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The common realization of the necessity to reduce the use of mineral sources is promoting the use of alternative fuels. Big efforts are being made to replace petroleum derivatives in the internal combustion engines (ICEs). For this purpose it is mandatory to evaluate the behavior of non-conventional fuels in the ICEs. The optical diagnostics have proven to be a powerful tool to analyze the processes that take place inside the engine. In particular, 2d imaging in the infrared range can reveal new details about the effect of the fuel properties since this technique is still not very common. In this work, a comparison between commercial diesel fuel and two non-conventional fuels has been made in an optically accessible diesel engine. The non-conventional fuels are: the first generation biofuel Rapeseed Methyl Ester (RME) and an experimental blend of diesel and a fuel with high glycerol content (HG).

The use of oxygenated and renewable fuels is nowadays a widespread means to reduce regulated pollutant emissions produced by internal combustion engines, as well as to reduce the greenhouse impact of transportation. Besides PM, NOx and HC emissions, also the size distribution of particles emitted at the engine exhaust represent meaningful information, considering its adverse effects on the environment and human health. In this work, the results of a comprehensive investigation on the combustion characteristics and the exhaust emissions of a GDI high performance engine, fuelled with pure bio-ethanol and European gasoline, are shown. The engine is a 4-cylinder, 4-stroke, 1750 cm₃ displacement, and turbocharged. The engine was operated at different speed/load conditions and two fuel injection strategies were investigated: homogeneous charge mode and stratified charge mode.

In order to meet the requirements in the stringent emission regulations, more and more research work has been focused on homogeneous charge compression ignition (HCCI) and partially premixed combustion (PPC) or partially premixed compression ignition (PCCI) as they have the potential to produce low NOx and soot emissions without adverse effects on engine efficiency. The mixture formation and charge stratification influence the combustion behavior and emissions for PPC/PCCI, significantly. An ultra-high speed burst-mode laser is used to capture the mixture formation process from the start of injection until several CADs after the start of combustion in a single cycle. To the authors’ best knowledge, this is the first time that such a high temporal resolution, i.e. 0.2 CAD, PLIF could be accomplished for imaging of the in-cylinder mixing process. The capability of resolving single cycles allows for the influence of cycle-to-cycle variations to be eliminated.

In this work, optical diagnostics were applied in a transparent DI diesel engine equipped with the head of Euro5 commercial engine and the last generation CR injection system. In order to realize the PCCI combustion the injection of neat bio-ethanol was performed in the intake manifold and European commercial diesel fuel was injected into the cylinder. Different amounts of bio-ethanol were injected in order to create PCCI combustion with high levels of pre-combustion mixing, and to ensure low equivalence ratio and low flame temperatures too. UV-Visible imaging and spectroscopic measurements were performed in the engine in order to investigate the autoignition of the charge and the combustion process, respectively. In particular, the detection of the species involved in the combustion, like OH, HCO, and CH, was performed. The relevance of the radicals and species on PCCI were evaluated and compared with the data from thermodynamic analysis.

Premixed charge compression ignition (PCCI) has been shown to be a promising strategy to simultaneously reduce emissions while realizing improved fuel economy. PCCI combustion uses high levels of pre-combustion mixing to lower both NOx and soot emissions by ensuring low equivalence ratio and low flame temperatures. The high level of pre-combustion mixing results in a primarily kinetics controlled combustion process. In this work, optical diagnostics have been applied in a transparent DI diesel engine equipped with the head of Euro5 commercial engine and the last generation CR injection system. In order to realize the PCCI combustion the injection of neat ethanol has been performed in the intake manifold. The engine run in continuous way at 1500 rpm engine speed and commercial diesel fuel has been injected into the cylinder. The PCCI combustion has been analyzed by means of UV- Visible digital imaging and the mixing process, the autoignition of the charge have been investigated.

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.

