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The containment of air pollution resulting from transportation sector is worldwide considered as one of the most important targets to be still achieved. The emissions of particulate matter (PM) assume a relevant role when Diesel engine-based vehicles are concerned. In the present paper, an innovative model for the prediction of engine-out emitted PM mass has been developed and assessed on two different Diesel engines. A semi-empirical approach is defined as reference model and initially validated through the ISO/IEC Guide 98-3:2008 procedure and its potentials of the being coupled to a predictive combustion model are demonstrated. Then, relying on the findings of the semi-empirical approach, a Random Forest (RF) algorithm has been thoroughly analysed and selected as a promising solution for real-time testing with on-line computed variables. Furthermore, an automatic feature selection and calibration procedure of the algorithm hyper-parameters has been developed. Very interesting performances were recorded as the reference model prediction accuracies were reproduced and comparable results were obtained when only ECU-measured variables were considered. The presented Random Forest model can be intended to be part of a pollution-oriented real-time powertrain control strategy that could on accurate and repeatable PM estimations.

Development and assessment of a random-forest model for real-time prediction of diesel engine-out particulate matter emissions / Misul, Daniela Anna; Maino, Claudio; Spessa, Ezio; Finesso, Roberto. - In: INTERNATIONAL JOURNAL OF MECHANICS AND CONTROL. - ISSN 1590-8844. - 21:No.02(2020), pp. 95-110.

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