applied to improvemodel, thewhile model accuracy, an dynamics, toalgorithm total ammonia adsorption capacity Ω, aToone-state in which the ammonia coverage ratio θ related is the only dynamic process taken into account. The recursive enhanced is considered conceiving theadditional EKF. ais one-state model, in which the ammonia coverage ratio θ is EKF the only dynamicallows processachieving taken intoenhanced account. considered while conceiving the EKF. The recursive EKF algorithm allows achieving To improve the model accuracy, an additional dynamics, related to total ammonia adsorption capacity Ω, estimation with a slight an increase of the computational burden is still adsorption suitable with on-board To improveaccuracy the model accuracy, additional dynamics, relatedburden to totalthat ammonia capacity Ω, estimation accuracy with a slight increase of the computational that is still suitable with on-board is considered while conceiving the EKF. The recursive EKF algorithm allows achieving enhanced application. is considered while conceiving the EKF. The recursive EKF algorithm allows achieving enhanced application.accuracy with a slight increase of the computational burden that is still suitable with on-board estimation estimation accuracy with increase of Model-based the computational burden is still suitable with on-board Keywords: Exhaust Gasa slight Aftertreatment, Plant Modelling and System © 2019, IFAC (International Federation of Automatic Control)Calibration, Hosting by that Elsevier Ltd. All rights reserved. application. Keywords: Exhaust Gas Aftertreatment, Model-based Calibration, Plant Modelling and System application. Identification, SCR modeling, NOX control, Model-based Diagnostics Keywords: Exhaust Gas Aftertreatment, Calibration, Plant Modelling and System Identification, SCR modeling, NOX control, Model-based Diagnostics Keywords: Exhaust Gas Aftertreatment, Model-based Calibration, Plant Modelling and System Identification, SCR modeling, NOX control, Model-based Diagnostics Identification, SCR modeling, NOX control, Model-based Diagnostics a NOX absorber which is able to capture 65% of NOX produced 1. INTRODUCTION absorber which is able to capture 65% of NO produced awhen NOX SCR temperature is less than 150°C, andX passively 1. INTRODUCTION when SCR temperature is less than 150°C, and passively a NO absorber which is able to capture 65% of NO produced X X in case of higher temperature. In65% the last decade, SCR One of the main issue to Diesel engines application in arelease NOX absorber which is able to captureIn of NO 1. related INTRODUCTION X produced in case of higher the last decade, One of the main issue to Dieselby engines application in release when temperature isfocused less than and passively 1. related INTRODUCTION systemSCR design has beentemperature. on 150°C, SCR coated on SCR DPF the automotive field is represented the high level of when SCR temperature is less than 150°C, and passively release case ofhas higher theinlast decade, design beentemperature. focused onIn SCR coated on SCR DPF the automotive field related is response represented the high level of One of the emissions. main issue to Diesel engines application in system system. in The combination of SCR and DPF one component pollutants In to by the numerous reports release in case of higher temperature. In the last decade, SCR One of the main issue related to Diesel engines application in system design has been focused on SCR coated on DPF system. The combination of SCR and DPF in one component the automotive field is represented by the high level of pollutants emissions. In response to the numerous reports allows getting the SCR closer to theonengine, faster conducted by International Agency for research cancer system getting design the hasSCR been focused SCR promoting coated on faster DPF the automotive field In is response represented by the highon of allows closer to the engine, pollutants emissions. theresearch numerous reports conducted by International Agencytofor onlevel cancer system. The combination of SCR and DPF inpromoting onedeNOX component light-off. The effect of combined SCR-DPF on and (IARC, 2010), European Commission is tightening the system. The combination of SCR and DPF in one component pollutants emissions. In response to the numerous reports light-off. The processes effect of closer combined SCR-DPF onquestion deNOXfaster and (IARC, amount 2010), European Commission isVItightening the allows getting the SCR to the engine, promoting conducted by International Agency for research on cancer regeneration is still an open and allowed of released emissions. Euro regulations in allows getting processes the SCR closer to thean engine, faster conducted by International Agency for research on cancer is still openpromoting allowed ensures amount released emissions. Euro regulations in regeneration light-off. Theaddressed effect ofby combined SCR-DPF onquestion deNOX and (IARC, 2010), of European Commission isVItightening the thoughtfully literature (Walker, 2012 Folic and Europe that emissions are contained for both typelight-off. Theaddressed effect ofby combined SCR-DPF on deNOX (IARC, 2010), that European Commission is tightening the thoughtfully literature (Walker, - Folic and Europe ensures emissions are contained for both typeprocesses is still an open2012 question and allowed amount of released emissions. Euro VIthis regulations in regeneration Johansen, 2012 Schrade et al., 2012). approval and real driving profiles. In direction, processes is al., still an open question and allowed amount released emissions. Euro regulations in regeneration 2012 - Schrade 2012). approvalensures andandof real driving profiles. In VIthis direction, thoughtfully addressed by et literature (Walker, 2012 - Folic and Europe that emissions are contained for both type- Johansen, manufactures car-makers propose complex configuration thoughtfully addressed by literature (Walker, 2012 - Folic and Europe ensures that emissions are contained for both typeUp to now, most studies focus on manufactures car-makers propose complex configuration Johansen, 2012 - Schrade et al., 2012).either engine or afterapproval andandreal driving profiles. In this direction, of exhaust after-treatment (ATs) and powertrain concepts with Up to