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In pursuing sustainable automotive technologies, exploring alternative fuels for hybrid vehicles is crucial in reducing environmental impact and aligning with global carbon emission reduction goals. This work compares methanol and naphtha as potential suitable alternative fuels for running in a battery-driven light-duty hybrid vehicle by comparing their performance with the diesel baseline engine. This work employs a 0-D vehicle simulation model within the GT-Power suite to replicate vehicle dynamics under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The vehicle choice enables the assessment of a delivery application scenario using distinct payload capacities: 0%, 25%, 50%, and 100%. The model is fed with engine maps derived from previous experimental work conducted in the same engine, in which a full calibration was obtained that ensures the engine's operability in a wide region of rotational speed and loads.

Ammonia has emerged as a promising carbon-free alternative fuel for internal combustion engines (ICE), particularly in large-bore engine applications. However, integrating ammonia into conventional engines presents challenges, prompting the exploration of innovative combustion strategies like dual-fuel combustion. Nitrous oxide (N2O) emissions have emerged as a significant obstacle to the widespread adoption of ammonia in ICE. Various studies suggest that combining exhaust gas recirculation (EGR) with adjustments in inlet temperature and diesel injection timing can effectively mitigate nitrogen oxides (NOx) emissions across diverse operating conditions in dual-fuel diesel engines.

Thermal runaway is a critical safety concern in lithium-ion battery systems, emphasising the necessity to comprehend its behaviour in various modular setups. This research compares thermal runaway propagation in different modular configurations of lithium-ion batteries by analysing parameters such as cell spacing and distribution, application of phase change materials (PCMs), and implementing insulating materials. The study at the module level includes experimental validation and employs a comprehensive model considering heat transfer due to electrical performance and thermal runaway phenomena. It aims to identify the most effective modular configuration for mitigating thermal runaway risks and enhancing battery safety. The findings provide valuable insights into the design and operation of modular lithium-ion battery systems, guiding engineers and researchers in implementing best practices to improve safety and performance across various applications.

The increasing awareness on the harmful effects on the environment of traditional Internal Combustion Engines (ICE) is driving the industry toward cleaner powertrain technologies such as battery-driven Electric Vehicles. Nonetheless, the high energy density of Li-Ion batteries can cause strong exothermic reactions under certain conditions that can lead to catastrophic results, called Thermal Runaway (TR). Hence, a strong effort is being placed on understanding this phenomena and increase battery safety. Specifically, the vented gases and their ignition can cause the propagation of this phenomenon to adjancent batteries in a pack. In this work, Computational Fluid Dynamics (CFD) are employed to predict this venting process in a LG18650 cylindrical battery. The ejection of the generated gases was considered to analyze its dispersion in the surrounding volume through a Reynolds-Averaged Navier-Stokes (RANS) approach.

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.

Upcoming legislation towards zero carbon emission is pushing the electric vehicle as the main solution to achieve this goal. However, electric vehicles still require further battery development to meet customer’s requirements as fast charge and high energy density. Both demands come with the cost of higher heat dissipation as lithium transport and chemical reaction inside the battery need to be performed faster, increasing the joule effect inside the battery. Due to its working principle, which guarantees an adiabatic environment, an accelerating rate calorimeter is used to study thermal phenomena in batteries like a thermal runaway. However, this equipment is not prepared to work with optical access, which helps to study and to comprehend battery surface distribution and other thermal aspects. This paper aims to show a methodology to correct temperature measurement when using a thermographic camera and optical access of sapphire in an accelerating rate calorimeter.

The employment of hydrogen as energy carrier for transportation sector represents a significant challenge for powertrains. Spark-ignition (SI) engines are feasible and low-cost devices to convert the hydrogen chemical energy into mechanical work. However, significant efforts are needed to successfully retrofit the available configurations. The computational fluid dynamics (CFD) modelling represents a useful tool to support experiments, clarifying the impact of the engine characteristics on both the mixture preparation and the combustion development. In this work, a CFD investigation is carried out on typical light-duty SI engine configurations, exploring the two main strategies of hydrogen addition: port fuel injection (PFI) and direct injection (DI). The purpose is to assess the behaviour of widely-used numerical models and methodologies when hydrogen is employed instead of traditional carbon-based fuels.

