bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Fluid Evidence on Various Vehicle Components
Authors: Boysen Kevin , Parenteau Chantal , Toomey Daniel , Gregg Richard H.
Published: April 2024

Determining occupant kinematics in a vehicle crash is essential when understanding injury mechanisms and assessing restraint performance. Identifying contact marks is key to the process. This study was conducted to assess the ability to photodocument the various fluids on different vehicle interior component types and colors with and without the use of ultraviolet (UV) lights.

Biological (blood, saliva, sweat and skin), consumable and chemical fluids were applied to vehicle interior components, such as seatbelt webbing, seat and airbag fabrics, roof liner and leather steering wheel. The samples were photodocumented with natural light and UV light (365 nm) exposure immediately after surface application and again 14 days later.

The review of the photos indicated that fabric type and color were important factors. The fluids deposits were better visualized on non-porous than porous materials. For example, blood was better documented on curtain airbags than side or driver airbags. Blood and chemical fluids were more difficult to document on black than beige seatbelt webbing, while skin showed better on black webbing. Biological fluids were better detected with UV light. The visual presentation did not change substantially between initial and follow-up documentation with and without UV light. Fluids and components with luminescent properties were also factors.

This study provides a photodocumentation summary of biological and other fluids on vehicle interior components. In conclusion, UV light exposure was helpful in highlighting some fluid deposits during crash investigation. While UV illumination provided additional insight, further investigation is necessary to discriminate and differentiate fluids present.
Video and Object Tracking for Speed Determination Using Aerial LiDAR
Authors: Terpstra Toby , McDonough Sean , Helms Ethan , Beier Steven , Hessell David
Published: April 2024

Video of an event recorded from a moving camera contains information not only useful for reconstructing the locations and timing of an event, but also the velocity of the camera attached to the moving object or vehicle. Determining the velocity of a video camera recording from a moving vehicle is useful for determining the vehicle’s velocity and can be compared with speeds calculated through other reconstruction methods, or to data from vehicle speed monitoring devices. After tracking the video, the positions and speeds of other objects within the video can also be determined. Video tracking analysis traditionally has required a site inspection to map the three-dimensional environment. In instances where there have been significant site changes, where there is limited or no site access, and where budgeting and timing constraints exist, a three-dimensional environment can be created using publicly available aerial imagery and aerial LiDAR. This paper presents a methodology for creating a three-dimensional environment and performing video tracking analysis without a site visit. To validate the methodology, a blind study was conducted where three different videos were tracked. Each video presented a different traffic scenario including oncoming traffic, cross traffic, and passing traffic. The speed of the vehicle from which the video was recorded was determined through the video tracking process, and speed was also determined for a second vehicle visible within the videos. The speed of both vehicles in each video was then compared to vehicle speeds measured with vehicle instrumentation using Harry’s LapTimer to evaluate the accuracy of vehicle speeds determined through video and object tracking.
Comparison of a Tractor-Semitrailer Rollover Test and HVE Simulations
Authors: Honeycutt Daniel , Rogers Gary , Yang Shu , Chinni James
Published: April 2024

Building upon prior research, this paper compares computer simulations to a previously conducted rollover crash test of a tractor-semitrailer. The effects of torsional stiffness were elucidated during the correlation of simulations to the rollover test. A commercially available vehicle dynamics and reconstruction software was used for the simulation. Unique aspects of the rollover crash test were modeled in the simulation.

