ECU-oriented models for NO x prediction. Part 2: adaptive estimation by using an NO x sensor

Abstract

The implantation of nitrogen oxide sensors in diesel engines is necessary in order to track emissions at the engine exhaust line for diagnosis and control of the after-treatment devices. However, the use of models is still necessary since the sensor outputs are delayed and filtered. The present paper deals with the problem of the nitrogen oxide estimation in two parts; Part 1 deals with a control-oriented model for the nitrogen oxide estimation, while Part 2 presents data fusion of the model and the sensor to improve the estimation, which is presented in the following. The use of models for the nitrogen oxide estimation is an alternative but the drift and the ageing are still issues. In order to overcome this problem, the fusion of different signals can be carried out in a smart way by means of a Kalman filter. There exist different ways of presenting this fusion, from directly tracking the bias to updating the model parameters. For this, different algorithms are proposed in this paper with the aim of correcting the model output. Furthermore, the estimation of the actual nitrogen oxide concentration, by preventing sensor delay and filtering, is also integrated in the algorithm, which is a suitable strategy for combining nitrogen oxide sensors and models on an onboard basis.