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The hydrogen engine is one of the promising technologies that enables carbon-neutral mobility, especially in heavy-duty on- or off-road applications. In this paper, a methodological procedure for the design of the combustion system of a hydrogen-fueled, direct injection spark ignited commercial vehicle engine is described. In a preliminary step, the ability of the commercial 3D computational fluid dynamics (CFD) code AVL FIRE classic to reproduce the characteristics of the gas jet, introduced into a quiescent environment by a dedicated H2 injector, is established. This is based on two parts: Temporal and numerical discretization sensitivity analyses ensure that the spatial and temporal resolution of the simulations is adequate, and comparisons to a comprehensive set of experiments demonstrate the accuracy of the simulations. The measurements used for this purpose rely on the well-known schlieren technique and use helium as a safe substitute for H2.

Ducted Fuel Injection (DFI) engines have emerged as a promising technology in the pursuit of a clean and efficient combustion process. This article aims at elucidating the effect of piston geometry on the engine performance and emissions of a metal DFI engine. Three different types of pistons were investigated and the main piston design features including the piston bowl diameter, piston bowl slope angle, duct angle and the injection nozzle position were examined. To achieve the target, computational fluid dynamics (CFD) simulations were conducted coupled to a reduced chemical kinetics mechanism. Extensive validations were performed against the measured data from a conventional diesel engine. To calibrate the soot model, genetic algorithm and machine learning methods were utilized. The simulation results highlight the pivotal role played by piston bowl diameter and fuel injection angle in controlling soot emissions of a DFI engine.

Heavy duty truck engines are quite difficult to electrify, due to the large amount of energy required on-board, in order to achieve a range comparable to that of diesels. This paper considers a commercial 6-cylinder engine with a displacement of 12.8 L, developed in two different versions. As a standard diesel, the engine is able to deliver more than 420 kW at 1800 rpm, whereas in the CNG configuration the maximum power output is 330 kW at 1800 rpm. Maintaining the same combustion chamber design of the last version, a theoretical study is carried out in order to run the engine on Hydrogen, compressed at 700 bar. The study is based on GT-Power simulations, adopting a predictive combustion model, calibrated with experimental results. The study shows that the implementation of a combustion system running on lean mixtures of Hydrogen, permits to cancel the emissions of CO2, while maintaining the same power output of the CNG engine.

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.

The current work presents the results of an investigation on the impact of renewable fuels on the combustion and emissions of a turbocharged compression-ignition internal combustion engine. An experimental study was undertaken and the engine settings were not modified to account for the fuel's chemical and physical properties, to analyze the performance of the fuel as a potential drop-in alternative fuel. Three fuels were tested: mineral diesel, a blend of it with waste cooking oil biodiesel and a hydrogenated diesel. The analysis of the emissions at engine exhaust highlights that hydrogenated fuel allows to reduce CO, total hydrocarbon emissions, particulate matter and NOx.

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.

The present study focuses on the impacts of pistachio shell particles (2–10 wt.%) on the mechanical and microstructures properties of Al–Cu–Mg/pistachio shell particulate composites. To inspect the impact of the pistachio shell powder content with Al–Cu–Mg alloys, the experimentation was carried out with different alloy samples with constant copper (Cu) and magnesium (Mg) content. Parameters such as hardness, tensile strength with yield strength and % elongation, impact energy, and microstructure were analyzed. The outcomes demonstrated that the uniform dissemination of the pistachio shell particles with the microstructure of Al–Cu–Mg/pistachio shell composite particulates is the central point liable for the enhancement of the mechanical properties. Incorporating pistachio shell particles, up to 10 wt.%, is a cost-effective reinforcement in the production of metal matrix composites for various manufacturing applications.

Vehicle emissions, which are rising alarmingly quickly, are a significant contributor to the air pollution that results. Incomplete combustion, which results in the release of chemicals including carbon monoxide, hydrocarbons, and particulate matter, is the main cause of pollutants from vehicle emissions. However, CO2 contributes more than the aforementioned pollutants combined. Carbon dioxide is the main greenhouse gas that vehicles emit. For every liter of gasoline burned by vehicles, around 2,347 grams of carbon dioxide are released. Therefore, it’s important to reduce vehicle emissions of carbon dioxide. The ability of materials like zeolite and silicon dioxide to absorb CO2 is outstanding. These substances transform CO2 into their own non-polluting carbonate molecules. Zeolite, silicon dioxide, and calcium oxide are combined to form the scrubbing material in a ratio based on their increasing adsorption propensities, along with enough bentonite sand to bind the mixture.

