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In modern Diesel engine control strategies the guideline is to perform an efficient combustion control, mainly due to the increasing request to reduce pollutant emissions. Innovative control algorithms for optimal combustion positioning require the on-board evaluation of a large number of quantities. In order to perform closed-loop combustion control, one of the most important parameters to estimate on-board is MFB50, i.e. the angular position in which 50% of fuel mass burned within an engine cycle is reached. Furthermore, MFB50 allows determining the kind of combustion that takes place in the combustion chamber, therefore knowing such quantity is crucial for newly developed low temperature combustion applications (such as HCCI, HCLI, distinguished by very low NOx emissions). The aim of this work is to develop a virtual combustion sensor, that provides MFB50 estimated value as a function of quantities that can be monitored real-time by the Electronic Control Unit (ECU).

The present paper describes a study of the basic parameters, which govern particulate (soot) formation within the combustion chamber of a small displacement (1.3 liter) turbocharged European CR-diesel engine. The main tools used for the study are a real fired engine, a numerical virtual engine and a special high ambient pressure vessel for injector spray visualization. The paper describes an improved soot formation model implemented in the virtual engine setup. A comparison is presented between measured and computed combustion data at 8 different load points. The paper concludes with a discussion of the means, which can be used to minimize the particulate matter formation in the design phase of both the combustion layout and the fuel injector atomizer as well as in the design of the injection control strategies.

Dual Mass Flywheel (DMF) systems are today widely adopted in compression ignition automotive powertrains, due to the well-known positive effects on vehicle drivability and fuel consumption. This work deals with the analysis of undesirable effects that the installation of a DMF may cause to engine and transmission dynamics, with the objective of understanding the causes and of determining possible solutions to be adopted. The main results of an experimental and simulation analysis, focused on the rotational dynamics of a powertrain equipped with a DMF system, are presented in the paper. A mathematical model of the physical system has been developed, validated, and used to investigate, in a simulation environment, the anomalous behavior of the powertrain that had been experimentally observed under specific conditions. Particular attention has been devoted to two aspects that are considered critical: engine cranking phase; interactions between powertrain dynamics and idle speed control.

A computer code oriented to S.I. engine control and powertrain simulation is presented. The model, developed in Matlab-Simulink® environment, predicts engine and driveline states, taking into account the dynamics of air and fuel flows into the intake manifold and the transient response of crankshaft, transmission gearing and vehicle. The model, derived from the code O.D.E.C.S. for the optimal design of engine control strategies now in use at Magneti Marelli, is suitable both for simulation analysis and to achieve optimal engine control strategies for minimum consumption with constraints on exhaust emissions and driveability via mathematical programming techniques. The model is structured as an object oriented modular framework and has been tested for simulating powertrain system and control performance with respect to any given transient and control strategy.

A set of models for the prediction of mechanical efficiency as function of the operating conditions for an automotive spark ignition engine is presented. The models are embedded in an integrated system of models with hierarchical structure for the analysis and the optimal design of engine control strategies. The validation analysis has been performed over a set of more than 400 steady-state operating conditions, where classical engine variables and pressure cycles were measured. Models with different functional structures have been tested; parameter values and indices of statistical significance have been determined via non-linear and step-wise regression techniques. The Neural Network approach (Multi Layer Perceptrons with Back-Propagation) has been also used to evaluate the feasibility of using such an approach for fast black-box modelization.