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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.

Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized Model Predictive Control (MPC) approach that incorporates Kalman Filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a Reinforcement Learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability.

The engine acoustic character has always represented the product DNA, owing to its strong correlation with in-cylinder pressure gradient, components design and perceived quality. Best practice for engine acoustic characterization requires the employment of a hemi-anechoic chamber, a significant number of sensors and special acoustic insulation for engine ancillaries and transmission. This process is highly demanding in terms of cost and time due to multiple engine working points to be tested and consequent data post-processing. Since Neural Networks potentially predicting capabilities are apparently un-exploited in this research field, the following paper provides a tool able to acoustically estimate engine performance, processing system inputs (e.g. Injected Fuel, Rail Pressure) thanks to the employment of Multi Layer Perceptron (MLP, a feed forward Network working in stationary points).

Ammonia is a new type of carbon-free fuel with low cost, clean and safe. The research and application of zero-carbon fuel internal combustion engines has become the mainstream of future development. However, there still exist problems should be solved in the application of ammonia fuel. Due to the lower flame laminar speed and higher ignition temperature, ammonia may have unstable combustion phenomena. In this work, the characteristics of ammonia combustion have been investigated, based on controllable thermal activated atmosphere burner. The ignition delay has been used to analyze the ammonia combustion characteristics. With the increase in co-flow temperature, the ignition delay of ammonia/air has an obvious decline. In order to investigate the emission characteristics of ammonia, CHEMKIN is used to validate the different chemical reaction mechanisms and analyse the ammonia emissions.

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.

The demand of game-changing technologies to improve efficiency and abate emissions of heavy-duty trucks and off-road vehicles promoted the development of novel engine concepts. The Recuperated Split-Cycle (R-SC) engine allows to recover the exhaust gases energy into the air intake by separating the compression and combustion stages into two different but connected cylinders: the compressor and expander, respectively. The result is a potential increase of the engine thermal efficiency. Accordingly, the 3D-computational fluid dynamics (CFD) modelling of the gas exchange process and the combustion evolution inside the expander becomes essential to control and optimize the R-SC engine concept. This work aims to address the most challenging numerical aspects encountered in a 3D numerical simulation of an R-SC engine.

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.

Fuel cells are considered one of the promising technologies as possible replacement of Internal Combustion Engine (ICE) for the transportation sector due to their high efficiency, ultra-low (or zero) emissions and for the higher drive range. The Membrane Electrode Assembly (MEA) is what mainly influences the Fuel Cell FC performance, durability, and cost. In PEMFC the proton conductivity of the membrane is a function of the humidification level of the FC membrane, hence the importance of keeping the membrane properly humidified to achieve the best possible fuel cell performance. To have the optimal water content inside the fuel cell’s membrane several strategies could be adopted, dealing with the use of external device (such as membrane humidifier) or to adopt an optimal set of parameters (gas flow rate and temperature for example) to use the water produced at fuel cell cathode as humidity source. The aim of this paper is to study the behavior of a FC vehicle humidification system.

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.

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 last years, the design in the automotive sector is mainly led by emission reduction and circular economy. To satisfy the first perspective, composites materials are being increasingly used to produce lightweight structural and semi-structural components. However, the automotive mass production arises the problem of the end-of-life disposal of the vehicle and the reduction of the wastes environmental impact. The circular economy of the composite materials has therefore become a challenge of primary importance for car manufacturers and tier 1 suppliers. It is necessary to pursue a different economic model, combining traditional raw materials with the intensive use of materials from recycling processes. New technologies are being studied and developed concerning the reuse of in-line production scraps with out-of-autoclave process that makes them desirable for high production rate applications.

In the recent years, the interest in heavy-duty engines fueled with Compressed Natural Gas (CNG) is increasing due to the necessity to comply with the stringent CO2 limitation imposed by national and international regulations. Indeed, the reduced number of carbon atoms of the NG molecule allows to reduce the CO2 emissions compared to a conventional fuel. The possibility to produce synthetic methane from renewable energy sources, or bio-methane from agricultural biomass and/or animal waste, contributes to support the switch from conventional liquid fuels to CNG. To drive the engine development and reduce the time-to-market, the employment of numerical analysis is mandatory. This requires a continuous improvement of the simulation models toward real predictive analyses able to reduce the experimental R&D efforts. In this framework, 1D numerical codes are fundamental tools for system design, energy management optimization, and so on.

Dimethyl ether (DME) represents a promising fuel for heavy-duty engines thanks to its high cetane number, volatility, absence of aromatics, reduced tank-to-wheel CO2 emissions compared to Diesel fuel and the possibility to be produced from renewable energy sources. However, optimization of compression-ignition engines fueled with DME requires suitable computational tools to design dedicated injection and combustion systems: reduced injection pressures and increased nozzle diameters are expected compared to conventional Diesel engines, which influences both the air-fuel mixing and the combustion process. This work intends to evaluate the validity of two different combustion models for the prediction of performance and pollutant emissions in compression-ignition engines operating with DME. The first one is the Representative Interactive Flamelet while the second is the Approximated Diffusive Flamelet.

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.

Flow noise produced by the turbulent motion of the exhaust gases is one of the main contributions to the noise generation for a heavy-duty vehicle. The exhaust system has therefore to be optimized since the early stages of the design to improve the engine’s Noise Vibration Harshness (NVH) performance and to comply with legislation noise limits. In this context, the availability of reliable Computational Aero-Acoustics (CAA) methodologies is crucial to assess the noise mitigation potential of different exhaust system designs. In the present work, a characterization of the sound generation in a heavy-duty exhaust system was carried out evaluating the noise attenuation potential of a design modification, by means of a hybrid CAA methodology.

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.

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.

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.

The detection and evaluation of damage in composite materials components is one of the main concerns for automotive engineers. It is acknowledged that defects appeared in the manufacturing stage or due to the impact and/or fatigue loads can develop along the vehicle riding. To avoid an unexpected failure of structural components, engineers ask for cheap methodologies assessing the health state of composite parts by means of continuous monitoring. Non Destructive Technique (NDT) for the damage assessment of composite structures are nowadays common and accurate, but an on-line monitoring requires properties as low cost, small size and low power that do not belong to common NDT. The presence of a damage in composite materials, either due to fatigue cycling or low-energy impact, leads to progressive degradation of elastic moduli and strengths.