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Nowadays, green hydrogen can play a crucial role in a successful clean energy transition, thus reaching net zero emissions in the transport sector. Moreover, hydrogen exploitation in internal combustion engines is favoured by its suitable combustion properties and quasi-zero harmful emissions. High flame speeds enable a lean combustion approach, which provides high efficiency and reduces NOx emissions. However, high air flow rates are required to achieve the load levels typical of heavy-duty applications. In this framework, the present study aims to investigate the required boosting system of a 6-cylinder, 13-liter heavy-duty spark ignition engine through 1D numerical simulation. A comparison among various architectures of the turbocharging system and the size of each component is presented, thus highlighting limitations and potentialities of each architecture and providing important insights for the selection of the best turbocharging system.

In recent years, the urgent need to fully exploit the fuel economy potential of the Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) have led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure, in which an agent is able to learn the optimal control strategy through the feedback received by a direct interaction with the environment. Therefore, in this study, a new Soft Actor-Critic agent (SAC), which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability.

Compression ignition engine-based transportation is nowadays looking for cleaner combustion solutions. Among them, ducted fuel injection (DFI) is emerging as a cutting-edge technology due to its potential to drastically curtail engine-out soot emissions. Although the DFI capability to abate soot formation has been demonstrated both in constant-volume and optical engine conditions, its optimization and understanding is still needed for its exploitation on series production engines. For this purpose, computational fluid dynamics (CFD) coupled with low-cost turbulence models, like RANS, can be a powerful tool, especially in the industrial context. However, it is often challenging to obtain reliable RANS-based CFD simulations, especially due to the high dependence of the various state-of-the-art turbulence models on the case study.

Nowadays numerical simulations play a major role in the development of future sustainable powertrain thanks to their capability of investigating a wide spectrum of innovative technologies with times and costs significantly lower than a campaign of experimental tests. In such a framework, this paper aims to assess the predictive capabilities of an 1D-CFD engine model developed to support the design and the calibration of the innovative highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. As a matter of fact, the availability of a reliable simulation platform is crucial to achieve the project target of 47% peak indicating efficiency, by synergistically exploiting the combination of innovative in-cylinder charge motion, Miller cycle with high compression ratio, lean mixture with cooled Exhaust Gas Recirculation (EGR) and electrified turbocharger.

In modern internal combustion engines, oil represents a real component. It carries out the essential tasks: lubrication and heat dissipation. On one hand, it directly influences the vehicle performances and, on the other hand, it is subjected to an unavoidable dirtying and degradation process during operation. For these reasons, it requires a dedicated maintenance program which traditionally consists in a scheduled substitution without the analysis of its actual state. To this purpose, the current work aims to show the potential use of nanostructured metal oxides (MOX) gas sensors to develop a new online, on-board, non-invasive device for the oil monitoring. Indeed, they could analyze the oil vapors from the recirculation pipe directly in the engine head. For this analysis, two traditional engine oils have been considered and used in the same test bench.

The last environmental regulations on passenger vehicles’ emissions harden constraints on designing powertrains. A promising solution consists in vehicle electrification leading to hybrid configurations: the tank-to-wheel pollutant emissions can be drastically reduced combining features of typical battery electric vehicles adding an Internal Combustion Engine (ICE) controlled as a Range Extender (REX). Furthermore, HC and CO/CO2 emissions can be avoided using green hydrogen as fuel for the ICE; moreover, in absence of a mechanical coupling between REX and wheels the best operating conditions in terms of maximum ICE efficiency may be easily achieved. In this work, a light quadricycle (EU L6e, classification) series hybrid vehicle with four in-wheel motors is studied for the application of a range extender system.

The growing concern about Greenhouse Gas (GHG) emissions led institutions to further reduce the limits on vehicle-related CO2 emissions. Therefore, car manufacturers are developing vehicles with low environmental impact, like Hybrid-Electric Vehicles (HEVs), which in the series architecture employ an Internal Combustion Engine (ICE) coupled with an electric generator for battery recharging, thus extending the range of a Battery Electric Vehicle (BEV). For this kind of application, small four-stroke Spark Ignition (SI) engines are preferred, as they are a proven and reliable solution to increase the driving range with very low environmental impact. In series hybrid-electric powertrains, the ICE is decoupled from the drive wheels, then it can operate in a steady-state high-efficiency working point, regardless of the power required by the mission profile. The benefits of lean combustion can be exploited to increase efficiency and reduce CO2 and NOx emissions.

The urban mobility electrification has been proposed as the main solution to the vehicle emission issues in the next years. However, internal combustion engines have still great potential to decarbonize the transport sector through the use of low/zero-carbon fuels. Alcohols such us methanol, have long been considered attractive alternative fuels for spark ignition engines. They have properties similar to those of gasoline, are easy to transport and store. Recently, great attention has been devoted to gaseous fuels that can be used in existing engine after minor modification allowing to drastically reduce the pollutant emissions. In this regard, this study tries to provide an overview on the use of alternative fuels, both liquid and gaseous in spark ignition engines, highlighting the benefits as well as the criticalities. The investigation was carried out on a small displacement spark ignition engine capable to operate both in port fuel and direct injection mode.

The role of numerical simulations in the development of innovative and sustainable powertrains is constantly growing thanks to their capabilities to significantly reduce the calibration efforts and to point out potential synergies among different technologies. In such a framework, this paper describes the development of a fully physical 1D-CFD engine model to support the calibration of the highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. The availability of a reliable simulation platform is essential to effectively exploit the combination of the several features introduced to achieve the project target of 47% peak gross indicated efficiency, such as SwumbleTM in-cylinder charge motion, Miller cycle combined with high Compression Ratio (CR), lean mixture exploiting cooled low pressure Exhaust Gas Recirculation (EGR) and electrified turbocharging.

