Development of a Kalman filter estimator for simulation and controlof NOx and PM in a SCR catalyston a DPF

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Department of Mechanical Engineering-Engineering Mechanics


The goal of a control system for a SCR catalyst on a DPF (SCR-F) is to minimize the fuel consumption due to back pressure while maximizing the NOx conversion across the SCR-F. To meet these goals, prediction of the internal states of the 2D spatial distribution of temperature, PM mass retained and NH3 coverage fraction in an SCR-F is required. To predict these internal states, a state estimator capable of simulating the PM oxidation rate, SCR reactions, filtration efficiency and pressure drop across the SCR-F based on inlet conditions and outlet sensor data is required. A 2D model of the SCR-F capable of predicting the SCR-F internal states was developed and validated using experimental data from a Johnson Matthey SCRF® and Cummins 2013 ISB engine. To reduce the computational cost, an estimator model with a coarser mesh and a quasi-steady state solution for the chemical species and energy conservation equations was developed which was faster than real time. The model was combined with the outlet thermocouple, pressure drop and NOx sensor data from the engine control unit using an extended Kalman filter to create the SCR-F state estimator. The estimator was able to predict the target internal states to within 5% of the experimental data. The estimator was able to compensate for calibration parameter errors by up to 5% using the sensor data along with filtering of the sensor noise. A DOC estimator was used to obtain the SCR-F inlet NOx concentrations and temperatures.

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© IMechE 2020. Publisher’s version of record: https://doi.org/10.1177/1468087420954041

Publication Title

International Journal of Engine Research