Mitigating human-made climate change means cutting greenhouse gas (GHG) emissions, especially carbon dioxide (CO2), which causes climate change. One approach to achieving this is to move to a carbon-free economy where carbon emissions are offset by carbon removal or sequestration. Transportation is a significant contributor to CO2 emissions, so finding renewable alternatives to fossil fuels is crucial. Green hydrogen-fueled engines can reduce the carbon footprint of transportation and help achieve a carbon-free economy. However, hydrogen combustion is challenging in an internal combustion engine due to flame instabilities, pre-ignition, and backfire. Numerical modeling of hydrogen combustion is necessary to optimize engine performance and reduce emissions. In this work, a numerical methodology is proposed to model lean hydrogen combustion in a turbocharged port fuel injection (PFI) spark-ignition (SI) engine for automotive applications.

Accelerating rate calorimetry (ARC) has emerged as a powerful tool for evaluating the thermal behavior of Li-ion cells and identifying potential safety hazards. In this work, a new physical thermal model has been developed based on the first law of thermodynamics for analyzing heat and mass generated by Lithium-ion battery cells under thermal abuse conditions during EV-ARC tests. The analysis is based on the experimental data gathered from an ARC, including different temperatures and pressure inside a gas-tight canister located in the calorimeter chamber, as well as the gas composition at the end of the test. The energy balance of the battery cell includes: the energy released by the cell, the internal energy of the elements inside the canister, heat transfer between elements inside the canister, as well as the mass transfer between the cell and the gases inside the canister.

SI engines fueled with hydrogen represent a promising powertrain solution to meet the ambitious target of carbon-free emissions at the tailpipe. Therefore, fast and reliable numerical tools can significantly support the automotive industry in the optimization of such technology. In this work, a 1D-3D methodology is presented to simulate in detail the combustion process with minimal computational effort. First, a 1D analysis of the complete engine cycle is carried out on the user-defined powertrain configuration. The purpose is to achieve reliable boundary conditions for the combustion chamber, based on realistic engine parameters. Then, a 3D simulation of the power-cycle is performed to mimic the combustion process. The flow velocity and turbulence distributions are initialized without the need of simulating the gas exchange process, according to a validated technique.

Fuel film deposits on combustion chamber walls are understood to be the main source of particle emissions in GDI engines under homogenous charge operation. More precisely, the liquid film that remains on the injector tip after the end of injection is a fuel rich zone that undergoes pyrolysis reactions leading to the formation of poly-aromatic hydrocarbons (PAH) known to be the precursors of soot. The physical phenomena accompanying the fuel film deposit, evaporation, and the chemical reactions associated to the injector film are not yet fully understood and require high fidelity CFD simulations and controlled experimental campaigns in optically accessible engines. To this end, a simplified model based on physical principles is developed in this work, which couples an analytical model for liquid film formation and evaporation on the injector tip with a stochastic particle dynamics model for particle formation.

Lithium-ion batteries have a well-documented failure tendency under abuse conditions with a significant release of gases and heat. This failure originated from the decomposition reactions within the battery’s electrochemical components, resulting in gas generation and increased internal pressure. To optimize battery safety, it is crucial to understand their behaviors when subjected to abuse conditions. The 18650 format cell incorporates a vent mechanism within a crimped cap to relieve pressure and mitigate the risk of rupture. However, cell venting introduces additional safety concerns associated with flammable gases and liquid electrolyte that flow into the environment. Experiments were performed with two venting caps with well-known geometries to quantify key parameters in describing the external dynamic flow of battery venting and to validate a CFD model.

Fleet electrification has been demonstrated as a feasible solution to decarbonize the heavy-duty transportation sector. The combination of hybridization and advanced combustion concepts may provide further advantages by also introducing reductions on criteria pollutants such as nitrogen oxides and soot. In this scenario, the interplay among the different energy paths must be understood and quantified to extract the full potential of the powertrain. One of the key devices in such powertrains is the battery, which involves different aspects regarding operation, safety, and degradation. Despite this complexity, most of the models still rely on resistance-capacity models to describe the battery operation. These models may lead to unpractical results since the current flow is governed by limiters rather than physical laws. Additionally, phenomena related with battery degradation, which decreases the nominal capacity and enhances the heat generation are also not considered in this approach.

Electrification and hybridization of powerplants in the transportation sector is one of the most important changes in the last few decades. Lithium-ion batteries are the main energy storage systems, but despite the maturity of this technology, it has certain constrains compared to traditional internal combustion engines in the day-to-day usage. As the operating conditions of the batteries are pushed to the limits to overcome certain disadvantages relative to other conventional systems like charge and discharge times or vehicle driving range, new concerns and safety limitations must be considered. High power rates and cooling deficiencies can produce excessive operating temperatures within the cells, leading to problems with degradation or even unchain chemical reactions that can end in thermal runaway, one of the most worrying failure modes attaining electric platforms nowadays.