This study aims at building efficient and robust artificial neural networks (ANN) able to reconstruct the in-cylinder pressure of Diesel engines and to identify engine conditions starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. In this view, the artificial neural network is meant to be efficient in terms of response time, i.e. fast enough for on-line use. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. Here we consider a feed-forward neural network based on radial basis functions (RBF) for signal reconstruction, and a feed-forward multi-layer perceptron network with tan-sigmoid transfer function for signal classification. The networks are trained using measurements from a three-cylinder real engine for various operating conditions.

The electrochemical performance of a lithium-ion battery is strongly affected by the temperature. During charge and discharge cycles, batteries are subjected to an increment of temperature that can accelerate aging and loss of efficiency if critical values are reached. Knowing the thermal parameters that affect the heat exchange between the battery surface and the surrounding environment (air, cooling fins, plates, etc…) is fundamental to their thermal management. In this work, thermal imaging is applied to a laminated lithium-polymers battery as a non-invasive temperature-indication method. Measurements are taken during the discharge phase and the following cooling down until the battery reaches the ambient temperature. The 2d images are used to analyze the homogeneity of the temperature distribution on the battery surface. Then, experimental results are coupled with mathematical correlations.

Direct injection spark ignition engines represent an effective technology to achieve the goal of carbon dioxide emission reduction. Further reduction of the carbon footprint can be achieved by using carbon-neutral fuels. Oxygenated alcohols are well consolidated fuels for spark ignition engines providing also the advantages of knock resistance and low soot tendency production. Methanol and ethanol are possible candidates as alternative fuels to gasoline due to their similar properties. In this study a blend at 25 % v/v of ethanol in gasoline (E25) and a blend with 80% gasoline, 5 % v/v ethanol and 15% v/v of methanol (GEM) were tested. These blends were considered since E25 is already available at fuel pump in some countries. The GEM blend, instead, could represent a valid alternative in the next future. Experiments were carried out on a high performance, turbocharged 1.8 L direct injection spark ignition engine over the Worldwide Harmonized Light Vehicles Test Cycle.

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.

The aim of this study is to investigate how the fouling that injectors undergo after several operating hours on a vehicle can affect the injection and combustion phases. The impact of the injector fouling on the pollutant formation has been also investigated. Moreover, the effects of the injector cleaning by deposits through the top quality diesel fuel commercialized by eni that is FAME free and contains multi performance additives have been investigated. The experimentation has been carried out on transparent compression ignition engine. It is a single cylinder equipped with a Euro 5 multi-cylinder head and a second-generation common rail injection system. Three indirect-acting piezoelectric injectors have been tested. The first one has been fouled with European commercial diesel fuel through the CEC DW10 injector-coking test. The second one has been fouled in the same way and, then, it has been cleaned with eni top quality diesel fuel. This fuel has fed the third injector too.

The aim of the present work is the study of the combustion process in Gasoline Direct Injection (GDI) engine fuelled with ethanol mixed with gasoline at percentages of 10 and 85. The characterization has been made in terms of performance and emission for different injection pressure conditions and the results correlated to the unperturbed non-evaporating evolution of the fuel injected in a pressurized quiescent vessel. Measurements were performed in the optically accessible combustion chamber made by modifying a real 4-stroke, 4-cylinder, high performance GDI engine. The cylinder head was instrumented by using an endoscopic system coupled to high spatial and temporal resolution camera in order to allow the visualization of the fuel injection and the combustion process. The engine is equipped with solenoid-actuated six-hole GDI injectors, 0.14 mm hole diameter, 9.0 g/s @ 10 MPa static flow.

The present paper describes the results of a cooperative research project between GM Powertrain Europe and Istituto Motori - CNR aimed at studying the impact of both fresh and highly oxidized RME at two levels of blending on spray formation and combustion in modern automotive diesel engines. The tests were performed on an optical single-cylinder engine sharing combustion system configuration with the 2.0L Euro5 GM diesel engine for passenger car application. Two blends (B50 and B100) blending were tested for both fresh and aged RME and compared with commercial diesel fuel in two different operating points typical of NEDC (1500rpm/2bar BMEP and 2000rpm/5bar BMEP). The experimental activity was devoted to an in-depth investigation of the spray density, breakup and penetration, mixture formation, combustion and soot formation, by means of optical techniques.

