The NOx health and environmental problems make necessary to reduce this gaseous emission from different sources. Furthermore, its increase in the last years and the difficulties to remove it with after-treatment systems already in the market make more urgent the development of new techniques. The purpose of this investigation is to study the low temperature catalytic oxidation of NO to NO2 and its adsorption over a renewable activated carbon (AC) from coconut shell. The present research presents the results of experimental work carried out using a laboratory scale reactor to investigate the low temperature catalytic oxidation of NO. Activated carbon was housed in the reactor and tests were carried out with different reactor sizes, different activated carbon forms and shapes, different gas mixtures at different temperatures and different levels of humidity to simulate dry and wet particulate-free diesel engine exhaust gas. The effects of addition of ozone in the gas on the NO oxidation were also explored. Gas analysis upstream and downstream of the catalytic reactor was carried out in all cases during the charge and regeneration of the AC. An extensive literature review in conjunction with measurement of some properties of the activated carbon helped to understand better its characteristics and behaviour. The results of this study indicate that in the case of dry gas, the activated carbon initially acts as an adsorber and only after operation of several hours, the NO oxidation that takes place in the reactor results in increased NO2 levels in the product gas. The NO conversion is affected by the activated carbon form and reaction conditions including temperature, humidity, oxygen, NO, CO2 content in the inlet gas, temperature, space velocity, linear gas velocity, residence time, reactor shape, AC pretreatment and lifespan. Water vapour has a detrimental effect on the conversion of NO to NO2 before the AC reaches the steady-state conditions. On the other hand, ozone is effective in converting NO to NO2 at room temperature. This research has developed some findings not studied or reported by other researches before and confirms and/or complements results reported in the literature review by other groups, which will benefit the development of a renewable after-treatment system of NOx emissions.

Tougher environmental legislations are a driving force for development of aftertreatment technologies for truck and car exhaust gases. In particular, the emission requirements are high on nitrogen oxides (NOx) and particulate matter. Focus of this thesis work is to develop a component in the exhaust system, a NOx level reduction system. The currently used technology with urea evaporator has problem with formation of urea crystals due to delayed urea evaporation. Crystalline urea causes reduced exhaust flow and thus build up a pressure in the system that has negative impact on the performance of the engine. Feasibility study was done to understand function, advantage and disadvantages of current design and the need for a new design. The main task of this project was to investigate and propose a new design of the helix-rotary evaporator and to present it in the form of parametric model. Material selection needed for urea injection arrangement, 3D printed model for visualization of the concept and integration of the model to next generation aftertreatment system (NGA) are examples of sub-tasks that was performed to reach the main objective. Several generations of selected concept were developed in 3D design which later was 3D printed to visualize the ideas. The parametric 3D model was designed so that it later serves as input model for a later phase in the development project, where computational fluid dynamics is utilized. Parametric modelling is used to provide wide range of possibility to generate different models for simulation and reduce pre-simulation works. Selected concept parametric model has six different parameters that can be analysed. Material selection carried out to injection manifold thought CES Edupack and consultancy of material engineers. Three different austenitic stainless steels were recommended.

Yes / This 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)

Producing more fuel efficient vehicles as well as lowering emissions are of high importance among heavy duty vehicle manufactures. One functionality of lowering fuel consumption is to use a so called \emph{look-ahead control strategy}, which uses the GPS and topography data to determine the optimal velocity profile in the future. When driving downhill in slopes, no fuel is supplied to the engine which lowers the temperature in the aftertreatment system. This results in a reduced emission reduction capability of the aftertreatment system. This master thesis investigates the possibilities of using preheating look-ahead control actions to heat the aftertreatment system before entering a downhill slope, with the purpose of lowering fuel consumption and $NO_x$ emissions. A temperature model of a heavy duty aftertreatment system is produced, which is used to analyse the fuel consumption and $NO_x$ reduction performance of a Scania truck. A Dynamic Programming algorithm is also developed with the purpose of defining an optimal control trajectory for minimizing the fuel consumption and released $NO_x$ emissions. It is concluded that the Dynamic Programming optimization initiates preheating control actions with results of fuel consumption reduction as well as $NO_x$ emissions reductions. The best case for reducing the maximum amount of fuel consumption results in 0.14\% lower fuel consumption and 5.2\% lower $NO_x$ emissions.

yes / This paper presents a methodology for fault detection, fault prediction and fault isolation based on the integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid framework that captures causality in the complex engineered system. The proposed methodology is based on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of detected/predicted faults, using the information propagated from the HMM model as empirical evidence. The feasibility and effectiveness of the presented approach are discussed in conjunction with the application to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the implementation of the methodology to this case study, with data available from real-world usage of the system. The results show that the proposed methodology identifies the fault faster and attributes the fault to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its applicability is much wider to the fault detection and prediction problem of any similar complex engineered system.

The complexity of engineered systems has increased remarkably to meet customer needs. In the continuously growing global market, it is essential for engineered systems to keep their productivities which can be achieved by higher reliability and availability. Integrated health management based on diagnostics and prognostics provides significant benefits, which includes increasing system safety and operational reliability, with a significant impact on the life-cycle costs, reducing operating costs and increasing revenues. Characteristics of complex systems such as nonlinearity, dynamicity, non-stationarity, and non-Gaussianity make diagnostics and prognostics more challenging tasks and decrease the application of classic reliability methods remarkably – as they cannot address the dynamic behaviour of these systems. This research has focused on detecting, predicting and isolating faults in engineered systems, using operational data with multifarious data characteristics. Complexities in the data, including non-Gaussianity and high nonlinearity, impose stringent challenges on fault analysis. To deal with these challenges, this research proposed an integrated data-driven methodology in which hidden Markov modelling (HMM) and Bayesian network (BN) were employed to detect, predict and isolate faults in a system. The fault detection and prediction were based on comparing and exploiting pattern similarity in the data via the loglikelihood values generated through HMM training. To identify the root cause of the faults, the probability values obtained from updating the BN were used which were based on the virtual evidence provided by HMM training and log-likelihood values. To set up a more accurate data-driven model – particularly BN structure – engineering analyses were employed in a structured way to explore the causal relationships in the system which is essential for reliability analysis of complex engineered systems. The automotive exhaust gas Aftertreatment system is a complex engineered system consisting of several subsystems working interdependently to meet emission legislations. The Aftertreatment system is a highly nonlinear, dynamic and non-stationary system. Consequently, it has multifarious data characteristics, where these characteristics raise the challenges of diagnostics and prognostics for this system, compared to some of the references systems, such as the Tennessee Eastman process or rolling bearings. The feasibility and effectiveness of the presented framework were discussed in conjunction with the application to a real-world case study of an exhaust gas Aftertreatment system which provided good validation of the methodology, proving feasibility to detect, predict, and isolate unidentified faults in dynamic processes.

