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The paper provides an overview of a developed methodology and a toolchain for modeling and control of a complex aftertreatment system for passenger cars. The primary objective of this work is to show how the use of this methodology allows to streamline the development process and to reduce the development time thanks to a model based semi-automatic control design methodology combined with piece-wise optimal control. Major improvements in passenger car tailpipe NOx removal need to be achieved to fulfil the upcoming post EURO 6 norms and Real Driving Emissions (RDE) limits. Multi-brick systems employing combinations of multiple Selective Catalytic Reduction (SCR) catalysts with an Ammonia Oxidation Catalysts, known also as Ammonia Clean-Up Catalyst (CUC), are proposed to cover operation over a wide temperature range. However, control of multi-brick systems is complex due to lack of available sensors in the production configurations.

Many control approaches for selective catalytic reduction (SCR) systems require knowledge of ammonia storage (NH3 storage) to dose urea accurately. Currently there are no technologies to directly measure internal NH3 storage in a vehicle, so it can only be inferred from hardware sensors located upstream, downstream, or in the catalyst. This paper describes an application of extended Kalman filter (EKF) state estimator used as a virtual sensor for urea injection control of a multi-brick aftertreatment system. The proposed estimator combines mean-value physics-based models of combined SCR and diesel particulate filter (SCR/DPF), SCR and clean-up catalyst (CUC). It uses hardware sensors at the inlet and outlet of the aftertreatment system, and includes no sensors between the catalysts. Performance of the proposed estimator was validated in simulations against a high-fidelity model of the aftertreatment system.

Powertrain software development for series production faces multifaceted challenges related to high functional complexity, high quality standards, reduced time to market and high development costs. Software architecture tackles the above mentioned challenges by breaking down the complexity of application software into modular components. Hence, design errors introduced during that phase cause significant cost and time deviations. Early and repeated analysis of new and modified architecture artifacts is required to detect design errors and the impact of the subsequent changes in the software architecture. Engine management software has a high degree of functional complexity and large number of system variants depending upon market requirements. This paper deals with the methods to perform automated evaluation of Renault’s EMS 2012 Engine Management Software in a Continuous Integration Framework.

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.