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The potential of internal EGR (iEGR) and external EGR (eEGR) in reducing the engine-out NOx emissions in a heavy-duty diesel engine has been investigated by means of a refined 1D fluid-dynamic engine model developed in the GT-Power environment. The engine is equipped with Variable Valve Actuation (VVA) and Variable Geometry Turbocharger (VGT) systems. The activity was carried out in the frame of the CORE Collaborative Project of the European Community, VII FP. The engine model integrates an innovative 0D predictive combustion algorithm for the simulation of the HRR (heat release rate) based on the accumulated fuel mass approach and a multi-zone thermodynamic model for the simulation of the in-cylinder temperatures. NOx emissions are calculated by means of the Zeldovich thermal and prompt mechanisms.

The combustion propagation and burned-gas expansion processes in a bi-fuel CNG SI engine were characterized by applying a newly developed diagnostic tool, in order to better understand how these processes are related to the fuel composition, to the engine operating variables as well as to the exhaust emissions. The diagnostic tool is based on an original multizone heat-release model that is coupled with a CADmodel of the burned-gas containing surface for the computation of the burning speed and the burned-gas mean expansion velocity. Furthermore, the thermal and prompt NO sub-models, embedded in the diagnostic code, were employed to study the effects ofNO formation mechanisms and thermodynamic parameters on nitric oxide emissions.

The present work concerns the study of the potentialities of high-boost small-displacement C.R. (Common Rail) diesel engines where the compressor and the expander are mechanically disengaged for the purpose of power cogeneration from the exhaust gas. This objective can be achieved by means of advanced concept electrical devices capable of delivering the energy produced by the expander either to the drivetrain transmission or to the even more power-demanding auxiliary equipment of both the engine and the vehicle. The performance of a small-displacement boosted diesel engine with a common-rail injection system has been predicted by means of a computational code obtained by integrating different in-house non-commercial codes that simulate the intake, combustion and exhaust processes. The model validation has been carried out by means of the experimental data obtained at Fiat Research Center on a commercial small-displacement C.R. turbocharged diesel engine.

An experimental investigation was performed on a turbocharged spark-ignition 4-cylinder production engine fuelled with natural gas and with two blends of natural gas and hydrogen (15% and 25% in volume of H₂). The engine was purposely designed to give optimal performance when running on CNG. The first part of the experimental campaign was carried out at MBT timing under stoichiometric conditions: load sweeps at constant engine speed and speed sweeps at constant load were performed. Afterwards, spark advance sweeps and relative air/fuel ratio sweeps were acquired at constant engine speed and load. The three fuels were compared in terms of performance (fuel conversion efficiency, brake specific fuel consumption, brake specific energy consumption and indicated mean effective pressure) and brake specific emissions (THC, NOx, CO).

Fuel cell electrified powertrains are currently a promising technology towards decarbonizing the heavy-duty transportation sector. In this context, extensive research is required to thoroughly assess the hydrogen economy potential of fuel cell heavy-duty electrification. This paper proposes a real-time capable energy management strategy (EMS) that can achieve improved hydrogen economy for a fuel cell electrified heavy-duty truck. The considered heavy-duty truck is modelled first in Simulink® environment. A baseline heuristic map-based controller is then retained that can instantaneously control the electrical power split between fuel cell system and the high-voltage battery pack of the heavy-duty truck. Particle swarm optimization (PSO) is consequently implemented to optimally tune the parameters of the considered EMS.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.