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Automotive engines and control systems are more and more sophisticated due to increasingly restrictive environmental regulations. Particularly in both diesel and SI lean-burn engines NOx emissions are the key pollutants to deal with and sophisticated Engine Management System (EMS) strategies and after-treatment devices have to be applied. In this context, the in-cylinder oxygen mass fraction plays a key-role due its direct influence on the NOx formation mechanism. Real-time estimation of the intake O₂ charge enhances the NOx prediction during engine transients, suitable for both dynamic adjustments of EMS strategies and management of aftertreatment devices. The paper focuses on the development and experimental validation of a real-time estimator of O₂ concentration in the intake manifold of an automotive common-rail diesel engine, equipped with turbocharger and EGR system.

In the paper, a rule-based (RB) control strategy is proposed to optimize on-board energy management on a Hybrid Solar Vehicle (HSV) with series structure. Previous studies have shown the promising benefits of such vehicles in urban driving in terms of fuel economy and carbon dioxide reduction, and that economic feasibility could be achieved in a near future. The control architecture consists of two main loops: one external, which determines final battery state of charge (SOC) as function of expected solar contribution during next parking phase, and the second internal, whose aim is to define optimal ICE- EG power trajectory and SOC oscillation around the final value, as addressed by the first loop. In order to maximize the fuel savings achievable by a series architecture, an intermittent ICE scheduling is adopted for HSV. Therefore, the second loop yields the average power at which the ICE is operated as function of the average values of traction power demand and solar power.

The paper focuses on the experimental identification and validation of recurrent neural networks (RNN) for virtual sensing of NO emissions in internal combustion engines (ICE). Suited training procedures and experimental tests are proposed to improve RNN precision and generalization in predicting NO formation dynamics. The reference Spark Ignition (SI) engine was tested by means of an integrated system of hardware and software tools for engine test automation and control strategies prototyping. A fast response analyzer was used to measure NO emissions at the exhaust valve. The accuracy of the developed RNN model is assessed by comparing simulated and experimental trajectories for a wide range of operating scenarios. The results evidence that RNN-based virtual NO sensor will offer significant opportunities for implementing on-board feedforward and feedback control strategies aimed at improving the performance of after-treatment devices.

This paper deals with modeling and analysis of the integration of ThermoElectric generators (TEG) into a conventional vehicle, specifically aimed at recovering waste heat from exhaust gases. The model is based on existing and commercial thermoelectric materials, specifically Bi2Te3, having ZTs not exceeding 1 and efficiency below 5%, but a trade-off between cost and performance that would be acceptable for automotive applications. TEGs operate on the principle of thermoelectric energy conversion via Seebeck effect, utilizing thermal gradients to generate electric current, with exhaust gases at the hot side and coolant at the cold side. In the simulated configuration the TEG converters are interfaced with the battery/alternator supporting the operation of the vehicle, reducing the energy consumption due to electrical accessories and HVAC.

In the last few years, concern about the environmental impact of vehicles has increased, considering the growth of the dangerous effects on health of noxious exhaust emissions. For this reason, car manufacturers are moving towards more efficient combustion systems for Spark Ignition (SI) engines, aiming to comply with the increasingly stringent regulation imposed by EU and other legislators. Engine operation with very lean air/fuel ratios has demonstrated to be a viable solution to this problem. Stable ultra-lean combustion can be obtained with a Pre-Chamber (PC) ignition system, installed in place of the conventional spark plug. The efficiency of this configuration in terms of performance and emissions is due to its combustion process, that starts in the PC and propagates in the main chamber in the form of multiple hot turbulent jets.

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.

An integrated system of phenomenological models is applied in conjunction with identification techniques to simulate SI engine performance and emissions. In the framework of a hierarchical model architecture, the model structure provides the steady state engine data required for the design and validation of synthetic engine models. This approach allows limiting the recourse to the experimental data and speeds up the engine control strategies prototyping. The model structure is composed of a multi-zone thermodynamic engine model linked to a 1-D commercial fluid-dynamic model for intake and exhaust gas flow and to a physical model for NOx exhaust emissions. In order to improve model accuracy and generalization, an identification methodology is applied to estimate the optimal parameters for the turbulent combustion model. Due to the built-in physical content, the proposed methodology requires a relatively limited amount of experimental data for characterizing the under-study engine.

