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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.

Nowadays, the push for more ecological low-carbon propulsion systems is high in all mobility sectors, including the recreational or light-commercial boating, where propulsion is usually provided by internal combustion engines derived from road applications. In this work, the effects of replacing conventional fossil-derived B7 diesel with Hydrotreated Vegetable Oil (HVO) were experimentally investigated in a modern Medium-Duty Engine, using the advanced biofuel as drop-in and testing according to the ISO 8178 marine standard. The compounded results showed significant benefits in terms of NOx, Soot, mass fuel consumption and WTW CO2 thanks to the inner properties of the aromatic-free, hydrogen-rich renewable fuel, with no impact on the engine power and minimal deterioration of the volumetric fuel economy.

High cycle-to-cycle variations (CTCV) in a Hydrogen-Fueled Internal Combustion Engine (H2-ICE), especially in the lean-burn condition, not only lower the engine’s efficiency but also increase emissions and torque variations. High CTCV are mainly due to the variations in: mixture motion within the cylinder at the time of spark, amount of air and fuel fed to the cylinder, and mixing of the fresh mixture and residual gases within the cylinder during each cycle. In this article, multiple cycle-based methodologies were compared and analyzed specifically for H2-ICEs based on systematic experimentation. The experimental test campaign was performed on a Port Fuel Injection (PFI) H2-ICE designed by PUNCH Torino and data is processed with MATLAB. A MATLAB code is also proposed as a tool for comparing multiple methodologies for the analysis of CTCV specifically for H2-ICE.

Water removal from Proton Exchange Membrane (PEM) Fuel Cell (FC) mainly involves two phenomena: some of the emerging droplets will roll on the Gas Diffusion Layer (GDL), others may impact channel walls and start sliding along the airflow direction. This different behaviour is linked to the hydrophobic/hydrophilic nature of the surface the water is moving on. In this paper, the walls of the channel of a FC were characterized by applying optical techniques. The deposition of droplets on the channel wall led to an evaluation of the proper range for Contact Angle Hysteresis (CAH = 55° - 45°), and due to the high wettability of the surface, droplets dimension was defined with a dimensionless parameter B/H. Under high crossflow condition (15 m/s) a sliding behaviour was observed. The channel features determined through image processing were used as boundary conditions for a 2D CFD two phase simulation employing the Volume of Fluid (VOF) model to keep track of the fluids interface.

Predicting and preventing combustion anomalies leads to safe and efficient operation of the hydrogen internal combustion engine. This research presents the application of three machine learning (ML) models – K-Nearest Neighbors (KNN), Random Forest (RF) and Logistic Regression (LR) – for the prediction of combustion anomalies in a hydrogen internal combustion engine. A small experimental dataset was used to train the models and posterior experiments were used to evaluate their performance and predicting capabilities (both in operating points -speed and load- within the training dataset and operating points in other areas of the engine map). KNN and RF exhibit superior accuracy in classifying combustion anomalies in the training and testing data, particularly in minimizing false negatives, which could have detrimental effects on the engine.

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.

Methanol is currently being evaluated as a promising alternative fuel for internal combustion engines, due to being attainable by carbon neutral or negative pathways (renewable energy and carbon capture technology). The low ignitability of methanol has made it attractive mostly as a fuel for spark ignition engines, however the low sooting properties of the fuel could potentially reduce the NOx-soot tradeoff present in compression ignition engines. In this work, using a 4-cylinder engine with compression ratio modified from 16:1 to 19:1, methanol combustion is evaluated under five operating conditions in terms of fuel consumption, criteria pollutants, CO2 emissions and engine efficiency in addition to the qualitative assessment of the combustion stability. It was found that combustion is stable at medium to high loads, with medium load NOx emissions levels at least 30% lower than the original diesel engine and comparable emissions at maximum load conditions.

Hydrogen is an energy vector with low environmental impact and will play a significant role in the future of transportation. Converting a spark ignition (SI) engine powered vehicle to H2 fueling has several challenges, but was overall found to be feasible with contained cost. Fuel delivery directly to the cylinder features numerous advantages and can successfully mitigate backfire, a major issue for H2 SI engines. Within this context, the present work investigated the specific fuel system requirements in port- (PFI) and direct-injection (DI) configurations. A 0D/1D model was used to simulate engine operating characteristics in several working conditions. As expected, the model predicted significant improvement of volumetric efficiency for DI compared to the PFI configuration. Boosting requirements were predicted to be at levels quite close to those for gasoline fueling.