now, most studies focus on either engine or afterJohansen, 2012 Schrade et al., 2012). approval and real driving profiles. In this direction, of exhaust after-treatment and powertrain with treatment control separately, with individual targets set for manufactures and car-makers propose complex concepts configuration the aim to satisfy the (ATs) restrictive upcoming legislation. treatment control separately, with individual targets for Up to now, most studies focus on either engine orset aftermanufactures and car-makers propose complex configuration emissions and catalysts performance. As the emission of after-treatment (ATs) and powertrain concepts with engine theexhaust aim to restrictive upcoming legislation. Up to emissions now, mostand studies focus on either engine or afterCurrently, thesatisfy Diesel the Oxidation Catalyst (DOC) is adopted engine catalysts performance. As the emission treatment control separately, with individual targets set for of exhaust after-treatment (ATs) and powertrain concepts with getcontrol lower,separately, especiallywith during real-life operating the aimfortohydrocarbons restrictive upcoming Currently, thesatisfy Diesel the Oxidation Catalyst (DOC) is adopted limits treatment individual targets set for mainly emissions control, Diesel legislation. Particulate limits get lower, especially during real-life operating engine emissions and catalysts performance. As the emission the aim to satisfy the restrictive upcoming legislation. conditions, the trade-off between catalyst performance and Currently, the Diesel Oxidation Catalyst (DOC) isthe adopted mainly(DPF) for hydrocarbons emissions control, Diesel engine emissions and catalysts performance. As the emission Filter is employed for particulate control andParticulate Urea- conditions, trade-off between performance and limits lower, during real-life Currently, the Diesel Oxidation Catalyst (DOC) isthe adopted system get coststhe can beespecially achieved bycatalyst exploiting theoperating synergy Filter (DPF) is employed for particulate control and Urealimits get lower, especially during real-life operating mainly for hydrocarbons emissions control, Diesel Particulate Selective Catalytic reduction (SCR) coupled withParticulate ammonia system conditions, the trade-off between catalyst performance and costs can be achieved by exploiting the synergy mainly for hydrocarbons emissions control, Diesel between engine and after-treatment system. This requires Selective Catalytic reduction (SCR) coupled ammonia conditions, thecan trade-off betweenbycatalyst performance and Filter (DPF) is employed for particulate controlwith and Urea- between oxidation (AMOX) are considered thethe costs beinteraction achieved exploiting theEGR-SCR synergy engine and after-treatment system. This requires Filter (DPF)catalyst is employed for particulate control and theleading Urea- system optimal engine-ATs by the so called oxidation catalyst (AMOX) are considered the leading system costs can be achieved by exploiting the synergy Selective Catalytic reduction (SCR) coupled with ammonia technology for the abatement NOX coupled emissionswith in traditional engine-ATs interaction by thesystem. so called between engine and2013). after-treatment ThisEGR-SCR requires Selective Catalytic reduction of (SCR) ammonia optimal balancing (Willems, emissions traditional technology for the abatement of NO between engine and2013). after-treatment system. This requires Xconsidered oxidation catalyst (AMOX) are the leading Diesel engines (Lambert, 2004 – Jiang, 2016).in Due to the balancing (Willems, optimal engine-ATs interaction by the so called EGR-SCR oxidation catalyst (AMOX) are considered the leading Diesel engines (Lambert, 2004 – Jiang, 2016). to the optimal engine-ATs interaction(NH by3)theassoreducing called EGR-SCR technology for the abatement of NO traditional X emissions increasing demand to meet the tightening cold startinDue and deNO SCR system uses ammonia agent to X balancing (Willems, 2013). technology for the abatement of NO X emissions in traditional Diesel engines (Lambert, 2004 – Jiang, 2016). Due to the increasing demand to meet the tightening cold start and deNO SCR system uses ammonia (NH balancing (Willems, 2013). X 3) as reducing agent to efficiency requirements under – low-load urban driving catalytically convert NOX in the exhaust to nitrogen molecules Diesel engines (Lambert, 2004 Jiang, 2016). Due to the increasing demand to meetto the tightening cold start andlow-cost deNOX SCR systemThe uses ammonia asto reducing agent to the(NH exhaust nitrogen molecules efficiency under low-load urban driving catalytically convert NOX incannot 3) directly conditions, itrequirements is desirable develop small size and water. ammonia carried in the increasing demand to meetto thedevelop tightening cold start andlow-cost deNOX and SCR systemTheuses ammonia (NHbe 3) as reducing agent to and water. ammonia be directly carried in the conditions, it is desirable small size and X incannot efficiency requirements under low-load urban driving catalytically convert NO the exhaust to nitrogen molecules after-treatment systems under layout low-load to integrate multiple tail pipe for NO reason of toxicity (Shimizu, 2007), thus efficiency requirements urban multiple driving exhaust catalytically convert X in the exhaust to nitrogen molecules exhaust tail urea pipe for reason of toxicity (Shimizu, 2007), after-treatment systems to integrate conditions, it isand desirable tolayout develop small size and low-cost and water. The ammonia cannot directly carried in thus the functionalities exploit any possible synergies between an aqueous solution (which is be referred as Diesel Exhaust conditions, it isand desirable to any develop smallsynergies size and low-cost andaqueous water. urea The solution ammonia(which cannotis be directly carriedExhaust in the an referred as Diesel functionalities exploit possible between after-treatment systems layout to integrate multiple exhaust tail pipe for reason of toxicity (Shimizu, 2007), thus devices. To accomplish thislayout task, thetoproper systemsmultiple design fluid in the United States and by the brand name2007), AdBlue in after-treatment systemsthis integrate exhaust tail United pipe forStates reasonand of by toxicity (Shimizu, thus fluid in the the brand AdBlue in devices. To accomplish task, the proper systems design functionalities and exploit any possible synergies between an aqueous urea solution (which is referred asname Diesel Exhaust and optimal control of such advanced exhaust line Europe) is injected upstream the catalyst. The injected urea is functionalities and exploit possible synergies between an aqueous urea solution (which is referred as injected Diesel Exhaust is injected The urea in is and optimal control of any such advanced exhaust line Europe) devices. To accomplish this task, proper design fluid the United States andthe by the brand name configurations are required. Forthe light dutysystems applications, then in converted to upstream ammonia bycatalyst. pyrolysis and AdBlue hydrolysis devices. To accomplish this task, the proper systems design fluid in the United States and by the brand name AdBlue in converted to upstream ammonia pyrolysis and hydrolysis configurations are Kwee required. For advanced light duty exhaust applications, and optimal and control of (2009) such line then Europe) is injected thebycatalytic catalyst. The injected is Holderbaum evaluated the optimal reactions, then adsorbed into substrate ofurea SCR and optimal and control of (2009) such advanced exhaust line reactions, Europe) is injected upstream thebycatalytic catalyst. The injected urea is configurations are required. For light duty applications, then converted to ammonia pyrolysis and hydrolysis then adsorbed into substrate of SCR Holderbaum Kwee evaluated the optimal configuration ofare the required. SCR and DPF along the exhaust line by then converter (Hsieh,to2011). Since only the adsorbed ammonia is configurations For light duty applications, converted ammonia by pyrolysis and hydrolysis Holderbaum and (2009) evaluated the optimal reactions, thenwhich adsorbed intoonly catalytic substrate of (Hsieh, 2011). theNO adsorbed ammonia is configuration the Kwee SCR and exhaust line by converter considering theof fuel required toDPF heatalong up thethe SCR and the DPF the reductant canSince react with specie, the SCR key X Holderbaumthe and Kwee (2009) evaluated the optimal reactions, thenwhich adsorbed into catalytic substrate of SCR the key the reductant can react with NO considering fuel required to heat up the SCR and the DPF X specie, configuration of the SCR and DPF along the exhaust line by converter (Hsieh, 2011). Since only the adsorbed ammonia is regeneration. The authors recognise the SCR in frontline of the of NH3-SCR chemistry arethe theadsorbed ammoniaammonia adsorption configuration The of the SCR and DPF along the exhaust by processes converter (Hsieh, 2011).chemistry Since only is -SCR are the ammonia adsorption processes of NH regeneration. authors recognise the SCR in front of the X specie, 3 considering the fuel required to heat up the SCR and the DPF the reductant which can react with NO the key DPF as the best configuration due to up the the fuelSCR penalty incurs in and desorptionwhich from the catalyst surface (Colombo, 2012), considering the fuel required to heat and the DPF the reductant can react with NO X specie, the key desorption catalyst surface (Colombo, 2012), DPF as the configuration due to the fuel penalty incurs in and 3-SCRthe regeneration. The authors recognise the SCR in front of the processes NHfrom chemistry the ammonia adsorption case of SCRbest behind the DPF. Henry et al. (2011) investigate which areof highly affected by theare rapid variation of engine regeneration. The authors recognise the SCR in front of the which processes ofhighly NH3-SCR chemistry are the ammonia adsorption are affected by the rapid variation of engine case of SCR behind the DPF. Henry et al. (2011) investigate DPF as the best configuration due to the fuel penalty incurs in and desorption from the catalyst surface (Colombo, 2012), the performance characteristicsdue when an fuel SCRpenalty is coupled with working conditions. DPFperformance as the best configuration to the incurs in working and desorption from the catalyst surface (Colombo, 2012), conditions. the characteristics when anal. SCR is coupled with case of SCR behind the DPF. Henry et (2011) investigate which are highly affected by the rapid variation of engine case of SCR behind the DPF. Henry et al. (2011) investigate which are highly affected by the rapid variation of engine the performance characteristics when an SCRofisAutomatic coupled with 538Hosting working conditions. Copyright © 2019, 2019 IFAC 2405-8963 © IFAC (International Federation Elsevier Ltd. All rights reserved. the performance characteristics when an SCR is coupled Control) with workingbyconditions. Copyright 2019 responsibility IFAC 538Control. Peer review©under of International Federation of Automatic 10.1016/j.ifacol.2019.09.085 Copyright © 2019 IFAC 538 Copyright © 2019 IFAC 538
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The urea dosing and NOX conversion efficiency are strictly related to the engine out NOX concentration and flow rate, catalyst temperature and the amount of ammonia stored in the catalyst. The most common Cu-Zeolite SCR catalyst has very high ammonia storage capacity and ammonia adsorption and desorption rates appear slow at relatively low temperatures. These characteristics imply that the NOX conversion is not immediately controllable by the urea injection rate. Thus, the urea injection is controlled to keep a certain level of ammonia storage on the catalyst surface to assure high NOX conversion, while avoiding excessive ammonia slip in case of a sudden temperature increase (Cloudt, 2011). Since the urea injection rate cannot control the short-term NOX conversion, it is not possible to reach 100 % of NOX reduction during transient conditions due to the much slower dynamics of the catalyst compared to the engine (the catalyst typically requires several minutes before reaching chemical equilibrium). Instantaneous tailpipe NOX emissions are controlled by the engine, exhaust gas flow and temperature. Therefore, the objective of the SCR control system design is to find an appropriate control strategy in terms of rate and timing of injected urea which can prevent NH3 slip phenomena while optimizing NOX conversion efficiency with the minimum dosage of AdBlue injected.
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the NH3 sensor can be considered as a potential candidate for SCR control to avoid ammonia cross sensitivity of NOx sensors. Although model based feedback control have demonstrated good accuracy and efficient feasibility in the urea dosing control, system vs. model mismatching conditions, measurements errors and system aging could negatively affect the SCR performance. Nonlinear closed loop estimator is motivated based on the rapid variation of Diesel engine operating conditions, which expose the SCR system to changing flow rate, composition and temperature of exhaust gas. For this task, Zhou and Jorgenses (2012) develop Model Predictive Control (MPC) of the SCR process based on Extended Kalman filter for state estimation in SCR process based on four state modelling approach. While Hsieh and Wang (2011) use an extended Kalman Filter for three state model and report good performance and robustness to measurements errors. Mora et al. (2018) propose an observer based on a five states Extended Kalman filter and the SCR downstream NOX and NH3 measurements; the observer is able to recognize the cause of a decrease in SCR performance, which can be due to SCR ageing, urea dilution or urea injector drift. In this paper a two state Extended Kalman Filter (EKF) estimator is developed to be integrated in the closed loop control of SCR urea dosing. To satisfy OBD requirements and industrial-scale application, the one state 0-D model, presented in previous works Arsie et al. (2017a, 2018), is adopted to estimate the states in the EKF. The application of EKF for SCR control is intended to provide robustness and adaptation features to SCR feedback control. The observer is useful and suitable to support the light physics-based models implemented and to run on state-of-the-art Euro VI production engines (Cloudt et al., 2010). The paper is organized as follows: section 2 briefly describes the main chemical phenomena and the overall structure of one state model; section 3 presents the development of EKF; in the latter section results and discussion are addressed.
In standard industrial application, there is a closed-loop feedback control using NOX and NH3 sensors downstream the SCR. the downstream NOX sensor is mandatory to perform SCR diagnostics. However, its cross-sensitivity with NH3 opens several possibilities, taking also into account that an AMOX catalyst is placed after the SCR. On the one hand, the NOx sensor could be placed after the AMOX if an NH3 sensor is used between the SCR and the AMOX. On the other hand, if the NH3 signal is to be measured isolating NH3 from the NOx sensor signal, the NOx sensor should be located between the SCR and the AMOX. The separation of the NOx upstream signal into NO and NO2, which can be obtained by an upstream NOx sensor, is already used in Euro VI production engines. SCR control is, actually, the great challenge due to its complicated dynamics and limited feedback (Hsieh, 2011). Due to the system complexity, to systematically assess ATs layout in terms of interactions, calibration effects, performance and critical states, a massive amount of data should be exploited, alternately control oriented models and simulation tools are effective in predicting systems behaviour and critical states. Several control strategies are developed and presented by Willems et al. (2007) by concluding that feedback control is necessary to achieve emissions consistent with upcoming legislation. Therefore, Devarakonda et al. (2009) developed a model based estimator for the relevant species (i.e. NO, NO 2 and NH3) and a control strategy of the urea-SCR plant based on a four states model. In Schar et al. (2006) a model-based feedforward controller (FFC) and a model-based feedback controller (FBC) are designed to achieve an effective control strategy for the urea solution injection. The feedback control is accomplished by means of a NOx sensor located downstream of the SCR. Nevertheless, due to NOX sensor slow time response and cross-sensitivity towards NH3, real sensors can be replaced by NOX virtual sensors based on suitable modelling and identification approaches (Arsie, 2017 b). In addition, as evidenced by Devarakonda et al. (2009a, 2009b),
2. CONTROL ORIENTED MODEL FOR SCR While the dynamics of chemical reactions occurring within the SCR catalyst are quite complex, lumped-approach 0-D mathematic models are preferred for real-time estimation of main variables and system control design. The accurate modelling of chemical reaction mechanism and fluid dynamics inside the catalyst requires the use of partial differential equations (PDE) to offer insightful investigation of spatialtemporal distribution of specie concentrations. Since PDE are computationally expensive and hard to be employed for real time control, the SCR catalyst is assumed to be a continuous stirred tank reactor (CSTR) for developing a 0-D model (Nova et Tronconi, 2014). The processes taking place within the catalyst can be summarized by means of three main steps. Firstly, the urea solution (AdBlue) is converted into ammonia; secondly, the ammonia in the catalyst is adsorbed on the catalytic substrate. In the latter, the adsorbed ammonia catalytically reacts with NOX and converts them to nitrogen and water. The adopted modelling approach is described in detail in previous works (Arsie et al., 2017a – Arsie et al.,2018) and for reason of brevity is not reported in the current paper. Particularly, the model is based on mass conservation law and 539
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chemical kinetics for NO, NO2 and NH3 species while the interaction of ammonia with catalytic substrate is summarized in ammonia coverage ratio (𝜗𝜗 - defined in the equation (1)). The formation processes for NO, NO2 and NH3 species are assumed to be in chemical equilibrium due to their faster dynamics than ammonia storage one, thus the ammonia storage is the only relevant dynamic process to be modelled and 𝜗𝜗 (defined below) is the only state variable of the model. 𝜗𝜗 =
∗ 𝐶𝐶𝑁𝑁𝑁𝑁 3 Ω
(1)
Ω is the total adsorption capacity of catalyst evaluated at the minimum temperature and kept constant. The model state equations are reported in Eq. 2 to 5. 𝜗𝜗̇ = −(𝐾𝐾𝑑𝑑𝑑𝑑𝑑𝑑 + 𝐾𝐾𝑜𝑜𝑜𝑜𝑜𝑜 )𝜗𝜗 + 𝐾𝐾𝑎𝑎𝑎𝑎𝑎𝑎 𝐶𝐶𝑁𝑁𝑁𝑁 3 (1 − 𝜗𝜗) − 𝐾𝐾𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝜗𝜗𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑁𝑁𝑁𝑁 2 − 𝐾𝐾𝑠𝑠𝑠𝑠𝑠𝑠 𝜗𝜗𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑂𝑂 2 − 𝐾𝐾𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜗𝜗𝐶𝐶𝑁𝑁𝑁𝑁 2 −𝑄𝑄̅ 𝐶𝐶𝑁𝑁𝑁𝑁 − 𝐾𝐾𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝜗𝜗𝛺𝛺𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑁𝑁𝑁𝑁 2 − 𝐾𝐾𝑠𝑠𝑠𝑠𝑠𝑠 𝜗𝜗𝛺𝛺𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑂𝑂 2 + 𝑄𝑄̅ 𝐶𝐶𝑁𝑁𝑂𝑂𝑖𝑖𝑖𝑖 = 0 𝐾𝐾𝑑𝑑𝑑𝑑𝑑𝑑 𝛺𝛺𝜗𝜗 − [𝐾𝐾𝑎𝑎𝑎𝑎𝑎𝑎 Ω(1 − 𝜗𝜗) + 𝑄𝑄]𝐶𝐶𝑁𝑁𝑁𝑁 3 + 𝑄𝑄̅𝐶𝐶𝑁𝑁𝐻𝐻3𝑖𝑖𝑖𝑖 = 0 −𝑄𝑄̅ 𝐶𝐶𝑁𝑁𝑂𝑂2 − 𝐾𝐾𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝜗𝜗𝛺𝛺𝐶𝐶𝑁𝑁𝑁𝑁 𝐶𝐶𝑁𝑁𝑁𝑁 2 − 𝐾𝐾𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜗𝜗𝛺𝛺𝐶𝐶𝑁𝑁𝑁𝑁 2 + 𝑄𝑄̅ 𝐶𝐶𝑁𝑁𝑂𝑂2 𝑖𝑖𝑖𝑖 = 0
(2)
(3)
coverage ratio/storage variations, which require complex formulation despite having limited measurable feedback. In addition, measurements uncertainties related to engine-out gas temperature, flow and composition, cross-sensitivity of NOX sensors toward NH3 and sensors time response has to be considered. The set of trouble described above, made SCR map-based control unsuitable to optimize NOX conversion efficiency whilst prevent NH3 slip during transients. Thus feedback non-linear model-based control is adopted to infer adaptive features vs. systems aging and plant-model mismatch. In the perspective of supporting this need, an Extended Kalman Filter (EKF) is established. The EKF is a recursive algorithm able to update model prediction with onboard measurements by considering both system and measurement noises for developing robust and reliable online estimator, directly embeddable on-board the vehicle with the aim of operating jointly with the physical sensors. The integration of measurements, model prediction and EKF estimation in the closed-loop control scheme is shown in Figure 1.
(4) (5)
In order to reach the most suited trade-off between model accuracy and computation efficiency, the lumped (nondimensional) approach is adopted. The inaccuracies due to the approximation of averaged vs. non-uniform distribution of ammonia adsorption capacity and thermo-fluid dynamic variables (spatial velocity, gas temperature and specie concentrations) cannot be overcome. Nevertheless, by partitioning the SCR catalyst in several slices the approximation error can be reduced. For this goal, the SCR model equations (Eqs. 1-5) are replied into four sections along axial direction in accordance with Willems et al. (2007) approach. Continuity conditions on spatial velocity and inlet concentrations are applied at the interface, while pressure drop and temperature dynamic are neglected, particularly temperature is considered homogeneous and constant for overall catalyst. The resulting model can be expressed by a set of ordinary differential equations (ODE) with a much lower computational burden.
Figure 1: EKF integration on model-based control. The EKF allows relating the actual state, not only to the previous state, but also to the stochastic relationship between model prediction and on-board measurements (Gelb, 1974 – Ljung, 1987). Generally, the EKF includes two steps, as referred in Fig 2: prediction and update.
3. DEVELOPMENT OF SCR ESTIMATOR BASED ON EXTENDED KALMAN FILTER
Figure 2: time updating and measurement updating in the EKF algorithm.
The optimization of urea dosing strategy in SCRs, aimed reaching the balance between high NOX reduction efficiency and low NH3 slip in the tailpipe, especially during transients, is the most challenging task in SCR control. The NOX reduction processes occurring in SCR system are represented by very complicated thermo-fluid dynamic/chemical phenomena and secondary effects (such as urea/NH 3 deposits upstream the catalyst and mass transfer phenomena) coupled with aging of SCRs could negatively affect the correct estimation of main states. The system representation requires careful and accurate modelling approach because of i) the nonlinearity of the system due to reactions kinetic; ii) the close link between system dynamics and catalyst temperature (highly variable during real driving conditions) and iii) NH3
In the prediction phase, the control oriented model (refer to Eqs. 1 to 5, section 2) described by coupled Differential and Algebraic Equations (DAEs), is adopted to provide a prior prediction of the main states. The EKF has been traditionally applied to models described by ordinary differential equations, while the design of observer for nonlinear DAE systems has been addressed by Becerra et al. (2001), who transform the DAE system into a corresponding explicit ordinary differential equation, thus allowing the use of standard observer design techniques. The one state model is then linearized following the first order Taylor expansion, to use it in state-space formulation. The system schematization is shown in Fig.3.
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𝑘𝑘𝑘𝑘 = Pk− HkT [𝐻𝐻𝑘𝑘 Pk− 𝐻𝐻𝑘𝑘𝑇𝑇 + 𝑅𝑅𝑘𝑘 ]−1
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(12)
In Eqs. 11 and 12, 𝐻𝐻𝑘𝑘 is the Jacobian matrix of the observation function ℎ(𝑥𝑥̂𝑘𝑘 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘). 4. RESULTS AND DISCUSSION Simulation results and EKF vs. open loop model improvements are proved in this section. In-depth analysis of experimental data is presented in (Arsie, 2018). The experimental results were collected on a commercial Cuzeolite catalyst (Volume: 2.37 l) with honeycomb monolith (400 cpsi) washcoated. The catalyst was placed at the exhaust of a light-duty Diesel engine and the experimental data were measured at the engine test bench during the typical engine transients performed for tuning the map based SCR control. NO, NO2 and NH3 concentrations were measured downstream of the converter by a FTIR Analyser while direct measurements of the inlet concentrations were not available. Nevertheless, in order to exploit the one state model, the inlet concentrations of relevant species are needed and their values were assumed to equal the available outlet concentrations before the first AdBlue injection occurs. The test was carried out by setting engine speed equal to 2500 rpm and exhaust gas temperature is 350°C. In figure 4 the inlet concentration of relevant species (i.e. NO, NO 2 and NH3) and the temperature evolution during the test are depicted.
Figure 3: state space SCR model 𝑢𝑢𝑘𝑘 represents the model inputs: 𝐶𝐶𝑁𝑁𝑂𝑂𝑖𝑖𝑖𝑖 , 𝐶𝐶𝑁𝑁𝑂𝑂2 𝑖𝑖𝑖𝑖 and 𝐶𝐶𝑁𝑁𝐻𝐻3𝑖𝑖𝑖𝑖 inlet concentrations, evaluated at time tk ; 𝜗𝜗 𝑥𝑥̂𝑘𝑘 are the observer states, 𝑥𝑥̂𝑘𝑘 = [ ] ; 𝛺𝛺 𝐶𝐶𝑁𝑁𝑁𝑁 𝑧𝑧𝑘𝑘 represents the algebraic states vector, 𝑧𝑧𝑘𝑘 = [𝐶𝐶 ]; 𝑁𝑁𝑂𝑂2 𝑦𝑦̂𝑘𝑘 is the model output, 𝑦𝑦̂𝑘𝑘 = 𝐶𝐶𝑁𝑁𝐻𝐻3. As previously discussed, to satisfy OBD requirements, the ammonia coverage ratio θ was the only modelled dynamic process in the control oriented model development (refer to section 2); while the total adsorption capacity (Ω), whose value has been identified at minimum temperature, was kept as constant. Thus, to improve the observer accuracy, an additional dynamic related to Ω is considered while conceiving EKF. The set of equations below (refer to Eqs. 6 to 9) defines the observer during prediction phase. The state variable 𝑥𝑥̂ and the system error covariance matrix 𝑃𝑃 are time updated by following the Eqs. 6 and 9. Time updating phase consists of propagating the state estimates and error covariance through the system dynamics from time step tk-1+ to tk- before on-board measurement is taken: + (6) 𝑥𝑥̂𝑘𝑘− = 𝑓𝑓(𝑥𝑥̂𝑘𝑘−1 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘) 𝑔𝑔(𝑥𝑥̂𝑘𝑘 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘) = 0
(7)
𝑦𝑦̂(𝑘𝑘) = ℎ(𝑥𝑥̂𝑘𝑘 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘)
(8)
+ 𝑃𝑃𝑘𝑘− = ∅𝑘𝑘−1 𝑃𝑃𝑘𝑘−1 ∅𝑇𝑇𝑘𝑘−1 + 𝑄𝑄𝑘𝑘−1
(9)
The 𝑓𝑓 function of Eq. 6 is the linear model explained in Eq.2. The 𝑔𝑔 function proposed in equation 7 is the state-space formulation for NO and NO2 algebraic balances (refer to Eqs. 3 and 5). The ℎ function reported in Eq. 8 represent the state space representation for the output state (i.e. 𝐶𝐶𝑁𝑁𝐻𝐻3. )whose explicit formulation is proposed in equation 4. As widely referred by literature (Gelb, 1974 – Ljung, 1987), in the Kalman filter, In addition to the control input 𝑢𝑢(𝑘𝑘), the system is driven by two zero-mean, Gaussian and independent white noise sources: the state and measurement noises with covariance matrices Q and R respectively, while ∅𝑘𝑘−1 is the Jacobian matrix of the 𝑓𝑓 function. After the time update, taking the sensor measurements 𝑦𝑦𝑘𝑘 at the time tk, the measurements update is performed by achieving the new time step tk+. The state and error covariance update, refer to Eqs. 10 and 11, is achieved by means an optimal Kalman gain 𝑘𝑘𝑘𝑘 whose formulation is based on solution of Riccati covariance equation (refer to Eq.12). (10) 𝑥𝑥̂𝑘𝑘+ = 𝑥𝑥̂𝑘𝑘− + 𝑘𝑘𝑘𝑘 [𝑦𝑦𝑘𝑘 − ℎ(𝑥𝑥̂𝑘𝑘 ; 𝑢𝑢𝑘𝑘 ; 𝑧𝑧𝑘𝑘 , 𝑘𝑘)] 𝑃𝑃𝑘𝑘+ = [𝐼𝐼 − 𝑘𝑘𝑘𝑘 𝐻𝐻𝑘𝑘 ]𝑃𝑃𝑘𝑘−
Figure 4: Inlet concentration of NO, NO2 and NH3 and exhaust gas temperature vs. time during validation test transient. During the validation transient, both exhaust temperature and NO/NO2 inlet concentrations are assumed to be constant due to the steady state engine operating conditions. Four set-points of AdBlue injection are performed during the test, corresponding to different ratio between injected and stoichiometric amount of AdBlue (respectively 50%, 80%, 100% and 120%). The rapid increase of NH3 inlet concentration allows exciting the dynamics of adsorption/desorption phenomena; in addition, in the last part of the maneuver, the urea injection prolonged over time, allows reaching the ammonia slip convergence. During the test, the inlet oxygen concentration is measured by a UEGO sensor, thus the ammonia oxidation is taken into account along the transient. Figures 5, 6 and 7 compare the experimental data provided by the FTIR gas analyzer with the corresponding open loop one-state model and EKF observer predictions.