Testing of ducted fuel injection (DFI) in a single-cylinder engine with production-like hardware previously showed that adding a duct structure increased soot emissions at the full load, rated speed operating point [1]. The authors hypothesized that the DFI flame, which travels faster than a conventional diesel combustion (CDC) flame, and has a shorter distance to travel, was being re-entrained into the on-going fuel injection around the lift-off length (LOL), thus reducing air entrainment into the on-going injection. The engine operating condition and the engine combustion chamber geometry were duplicated in a constant pressure vessel. The experimental setup used a 3D piston section combined with a glass fire deck allowing for a comparison between a CDC flame and a DFI flame via high-speed imaging. CH* imaging of the 3D piston profile view clearly confirmed the re-entrainment hypothesis presented in the previous engine work.

Reducing consumption of fossil fuels and resulting emissions remains a goal of the worlds’ population. Perhaps as an aid to encourage more fuel-efficient driving style, many modern motor vehicles are equipped with digital displays of average and instantaneous display of fuel efficiency in miles per gallon (mileage) or liters per 100 kilometers. The display may be interesting to drivers concerned with fuel efficiency, but may not yield desired or best results. What is missing is impact on time of travel; what is the fuel- and time-efficient way to accelerate, what steady speed to travel, and how to decelerate? The author has defined a new fuel efficiency measure described as Dynamic Fuel Cost, in units of currency such as dollars, per travel time in hours, and a microprocessor to compute and display same. Using this display, vehicle operators can choose a maximum steady highway speed, accelerate and decelerate in ways that are fuel-, time- and cost-effective and minimize emissions.

The paper presents a robust adaptive control technique for precise regulation of a port fuel injection + direct injection (PFI+DI) system, a dual fuel injection configuration adopted in modern gasoline engines to boost performance, fuel efficiency, and emission reduction. Addressing parametric uncertainties on the actuators, inherent in complex fuel injection systems, the proposed approach utilizes an indirect model reference adaptive control scheme. To accommodate the increased control complexity in PFI+DI and the presence of additional uncertainties, a nonlinear plant model is employed, incorporating dynamics of the exhaust burned gas fraction. The primary objective is to optimize engine performance while minimizing fuel consumption and emissions in the presence of uncertainties. Stability and tracking performance of the adaptive controller are evaluated to ensure safe and reliable system operation under various conditions.

Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling.

In the perspective of a reduction of emissions and a rapid decarbonisation, especially for compression ignition engines, hydrogen plays a decisive role. The dual fuel technology is perfectly suited to the use of hydrogen, a fuel characterized by great energy potential. In fact, replacing, at the same energy content, the fossil fuel with a totally carbon free one, a significant reduction of the greenhouse gases, like carbon dioxide and total hydrocarbon, as well as of the particulate matter can be obtained. The dual fuel with indirect injection of gaseous fuel in the intake manifold, involves the problem of hydrogen autoignition. In order to avoid this difficulty, the optimal conditions for the injection of the incoming mixture into the cylinder were experimentally investigated. All combustion processes are carried out on a research engine with optical access. The engine speed has is set at 1500 rpm, while the EGR valve is deactivated.

Dual-fuel engines powered by renewable fuels provide a potential solution for reducing the carbon footprint and emissions of transportation, contributing to the goal of achieving sustainable mobility. The investigation presented in the following uses a dual-fuel engine concept running on biogas (referred to as CNG in this paper) and the e-fuel polyoxymethylene dimethyl ether (OME). The current study focuses on the effects of exhaust gas rebreathing and external exhaust gas recirculation (EGR) on emissions and brake thermal efficiency (BTE). A four-cylinder heavy-duty engine converted to dual-fuel operation was used to conduct the engine tests at a load point of 1600 min-1 and 9.8 bar brake mean effective pressure (BMEP). The respective shares of high reactivity fuel (HRF, here: OME) and low reactivity fuel (LRF, here: CNG) were varied, as were the external and internal EGR rates and their combinations.

