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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.

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.

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.

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 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.

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.

A comparison of the flame structure for two different fuels, dodecane and oxymethylene dimethyl ether (OMEX), has been performed under condition of Spray A of the Engine Combustion Network (ECN). The experiments were carried out in a constant pressure vessel with wide optical access, at high pressure and temperature and controlled oxygen concentration. The flame structure analysis has been performed by measuring the formaldehyde and OH radical distributions using planar Laser-Induced Fluorescence (PLIF) techniques. To complement the analysis, this information was combined with that obtained with high-speed imaging of OH* chemiluminescence radiation in the UV. Formaldehyde molecules are excited with the 355-nm radiation from the third harmonic of a Nd:YAG laser, whilst OH is excited with a wavelength of 281.00-nm from a dye laser.

In order to comply with the increasingly restrictive limits of emissions and fuel consumption, researches are focusing on improving the efficiency of combustion engines. In this area, the aging of the injector and its effect on the injection development is not entirely analyzed. In this work, the rate of injection of a diesel injector at different stages of its lifetime is analyzed. To this end, a multi-hole piezoelectric injector was employed, comparing the injection rate measured at the beginning of its lifetime to the rate provided by the injector after aging, maintaining the same boundary conditions in both measurements. Injection pressures up to 200 MPa were used throughout the experiments. The results showed that the steady-state rate of injection was lower after the injector aged. Furthermore, the injector took a longer time to close the needle and end the injection, in comparison with the measurements done at earlier stages of its lifetime.

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.

Experimental visualization of current gasoline direct injection (GDi) systems are even more complicated especially due to the proximity of spray plumes and the interaction between them. Computational simulations may provide additional information to understand the complex phenomena taking place during the injection process. Nozzle flow simulations with a Volume-of-Fluid (VOF) approach can be used not only to analyze the flow inside the nozzle, but also the first 2-5 mm of the spray. A methodology to obtain plume direction and spray angle from the simulations is presented. Results are compared to experimental data available in the literature. It is shown that plume direction is well captured by the model, whilst the uncertainty of the spray angle measurements does not allow to clearly validate the developed methodology.

The Engine Combustion Network (ECN) is a coordinate effort from research partners from all over the world which aims at creating a large experimental database to validate CFD calculations. Two injectors from ECN, namely Spray C and D, have been compared in a constant pressure flow vessel, which enables a field of view of more than 100 mm. Both nozzles have been designed with similar flow metrics, with Spray D having a convergent hole shape and Spray C a cylindrical one, the latter being therefore more prone to cavitation. Although the focus of the study is on reacting conditions, some inert cases have also been measured. High speed schlieren imaging, OH* chemiluminescence visualization and head-on broadband luminosity have been used as combustion diagnostics to evaluate ignition delay, lift off length and reacting tip penetration. Parametric variations include ambient temperature, oxygen content and injection pressure variations.

In order to improve understanding of the primary atomization process for diesel-like sprays, a collaborative experimental and computational study was focused on the near-nozzle spray structure for the Engine Combustion Network (ECN) Spray D single-hole injector. These results were presented at the 5th Workshop of the ECN in Detroit, Michigan. Application of x-ray diagnostics to the Spray D standard cold condition enabled quantification of distributions of mass, phase interfacial area, and droplet size in the near-nozzle region from 0.1 to 14 mm from the nozzle exit. Using these data, several modeling frameworks, from Lagrangian-Eulerian to Eulerian-Eulerian and from Reynolds-Averaged Navier-Stokes (RANS) to Direct Numerical Simulation (DNS), were assessed in their ability to capture and explain experimentally observed spray details. Due to its computational efficiency, the Lagrangian-Eulerian approach was able to provide spray predictions across a broad range of conditions.