Exhaust valve phasing controllers for cold start NOx emissions reduction in heavy duty diesel engines

Abstract

In this paper, early exhaust valve opening (EVO) is applied to a diesel engine for fast warm up of the selective catalytic reduction (SCR) system with the ultimate goal of tailpipe NOx emissions reduction. By advancing EVO from top dead center, the exhaust gas temperature increases and the exhaust flow reduces, influencing the enthalpy available to warm up the SCR, and the engine-out NOx emissions increase or decrease depending on the engine’s operating conditions. Therefore, proper management of EVO is required to ensure that (1) engine-out NOx emissions do not increase when the SCR catalyst is cold; (2) heat transfer to the SCR increases and it warms up faster than the baseline operation (without EVO phasing); and (3) fuel consumption increase is minimal. A novel model predictive controller (MPC) is proposed for this application, assuming a limited preview of the drive cycle is available. For the MPC, an optimization objective function is applied such that a sequential warm up strategy can be implemented for the aftertreatment system catalysts. Using this technique, the prediction horizon for effective thermal management of the slow SCR system is reduced. In addition, a rule-based logic is offered as an alternative to the predictive controller to calculate the EVO trajectory with less computational power. Observations based on optimization problems solved by dynamic programing (DP) were used to develop the rule-based controller. Both the rule-based logic and model-based MPC are tested with a detailed high fidelity one-dimensional model in a model-in-the-loop simulator. Results indicate the potential of an EVO phasing system with the proposed controllers to reduce tailpipe NOx by 10% and 25% for the world harmonized transient cycle (WHTC) and federal test procedure (FTP), respectively. The rule-based controller has been found to be sensitive to the test drive cycle while the model based MPC shows a consistent performance, that is, independent of the test trajectory.