Nowadays the availability of a wide portfolio of powertrain solutions represents the most effective approach to reduce the environmental impact of the transport sector. In such a framework, the PHOENICE project aims at assessing the capabilities of a plug-in hybrid electric powertrain to minimize the fuel consumption of a C-class Sport Utility Vehicle (SUV) while ensuring its compliance with the upcoming EU7 regulations. The achievement of these ambitious goals required the development of an innovative Spark Ignition (SI) engine concept able to maximize its efficiency and, at the same time, minimize its emissions through the synergic use of advanced in-cylinder charge motion, lean mixture with cooled EGR, electrified turbocharged and a dedicated aftertreatment composed by an electrically heated TWC, a GPF and a SCR. This paper will therefore provide a comprehensive overview on the powertrain design and optimization process which requires a hierarchical exploitation of both numerical simulations and experimental measurements to handle the increased complexity derived from the integration of the abovementioned technologies. More in details, simplified map-based models were preliminary used to assess the potential of selected powertrain configuration while detailed 3D CFD simulations allowed the optimization of combustion concept in highly diluted mixture conditions. Then, a 1D digital twin of the whole engine was developed to investigate a wide range of the operating parameters and to define their best calibration. The refinements of powertrain calibration and the achievement of the project targets will be finally assessed through an extensive set of experimental measurements performed in regulatory and real-world driving conditions. The paper will pay particular attention to the analysis of the double diluted combustion system and of the aftertreatment performance. At the present state of the research activity, the preliminary experimental tests carried out on the first engine prototype under steady state conditions showed promising results achieving a maximum indicated efficiency of about 45%.

Prof. Luciano Rolando, Associate Professor, Politecnico Di Torino