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The amount of ammonia stored on the walls of the catalyst (or ammonia storage) is a parameter with significant impact on NOx reduction efficiency and undesired ammonia slip of Selective Catalytic Reduction catalysts. This makes the ammonia storage interesting for utilization in urea injection control. However, ammonia storage is not directly measurable onboard vehicles, it can only be estimated. Model-based online estimation requires models that are capable of capturing the main phenomena of the SCR and at the same time can be computed onboard vehicle. While the modeling of SCR and model-based control is well present in the literature, it is apparent that few attempts of implementing the models on production ECUs were published. This paper reviews literature on ammonia storage, outlet NH3 and NOx concentration estimation in SCR and SCR/DPF systems-including the estimation of NOx sensor cross-sensitive to NH3-in order to present the state of the art.

The interest for NOx estimators (also known as virtual sensors or inferential sensors) has increased over the recent years due to benefits attributed to cost and performance. NOx estimators are typically installed to improve On-Board Diagnostics (OBD) monitors or to lower bill of material costs by replacing physical NOx sensors. This paper presents initial development results of a virtual engine-out NOx estimator planned for the implementation on an ECM. The presented estimator consists of an airpath observer and a NOx combustion model. The role of the airpath observer is to provide input values for the NOx combustion model such as the states of the gas at the intake and exhaust manifolds. It contains a nonlinear mean-value model of the airpath suitably transformed for an efficient and robust implementation on an ECM. The airpath model uses available sensory information in the vehicle to correct predictions of the gas states.

Over the past few years, innovative engine layouts have enabled significant reductions in both fuel consumption and pollutant emissions. However, exponential growth of powertrain control strategies complexity has inevitably accompanied these achievements. As a result, control and calibration development time and effort have become an ever-growing concern in powertrain design. An illustrative example of this complexity is Diesel Particulate Filters (DPF), which requires periodic regeneration to eliminate the accumulated soot. The main challenge for a DPF is to enhance the efficiency of these regeneration events, which depend largely on the quality of the regeneration temperature control. In this paper, we describe the DPF regeneration process, especially the main constraints and identification tests. We then give a simulation based comparison of two model based control solutions for the DPF thermal control during regeneration.

Recently, numerous researchers and Original Equipment Manufacturers (OEMs) have developed diesel engine-out nitrogen oxides (NOx) estimation algorithms that are capable of running in real-time on production Electronic Control Units (ECUs). These are generally referred to as virtual sensors or inferential sensors. NOx estimators are typically installed to improve On-Board Diagnostics (OBD) monitors or to lower bill of material costs by replacing physical NOx sensors. This paper reviews the literature of on-ECU NOx models in order to document the state of the art and identify directions for future work. The discussion includes applications of NOx estimators, accuracy of NOx estimators, required sensor inputs, sources of error, calibration effort, and ECU resource consumption.