Team NeCS

Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry
Other Grants and Activities

Section: New Results

Observers design for multi-sensor systems: application to HDI engines

Participants : C. Canudas-de-Wit [ contact person ] , R. Ceccarelli.

Collaboration with IFP (Institut Français du Petrole).

Research activity in vehicle industry targets pollutant emission reduction. Homogeneous charge compression engine ignition (HCCI) is an interesting alternative to this problem. New European community laws impose new stringer constraints to pollution, and as a consequence, forces the car industry to realize on board diagnosis system in order to detect engine failures that may result in an increase in the engine pollution. The control of pollutant emission, in diesel engine, is ensured by exhaust gas recirculation system (EGR). Its functioning is very important and a fault detection and isolation system (FDI) is necessary in order to ensure good performances and poor emissions. Exhaust gas could be taken before or after compressor: respectively called high and low pressure EGR.

In this project carried out in collaboration with the IFP (Institut Français du Petrole), we aim at developing model-based observer allowing to identify several types of engine failures, like: gas leakage in the low and high pressure recirculation circuits, ill functioning of some of the sensors, and actuators (valves).

First year has been dedicated to system modeling with respect to leakage detection and estimation. In a first phase, leakage in intake receiver has been studied by the mean of two different nonlinear adaptive observers. The observers have been tested on a AMEsim - Simulink co-simulation environments.. For this first application, no differences come out from the use of these two observers. The second part of the work (current task) is dedicated to the design of a variable threshold in order to be less conservative and avoid false alarms. For this purpose I am investigating a study of possible causes (modeling and measures error) whose effect drive leakage estimation away from the correct value [34] .


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