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.

The continuous tightening of CO2 emission targets along with the introduction of Real Driving Emissions (RDE) tests make Water Injection (WI) one of the most promising solutions to improve efficiency, enhance performance and reduce emissions of turbocharged high-performance Spark Ignition engines. This technology, by reducing local in-cylinder mixture temperature, enables higher compression ratios, optimal spark timing and stoichiometric combustion over the entire engine operating range. This research activity, therefore, aims to assess the benefits in terms of CO2 emission reduction of a Port Water Injection (PWI) system integrated in a Downsized Turbocharged Direct Injection Spark Ignition (T-DISI) Engine. In this regard, a 1D-CFD model of the engine capable to predict the impact of the water content on both the combustion process and the knock likelihood was firstly developed.

Hydrogen Internal Combustion Engines (H2-ICEs) are subject to increased attention thanks to their extremely low criteria pollutant emission and near-zero CO2 tailpipe emissions. However, to further minimize exhaust emissions and increase the efficiency of a H2-ICE, it is important to carefully control the relative air-fuel ratio of operation, i.e. Lambda (λ), which will lead in turn to an optimal combustion process. The precise λ control mainly relies upon the methodology to calculate λ on board of the engine, where the availability of reliable sensors specifically-developed for hydrogen combustion is currently limited. In this article, a comparative analysis of different methodologies for the calculation of λ is performed, comparing four methodologies: exhaust gas analysis through a Spindt-Brettschneider approach (λEMI), raw Universal Exhaust Gas Oxygen (λR-UEGO), processed Universal Exhaust Gas Oxygen (λP-UEGO) and speed-density (λSD) outputs.

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.

In the context of increasing efforts towards zero emissions transport, hydrogen represents a valid alternative to electric powertrains. Spark ignition (SI) engines are well suited for this alternative fuel and its specific application requires relatively minor changes with respect to added components. Limited range is one of the main issues with hydrogen as an energy source for transportation, due to its low energy density. The present study looked at the possibility of converting a small size passenger car powered by a turbocharged SI unit to hydrogen fueling. Taking the electric version of the vehicle as benchmark, the initial evaluation of the hydrogen SI alternative appears feasible with an additional gas container comparable in size to the gasoline tank. As a result, further investigation was aimed at actual engine operation in port fuel injection mode, with a focus on rated power and injection phasing effects.

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.

De-fossilization is an increasingly important trend in the energy sector. In the transport sector the de-fossilization efforts have been centered in promoting the electrification of vehicles, nonetheless other pathways, like the use of carbon neutral or carbon-offsetting fuels under current vehicle fleets, are also worth considering. Low-carbon fuels (LCF) can be synthetized from sources that can take advantage of the carbon already present in the atmosphere (either by technologies like direct carbon capture or biological processes like photosynthesis in biofuels) and use energy from renewable sources for the necessary industrial processes. Although, LCFs can be compared to fossil fuels as energy sources for internal combustion engines, their composition is not the same and their properties can modify the engine combustion and emissions.

Lean operation of spark ignition engines is a promising strategy for increasing thermal efficiency and minimize emissions. Variability on the other hand is one of the main shortcomings in these conditions. In this context, the present study looks at the interaction between the spark produced by a J-type plug and the surrounding fluid flow. A combined experimental and numerical approach was implemented so as to provide insight into the phenomena related to the ignition process. A sweep of cross-flow velocity of air was performed on a dedicated test rig that allowed accurate control of the volumetric flow and pressure. This last parameter was varied from ambient to 10 bar, so as to investigate conditions closer to real-world engine applications. Optical diagnostics were applied for better characterization of the arc in different operating conditions. The spatial and temporal evolution of the arc was visualized with high-speed camera to estimate the length, width and stretching.

In the intermediate stage towards zero-emissions, use of methane-hydrogen blends in spark ignition (SI) engines could represent an attractive application. The present work investigated the relevance of empirical base rules for choosing maximum brake torque spark timing settings when using methane-hydrogen blends. A 0D/1D model was used for investigating the optimized ignition for maximizing engine output. Calibration was performed by using in-cylinder pressure data recorded on a methane fueled small size SI engine for two-wheel applications. After adaptations of the model such as valves timing, for rendering it more representative for power generation applications, the investigation was focused on how MBT spark advance was correlated to the 50% mass fraction burned mark (CA50) and peak pressure location. The fact that they were optimized for methane was found to be essential only for high concentrations of hydrogen.