<div>Stoichiometric air-fuel ratio (AFR) and air/EGR flow control are essential control problems in today’s advanced spark-ignited (SI) engines to enable effective application of the three-way-catalyst (TWC) and generation of required torque. External exhaust gas recirculation (EGR) can be used in SI engines to help mitigate knock, reduce enrichment and improve efficiency[1 ]. However, the introduction of the EGR system increases the complexity of stoichiometric engine-out lambda and torque management, particularly for high BMEP commercial vehicle applications. This thesis develops advanced frameworks for sensing and control architecture designs to enable robust air handling system management, stoichiometric cylinder air-fuel ratio (AFR) control and three-way-catalyst emission control.</div><div><br></div><div><div>The first work in this thesis derives a physically-based, control-oriented model for turbocharged SI engines utilizing cooled EGR and flexible VVA systems. The model includes the impacts of modulation to any combination of 11 actuators, including the throttle valve, bypass valve, fuel injection rate, waste-gate, high-pressure (HP) EGR, low-pressure (LP) EGR, number of firing cylinders, intake and exhaust valve opening and closing timings. A new cylinder-out gas composition estimation method, based on the inputs’ information of cylinder charge flow, injected fuel amount, residual gas mass and intake gas compositions, is proposed in this model. This method can be implemented in the control-oriented model as a critical input for estimating the exhaust manifold gas compositions. A new flow-based turbine-out pressure modeling strategy is also proposed in this thesis as a necessary input to estimate the LP EGR flow rate. Incorporated with these two sub-models, the control-oriented model is capable to capture the dynamics of pressure, temperature and gas compositions in manifolds and the cylinder. Thirteen physical parameters, including intake, boost and exhaust manifolds’ pressures, temperatures, unburnt and burnt mass fractions as well as the turbocharger speed, are defined as state variables. The outputs such as flow rates and AFR are modeled as functions of selected states and inputs. The control-oriented model is validated with a high fidelity SI engine GT-Power model for different operating conditions. The novelty in this physical modeling work includes the development and incorporation of the cylinder-out gas composition estimation method and the turbine-out pressure model in the control-oriented model.</div></div><div><br></div><div><div>The second part of the work outlines a novel sensor selection and observer design algorithm for linear time-invariant systems with both process and measurement noise based on <i>H</i>2 optimization to optimize the tradeoff between the observer error and the number of required sensors. The optimization problem is relaxed to a sequence of convex optimization problems that minimize the cost function consisting of the <i>H</i>2 norm of the observer error and the weighted <i>l</i>1 norm of the observer gain. An LMI formulation allows for efficient solution via semi-definite programing. The approach is applied here, for the first time, to a turbo-charged spark-ignited (SI) engine using exhaust gas recirculation to determine the optimal sensor sets for real-time intake manifold burnt gas mass fraction estimation. Simulation with the candidate estimator embedded in a high fidelity engine GT-Power model demonstrates that the optimal sensor sets selected using this algorithm have the best <i>H</i>2 estimation performance. Sensor redundancy is also analyzed based on the algorithm results. This algorithm is applicable for any type of modern internal combustion engines to reduce system design time and experimental efforts typically required for selecting optimal sensor sets.</div></div><div><br></div><div><div>The third study develops a model-based sensor selection and controller design framework for robust control of air-fuel-ratio (AFR), air flow and EGR flow for turbocharged stoichiometric engines using low pressure EGR, waste-gate turbo-charging, intake throttling and variable valve timing. Model uncertainties, disturbances, transport delays, sensor and actuator characteristics are considered in this framework. Based on the required control performance and candidate sensor sets, the framework synthesizes an H1 feedback controller and evaluates the viability of the candidate sensor set through analysis of the structured</div><div>singular value μ of the closed-loop system in the frequency domain. The framework can also be used to understand if relaxing the controller performance requirements enables the use of a simpler (less costly) sensor set. The sensor selection and controller co-design approach is applied here, for the first time, to turbo-charged engines using exhaust gas circulation. High fidelity GT-Power simulations are used to validate the approach. The novelty of the work in this part can be summarized as follows: (1) A novel control strategy is proposed for the stoichiometric SI engines using low pressure EGR to simultaneously satisfy both the AFR and air/EGR-path control performance requirements; (2) A parametrical method to simultaneously select the sensors and design the controller is first proposed for the internal combustion engines.</div></div><div><br></div><div><div>In the fourth part of the work, a novel two-loop estimation and control strategy is proposed to reduce the emission of the three-way-catalyst (TWC). In the outer loop, an FOS estimator consisting of a TWC model and an extended Kalman-filter is used to estimate the current TWC fractional oxygen state (FOS) and a robust controller is used to control the TWC FOS by manipulating the desired engine λ. The outer loop estimator and controller are combined with an existing inner loop controller. The inner loop controller controls the engine λ based on the desired λ value and the control inaccuracies are considered and compensated by the outer loop robust controller. This control strategy achieves good emission reduction performance and has advantages over the constant λ control strategy and the conventional two-loop switch-type control strategy.</div></div>