Hydrogen–diesel dual-fuel combustion processes were visualized using an optically accessible rapid compression and expansion machine (RCEM). A hydrogen-air mixture was introduced into the combustion chamber, and a pilot injection of diesel fuel was used as the ignition source. A small amount of diesel fuel was injected as the pilot fuel at injection pressures of 40, 80, and 120 MPa using a common rail injection system. The injection amounts of diesel fuel were varied as 3, 6, and 13 mm3. The amount of hydrogen was manipulated by varying the total excess air ratio (λtotal) at 3 and 4. The RCEM was operated at a constant speed of 900 rpm, and the in-cylinder pressure and temperature at the top dead center (TDC) were set as 5 MPa and 700 K, respectively. The combustion processes were visualized via direct photography and hydroxyl (OH*) chemiluminescence photography using a high-speed camera and an image intensifier.

Soot and carbon dioxide released from internal combustion engines became the key issues when using fossil fuels. Ammonia and hydrogen having zero-carbon species can reduce carbon-related emissions and enhance the reliance on renewable fuels. A comparative study of ammonia and hydrogen impact on combustion and emission characteristics of iso-octane flame was performed under different combustion conditions. Arrhenius equation, soot surface reactions, and modified kinetic mechanism were used to study the flame growth, soot nucleation, and surface growth rates. The results show that hydrogen increased the temperature about 20.74 K and 59.30 K, whereas ammonia reduced it about 82.17 K and 66.03 K at premixed and counterflow conditions, respectively. The flame speed of iso-octane was increased 43.83 cm/s by hydrogen and decreased 34.36 cm/s by ammonia. A reduction in CH2O caused a reduction in CO and CO2 emissions.

Net-Zero emission ambitions coupled with availability of oxygenated fuels like ethanol encouraged the Government towards commercial implementation of fuels like E20. In this background, a study was taken up to assess the impact of alcohol blended fuels on performance and emission characteristics of a BS-VI complaint motorbike. A single cylinder, 113-cc spark ignition, ECU based electronic fuel injection motorbike was used for conducting tests. Pure gasoline (E0), 10% ethanol-gasoline (E10), 20% ethanol-gasoline (E20) and 15% methanol-gasoline (M15) blends meeting respective IS standards were used as test fuels. The oxygen content of E10, E20 and M15 fuels were 3.7%, 7.4% and 8.35% by weight respectively. Experiments were conducted following worldwide motorcycle test cycle (WMTC) as per AIS 137 standard and wide-open-throttle (WOT) test cycle, using chassis dynamometer.

The present study aims to determine the comparative performance evaluation in terms of fuel economy (kmpl) and wide open throttle (WOT) power derived from set of different blends of high octane gasoline fuel(s) i.e., Neat Gasoline (E0), E10 & E20 (With different dosages of additives) in high compression ratio (HCR) motorcycle on chassis dynamometer facility. With the Government of India focus on use of alcohol as co-blend of gasoline with the endeavour to save foreign exchange and also to reduce greenhouse gases (GHG) emissions. The commercially available blended fuels, E10 & E20, have high research octane number (RON, 92-100) and as per the available literature high RON fuel have the better anti-knocking tendencies thereby lead to higher fuel economy. There are various routes to formulate high octane fuel (refining technologies, additive approach & ethanol blending route) in the range of 92-100 octane number which are currently commercialized in Indian market.

Recently, as part of the effort to enhance fuel efficiency and reduce costs for eco-friendly vehicles, the R-gearless system has been implemented in the TMED (P)HEV system. Due to the removal of the reverse gear, a distinct backward driving method needs to be developed, allowing the Electronic Motor (e-Motor) system to facilitate backward movement in the TMED (P)HEV system. However, the capability of backward driving with the e-Motor is limited because of partial failure in the high-voltage system of an R-gearless system. Thus, we demonstrate that it is possible to improve backward driving problems by applying a new fail-safe strategy. In the event of a high-voltage battery system failure, backward driving can be achieved using the e-Motor with constant voltage control by the Hybrid Starter Generator (HSG), as proposed in this study.

The Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables.