Search Results

Biofuel usage is increasingly expanding thanks to its significant contribution to a well-to-wheel (WTW) reduction of greenhouse gas (GHG) emissions. In addition, stringent emission standards make mandatory the use of Diesel Particulate Filter (DPF) for the particulate emissions control. The different physical properties and chemical composition of biofuels impact the overall engine behaviour. In particular, the PM emissions and the related DPF regeneration strategy are clearly affected by biofuel usage due mainly to its higher oxygen content and lower low heating value (LHV). More specifically, the PM emissions and the related DPF regeneration strategy are clearly affected by biofuel usage due mainly to its higher oxygen content and lower low heating value, respectively. The particle emissions, in fact, are lower mainly because of the higher oxygen content. Subsequently less frequent regenerations are required.

The present paper describes the first results of a cooperative research project between GM Powertrain Europe and Istituto Motori of CNR aimed at studying the impact of Fatty-Acid Methyl Esters (FAME) and gas-to-liquid (GTL) fuel blends on the performance, emissions and fuel consumption of modern automotive diesel engines. The tests were performed on the architecture of GM 1.9L Euro4 diesel engine for passenger car application, both on optical single-cylinder and on production four-cylinder engines, sharing the same combustion system configuration. Various blends of biodiesels as well as reference diesel fuel were tested. The experimental activity on the single-cylinder engine was devoted to an in-depth investigation of the combustion process and pollutant formation, by means of different optical diagnostics techniques, based on imaging multiwavelength spectroscopy.

The present paper describes some results of a cooperative research project between GM Powertrain Europe and Istituto Motori of CNR aimed at studying the impact of FAME and GTL fuel blends on the performance, emissions and fuel consumption of the latest-generation automotive diesel engines. The investigation was carried out on the newly released GM 2.0L 4-cylinder “torque-controlled” Euro 5 diesel engine for PC application and followed previous tests on its Euro 4 version, in order to track the interaction between the alternative fuels and the diesel engine, as the technology evolves. Various blends of first generation biodiesels (RME, SME) and GTL with a reference diesel fuel were tested, notably B20, B50 and B100. The tests were done in a wide range of engine operation points for the complete characterization of the biodiesels performance in the NEDC cycle, as well as in full load conditions.

The present paper describes some results of a research project aimed at studying the impact of alternative fuels blends on the emissions and fuel consumption of an Euro 5 automotive diesel engine. Two alternative fuels were chosen for the experiments: RME and GTL. The tests were done in the three most important operating conditions for the engine emission calibration. Moreover, the NOx-PM trade-off by means of EGR sweep was performed in the same operating conditions, in order to evaluate the engine EGR tolerability when burning low sooting fuels as the RME. The investigations put in evidence that the impact of the alternative fuels on modern diesel engines remains significant. This also depends on the interaction between the alternative fuel characteristics and the engine-management strategies, as described in detail in the paper.

In view of the new emission regulations seeking to lower the particle cut-off size down to the current 23 nm, an extensive comprehension on the nature of sub-23 nm particles is crucial. In this regard, a new challenge lies ahead considering an even more massive use of biofuels. The objective of this research study was to characterize the sub-23 nm particles and to evaluate their volatile organic fraction (VOF) from a high performance, 1.8 L gasoline direct injection (GDI) engine under the Worldwide harmonized Light vehicles Test Cycle (WLTC). Particle emissions were measured through an Engine Exhaust Particle Sizer (EEPS) capable of particle sizing and counting in the range 5.6 - 560 nm. The sampling and conditioning were performed by both a single diluter and the Dekati Engine Exhaust Diluter (DEED) a Particle Measurement Programme (PMP) compliant sample conditioning system.

With the aim of identifying technical solutions to lower the particulate matter emissions, the engine research community made a consistent effort to investigate the root causes leading to soot formation. Nowadays, the computational power increase allows the use of advanced soot emissions models in 3D-CFD turbulent reacting flows simulations. However, the adaptation of soot models originally developed for Diesel applications to gasoline direct injection engines is still an ongoing process. A limited number of studies in literature attempted to model soot produced by gasoline direct injection engines, obtaining a qualitative agreement with the experiments. To the authors’ best knowledge, none of the previous studies provided a methodology to quantitatively match particulate matter, particulate number and particle size distribution function measured at the exhaust without a case-by-case soot model tuning.

The diesel particulate filters (DPF) are considered the most robust technologies for particle emission reduction both in terms of mass and number. On the other hand, the increase of the backpressure in the exhaust system due to the accumulation of the particles in the filter walls leads to an increase of the engine fuel consumption and engine power reduction. To limit the filter loading, and the backpressure, a periodical regeneration is needed. Because of the growing interest about particle emission both in terms of mass, number and size, it appears important to monitor the evolution of the particle mass and number concentrations and size distribution during the regeneration of the DPFs. For this matter, in the presented work the regeneration of a catalyzed filter was fully analyzed. Particular attention was dedicated to the dynamic evolution both of the thermodynamic parameters and particle emissions.

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.