Present work investigates both experimentally and numerically the benefits deriving from the use of split injections in increasing the engine power output and reducing the tendency to knock of a gasoline direct injection (GDI) engine. The here considered system is characterized by an optical access to the combustion chamber. Imaging in the UV-visible range is carried out by means of a high spatial and temporal resolution camera through an endoscopic system and a transparent window placed in the piston head. This last is modified to allow the view of the whole combustion chamber almost until the cylinder walls, to include the so-called eng-gas zones of the mixture, where undesired self-ignition may occur under some circumstances. Optical data are correlated to in-cylinder pressure oscillations on a cycle resolved basis.

This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality.

In order to meet the increasingly strict emission regulations, several solutions for NOx and PM emissions reduction have been studied. Exhaust gas recirculation (EGR) technology has become one of the more used methods to accomplish the NOx emissions reduction. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle size and density. These latter have a great importance both for the optimal functioning of after-treatment systems, but also for the adverse effects that small particles have on human health. Epidemiological studies, in fact, highlighted that the toxicity of particulate particles increases as the particle size decreases. The aim of this paper is to present a Neural Network model able to provide real time information about the characteristics of exhaust particles emitted by a Diesel engine.

Within the context of distributed power generation, small size systems driven by spark ignition engines represent a valid and user-friendly choice, that ensures good fuel flexibility. One issue is that such applications are run at part load for extensive periods, thus lowering fuel economy. Employing an inverter (fitted between the generator and load) allows engine operation within a wide range of crankshaft rotational velocity, therefore improving efficiency. For the purpose of evaluating the benefits of this technology within a co-generation framework, two configurations were modeled by using the GT-Power simulation software. After model calibration based on measurements on a small size engine for two-wheel applications, the downsized version was compared to a larger power unit operated at constant engine speed for a scenario that featured up to 10 kW rated power.

This paper deals with the combustion behavior and exhaust emissions of a small compression ignition engine modified to operate in diesel/methane dual fuel mode. The engine is a three-cylinder, 1028 cm3 of displacement, equipped with a common rail injection system. The engine is provided with the production diesel oxidation catalyst. Intake manifold was modified in order to set up a gas injector managed by an external control unit. Experiments were carried out at different engine speeds and loads. For each engine operating condition, the majority of the total load was supplied by methane while a small percentage of the load was realized using diesel fuel; the latter was necessary to ignite the premixed charge of gaseous fuel. Thermodynamical analysis of the combustion phase was performed by in-cylinder pressure signal. Gas emissions and particulate matter were measured at the exhaust by commercial instruments.

This paper reports the results of an experimental investigation on the combustion characteristics and exhaust particulate emissions of a GDI high performance engine, fuelled with blends of bio-ethanol and European gasoline fuel. The engine is a 4-cylinder, 4-stroke, 1750 cm₃ displacement, and turbocharged. The engine was operated at fixed speed and load, namely 1500 rpm and 110 Nm, and fuelled with gasoline (E0), ethanol (E100) and two blends 50% v/v (E50) and 85% v/v (E85) of ethanol in gasoline. Two fuel injection strategies were investigated: homogeneous charge and stratified charge combustion mode. The study mainly focuses on the effects of fuel injection strategy and ethanol upon the emissions of particulate matter (PM), in terms of mass, number concentration and size distribution.

Fuel depletion as well as the growing concerns on environmental issues prompt to the use of more eco-friendly fuels. The compressed natural gas (CNG) is considered one of the most promising alternative fuel for engine applications because of the lower emissions. Nevertheless, recent studies highlighted the presence of ultrafine particle emissions at the exhaust of CNG engines. The present study aims to investigate the effect of CNG on particle formation and emissions when it was direct injected and when it was dual fueled with gasoline. In this latter case, the CNG was direct injected and the gasoline port fuel injected. The study was carried out on a transparent single cylinder SI engine in order to investigate the in-cylinder process by real time non-intrusive diagnostics. In-cylinder 2D chemiluminescence measurements from UV to visible were carried out.