A tractor-semitrailer quarter-turn rollover crash test conducted by IMMI was reconstructed using impact and vehicle dynamics models within the simulation software HVE (Human, Vehicle & Environment). The SIMON (SImulation MOdel Non-linear) module and the DyMESH (Dynamic MEchanical SHell) module within HVE were used. During the IMMI test, onboard instrumentation recorded acceleration and roll rate data in six degrees of freedom to characterize both tractor and semitrailer dynamics before and during the rollover event. The roll angle and roll rate behavior of the HVE tractor-semitrailer were correlated to the test data by varying the connection torsional stiffness values within HVE. The roll angle and roll rate temporal data from the test showed that the torsionally compliant combination vehicle stored and released mechanical energy. This effect was also observed in the calibrated HVE model. The effects of varying the crush stiffness values, suspension wheel rates, and the tire-to-pavement coefficient of friction were evaluated in the HVE simulations. Roll angle, roll rate, and impact acceleration temporal test data were compared to corresponding data from the HVE simulations. During the rollover test, a 16,460-lb block of ballast became dislodged. This event was comprehended by the HVE simulation and is compared to the test via still video frames. All known tractor-semitrailer rollover crash tests are included in the literature review. A brief review of rollover simulation literature is included in this paper.
Estimating How Long In-Vehicle Tasks Take: Static Data for Distraction and Ease-of-Use Evaluations
Authors: Green Paul
Published: April 2024

Often, when assessing the distraction or ease of use of an in-vehicle task (such as entering a destination using the street address method), the first question is “How long does the task take on average?” Engineers routinely resolve this question using computational models. For in-vehicle tasks, “how long” is estimated by summing times for the included task elements (e.g., decide what to do, press a button) from SAE Recommended Practice J2365 or now using new static (while parked) data presented here. Times for the occlusion conditions in J2365 and the NHTSA Distraction Guidelines can be determined using static data and Pettitt’s Method or Purucker’s Method. These first approximations are reasonable and can be determined quickly.

The next question usually is “How likely is it that the task will exceed some limit?” This question, addressed using discrete event simulations such as IMPRINT, requires the distribution types and parameters (mean, standard deviation, etc.) for each task element, data which generally are not available in the published literature for in-vehicle tasks (e.g., SAE J2365). Those distribution types and parameters are presented here, derived from further analyses of the data for 13 static task elements in Green et al. (2015). For example, for static task times, the time to press a function key could be modeled as Normal (2.36, 2.61) or Weibull with a Threshold (3.94, 1.52, -0.62). If only mean task element times are available in the chosen dataset, the standard deviations could be estimated as (0.77*mean time) + 0.12 for static conditions.
Effect of Cooling Airflow Intake Positioning on the Aerodynamics of a Simplified Battery Electric Road Vehicle
Authors: Upadhyaya Avaneesh , Sebben Simone , Willeson Emil , Minelli Guglielmo
Published: April 2024

The transition towards battery electric vehicles (BEVs) has increased the focus of vehicle manufacturers on energy efficiency. Ensuring adequate airflow through the heat exchanger is necessary to climatize the vehicle, at the cost of an increase in the aerodynamic drag. With lower cooling airflow requirements in BEVs during driving, the front air intakes could be made smaller and thus be placed with greater freedom. This paper explores the effects on exterior aerodynamics caused by securing a constant cooling airflow through intakes at various positions across the front of the vehicle. High-fidelity simulations were performed on a variation of the open-source AeroSUV model that is more representative of a BEV configuration. To focus on the exterior aerodynamic changes, and under the assumption that the cooling requirements would remain the same for a given driving condition, a constant mass flow boundary condition was defined at the cooling airflow inlets and outlets. A parametric study was conducted by spatially moving the cooling intakes, with constant total area, across the front of the vehicle. Power consumption of the fan had to be considered when maintaining the desired airflow rate through intakes without sufficient ram air. As expected, moving intakes away from the stagnation region increased drag. Lateral outward movements altered the flow fields around the front wheels, while vertical movements of the intake only showed marginal flow changes across the top and bottom halves of the vehicle. The flow characteristics towards the rear were unaffected, and the changes observed were mostly local. Including power requirements of the fan, increments up to 15% of total vehicle drag were observed.
The Effects of Solid Wall Wind Tunnel Blockage on Incremental Changes to Generic and Simplified Automotive Bodies
Authors: Gleason Mark , Riegel Eugen
Published: April 2024

In the authors’ previous work, a database was generated documenting the effects of variable blockage ratios on the drag and lift of simplified and generic automotive bodies in solid wall wind tunnels. This database displays significant differences in the responses of different vehicle architectures to changes in wind tunnel blockage. What was not examined in this previous work was the effect of wind tunnel blockage on the incremental values of geometry changes to these generic models. This is critical knowledge related to the aerodynamic development process of automotive vehicles in wind tunnels. To complement that work, the present paper examines the effects of changes in solid wall blockage on the incremental force values of geometry changes on the simplified sedan geometry known as the Pilot Fastback, the Pilot Squareback and the Ford GTU pickup. It is intended that this information will give initial insight as to the degree in which solid wall wind tunnel blockage effects can influence the aerodynamic development process utilized in the automotive industry and bluff body research activities. It is also intended to further inform the documentation process of the SAE Road Vehicle Aerodynamics Forum Committee (RVAC) and the Subsonic Aerodynamic Testing Association combined activity, Commonized Automotive Aerodynamic Test Standards, or CAATS. As in previous work the study is conducted utilizing Computational Fluid Dynamics (CFD) employing the Lattice-Boltzmann method of simulating the fluid flows.
Coordinated Longitudinal and Lateral Motions Control of Automated Vehicles Based on Multi-Agent Deep Reinforcement Learning for On-Ramp Merging
Authors: Li Wenchang , Zhao Zhiguo , Liang Kaichong , Zhao Kun
Published: April 2024

The on-ramp merging driving scenario is challenging for achieving the highest-level autonomous driving. Current research using reinforcement learning methods to address the on-ramp merging problem of automated vehicles (AVs) is mainly designed for a single AV, treating other vehicles as part of the environment. This paper proposes a control framework for cooperative on-ramp merging of multiple AVs based on multi-agent deep reinforcement learning (MADRL). This framework facilitates AVs on the ramp and adjacent mainline to learn a coordinate control policy for their longitudinal and lateral motions based on the environment observations. Unlike the hierarchical architecture, this paper integrates decision and control into a unified optimal control problem to solve an on-ramp merging strategy through MADRL. Firstly, a partially observable Markov game (POMG) is formulated to characterize the on-ramp merging control problem, where the observation space of each AV (agent) is defined as its states and the relative state between it and other AVs, and the joint action spaces are the longitudinal acceleration and front wheel steering angle of AVs. Then, with safety and traffic efficiency as the objective, the reward function of each AV is designed. Furthermore, the joint action for multi-agent is obtained by solving the POMG problem utilizing the multi-agent deep deterministic policy gradient (MADDPG) method. Finally, a rule-based action guidance strategy is presented to supervise further the joint action for enhancing the safety of AVs. Numerical experiments are performed under different conditions to verify the effectiveness of the proposed merging control framework for a multi-agent system. The proposed scheme is also compared with the method for a single agent, taking the deep deterministic policy gradient (DDPG) method as a benchmark. The results demonstrate superior performance of the proposed method than the DDPG method in terms of safety and traffic efficiency.
Spatio-Temporal Trajectory Planning Using Search And Optimizing Method for Autonomous Driving
Authors: Zhong Liang , Lu Changgang , Wu Jian
Published: April 2024