Despite the legislation targets set by several governments of a full electrification of new light-duty vehicle fleets by 2035, the development of innovative, environmental-friendly Internal Combustion Engines (ICEs) is still crucial to be on track toward the complete decarbonization of on road-mobility of the future. In such a framework, the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) project aims at developing a C SUV-class plug-in hybrid (P0/P4) vehicle demonstrator capable to achieve a -10% fuel consumption reduction with respect to current EU6 vehicle while complying with upcoming EU7 pollutant emissions limits. Such ambitious targets will require the optimization of the whole engine system, exploiting the possible synergies among the combustion, the aftertreatment and the exhaust waste heat recovery systems.

Hybrid electric vehicles are a suitable solution for the transition from fossil fuels-based transportation to electric mobility. They have the benefits of zero-emissions operation when only the electric engine is used preventing the typical range anxiety of full-electric vehicles. Also, they can have a low battery pack capacity and weight thanks to the continuous recharge from the internal combustion engine that becomes the only responsible for exhaust emissions. A practical solution to limit the combustion engine emissions is represented by the range extender configuration, where the engine works at a fixed operating point with the highest efficiency serving uniquely as a battery charger. In the face of the current world situation and future changes, research for alternative energy sources is crucial. Hydrogen can be used as an alternative fuel for common internal combustion engines; moreover, it has the great advantage of high efficiency (about 44%).

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.

The port-logistic sector has a crucial role in goods transport, as the 85-90% of international trade is achieved by means of maritime routes. The latest reports from the International Maritime Organization show that the port-logistic related activities are an important source of air pollution, both for the use of large auxiliary power systems on ships, which operate during port stays, as well as for the employment of fossil fueled road vehicles for on-site operations. As a matter of fact, the most important maritime facilities are located nearby urban areas and therefore reduction of the environmental impact in ports becomes of primary importance. Thus, in the pursuit of a greener in-port mobility, a progressive replacement of fossil fuels with cleaner alternatives must be promoted. This paper presents the analysis of the performance of a hydrogenfueled plug-in fuel cell/battery hybrid vehicle for cargo-handling in roll-on and roll-off port operations.

The use of Ducted Fuel Injection (DFI) for attenuating soot formation throughout mixing-controlled diesel combustion has been demonstrated impressively effective both experimentally and numerically. However, the last research studies have highlighted the need for tailored engine calibration and duct geometry optimization for the full exploitation of the technology potential. Nevertheless, the research gap on the response of DFI combustion to the main engine operating parameters has still to be fully covered. Previous research analysis has been focused on numerical soot-targeted duct geometry optimization in constant-volume vessel conditions. Starting from the optimized duct design, the herein study aims to analyze the influence of several engine operating parameters (i.e. rail pressure, air density, oxygen concentration) on DFI combustion, having free spray results as a reference.

The in-cylinder direct injection of fuels can be a further step towards cleaner and more efficient internal combustion engines. However, the injector design and its characterization, both experimental and from numerical simulation require accurate diagnostics and efficient models. This work aims to simulate the complex behavior of the gaseous and liquid jets through an outwardly opening injector characterized by optical diagnostics using a one-dimensional model without using three dimensional models. The behavior of the jet from an outwardly opening injector changes according to the type of fuel. In the case of the gas, the experimental investigations put in evidence three main jet regions: 1) near-field region where the jet shows a complex gas-dynamic structure; 2) transition region characterized by intense mixing; 3) far-field region characterized by a fully developed subsonic turbulent jet.

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.

Nowadays control system development in the automotive industry is evolving rapidly due to several factors. On the one hand legislation tightening is asking for simultaneous emission reduction and efficiency increase, on the other hand the complexity of the powertrain is increasing due to the spreading of electrification. Those factors are pushing for strong design parallelization and frontloading, thus requiring engine calibration to be moved much earlier in the V-Cycle. In this context, this paper shows how, coupling well known physical 1D engine models featuring predictive combustion and emission models with a fully physical aftertreatment system model and longitudinal vehicle model, a powerful virtual test rig can be built. This virtual test rig can be used for powertrain virtual calibration activities with reduced requirement in terms of experimental data.

Worldwide regulations concerning noise emissions of road vehicles are constantly demanding further reductions of acoustic emissions, which are considered a major environmental health concern in several countries. Among the different sources contributing to noise generation in vehicles equipped with internal combustion engines, exhaust flow noise is one of the most significant, being generated by turbulence development in the exhaust gases, and robust and reliable numerical methodologies for its prediction in early design phases are currently still needed. To this extent, Computational Aero-Acoustics (CAA) can be considered a valuable approach to characterize the physical mechanisms leading to flow noise generation and its propagation, and it could therefore be used to support exhaust system development prior to the execution of experimental testing campaigns.

This paper describes and compares different powertrain configurations for the retrofit of a heavy-duty Class 8 truck, powered by a 12.6 liters diesel engine. The engine is firstly equipped with an electrification-oriented organic Rankine cycle (ORC) system and then coupled to a traction electric machine into a hybrid powertrain. An electrification-oriented ORC system can produce enough energy to cover the ancillary loads, which in long-haul applications for freight transportation are quite demanding. Nevertheless, only powertrain hybridization can achieve significant improvements in the overall system efficiency. Both systems may thus be implemented in the same vehicle, but an efficiency improvement is guaranteed only if the system is carefully managed so as to reach a trade-off between the requirements and potential benefits of the ORC system and those of the hybrid powertrain.