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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.

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.

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 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.

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.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

The continuous improvement of internal combustion engines (ICEs) with strategies that can be applied to existing vehicle platforms, either directly or with minor modifications, can improve efficiency and reduce GHG emissions to help achieve Paris climate targets. Low carbon fuels (LCF) as diesel substitutes for light and heavy-duty vehicles are currently being considered as a promising alternative to reduce well-to-wheel (WTW) CO2 emissions by taking advantage of the carbon offset their synthesis pathway can promote, which could capture more CO2 than it releases into the atmosphere. Additionally, some low carbon fuels, like OMEx blends, have non-sooting properties that can significantly improve the NOx-soot tradeoff. The current work studies the calibration optimization of a EU6D-TEMP light-duty engine using various LCFs with different renewable contents with the goal of reduced NOx emissions.

Hydrogen Internal Combustion Engines (H2-ICEs) are being investigated due to their minimal criteria pollutant and zero CO2 tailpipe emissions. However, oil filters and non-hot joint gaskets have rubber material that can be damaged and deteriorate due to direct or indirect exposure to the high temperature and high-pressure hydrogen in a H2-ICE. Thus, the effects on the properties of a rubber exposed to a hydrogen environment need to be reviewed. In this review paper, the transportation, chemical and mechanical properties of a rubber exposed directly or indirectly to high temperature and high-pressure hydrogen in a H2-ICE have been reviewed. The compatibility of rubber materials used in H2-ICE has been explored. The effects of high-pressure hydrogen on the transportation, chemical and mechanical properties of NBR and HNBR have been reviewed.

Analysis-driven pre-calibration of a modern automotive engine is extremely valuable in significantly reducing hardware investments and accelerating engine designs compliant with stricter emission regulations. Advanced modelling tools, such as a Virtual Engine Model (VEM) using Computational Fluid Dynamics (CFD), are often used within the framework of a Design of Experiments for Powertrain Engineering (DEPE) with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. Results show excellent agreements in engine performance parameters (with R2 > 98%) and good agreements in NOx and combustion noise (with R2 > 87%), while the Carbon Monoxide (CO), Unburned Hydrocarbons (HC) and Smoke emissions predictions remain a challenge even with a large n-heptane mechanism consisting of 144 species and 900 reactions and refined mesh resolution.

In recent years the research on Diesel engines has been increasingly shifting from performance and refinement to ultra-low emissions and efficiency. In fact, the last two attributes are key for the powertrain competitiveness in the propulsion electrified future, especially in the European market where 95gCO2/km fleet average and Euro6D RDE Step2 are phasing in at the same time. The present paper describes some of the most innovative research that GM and Istituto Motori Napoli are performing in the field, exploring how the steel-based additive manufacturing can be used to create innovative combustion bowl features that optimize the combustion process to a level that was not compatible with standard manufacturing technologies.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

With demanding emissions legislations and the need for higher efficiency, new technologies for compression ignition engines are in development. One of them relies on reducing the heat losses of the engine during the combustion process as well as to devise injection strategies that reduce soot formation. Therefore, it is necessary a better comprehension about the turbulent kinetic energy (TKE) distribution inside the cylinder and how it is affected by the interaction between air flow motion and fuel spray. Furthermore, new diesel engines are characterized by massive decrease of NOx emissions. Therefore, considering the well-known NOx-soot trade-off, it is necessary a better comprehension and overall quantification of soot formation and how the different injection strategies can impact it.

The introduction of new light-duty vehicle emission limits to comply under real driving conditions (RDE) is pushing the diesel engine manufacturers to identify and improve the technologies and strategies for further emission reduction. The latest technology advancements on the after-treatment systems have permitted to achieve very low emission conformity factors over the RDE, and therefore, the biggest challenge of the diesel engine development is maintaining its competitiveness in the trade-off “CO2-system cost” in comparison to other propulsion systems. In this regard, diesel engines can continue to play an important role, in the short-medium term, to enable cost-effective compliance of CO2-fleet emission targets, either in conventional or hybrid propulsion systems configuration. This is especially true for large-size cars, SUVs and light commercial vehicles.

