A Study on In-Cycle Control of NO x Using Injection Strategy with a Fast Cylinder Pressure Based Emission Model as Feedback

Paper #:
  • 2013-01-2603

Published:
  • 2013-10-14
Citation:
Muric, K., Stenlaas, O., Tunestal, P., and Johansson, B., "A Study on In-Cycle Control of NOx Using Injection Strategy with a Fast Cylinder Pressure Based Emission Model as Feedback," SAE Technical Paper 2013-01-2603, 2013, https://doi.org/10.4271/2013-01-2603.
Pages:
11
Abstract:
The emission control in heavy-duty vehicles today is based on predefined injection strategies and after-treatment systems such as SCR (selective catalytic reduction) and DPF (diesel particulate filter). State-of-the-art engine control is presently based on cycle-to-cycle resolution. The introduction of the crank angle resolved pressure measurement, from a piezo-based pressure sensor, enables the possibility to control the fuel injection based on combustion feedback while the combustion is occurring. In this paper a study is presented on the possibility to control NOx (nitrogen oxides) formation with a crank angle resolved NOx estimator as feedback. The estimator and the injection control are implemented on an FPGA (Field-Programmable Gate Array) to manage the inherent time constraints. The FPGA is integrated with the rest of the engine control system for injection control and measurement.Studies of injection strategies show that one of the feasible approaches, using a solenoid injector to control NOx, is a split-main injection based strategy. Results suggest that it is hard to control the NOx in a satisfactory manner. Really low injection pressures and long injection durations had to be applied to achieve control of the NOx formation. This also implies inherently high smoke emissions. The strategy allows NOx reduction but measurement and emission estimation results indicate that the delay between fuel injection and the maximum NOx concentration is too long. The NOx target value defined could never be satisfied using the feedback-based split-main injection strategy. Prediction-based feedback will be necessary to improve the control.
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