Internal combustion engines for vehicle propulsion are more and more sophisticated due to increasingly restrictive environmental regulations. In case of heavy-duty engines, Compressed Natural Gas (CNG) fueling coupled with Three-Way Catalyst (TWC) and Exhaust Gas Recirculation (EGR) can help in meeting the imposed emission limits and preventing from thermal stress of engine components. To cope with the new issues associated with the more complex hardware and to improve powertrain performance and reliability and after-treatment efficiency, the engine control strategies must be reformulated. The paper focuses on the steady-state optimization of control parameters for a heavy-duty engine fueled by CNG and equipped with turbocharger and EGR. The optimization analysis is carried out to design EGR, spark timing and wastegate control, aimed at increasing fuel economy while reducing in-cylinder temperature to prevent from thermal stress of engine components.

Road topography has a remarkable impact on vehicle fuel consumption for both passenger and heavy duty vehicles. In addition, erroneous or non-optimized scheduling of both velocity set-point and gear shifting may be detrimental for fuel consumption and performance. Recent technologies have made road data, such as elevation or slope, either available or measurable on board, thus making possible the exploitation of this additional information in innovative controllers. The aim of this paper is the development of a smart, fuel-economy oriented controller adapting cruising speed and engaged gear to current road load (i.e. local slope). Unlike traditional cruise controllers, the velocity set-point is not constant, but it is set by applying a mathematical transformation of the current slope, accounting for the mission time duration as well.

In the recent years, growing attention has been focused on internal combustion engines, considered as the main sources of Particulate Matter (PM) in urban air. Small particles are associated to fine dust formation in the atmosphere and to pulmonary diseases. The legislation proposes a stronger restriction in terms of particulate mass concentrations for both Diesel and gasoline engines and a limitation on number concentration. Unfortunately, the experimental evaluation of particles number and size is a hard task as they are strongly affected by the dilution conditions, due to condensation and nucleation phenomena, which may occur during the sampling. Even if a considerable amount of basic research on particulate matter emitted by engines has been carried out, the mechanisms governing particle formation are still not fully understood, neither for Diesel nor for gasoline engines.

The paper deals with the steady-state optimal tuning of control variables for an automotive turbocharged Diesel engine. The optimization analysis is based on an engine simulation model, composed of a control oriented model of turbocharger integrated with a predictive multi-zone combustion model, which allows accounting for the impact of control variables on engine performance, NOx and soot emissions and turbine outlet temperature. This latter strongly affects conversion efficiency of after treatment devices therefore its estimation is of great interest for both control and simulation of tailpipe emissions. The proposed modeling structure is aimed to support the engine control design for common-rail turbocharged Diesel engines with multiple injections, where the large number of control parameters requires a large experimental tuning effort.

The paper addresses the potentialities of Recurrent Neural Networks (RNN) for modeling and controlling Air-Fuel Ratio (AFR) excursions in Spark Ignited (SI) engines. Based on the indications provided by previous studies devoted to the definition of optimal training procedures, an RNN forward model has been identified and tested on a real system. The experiments have been conducted by altering the mapped injection time randomly, thus making the effect of fuel injection on AFR dynamics independent of the other operating variables, namely manifold pressure and engine speed. The reference engine has been tested by means of an integrated system of hardware and software tools for engine test automation and control strategies prototyping. The developed forward model has been used to generate a reference AFR signal to train another RNN model aimed at simulating the inverse AFR dynamics by evaluating the fuel injection time as function of AFR, manifold pressure and engine speed.

The computer code ODECS (Optimal Design of Engine Control Strategies) for the design of Spark Ignition engine control strategies is presented. This code has been developed starting from the author's activity in this field, availing of some original contributions about engine stochastic optimization and dynamical models. This code has a modular structure and is composed of a user interface for the definition, the execution and the analysis of different computations performed with 4 independent modules. These modules allow the following calculations: (i) definition of the engine mathematical model from steady-state experimental data; (ii) engine cycle test trajectory corresponding, to a vehicle transient simulation test such as ECE15 or FTP drive test schedule; (iii) evaluation of the optimal engine control maps with a steady-state approach.

