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Automotive engines and control systems are more and more sophisticated due to increasingly restrictive environmental regulations. Particularly in both diesel and SI lean-burn engines NOx emissions are the key pollutants to deal with and sophisticated Engine Management System (EMS) strategies and after-treatment devices have to be applied. In this context, the in-cylinder oxygen mass fraction plays a key-role due its direct influence on the NOx formation mechanism. Real-time estimation of the intake O₂ charge enhances the NOx prediction during engine transients, suitable for both dynamic adjustments of EMS strategies and management of aftertreatment devices. The paper focuses on the development and experimental validation of a real-time estimator of O₂ concentration in the intake manifold of an automotive common-rail diesel engine, equipped with turbocharger and EGR system.

The paper describes suited methodologies for developing Recurrent Neural Networks (RNN) aimed at estimating NOx emissions at the exhaust of automotive Diesel engines. The proposed methodologies particularly aim at meeting the conflicting needs of feasible on-board implementation of advanced virtual sensors, such as neural network, and satisfactory prediction accuracy. Suited identification procedures and experimental tests were developed to improve RNN precision and generalization in predicting engine NOx emissions during transient operation. NOx measurements were accomplished by a fast response analyzer on a production automotive Diesel engine at the test bench. Proper post-processing of available experiments was performed to provide the identification procedure with the most exhaustive information content. The comparison between experimental results and predicted NOx values on several engine transients, exhibits high level of accuracy.