In the field of autonomous driving trajectory planning, it’s virtual to ensure real-time planning while guaranteeing feasibility and robustness. Current widely adopted approaches include decoupling path planning and velocity planning based on optimization method, which can’t always yield optimal solutions, especially in complex dynamic scenarios. Furthermore, search-based and sampling-based solutions encounter limitations due to their low resolution and high computational costs. This paper presents a novel spatio-temporal trajectory planning approach that integrates both search-based planning and optimization-based planning method. This approach retains the advantages of search-based method, allowing for the identification of a global optimal solution through search. To address the challenge posed by the non-convex nature of the original solution space, we introduce a spatio-temporal semantic corridor structure, which constructs a convex feasible set for the problem. Trajectory optimization is then performed through numerical optimization methods, resulting in real-time and robust spatio-temporal trajectory planning. The proposed approach initiates by constructing a 3D spatio- temporal map that incorporates information such as dynamic obstacles. Improved A* algorithm is used to search for a preliminary spatio- temporal trajectory, serving as an initial approximation for the trajectory. Based on this initial approximation, a spatio-temporal corridor is constructed as locally convex feasible driving area, then a quintic monomial polynomial is employed to represent a trajectory, considering vehicle kinematics, continuity, and other constraints, this formulation transforms the problem into a general quadratic programming problem. Ultimately, the generated trajectories are rigorously tested through simulations in scenarios involving overtaking and side car cut-in. The results indicate that the generated trajectories are feasible, reasonable, and exhibit good real-time performance.
RL-MPC: Reinforcement Learning Aided Model Predictive Controller for Autonomous Vehicle Lateral Control
Authors: Jia Muye , Tao Mingyuan , Xu Meng , Zhang Peng , Qiu Jiayi , Bergsieker Gerald , Chen Jun
Published: April 2024

This paper presents a nonlinear model predictive controller (NMPC) coupled with a pre-trained reinforcement learning (RL) model that can be applied to lateral control tasks for autonomous vehicles. The past few years have seen opulent breakthroughs in applying reinforcement learning to quadruped, biped, and robot arm motion control; while these research extend the frontiers of artificial intelligence and robotics, control policy governed by reinforcement learning along can hardly guarantee the safety and robustness imperative to the technologies in our daily life because the amount of experience needed to train a RL model oftentimes makes training in simulation the only candidate, which leads to the long-standing sim-to-real gap problem–This forbids the autonomous vehicles to harness RL’s ability to optimize a driving policy by searching in a high-dimensional state space. The problem of robustness and constraints satisfaction can be alleviated by using NMPC technique which has proved itself in various industrial control tasks; however, traditional NMPC usually uses one fixed set of parameter matrices in its cost function while the changing path-tracking conditions faced by an autonomous vehicle may require the optimizer to place varying emphasis on different terms of the objective. Therefore, we propose to use a RL model to dynamically select the weights of the NMPC objective function while performing real-time lateral control of the autonomous vehicle (we call this RL-NMPC). The RL weight-search model is trained in a simulator using only one reference path, and is validated first in a simulation environment and then on a real Lincoln MKZ vehicle; the RL-NMPC achieved considerably better performance in lateral tracking during simulation and on-board tests.
Combining Dynamic Movement Primitives and Artificial Potential Fields for Lane Change Obstacle Avoidance Trajectory Planning of Autonomous Vehicles
Authors: Liang Kaichong , Zhao Zhiguo , Yan Danshu , Li Wenchang
Published: April 2024

Lane change obstacle avoidance is a common driving scenario for autonomous vehicles. However, existing methods for lane change obstacle avoidance in vehicles decouple path and velocity planning, neglecting the coupling relationship between the path and velocity. Additionally, these methods often do not sufficiently consider the lane change behaviors characteristic of human drivers. In response to these challenges, this paper innovatively applies the Dynamic Movement Primitives (DMPs) algorithm to vehicle trajectory planning and proposes a real-time trajectory planning method that integrates DMPs and Artificial Potential Fields (APFs) algorithm (DMP-Fs) for lane change obstacle avoidance, enabling rapid coordinated planning of both path and velocity. The DMPs algorithm is based on the lane change trajectories of human drivers. Therefore, this paper first collected lane change trajectory samples from on-road vehicle experiments. Second, the DMPs parameters are learned from the lane change trajectories of human drivers and the human-like lane change trajectories are planned. Meanwhile, the artificial potential field, which considers driver characteristics, is utilized to adjust the human-like lane change trajectory, ensuring that the vehicle can dynamically avoid obstacles in real-time during the lane change process. Finally, simulations and vehicle experiments were conducted in challenging scenarios with static and dynamic obstacles. The results indicate that the proposed DMP-Fs method exhibits high computational efficiency, strong generalization capabilities, and trackability of the planned trajectories. Furthermore, the DMP-Fs can actively and dynamically avoid obstacles in real-time built upon generating human-like lane change trajectories. The minimum distance between the vehicle and obstacles has been increased from 0.725 to 1.205 m, ensuring the vehicle's driving safety.
A Guidance Strategy for Parking Space of Autonomous Valet Parking with Cooperative Vehicle Infrastructure System
Authors: Zhang Zhouping , Liu Weidong , Sun Zhipeng , Zhu Zuwei , Hu Yiming , Zeng Dequan
Published: April 2024