In the last decades, NOx emissions legislations for Diesel engines are becoming more stringent than ever before and the selective catalytic reduction (SCR) is considered as the most suitable technology to comply with the upcoming constraints. Model-based control strategies are promising to meet the dual objective of maximizing NOx reduction and minimizing NH3 slip in urea-selective catalytic reduction. In this paper, a control oriented model of a Cu-zeolite urea-SCR system for automotive diesel engines is presented. The model is derived from a quasi-dimensional four-state model of the urea-SCR plant. To make it suitable for the real-time urea-SCR management, a reduced order one-state model has been developed, with the aim of capturing the essential behavior of the system with a low computational burden. Particularly, the model allows estimating the NH3 slip that is fundamental not only to minimize urea consumption but also to reduce this unregulated emission.

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.

In the global quest for preventing fossil fuel depletion and reducing air pollution, hybridization plays a fundamental role to achieve cleaner and more fuel-efficient automotive propulsion systems. While hybrid powertrains offer many opportunities, they also present new developmental challenges. Due to the many variants and possible architectures, development issues, such as the definition of powertrain concepts and the optimization of operating strategies, are becoming more and more important. The paper presents model-based fuel economy analyses of different hybrid vehicle configurations, depending on the position of the electric motor generator (EMG). The analyses are intended to support the design of powertrain architecture and the components sizing, depending on the driving scenario, with the aim of reducing fuel consumption and CO2 emissions.

The wall-flow Diesel Particulate Filter (DPF) is currently the most common after-treatment system used to meet the particulate emission limits imposed by government regulations. Today’s technology shows the best balance between filtration efficiency and back-pressure in the engine exhaust pipe. Conventional filters consist in alternately plugged parallel square channels, so that the exhaust gases flow through the porous inner walls leading to particles trapping. During the accumulation phase the pressure drop across the filter increases, thus requiring periodic regeneration of the DPF through after and post fuel injection strategies. This paper deals with the experimental testing of a catalytic silicon carbide (SiC) wall flow DPFs with CuFe2O4 loading. The filter was built following an optimized procedure based on a preliminary controlled chemical erosion of the SiC porous structure.

In the last few years, several studies have proved the benefits of exploiting information about the road topography ahead of the vehicle to adapt vehicle cruising for fuel consumption reduction. Recent technologies have brought on-board more road information enabling the optimization of the driving profile for fuel economy improvement. In the present paper, a cruise controller able to lowering vehicle fuel consumption taking into account the characteristics of the road the vehicle is traveling through is presented. The velocity profile is obtained by minimizing via discrete dynamic programming the energy spent to move the vehicle. In order to further enhance vehicle fuel efficiency, also the gear shifting schedule is optimized, allowing to avoid useless gear shifts and choose the most suitable gear to match current road load and keeping the engine in its maximum efficiency range. Despite the optimality of the solution provided, dynamic programming entails high computational time.

1 The Selective Catalytic reduction (SCR) using urea as reducing agent is currently regarded as the most promising after-treatment technology in order to comply with strict RDE targets for NOX and particulate in Diesel application. Model-based control strategies are promising to satisfy the demands of high NOX conversion efficiency and low tailpipe ammonia slip. This paper deals with the development of a control oriented model of a Cu-zeolite urea-SCR system for automotive Diesel engines. The model is intended to be used for the real-time urea-SCR management, depending on engine NOX emissions and ammonia storage. In order to ensure suitable computational demand for the on-board implementation, a reduced order one-state model of ammonia storage has been derived from a quasi-dimensional four-state model of the urea-SCR plant.

In the last years, the research effort of the automotive industry has been mainly focused on the reduction of CO2 and pollutants emissions. In this scenario, concepts such as the engines downsizing, stop/start systems as well as more costly full hybrid solutions and, more recently, Waste Heat Recovery technologies have been proposed. These latter include Thermo-Electric Generator (TEG), Organic Rankine Cycle (ORC) and Electric Turbo-Compound (ETC) that have been practically implemented on few heavy-duty applications but have not been proved yet as effective and affordable solutions for passenger cars. The paper deals with modeling of ORC power plant for simulation analyses aimed at evaluating the opportunities and challenges of its application for the waste heat recovery in a compact car, powered by a turbocharged SI engine.