Internal combustion engines must be individually tested at the end of the manufacturing process. In recent years classical hot test stands, where the engine is run for several minutes, are being replaced by cold test alternatives. The latter allow fast testing cycles using an external motoring device without using any fuel. The absence of fuel and combustion lowers the health and safety requirements for the plant itself and subsequent engine transport, but this comes at the cost of additional difficulties for the verification of the correct assembly and operation of the combustion system hardware. This paper presents a cold test concept, which includes dedicated measurements and algorithms for the detection of common failures in the manufacturing process, including those of the combustion hardware.

The temperature of fuel injectors can affect the flow inside nozzles and the subsequent spray and liquid films on the injector tips. These processes are known to impact fuel mixing, combustion and the formation of deposits that can cause engines to go off calibration. However, there is a lack of experimental data for the transient evolution of nozzle temperature throughout engine cycles and the effect of operating conditions on injector tip temperature. Although some measurements of engine surface temperature exist, they have relatively low temporal resolutions and cannot be applied to production injectors due to the requirement for a specialist coating which can interfere with the orifice geometry. To address this knowledge gap, we have developed a high-speed infrared imaging approach to measure the temperature of the nozzle surface inside an optical diesel engine.

Split injection processes have been analyzed by means of a Quasi-1D spray model that has been recently coupled to a laminar tabulated unsteady-flamelet progress-variable (UFPV) combustion model. The modelling approach can predict ignition delay and lift-off for long injection profiles, and it is now extended to a two-pulse injection scheme. In spite of the simplicity of the approach, relevant phenomena are adequately reproduced. In particular, the faster penetration of the second injection pulse compared to the first one is captured by the model both under inert and reacting conditions. The second pulse ignites much faster than the first one due to the injection into the remnants of the first one, where high temperature oxygen-depleted regions can be found. Ignition of the second pulse happens as soon as the first pulse reaches this region, with a faster low- to high-temperature transition.

The need to control global warming by regulating automotive emission levels has led to a lot of changes in the policies of different countries globally, specifically the United States (US) and the European Union (EU). More recently, the governments have been strongly pushing the integration of Electric Vehicles (EVs) in the market to replace the conventional Internal Combustion Engine (ICE) vehicles for CO₂ emissions reduction, with the enforcement of 50% EV sales by 2030 in the US and complete 100% by 2035 in the EU for passenger cars. However, these policies are misleading by considering EVs as zero emission vehicles, as there is no such technology yet available that has zero emissions during its lifecycle. During the manufacturing phase, any vehicle produced gives out emissions, with EVs emitting even higher than the conventional ICE vehicles with their battery manufacturing.

The reduction of carbon footprint of compression ignition engines for road transport makes it necessary to search for clean fuels alternative to diesel and to evaluate them under engine conditions. For this reason, in this paper, the combustion behaviour of different blends of synthetic fuels has been analyzed in an optical single cylinder engine of Medium Duty size (0,8 liters per cylinder) by means of optical techniques. The aim is to evaluate the effect of synthetic fuels, both partly or completely fossil diesel, in terms of combustion behaviours and soot formation. Therefore, different blends of oxymethylene dimethyl ether (OMEX) with diesel and neat hydrotreated vegetable oil (HVO) were studied. A conventional common rail injection system and a single injection strategy was used. In addition, special care was taken to ensure that conditions inside the engine cylinder at the injection start were as close as possible to the conditions used in previous studies.

In recent years, the automotive industry has been increasingly committed to developing new solutions for better and more efficient engines. One of them is the use of new insulating materials (thermal conductivity < 0.4 W/m-K, heat capacitance < 500 kJ/m3-K) to coat the engine combustion chamber walls, as well as the exhaust manifold. The main idea when coating the combustion chamber with these materials is to obtain a reduction of the temperature difference (thermal swing) between gas and walls during the engine cycle and minimize heat losses. Experimental measurements of the possible performance improvements are very difficult to obtain, mainly because the techniques available to measure wall temperature are limited. Therefore, simulations are typically used to investigate insulated combustion chambers. Nevertheless, the new generation of insulating coatings is posing challenges to numerical modelling, as layer thickness is very small (~100 μm).