The state of the art in selective catalytic reduction control

Selective Catalytic Reduction (SCR) is a leading after treatment technology for the removal of nitrogen oxide (NOx) from exhaust gases (DeNOx). It presents an interesting control challenge, especially at high conversion, because both reagents (NOx and ammonia) are toxic, and therefore an excess of either is highly undesirable. Numerous system layouts and control methods have been developed for SCR systems, driven by the need to meet future emission standards. This paper summarizes the current state-of-the-art control methods for the SCR aftertreatment systems, and provides a structured and comprehensive overview of the research on SCR control. The existing control techniques fall into three main categories: traditional SCR control methods, model-based SCR control methods, and advanced SCR control methods. For each category, the basic control technique is defined. Further techniques in the same category are then explained and appreciated for their relative advantages and disadvantages. Thus this paper presents a snapshot of the current state of the art for the research area of SCR control. This is a very active field, and it is hoped that by providing a better understanding of the different control strategies already developed for SCR control, future areas of interest will be identified and developed with the ultimate aim of satisfying the increasingly stringent emissions legislation. Copyright © 2014 SAE International

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Nonlinear model predictive control applied to multivariable thermal and chemical control of selective catalytic reduction aftertreatment

Manufacturers of diesel engines are under increasing pressure to meet progressively stricter NOx emissions limits. A key NOx abatement technology is selective catalytic reduction (SCR) in which ammonia, aided by a catalyst, reacts with NOx in the exhaust stream to produce nitrogen and water. The conversion efficiency is temperature dependent: at low temperature, reaction rates are temperature limited, resulting in suboptimal NOx removal, whereas at high temperatures, they are mass transfer limited. Maintaining sufficiently high temperature to allow maximal conversion is a challenge, particularly after cold start, as well as during conditions in which exhaust heat is insufficient, such as periods of low load or idling. In this work, a nonlinear model predictive controller simultaneously manages urea injection and power to an electric catalyst heater, in the presence of constraints.<br/

Reliability challenges for automotive aftertreatment systems: a state-of-the-art perspective

YesThis paper provides a critical review and discussion of major challenges with automotive aftertreatment systems from the viewpoint of the reliability of complex systems. The aim of this review is to systematically explore research efforts towards the three key issues affecting the reliability of aftertreatment systems: physical problems, control problems and fault diagnostics issues. The review covers important developments in technologies for control of the system, various methods proposed to tackle NOx sensor cross-sensitivity as well as fault detection and diagnostics methods, utilized on SCR, LNT and DPF systems. This paper discusses future challenges and research direction towards assured dependability of complex cyber-physical systems.InPowerCare Project - JLR (Jaguar Land Rover

Automotive Powertrain Control — A Survey

This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd

A predictive dynamic model of a smart cogeneration plant fuelled with fast pyrolysis bio-oil

Small scale biomass-based cogeneration has the potential to contribute significantly to a clean, flexible, secure, and cost-efficient energy system. It provides flexibility to future energy systems by balancing variable intermittent renewable energy sources. To exploit its flexibility, a smart control unit is needed. To enable smart control of a cogeneration unit, and to determine its optimal working points, a dynamic system model is required. The purpose of this study is to develop, parameterize and tune a dynamic model of a cogeneration plant fuelled with fast pyrolysis bio-oil. The system is a hybrid diesel generator/flue gas boiler plant for electricity generation and water/space heating. The plant has two unique features: (i) pyrolysis bio-oil is a new fuel for both engine and boiler, and as such it influences their operation and emissions, (ii) power and heat generation are partially decoupled hence non-linearly correlated. The paper presents the integration of the components&rsquo; dynamic models into a system model. The model is parameterized and partially validated using measurements from a turbocharged four-cylinder diesel engine and a swirl burner both running on FPBO. Preliminary controls are designed and evaluated. Results show applicability and usefulness of the model for cogeneration system analysis and control design evaluation

An on-engine method for dynamic characterisation of NOx concentration sensors

An on-engine method for dynamic characterisation of automotive NOx concentration sensors is presented. Steps in start of injection on a diesel engine are employed to achieve step-like NOx concentration variations on exhaust flow. On the basis of the sensor response, delay and dynamic response can be easily identified; the paper shows a simple least squares procedure although other models and identification techniques could be used. Application data is presented for three NOx sensors: a research-grade chemiluminescence exhaust gas analyser, and two different commercial ZrO2-based sensors. © 2010 Elsevier Inc.The authors thanks R. Lujan and G. Couture for their valuable contribution in the experimental part of the present work. This work has been partially supported by Ministerio de Ciencia y Tecnologia through Project PLANUCO No. TRA2006-15620-C02-02.Galindo, J.; Serrano Cruz, JR.; Guardiola, C.; Blanco-Rodriguez, D.; Cuadrado, I. (2011). An on-engine method for dynamic characterisation of NOx concentration sensors. Experimental Thermal and Fluid Science. 35(3):470-476. https://doi.org/10.1016/j.expthermflusci.2010.11.010S47047635

Mathematical Methods for Design of Zone Structured Catalysts and Optimization of Inlet Trajectories in Selective Catalytic Reduction (SCR) and Three Way Catalyst (TWC)

Abgaskatalysatoren zählen zu den wichtigsten Maßnahmen, um Schadstoffemissionen von Verbrennungsmotoren zu vermindern. Mit der stetigen Verschärfung der Emissionsstandards nahm über die Jahre der Forschungsbedarf zu Abgasnachbehandlungssystemen signifikant zu. Der Fokus dieser Arbeit liegt auf der Lösung von Optimierungsproblemen im Bereich der Autoabgaskatalyse, um die Effizienz zu steigern. Dabei werden drei Problemklassen behandelt: 1) Die Light-Off-Verzögerung beim Kaltstart in Oxidationskatalysatoren, 2) Die effiziente Ammoniakdosierung bei der selektiven katalytischen Reduktion (SCR), um Ammoniakdurchbrüche zu vermeiden, 3) Die Spannungsstabilisierung der Lambda-Sonde im Drei-Wege-Katalysator (TWC) während einer Schubabschaltung. Das erste Problem wird durch eine modellbasierte mathematische Optimierung beschrieben, bei der das Beladungsprofil von gezont-strukturierten Katalysatoren auf Basis von Platingruppen-Metallen (PGM) optimiert wird. Dazu wird ein Optimierungsproblem aufgestellt, bei dem ein katalytisch aktiver Kanal in Zonen aufgeteilt wird, die mit unterschiedlichen Mengen von PGM beladen werden. Eine solche Beladung kann auch experimentell getestet werden. Die Effekte der Beladung auf Diffusionslimitierungen im Washcoat werden ebenso berücksichtigt. Ziel ist es, die axiale Verteilung der Beladung zu optimieren, wobei die Gesamtmenge an PGM konstant gehalten wird, um den Gesamtumsatz unter transienten Bedingungen zu maximieren. Dabei wird ein transientes 1D+1D-Modell mit dem impliziten Differentialgleichungslöser DASPKADJOINT numerisch gelöst und in ein nichtlineares Optimierungsproblem übersetzt, das mit einem beliebigen ableitungsbasierten nichtlinearen Optimierungslöser (NLP) behandelt werden kann. Dieses Modell wird auf zwei Beispielfälle angewandt: die CO-Oxidation auf einem Pt/Al2O3 Dieseloxidationskatalysator (DOC), um die Kaltstart-Emissionen zu minimieren, sowie die CH4-Oxidation auf Pd/Al2O3 unter Minimierung der Deaktivierungseffekte. In beiden Fällen wird beobachtet, dass bei der optimalen Lösung ein Beladungsmaximum am Kanaleingang zu einer Umsatzsteigerung führt. Die präsentierte Methode ist darüber hinaus allgemeingültig und kann auf andere Systeme mit unterschiedlicher Chemie angewandt werden, so dass auch signifikant andere Lösungen generiert werden können. Die Fähigkeit, NOx effizient durch Ammoniak zu reduzieren, ist Grundlage der SCR-Technologie für die Dieselabgasnachbehandlung. Ammoniak wird diskontinuierlich durch Zersetzung von Harnstoff-Wasser-Lösung dem SCR-Katalysator zugeführt. Bei der Anwendung im Fahrbetrieb ist es wegen hochgradig transienter Wechsel der Emissionen nicht sinnvoll, konstante Menge Ammoniak zu dosieren. Eine effiziente optimale Dosierungsstrategie ist wichtig, um einerseits hohen Umsatz zu gewährleisten und andererseits NH3-Schlupf zu vermeiden. Die Entwicklung einer optimalen Dosierungsstrategie erfordert die Anwendung einfacher, aber hinreichend akkurater mathematischer Modelle und robuster Optimierungsalgorithmen, um eine Lösung für eine große Anzahl zu optimierender Parameter zu erhalten. Mehrere Modellreduktionstechniken aus der Literatur wurden verwendet, um ein Grey-Box-Modell zu konstruieren. Die Methode der orthogonalen Kollokation über finiten Elementen (OCFE) wird genutzt, um die differential-algebraischen Gleichungen aus dem Optimierungsproblem in ein nichtlineares Programm zu überführen. Das Modell wird auf eine Simulation des WHTC-Testzyklus angewandt, um die NH3-Dosierung für jede Sekunde des Zyklus zu optimieren. Die optimale Lösung verbessert die Effizienz des Reduktion unter Einhaltung eines Schlupf-Maximums von 10 ppm zu jedem Zeitpunkt. Die präsentierte Methode lässt sich auch auf ähnliche Probleme zur Optimierung transienter Eingangsbedingungen anwenden. Im dritten Beispiel wird dieselbe Optimierungsmethode erweitert, um eine optimale Lambda-Trajektorie zu berechnen, die das Lambdasensorsignal am Katalysatorausgang stabilisiert, um Durchbrüche fetter Abgasgemische zu vermeiden. Zunächst wurde ein Beobachtermodell mit vereinfachter Kinetik entwickelt und gegen Versuchsstand-Experimente kalibriert. Direkte Kollokation auf Basis der OCFE wird genutzt, um das Optimierungsproblem in ein nichtlineares Programm zu überführen. Die optimale Lösung zeigt eine schnelle Stabilisierung der Ausgangssensor-Spannung ohne Überschwingungen. Diese Strategie verringert die Relaxationszeit der Sensorspannung signifikant, was wichtig für den Einsatz als Feedback-Controller in einem Dreiwegekatalysator wäre

Data-Driven Model for Real-Time Estimation of NOx in a Heavy-Duty Diesel Engine

The automotive sector is greatly contributing to pollutant emissions and recent regulations introduced the need for a major control of, and reduction of, internal combustion engine emissions. Artificial intelligence (AI) algorithms have proven to hold the potential to be the thrust in the state-of-the-art for engine-out emission prediction, thus enabling tailored calibration modes and control solutions. More specifically, the scientific literature has recently witnessed strong efforts in AI applications for the development of nitrogen oxides (NOx) virtual sensors. These latter replace physical sensors and exploit AI algorithms to estimate NOx concentrations in real-time. Still, the calibration of the algorithms, together with the appropriate choice of the specific metric, strongly affects the prediction capability. In the present paper, a machine learning-based virtual sensor for NOx monitoring in diesel engines was developed, based on the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The latter is commonly used in the literature to deploy virtual sensors due to its high performance, flexibility and robustness. An experimental campaign was carried out to collect data from the engine test bench, as well as from the engine electronic control unit (ECU), for the development and calibration of the virtual sensor at steady-state conditions. The virtual sensor has, since then, been tested throughout on an on-road driving mission to assess its prediction performance in dynamic conditions. In stationary conditions, its prediction accuracy was around 98%, whereas it was 85% in transient conditions. The present study shows that AI-based virtual sensors have the potential to significantly improve the accuracy and reliability of NOx monitoring in diesel engines, and can, therefore, play a key role in reducing NOx emissions and improving air quality