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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.

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.

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.

Focusing on the dual-mode dual-fuel (DMDF) combustion concept, a combined optimization of the piston bowl geometry with the fuel injection strategy was conducted at low, mid, and high loads. By coupling the KIVA-3V code with the enhanced genetic algorithm (GA), a total of 14 parameters including the piston bowl geometric parameters and the injection parameters were optimized with the objective of meeting Euro VI regulations while improving the fuel efficiency. The optimal piston bowl shape coupled with the corresponding injection strategy was summarized and integrated at various loads. Furthermore, the effects of the key geometric parameters were investigated in terms of organizing the in-cylinder flow, influencing the energy distribution, and affecting the emissions. The results indicate that the behavior of the DMDF combustion mode is further enhanced in the aspects of improving the fuel economy and controlling the emissions after the bowl geometry optimization.

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.

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).

The spatial and temporal locations of autoignition depend on fuel chemistry and the temperature, pressure, and mixing trajectories in the fuel jets. Dual-fuel systems can provide insight into fuel-chemistry aspects through variation of the proportions of fuels with different reactivities, and engine operating condition variations can provide information on physical effects. In this context, the spatial and temporal progression of two-stage autoignition of a diesel-fuel surrogate, n-heptane, in a lean-premixed charge of synthetic natural gas (NG) and air is imaged in an optically accessible heavy-duty diesel engine. The lean-premixed charge of NG is prepared by fumigation upstream of the engine intake manifold.

Compression ignition engines are widely used for transport and energy generation due to their high efficiency and low fuel consumption. To minimize the environmental impact of this technology, the pollutant emissions levels at the exhaust are strictly regulated. To reduce the after-treatment needs, alternative strategies as the low temperature combustion (LTC) concepts are being investigated recently. The reactivity controlled compression ignition (RCCI) uses two fuels (direct- and port- injected) with different reactivity to control the in-cylinder mixture reactivity by adjusting the proportion of both fuels. In spite of the proportion of the port-injected fuel is typically higher than the direct-injected one, the characteristics of the latter play a main role on the combustion process. Use of gasoline for direct injection is attractive to retard the start of combustion and to improve the air-fuel mixing process.

The use of predictive models in the study of Internal Combustion Engines (ICE) allows reducing developing cost and times. However, those models are challenging due to the complex and multi-phase phenomena occurring in the combustion chamber, but also because of the different spatial and temporal scales in different components of the injection systems. This work presents a methodology to accurately simulate the spray by Discrete Droplet Models (DDM) without experimentally measuring the injector mass flow rate and/or momentum flux. Transient nozzle flow simulations are used instead to define the injection conditions of the spray model. The methodology is applied to a multi-hole Gasoline Direct injection (GDi) injector. Firstly, the DDM constant values are calibrated comparing simulation results to Diffused Back-light Illumination (DBI) experimental technique results. Secondly, transient nozzle flow simulations are carried out.

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.

It is known that in-cylinder airflow structures during intake and compression strokes deeply affects the combustion process in compression ignition (CI) engines. This work presents a methodology for the analysis of the swirling structures by means of the CFD proprietary code Converge 2.3. The methodology is based on the CFD modelling and the comparison of results with in-cylinder velocity fields measured by particle image velocimetry (PIV). Furthermore, the analysis is extended to the accuracy evaluation of other methods available to define the flow in the cylinder of internal combustion engines, such as experiments in steady flow rigs. These methods, in junction with simple phenomenological models, have been traditionally used to determine some of the fundamental variables that define the in-cylinder flow in ICE engines. The CFD analysis is focused in the flow structures around top dead centre (TDC) at the end of the compression stroke.

It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort, and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise.

Engine Combustion Network promotes fundamental investigations on a number of different spray configurations with the goal of providing experimental results under highly controlled conditions for CFD validation. Most of the available experiments up to now have been obtained in spray vessels, which miss some of the interactions governing spray evolution in the combustion chamber of an engine, such as the jet wall interaction and the transient conditions in the combustion chamber. The main aim of the present research is to compare the results obtained with a three-hole, 90 μm injector, known as ECN’s Spray B, in these constant-volume vessels and more recent Heavy-Duty engines with those obtained in a Light Duty Single Cylinder Optical Engine, under inert and reactive conditions, using n-dodecane. In-cylinder conditions during the injection were estimated by means of a 1-D and 0-D model simulation, accounting for heat transfer and in-cylinder mass evolution.

A genetic algorithm (GA) optimization methodology is applied to the design of the combustion system of a heavy-duty (HD) Diesel engine fueled with dimethyl ether (DME). The study has two objectives, the optimization of a conventional diffusion-controlled combustion system aiming to achieve US2010 targets and the optimization of a stoichiometric combustion system coupled with a three way catalyst (TWC) to further control NOx emissions and achieve US2030 emission standards. These optimizations include the key combustion system related hardware, bowl geometry and injection nozzle design as input factors, together with the most relevant air management and injection settings. The GA was linked to the KIVA CFD code and an automated grid generation tool to perform a single-objective optimization. The target of the optimizations is to improve net indicated efficiency (NIE) while keeping NOx emissions, peak pressure and pressure rise rate under their corresponding target levels.