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SCR on Filter (SCRoF) is an efficient and compact NOX and PM reduction technology already used in series production for light-duty applications. The technology is now finding its way into the medium duty and heavy duty market. One of the key challenges for successful application is the robustness to real world variations. The solution to this challenge can be found by using model-based control algorithms, utilizing state estimation by physics-based catalyst models. This paper focuses on the development, validation and real time implementation of a physics-based control oriented SCRoF model. An overview of the developed model will be presented, together with a brief description of the model parameter identification and validation process using engine test bench measurement data. The model parameters are identified following a streamlined approach, focusing on decoupling the effects of deNOx and soot phenomena.

Heavy-duty diesel engines are used in a wide range of applications. For varying operating environments, the engine and aftertreatment system must comply with the real-world emission legislation limits. Simultaneously, minimal fuel consumption and good drivability are crucial for economic competitiveness and usability. Meeting these requirements takes substantial development and calibration effort, and complying with regulations results in a trade-off between emissions and fuel consumption. TNO's Integrated Emission Management (IEM) strategy finds online, the cost-optimal point in this trade-off and is able to deal with variations in operating conditions, while complying with legislation limits. Based on the actual state of the engine and aftertreatment system, an optimal engine operating point is computed using a model-based optimal-control algorithm.

This article describes a NOx sensor based urea dosing control strategy for heavy-duty diesel aftertreatment systems using Selective Catalytic Reduction. The dosing control strategy comprises of a fast-response, model-based ammonia storage control system in combination with a long-timescale tailpipe-feedback module that adjusts the dosing quantity according to current aftertreatment conditions. This results in a control system that is robust to system disturbances such as biased NOx sensors and variations in AdBlue concentrations. The cross-sensitivity of the tailpipe NOx sensor to ammonia is handled by a novel, smart signal filter that can reliably identify the contributions of NOx and NH3 in the tailpipe sensor signal, without requiring an artificial perturbation of the dosing signal.