(11)
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Figure 5: Simulated and measured NH3 concentration downstream of SCR converter.
Figure 7: Simulated and measured NO2 concentration downstream of SCR converter.
The NH3 outlet concentration depicted in Fig.5 proves that the one state model (red dashed line) captures the essential behavior of ammonia slip with a computational demand suitable for real-time application. In the first part of the transient (i.e. before 1000 s) inaccuracies are visible during the absorption and desorption phases mainly due to total ammonia capacity and the ammonia adsorption kinetic phenomena which cannot be optimally assessed during the identification process. Since the EKF application, considers an additional dynamic related to total ammonia adsorption capacity Ω allow improving overall level of ammonia downstream of the catalyst (refer to green dashed line in fig. 5). The recursive EKF algorithm allows achieving a gradually increasing estimation accuracy, thus the second part of the test evidences very good match. Figures 6 and 7 show the outlet concentration of NO and NO2 during the test transient. The comparison between predicted (open loop and EKF-2state models) and experimental concentrations evidence the overall good accuracy. A mismatch is observable mainly for NO-NO2 concentrations during under-stoichiometry and stoichiometry ammonia injection (refer to simulation results before 1000 s in Figs. 6 and 7) in which catalyst is empty, thus adsorption and desorption phenomena show great effects on modelled system dynamics while secondary effects inhibit the NOX reduction process in real system (mass transfer phenomena). More accurate results are obtained in over-stoichiometric conditions (refer to simulation results after 1000 s in Fig. 6 and 7) in which both adsorption/desorption dynamic and mass transfer phenomena can be neglected.
The EKF update, based on NH3 sensor feedback, improve prediction of NH3 slip mainly thanks 𝛺𝛺 additional dynamic, without compromising NO/NO2 concentrations. It is worth remarking that, in the current EKF application it is assumed constant values of covariance matrices Q and R; by considering that Kalman gain ki is the ratio between state estimate and measurements uncertainties the main differences in SCR phenomena under variable stoichiometric condition has to be taken into account. Q and R matrices could be a function of under/over stoichiometric conditions. 5. CONCLUSIONS The development of an EKF observer of ammonia storage for an automotive SCR has been presented. Besides the ammonia storage, the observer accounts for an additional dynamics related to the total ammonia adsorption capacity Ω. The validation results show that the EKF observer allows improving the prediction accuracy of NH3 downstream of the catalyst with respect to the feed-forward model. The improvement of NO/NO2 prediction is mainly due to the Ω additional dynamics and further improvements could be achieved by assuming Q and R matrices as function of under/over stoichiometric conditions. The application of the EKF for SCR control is intended to provide robustness and adaption features to feedback control. Further benefits include the extension of SCR operating range prior to malfunction indicator light appearance, as well as SCR monitoring for condition based maintenance. The observer is useful and suitable for real-time industrial-scale applications to support the light physics-based models implemented. REFERENCES Arsie, I., Cialeo, G., D'Aniello, F., Pianese, C., De Cesare, M., & Paiano, L. (2017). Control Oriented Modeling of SCR Systems for Automotive Application (No. 2017-24-0121). SAE Technical Paper. Arsie, I., Cricchio, A., De Cesare, M., Lazzarini, F., Pianese, C., & Sorrentino, M. (2017). Neural network models for virtual sensing of NOx emissions in automotive diesel engines with least square-based adaptation. Control Engineering Practice, 61, 11-20.
Figure 6: Simulated and measured NO concentration downstream of SCR converter.
Arsie, I., D'Aniello, F., Pianese, C., De Cesare, M., & Paiano, L. (2018). Development and Experimental Validation of a Control Oriented Model of SCR for Automotive Application (No. 2018-01-1263). SAE Technical Paper. 542
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Becerra, V. M., Roberts, P. D., & Griffiths, G. W. (2001). Applying the extended Kalman filter to systems described by nonlinear differential-algebraic equations. Control Engineering Practice, 9(3), 267-281.
