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In the last years the automotive industry has been involved in the development and implementation of CO2 reducing concepts such as the engines downsizing, stop/start systems as well as more costly full hybrid solutions and, more recently, waste heat recovery technologies. These latter include ThermoElectric Generator (TEG), Rankine cycle and Electric Turbo Compound (ETC) that have been practically implemented on few heavy-duty application but have not been proved yet as effective and affordable solutions for the automotive industry. The paper deals with the analysis of opportunities and challenges of the Electric Turbo Compound for automotive light-duty engines. In the ETC concept the turbine-compressor shaft is connected to an electric machine, which can work either as generator or motor. In the former case the power can satisfy the vehicle electrical demand to drive the auxiliaries or stored in the batteries.

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 paper describes suited methodologies for developing Recurrent Neural Networks (RNN) aimed at estimating NOx emissions at the exhaust of automotive Diesel engines. The proposed methodologies particularly aim at meeting the conflicting needs of feasible on-board implementation of advanced virtual sensors, such as neural network, and satisfactory prediction accuracy. Suited identification procedures and experimental tests were developed to improve RNN precision and generalization in predicting engine NOx emissions during transient operation. NOx measurements were accomplished by a fast response analyzer on a production automotive Diesel engine at the test bench. Proper post-processing of available experiments was performed to provide the identification procedure with the most exhaustive information content. The comparison between experimental results and predicted NOx values on several engine transients, exhibits high level of accuracy.

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).

In the field of passenger car engines, recent research advances have proven the effectiveness of downsized, turbocharged and direct injection concepts, applied to gasoline combustion systems, to reduce the overall fuel consumption while respecting particularly stringent exhaust emissions limits. Knock and turbocharger control are two of the most critical factors that influence the achievement of maximum efficiency and satisfactory drivability, for this new generation of engines. The sound emitted from an engine encloses many information related to its operating condition. In particular, the turbocharger whistle and the knock clink are unmistakable sounds. This paper presents the development of real-time control functions, based on direct measurement of the engine acoustic emission, captured by an innovative and low cost acoustic sensor, implemented on a platform suitable for on-board application.

The emissions limits of CO2 for vehicles are becoming more stringent with the aim of reducing greenhouse gas emissions and improve fuel economy. The New European Driving Cycle (NEDC) is adopted to measure emissions for all new internal combustion engines in the European Union, and it is performed on cold vehicle, starting at a temperature of 22°C ± 2°C. Consequently, the cold-start efficiency of internal combustion engine is becoming of predominant interest. Since at cold start the lubricant oil viscosity is higher than at the target operating temperature, the consequently higher energy losses due to increased frictions can substantially affect the emission cycle results in terms of fuel consumption and CO2 emissions. A suitable thermal management system, such as an exhaust-to-oil heat exchanger, could help to raise the oil temperature more quickly.

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.

Compression-ignited engines are still considered the most efficient and reliable technology for automotive applications. However, current and future emission regulations, which severely limit the production of NOx, particulate matter and CO2, hinder the use of diesel-like fuels. As a matter of fact, the spontaneous ignition of directly-injected Diesel leads to a combustion process that is heterogeneous by nature, therefore characterized by the simultaneous production of particulate matter and NOx. In this scenario, several innovative combustion techniques have been investigated over the past years, the goal being to benefit from the high thermal efficiency of compression-ignited engines, which results primarily from high Compression Ratio and lean and unthrottled operation, while simultaneously mitigating the amount of pollutant emissions.

Knock control systems based on engine block vibrations analysis are widely adopted in passenger car engines, but such approach shows its main limits at high engine speeds, since knock intensity measurement becomes less reliable due to the increased background mechanical noise. For small two wheelers engines, knock has not been historically considered a crucial issue, mainly due to small-sized combustion chambers and mixture enrichment. Due to more stringent emission regulations and in search of reduced CO2 emissions, an effective on-board knock controller acquires today greater importance also for motorcycle applications, since it could protect the engine when different fuel types are used, and it could significantly reduce fuel consumption (by avoiding lambda enrichment and/or allowing higher compression ratios to be adopted). These types of engines typically work at high rotational speeds and the reduced signal to noise ratio makes knock onset difficult to identify.

The next steps of the current European and US legislation, EURO 6c and LEV III, and the incoming new test cycles will impose more severe restrictions on pollutant emissions for Gasoline Direct Injection (GDI) engines. In particular, soot emission limits will represent a challenge for the development of this kind of engine concept, if injection and after-treatment systems costs are to be minimized at the same time. The paper illustrates the results obtained by means of a numerical and experimental approach, in terms of soot emissions and combustion stability assessment and control, especially during catalyst-heating conditions, where the main soot quantity in the test cycle is produced. A number of injector configurations has been designed by means of a CAD geometrical analysis, considering the main effects of the spray target on wall impingement.

