A least squares estimation-based parameterisation method for a control-oriented model of selective catalytic reduction systems

Abstract

Nowadays, heavy duty diesel engines are usually equipped with urea selective catalytic reduction (SCR) systems to remove the NOx emissions. To design a high performance urea dosage controller for the SCR systems, a control-oriented SCR model is essential. In the last decade, a few control-oriented SCR models have been proposed. However, a systematic calibration method is missing in the literature. To solve the calibration issue, we propose a novel parameterisation method. The main idea is to re-parameterise the model such that it becomes linear in the new parameters. This allows the model parameters to be determined analytically with the least squares estimation (LSE) technique. The proposed method is tested on our test bench with a few modifications to achieve improved robustness. The results confirm that the calibrated model is able to predict both the NOx and NH3 emissions reasonably well. The key system dynamics are always successfully captured.