In the last years, even more attention was paid to the alternative fuels which can allow both reducing the fuel consumption and the pollutant emissions. Among gaseous fuels, methane is considered one of the most interesting in terms of engine application. It represents an immediate advantage over other hydrocarbon fuels leading to lower CO₂ emissions; if compared to gasoline, CH₄ has wider flammable limits and better anti-knock properties, but lower flame speed. The addition of H₂ to CH₄ can improve the already good qualities of methane and compensate its weak points. In this paper a comparison was carried out between CH₄ and different CH₄/H₂ mixtures. The measurements were carried out in an optically accessible small single-cylinder, Port Fuel Injection spark ignition (PFI SI), four-stroke engine. It was equipped with the cylinder head of a commercial 250 cc motorcycle engine representative of the most popular two-wheel vehicles in Europe.

Nowadays, the regulation regards only the particles larger than 23 nm. The attention is shifting towards the sub-23 nm particles because of their large presence at the exhaust of the modern engines and their negative impact on human health. The main challenge of the regulation of these particles is the definition of a proper procedure for their measure. The nature of the sub-23 nm particles is not well understood, and their measure is strongly affected by the sampling conditions leading to not reliable measure. The aim of this paper is to provide information on the emissions of sub-23 nm particles from GDI vehicles/engines. At the same time, the presence of volatiles, which mainly contribute to the formation of sub-23 nm particles, was evaluated and the effect of sampling conditions was investigated. The analysis was performed on a 1.8L GDI powered vehicle, widely used both in North America and Europe, and a 4-cylinder GDI engine, whose features are similar to those of the vehicle.

The use of lean or ultra-lean ratios is an efficient and proven strategy to reduce fuel consumption and pollutant emissions. However, the lower fuel concentration in the cylinder hinders the mixture ignition, requiring greater energy to start the combustion. The prechamber is an efficient method to provide high energy favoring the ignition process. It presents the potential to reduce the emission levels and the fuel consumption, operating with lean burn mixtures and expressive combustion stability. In this paper the analysis of the combustion process of SI engines equipped with an innovative architecture and operating in different injection modes was described. In particular, the effect of the prechamber ignition on the engine stability and the efficiency was investigated in stoichiometric and lean-burn operation conditions. The activity was carried out in two parts.

Pollutant emission of vehicle cars is nowadays a fundamental aspect to take into account. In the last decays, the company have been forced to study new solutions, such as alternative fuel and learn burn mixture strategy, to reduce the vehicle’s pollutants below the limits imposed by emission regulations. Pre-chamber ignition system presents potential reductions in emission levels and fuel consumption, operating with lean burn mixtures and alternative fuels. As alternative fuels, methane is considered one of the most interesting. It has wider flammable limits and better anti-knock properties than gasoline. Moreover, it is characterized by lower CO2 emissions. The aim of this work is to study the evolution of the plasma jets in a different in-cylinder conditions. The activity was carried out in a research optical small spark ignition engine equipped alternatively with standard ignition system and per-chamber.

Within the context of environmental impact reduction for small size spark ignition (SI) engines, especially green-house gas emissions, this study looked at laminar flame speed as an optimization parameter for hydrogen-methane fueled micro-generators. To this aim, SI engine operation was modeled in a 0D/1D simulation framework, so as to identify the best choice of methane-hydrogen ratios in different conditions. Starting from experimental data recorded on a small size engine, an optimization method was implemented for achieving the proposed goal. One of the main conclusions is that high concentrations of hydrogen and resulting fast burn rates are beneficial at high engine speed settings, while the opposite is true at low engine speed. Hydrogen addition was also considered as an additional control margin during lean operation, given that stable combustion can be achieved even with very low equivalence ratios.

In the framework of an increasing demand for a more sustainable mobility, where the fuel consumption reduction is a key driver for the development of innovative internal combustion engines, Turbulent Jet Ignition (TJI) represents one of the most promising solutions to improve the thermal efficiency. However, details concerning turbulent jet assisted combustion are still to be fully captured, and therefore the design and the calibration of efficient TJI systems require the support of reliable simulation tools that can provide additional information not accessible through experiments. To this aim, an experimental investigation combined with a 3D-CFD study was performed to analyze the TJI combustion characteristics in a single-cylinder spark-ignition (SI) engine. Firstly, the model was validated against experiments considering stoichiometric mixture at 3000 rpm, wide open throttle operating conditions.

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.

The paper deals with the use of Recurrent Neural Networks (RNNs) for the Air-Fuel Ratio (AFR) control in Spark Ignition (SI) engines. Because of their features, Neural Networks can perform an adaptive control more efficiently than classical techniques. In the paper, a review of the most useful control schemes based on Neural Networks is presented and the potential use in the field of engine control is analyzed. A preliminary controller has been implemented making use of a Direct Inverse Modeling approach. The controller compensates for the wall wetting dynamics and estimates the right amount of fuel to be injected to meet the target AFR during engine transients. The Direct Inverse Controller has been tested within an engine/vehicle simulator. The simulation tests have been performed by imposing a set of throttle transients at different engine speeds. The results show that the Inverse Model can satisfactorily bound the AFR excursions around the target value.

The paper deals with the application of two techniques for the selection of the training data set used for the identification of Neural Network black-box engine models; the research starts from previous studies on Sequential Experimental Design for regression based engine models. The implemented methodologies rely on the Active Learning approach (i.e. active selection of training data) and are oriented to drive the experiments for the Neural Network training. The methods allow to select the most significant examples leading to an improvement of model generalization with respect to a heuristic choice of the training data. The data selection is performed making use of two different formulation, originally proposed by MacKay and Cohn, based on the Shannon's Statistic Entropy and on the Mean Error Variance respectively.

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.