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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.

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.

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.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

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.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints.

Modern vehicles use automated driving assistance systems (ADAS) products to automate certain aspects of driving, which improves operational safety. In the U.S. in 2020, 38,824 fatalities occurred due to automotive accidents, and typically about 25% of these are associated with inclement weather. ADAS features have been shown to reduce potential collisions by up to 21%, thus reducing overall accidents. But ADAS typically utilize camera sensors that rely on lane visibility and the absence of obstructions in order to function, rendering them ineffective in inclement weather. To address this research gap, we propose a new technique to estimate snow coverage so that existing and new ADAS features can be used during inclement weather. In this study, we use a single camera sensor and historical weather data to estimate snow coverage on the road. Camera data was collected over 6 miles of arterial roadways in Kalamazoo, MI.

Perception in adverse weather conditions is one of the most prominent challenges for automated driving features. The sensors used for mid-to-long range perception most impacted by weather (i.e., camera and LiDAR) are susceptible to data degradation, causing potential system failures. This research series aims to better understand sensor data degradation characteristics in real-world, dynamic environmental conditions, focusing on adverse weather. To achieve this, a dataset containing LiDAR (Velodyne VLP-16) and camera (Mako G-507) data was gathered under static scenarios using a single vehicle target to quantify the sensor detection performance. The relative position between the sensors and the target vehicle varied longitudinally and laterally. The longitudinal position was varied from 10m to 175m at 25m increments and the lateral position was adjusted by moving the sensor set angle between 0 degrees (left position), 4.5 degrees (center position), and 9 degrees (right position).

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase).

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.

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

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.

This study presents experimental and numerical examination of directly injected (DI) propane and iso-octane, surrogates for liquified petroleum gas (LPG) and gasoline, respectively, at various engine like conditions with the overall objective to establish the baseline with regards to fuel delivery required for future high efficiency DI-LPG fueled heavy-duty engines. Sprays for both iso-octane and propane were characterized and the results from the optical diagnostic techniques including high-speed Schlieren and planar Mie scattering imaging were applied to differentiate the liquid-phase regions and the bulk spray phenomenon from single plume behaviors. The experimental results, coupled with high-fidelity internal nozzle-flow simulations were then used to define best practices in CFD Lagrangian spray models.

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.