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Jiang, K., Geng, P., Meng, F., & Zhang, H. (2016). An extended Kalman filter for input estimations in diesel-engine selective catalytic reduction applications. Neurocomputing, 171, 569575.
Chi, J. N., & DaCosta, H. F. (2005). Modeling and control of a Lambert, C., Hammerle, R., McGill, R., Khair, M., & Sharp, C. urea-SCR aftertreatment system (No. 2005-01-0966). SAE (2004). Technical advantages of urea SCR for light-duty and Technical Paper. heavy-duty diesel vehicle applications (No. 2004-01-1292). SAE Technical Paper. Cloudt, R., & Willems, F. (2011). Integrated Emission Management strategy for cost-optimal engine-aftertreatment Ljung, L. (1987). System identification: theory for the user. operation. SAE International Journal of Engines, 4(1), 1784- Prentice-hall. 1797. Nova, I., & Tronconi, E. (Eds.). (2014). Urea-SCR technology Cloudt, R., Saenen, J., Eijnden, E. v. d., Rojer, C., apr 2010. for deNOx after treatment of diesel exhausts (pp. 6-25). New Virtual exhaust line for model-based diesel aftertreatment York: Springer. development. In: SAE 2010 World Congress & Exhibition. SAE Schar, C. M., Onder, C. H., & Geering, H. P. (2006). Control of International. an SCR catalytic converter system for a mobile heavy-duty Colombo, M., Koltsakis, G., Nova, I., & Tronconi, E. (2012). application. IEEE Transactions on Control Systems Modelling the ammonia adsorption–desorption process over an Technology, 14(4), 641-653. Fe–zeolite catalyst for SCR automotive applications. Catalysis Schrade, F., Brammer, M., Schaeffner, J., Langeheinecke, K., & today, 188(1), 42-52. Kraemer, L. (2012). Physico-chemical modeling of an Devarakonda, M., Parker, G., Johnson, J. H., & Strots, V. integrated SCR on DPF (SCR/DPF) system. SAE International (2009). Model-based control system design in a urea-SCR Journal of Engines, 5(3), 958-974. aftertreatment system based on NH 3 sensor feedback. Shimizu, K. I., & Satsuma, A. (2007). Hydrogen assisted ureaInternational Journal of Automotive Technology, 10(6), 653. SCR and NH3-SCR with silver–alumina as highly active and Devarakonda, M., Parker, G., Johnson, J. H., Strots, V., & SO2-tolerant de-NOx catalysis. Applied Catalysis B: Santhanam, S. (2009). Model-based estimation and control Environmental, 77(1-2), 202-205. system development in a urea-SCR aftertreatment system. SAE Walker, A. (2012, September). Current and future trends in International Journal of Fuels and Lubricants, 1(1), 646-661. catalyst-based emission control system design. In presentation Folić, M., & Johansen, K. (2012). SCR-DPF Integrations for at the SAE Heavy-Duty Diesel Emission Control Symposium. Diesel Exhaust Performance and Perspectives for High SCR Willems, F., Cloudt, R., Van Den Eijnden, E., Van Genderen, Loadings, presentation at the US Department of Energy M., Verbeek, R., de Jager, B., ... & van den Heuvel, I. (2007). Is Directions in Engine Efficiency and Emissions Research closed-loop SCR control required to meet future emission (DEER). Dearborn, MI, October. targets? (No. 2007-01-1574). SAE Technical Paper. Gelb, A. (Ed.). (1974). Applied optimal estimation. MIT press. Willems, F., Mentink, P., Kupper, F., & Van den Eijnden, E. Henry, C., Langenderfer, D., Yezerets, A., Ruth, M., Chen, H. (2013, September). Integrated emission management for cost Y., Hess, H., & Naseri, M. (2011). Passive catalytic approach to optimal EGR-SCR balancing in diesels. In Proc. IFAC Symp. low temperature NOx emission abatement. city, 15(570), 21-8. Adv. Automotive Contr (pp. 711-716). Holderbaum, B. (2009, April). Kwee,“. In Integration of DPF Yao, S., Shost, M., Yoo, J. H., Cabush, D., Racine, D., Cloudt, and SCR-Interfaces and Interactions”, presentation at the 5th R., & Willems, F. (2008). Ammonia sensor for closed-loop SCR International CTI Forum, SCR Systems, Fellbach, Germany. control. SAE international journal of passenger cars-electronic and electrical systems, 1(2008-01-0919), 323-333. Hsieh, M. F., & Wang, J. (2011). A two-cell backstepping-based control strategy for diesel engine selective catalytic reduction Zhou, G., Jørgensen, J. B., Duwig, C., & Huusom, J. K. (2012). systems. IEEE Transactions on Control Systems Technology, State estimation in the automotive SCR deNOx process. IFAC 19(6), 1504-1515. proceedings volumes, 45(15), 501-506. Hsieh, M. F., & Wang, J. (2011). Development and experimental studies of a control-oriented SCR model for a twocatalyst urea-SCR system. Control Engineering Practice, 19(4), 409-422. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans (2010). IARC monographs on the evaluation of carcinogenic risks to humans. Ingested nitrate and nitrite, and cyanobacterial peptide toxins. IARC monographs on the evaluation of carcinogenic risks to humans, 94, v-vii, 1-412.
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