Sector mesh modeling is the dominant computational approach for combustion system design optimization. The aim of this work is to quantify the errors descending from the sector mesh approach through three geometric modeling approaches to an optical diesel engine. A full engine geometry mesh is created, including valves and intake and exhaust ports and runners, and a full-cycle flow simulation is performed until fired TDC. Next, an axisymmetric sector cylinder mesh is initialized with homogeneous bulk in-cylinder initial conditions initialized from the full-cycle simulation. Finally, a 360-degree azimuthal mesh of the cylinder is initialized with flow and thermodynamics fields at IVC mapped from the full engine geometry using a conservative interpolation approach. A study of the in-cylinder flow features until TDC showed that the geometric features on the cylinder head (valve tilt and protrusion into the combustion chamber, valve recesses) have a large impact on flow complexity.

Diesel will continue to be an indispensable energy carrier for the car fleet CO2 emission targets in the short-term. This is particularly relevant for heavy-duty vehicles as for mid-size cars and SUVs. Looking at the latest technology achievements on the after-treatment systems, it can be stated that the concerning about the NOx emission gap between homologation test and real road use is basically solved, while the future challenge for diesel survival is to keep its competitiveness in the CO2 vs cost equation in comparison to other propulsion systems. The development of the combustion system design still represents an important leverage for further efficiency and emissions improvements while keeping the current excellent performance in terms of power density and low-end torque.

Powertrain electrification could be a key enabler for compliance with future exhaust emission standards and carbon dioxide (CO2) emissions limits or a customer facing product differentiator. The main objective of this study was to assess the potential of electrified propulsion systems in achieving a substantial reduction in CO2 emissions when applied to a representative full-size heavy-duty (HD) truck compared to the baseline configuration. A representative full-size HD four-wheel drive (4WD) truck of adjusted loaded vehicle weight (ALVW) 4082 kg or 9000 lbs with a 6.6 L diesel engine was simulated with various electrified drive configurations over the combined US FTP-72 (Federal Test Procedure) cycle and the Highway Fuel Economy Test (HWFET). Every hybrid vehicle configuration used in the study was designed using representative battery pack and electric drive components.

Diesel piston bowl geometry can affect turbulent mixing and therefore it impacts heat-release rates, thermal efficiency, and soot emissions. The focus of this work is on the effects of bowl geometry and injection timing on turbulent flow structure. This computational study compares engine behavior with two pistons representing competing approaches to combustion chamber design: a conventional, re-entrant piston bowl and a stepped-lip piston bowl. Three-dimensional computational fluid dynamics (CFD) simulations are performed for a part-load, conventional diesel combustion operating point with a pilot-main injection strategy under non-combusting conditions. Two injection timings are simulated based on experimental findings: an injection timing for which the stepped-lip piston enables significant efficiency and emissions benefits, and an injection timing with diminished benefits compared to the conventional, re-entrant piston.

The paper describes the results achieved in developing a new diesel combustion system for passenger car application that, while capable of high power density, delivers excellent fuel economy through a combination of mechanical and thermodynamic efficiencies improvement. The project stemmed from the idea that, by leveraging the high fuel injection pressure of last generation common rail systems, it is possible to reduce the engine peak firing pressure (pfp) with great benefits on reciprocating and rotating components light-weighting and friction for high-speed light-duty engines, while keeping the power density at competitive levels. To this aim, an advanced injection system concept capable of injection pressure greater than 2500 bar was coupled to a prototype engine featuring newly developed combustion system. Then, the matching among these features have been thoroughly experimentally examined.

In light-duty direct-injection (DI) diesel engines, combustion chamber geometry influences the complex interactions between swirl and squish flows, spray-wall interactions, as well as late-cycle mixing. Because of these interactions, piston bowl geometry significantly affects fuel efficiency and emissions behavior. However, due to lack of reliable in-cylinder measurements, the mechanisms responsible for piston-induced changes in engine behavior are not well understood. Non-intrusive, in situ optical measurement techniques are necessary to provide a deeper understanding of the piston geometry effect on in-cylinder processes and to assist in the development of predictive engine simulation models. This study compares two substantially different piston bowls with geometries representative of existing technology: a conventional re-entrant bowl and a stepped-lip bowl. Both pistons are tested in a single-cylinder optical diesel engine under identical boundary conditions.