Combustion phasing is crucial to achieve high performance and efficiency: for gasoline engines control variables such as Spark Advance (SA), Air-to-Fuel Ratio (AFR), Variable Valve Timing (VVT), Exhaust Gas Recirculation (EGR), Tumble Flaps (TF) can influence the way heat is released. The optimal control setting can be chosen taking into account performance indicators, such as Indicated Mean Effective Pressure (IMEP), Brake Specific Fuel Consumption (BSFC), pollutant emissions, or other indexes inherent to reliability issues, such as exhaust gas temperature, or knock intensity. Given the high number of actuations, the calibration of control parameters is becoming challenging.

Measurement of particle number was introduced in the Euro 5/6 light duty vehicle emissions regulation. Due to the complex nature of combustion exhaust particles, and to transportation, transformation and deposition mechanisms, such type of measurement is particularly complex, and regression analysis is commonly used for the comparison of different measurement systems. This paper compares various commercial instruments, developing a correlation analysis focused on PN (Particle Number) measurement, and isolating the factors that mainly influence each measuring method. In particular, the experimental activity has been conducted to allow critical comparisons between measurement techniques that are imposed by current regulations and instruments that can be used also on the test cell. The paper presents the main results obtained by analyzing instruments based on different physical principles, and the effects of different sampling locations and different operating parameters.

This paper presents an optimization algorithm, based on discrete dynamic programming, that aims to find the optimal control inputs both for energy and thermal management control strategies of a Plug-in Hybrid Electric Vehicle, in order to minimize the energy consumption over a given driving mission. The chosen vehicle has a complex P1-P4 architecture, with two electrical machines on the front axle and an additional one directly coupled with the engine, on the rear axle. In the first section, the algorithm structure is presented, including the cost-function definition, the disturbances, the state variables and the control variables chosen for the optimal control problem formulation. The second section reports the simplified quasi-static analytical model of the powertrain, which has been used for backward optimization. For this purpose, only the vehicle longitudinal dynamics have been considered.

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.

The paper presents the application of signal processing algorithms to racing engines acoustic emission signals. The proposed methodology has shown to be effective in extracting from such signals information related to the main powertrain performance parameters, such as engine speed, gear ratios and driver's strategy. The objective of the paper is to compare performance parameters of racing engines that have participated in two different Championships, FIA Formula One World Championship (Formula 1) and CART Champ Car Series (CART). The comparison is quite interesting, since the two formulas differ not only in terms of regulations (and therefore in terms of admissible powertrain layouts), but also in terms of circuits where the races take place. For example, ovals are quite common in CART, and that is not the case of Formula 1: This fact is reflected in the different way the engine and the gearbox are operated during the race.

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.

Due to the recent anti-pollution policies, the performance increase in Spark Ignition (SI) engines is currently under the focus of automotive manufacturers. This trend drives control systems designers to investigate accurate solutions and build more sophisticated algorithms to increase the efficiency of this kind of engines. The development of a control strategy is composed of several phases and steps, and the first part of such process is typically spent in defining and investigating the logic of the strategy. During this phase it is often useful to have a light engine simulator, which allows to have robust synthetic combustion data with a low calibration and computational effort. In the first part of this paper, a description of the control-oriented ANalytical Engine SIMulator (ANESIM) is carried out.

Modern internal combustion engine control systems require on-board evaluation of a large number of quantities, in order to perform an efficient combustion control. The importance of optimal combustion control is mainly related to the requests for pollutant emissions reduction, but it is also crucial for noise, vibrations and harshness reduction. Engine system aging can cause significant differences between each cylinder combustion process and, consequently, an increase in vibrations and pollutant emissions. Another aspect worth mentioning is that newly developed low temperature combustion strategies (such as HCCI combustion) deliver the advantage of low engine-out NOx emissions, however, they show a high cylinder-to-cylinder variation. For these reasons, non uniformity in torque produced by the cylinders in an internal combustion engine is a very important parameter to be evaluated on board.

This work focuses on the development and validation of a data-driven model capable of predicting the maximum in-cylinder pressure during the operation of an internal combustion engine, with the least possible computational effort. The model is based on two parameters, one that represents engine load and another one the combustion phase. Experimental data from four different gasoline engines, two turbocharged Gasoline Direct Injection Spark Ignition, a Naturally Aspirated SI and a Gasoline Compression Ignition engine, was used to calibrate and validate the model. Some of these engines were equipped with technologies such as Low-Pressure Exhaust Gas Recirculation and Water Injection or a compression ignition type of combustion in the case of the GCI engine. A vast amount of engine points were explored in order to cover as much as possible of the operating range when considering automotive applications and thus confirming the broad validity of the model.

The paper presents the main results obtained by developing and critically comparing different evaporative emissions leak detection diagnostic systems. Three different leak detection methods have been analyzed and developed by using a model-based approach: depressurization, air and fuel vapor compression, and natural vacuum pressure evolution. The methods have been developed to comply with the latest OBD II requirement for 0.5 mm leak detection. Detailed grey-box models of both the system (fuel tank, connecting pipes, canister module, engine intake system) and the components needed to perform the diagnostic test (air compressor or vacuum pump) have been used to analyze in a simulation environment the critical aspects of each of the three methods, and to develop “optimal” diagnostic model-based algorithms.