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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).

Proper design of the combustion phase has always been crucial for Diesel engine control systems. Modern engine control strategies' growing complexity, mainly due to the increasing request to reduce pollutant emissions, requires on-board estimation of a growing number of quantities. In order to feedback a control strategy for optimal combustion positioning, one of the most important parameters to estimate on-board is the angular position where 50% of fuel mass burned over an engine cycle is reached (MFB50), because it provides important information about combustion effectiveness (a key factor, for example, in HCCI combustion control). In modern Diesel engines, injection patterns are designed with many degrees of freedom, such as the position and the duration of each injection, rail pressure or EGR rate. In this work a model of the combustion process has been developed in order to evaluate the energy release within the cylinder as a function of the injection parameters.

Evaluation of MFB50 is very useful for combustion control, since it gives an evaluation of the combustion process effectiveness. Real-time monitoring its value enables to detect for example the kind of combustion that is taking place (useful for example for HCCI applications), or could provide important information to improve real-time combustion control. While it is possible to determine the position where the 50% of mass burned inside the cylinder is reached using an in-cylinder pressure sensor, this work proposes to obtain this information from the engine speed fluctuation measurement. In-cylinder pressure sensors in fact are still not so common for on-board applications, since their cost will constitute an important portion of the whole engine control system cost.

Regarding automotive applications, Internal Combustion Engines (ICE) have become very complex plants to comply with present and future requirements in reduction of fuel consumption, pollutant emissions and performance improvement. As a consequence, the development of engine control and diagnostic system is a key aspect in the powertrain design. Mathematical models are useful tools in this direction, with applications that range from the definition of optimised management systems, to Hardware- and Software-in-the-Loop testing (HiL and SiL) and to modelbased control strategies. To this extent an original library has been developed by the authors for the simulation of last generation automotive engines. Library blocks were used to assembly a sub-model of the typical intake and exhaust system of a turbocharged engine (with VGT, intercooler, EGR circuit with cooler and throttle).

Nowadays requirements towards a reduction in fuel consumption and pollutant emissions of Internal Combustion Engines (ICE) keep on pushing manufacturers to improve engines performance through the enhancement of existing subsystems (e.g.: electronic fuel injection, air systems) and the introduction of specific devices (e.g.: exhaust gas recirculation systems, SCR, …). Modern systems require a combined design and application of different after-treatment devices. Mathematical models are useful tools to investigate the complexity of different system layouts, to design and to validate (HIL/SIL testing) control strategies for the after-treatment management. This study presents a mean value model of an exhaust system with SCR; it has been coupled with a common rail diesel engine combustion black box model (Neural Network based). So, dedicated models for exhaust pipes, oxidation catalyst, diesel particulate filter and selective catalytic converter are developed.

The present paper is about the rotational speed measurement of an automotive turbocharger, obtained starting from the analysis of acoustic emission produced by an engine, which have been acquired by a microphone placed under the vehicle hood. In the first part of the paper several upgrades to increase the overall performance of the speed extraction algorithm are presented and discussed, starting from the basic algorithm that has already demonstrated the methodology capability in a previous paper. In particular it has been considered a different signal sampling rate in order to extend the applicability of the methodology to a wider range of engines. Also a new processing procedure has been defined to increase the capability of the algorithm to tune on the frequency signal.

Effective and precise torque estimation is a great opportunity to improve actual torque-based engine management strategies. Modern ECU often already implement algorithms to estimate on-board the torque that is being produced by the engine, even if very often these estimation algorithms are based on look-up tables and maps and cannot be employed for example for diagnostic purposes. The indicated torque estimation procedure presented in this paper is based on the measurement of the engine speed fluctuations, and is mainly based on two separated steps. As a first step a torsional behavior model of the powertrain configuration is developed. The engine-driveline torsional model enables to estimate the indicated torque frequency component amplitude from the corresponding component of the instantaneous engine speed fluctuation. This estimation can be performed cycle by cycle and cylinder by cylinder.

The paper presents a non-intrusive, indirect and low-cost methodology for a real time on-board measurement of an automotive turbocharger rotational speed. In the first part of the paper the feasibility to gather information on the turbocharger speed trend is demonstrated by comparing the time-frequency analysis of the acoustic signal with the direct measurement obtained by an optical sensor facing the compressor blades, mounted in the compressor housing of a spark ignited turbocharged engine. In the second part of the paper, a real time algorithm, to be implemented in the engine control unit, is proposed. The algorithm is able to tune on the turbocharger revolution frequency and to follow it in order to extract the desired speed information. The frequency range containing the turbocharger acoustic frequency can be set utilizing a raw estimation of the compressor speed, derived by its characteristic map.

Theoretical models are useful tools in the design of engine control systems, with applications that range from the design of engine layout, the definition of optimised management systems, to hardware-in-the-loop testing (HiL) and to model-based control strategies. To define theoretical models for control-oriented applications, an original library has been built up at the University of Parma for the simulation of the intake and exhaust systems of automotive turbocharged engines. Starting from this library, a Mean Value Model (MVM) of a Diesel engine, with variable-geometry turbocharger (VGT), EGR and throttle valve, has been developed for a small automotive application. In the paper the matching of the engine model with a detailed model of the exhaust system (developed by Magneti Marelli Powertrain) is presented.

Multi-jet injection strategies open significant opportunities for the combustion management of the modern diesel engine. Splitting up the injection process into 5 steps facilitates the proper design of the combustion phase in order to obtain the desired torque level, whilst attempting a reduction in emissions, particularly in terms of NOx. Complex 3-D models are needed in the design stage, where components such as the injector or combustion chamber shape have to be determined. Alternatively, zero-dimensional approaches are more useful when fast interpretation of experimental data is needed and an optimization of the combustion process should be obtained based on actual data. For example, zero-dimensional models allow a quick choice of optimum control settings for each engine operating condition, avoiding the need to test all the possible combinations of engine control parameters.

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.

The paper presents the development of a methodology for detecting the misfire event using the time-frequency analysis of the instantaneous engine speed signal. The diagnosis of this type of malfunctioning operating condition is enforced by OBD requirements over the whole operating range of the engine, and many different approaches have been developed in the past in order to solve this problem. The novel approach presented here is based on the observation that the misfire causes an impulsive lack of torque acting on the engine crankshaft, and thus it causes the excitation of damped torsional vibrations at frequencies characteristic of the system under study. In order to enlighten the presence of this torsional vibration (and therefore detect the misfire event), information contained in the instantaneous crankshaft speed fluctuations have been processed in the time-frequency domain.

A computer code oriented to S.I. engine control and powertrain simulation is presented. The model 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, clutch, transmission gearing and vehicle. The whole model is integrated in the code O.D.E.C.S., now in use at Magneti Marelli, and is based on a hierarchical structure composed of different classes of models, ranging from black-box Neural Network to grey-box mean value models. By adopting the proposed approach, a satisfactory accuracy is achieved with limited computational demand, which makes the model suitable for the optimization of engine control strategies. Furthermore, in order to simulate the driver behavior during the assigned vehicle mission profile, two drive controllers have been implemented for throttle and brakes actuation, based on classical PID and fuzzy-logic theory.

The large number of mechanical, electro-magnetic and oleo-dynamic systems for variable valve actuation developed by automotive suppliers demonstrates the great interest that is being devoted to their potential application on internal combustion engines. In the paper, a possible strategy to realize an original engine load control by means of both intake and exhaust variable valve timing (VVT) is briefly presented and the thermodynamic analysis of the performance obtainable with this solution is carried out. The peculiarity of this strategy is that it is possible to directly recirculate the desired mass of exhaust gas with less limitation with respect to the external duct architecture.

To optimize a shift transient during clutch engagement (third phase of a gearshift) it is fundamental to define the engine velocity reference and the more appropriate instant at which to begin the clutch engagement itself. An analytical procedure to calculate the engine velocity reference value during the third phase of a gearshift and the moment when to begin the clutch engagement is presented. Simulation results obtained considering upshift and downshift with engine torque either applied or not are presented. The analytical solution presented permits to tune the third phase of the gearshift in an easier way than previous strategy based on look-up tables.

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.