Lots of invalid volume of traffic occurs as the vehicle repeatedly seeking the valid berth location, due to the existence of information islands, which reduces traffic efficiency, increases traffic congestion and the emission of pollutants. Aiming at eliminating the existence of information islands, this paper proposed a guidance strategy for parking space of autonomous valet parking, as taking merits of cooperative vehicle infrastructure system. The guidance strategy consists of an optimal guidance model and an adaptive ant-colony algorithm. Firstly, the optimal guidance model takes the minimum total parking cost as the objective function and the capacity of parking space as the constraints. Both the objective costs of parking and the subjective cost of customer are taken into accounts in the objective function. Secondly, comparing with traditional method, the adaptive ant-colony algorithm taking two improvements, in order to accelerate the convergence of the algorithm and avoid falling into the local optimal solution in early time. The first improvement preserves the optimal solution for each iteration, and the second improvement changes pheromone evaporation coefficient according to certain rules. The illustrative example shows that the method presented in this paper can reduce the phenomenon of repeated berth seeking and the cost of parking system.
Art Meets Automotive: Design of a Curve-Adaptive Origami Gripper for Handling Textiles on Non-Planar Mold Surfaces
Authors: Strelkova Dora , Urbanic Ruth Jill
Published: April 2024

The handling of flexible components creates a unique problem set for pick and place automation within automotive production processes. Fabrics and woven textiles are examples of flexible components used in car interiors, for air bags, as liners and in carbon-fiber layups. These textiles differ greatly in geometry, featuring complex shapes and internal slits with varying material properties such as drape characteristics, crimp resistance, friction, and fiber weave. Being inherently flexible and deformable makes these materials difficult to handle with traditional rigid grippers. Current solutions employ adhesive, needle-based, and suction strategies, yet these systems prove a higher risk of leaving residue on the material, damaging the weave, or requiring complex assemblies. Pincer-style grippers are suitable for rigid components and offer strong gripping forces, yet inadvertently may damage the fabric, and introduce wrinkles / folded-over edges during the release process. Non-planar surfaces such as the curvature of a mold, introduce additional placement challenges. Thus, a contour-adapting end-effector able to manipulate textiles without damage is desired. This research explores the feasibility of a Miura-Origami fold for material handling. The geometric tessellation is to create a curve-fitting, self-collapsing gripper. Living hinge elements are integrated to achieve controlled compliance. Variants are built using a material extrusion additive manufacturing process. The design parameters are outlined, and a set of origami grippers are built for experimental testing. The compression forces and deflection are measured. These grippers are spring-like but exhibit some unique characteristics. More research needs to be performed to understand the merits and limitations of this gripper strategy.
Humidity Controlled Evaporative Cooler for Cooling of Automotive Cabin
Authors: Dube Devashish , Upkare Piyush Pradip
Published: April 2024

The discussed invention is centered on the evaporative cooling of a vehicle cabin, introducing a novel concept of humidity control. Unlike conventional Air Conditioning (AC) systems that operate on the Vapor Compression Refrigeration Cycle (VCRC), which tend to be costly and contribute to higher fuel consumption due to the engine-driven compressor in automobiles, there is currently no other Original Equipment Manufacturer (OEM) fitted cabin cooling option available to address this issue. This paper introduces the idea of a humidity-controlled evaporative cooler. The objective of humidity control is achieved through a controller unit that receives feedback from a humidity sensor, subsequently regulating the operation of the water pump. The ambient air is passed through a humidified honeycomb pad, cooling through the principle of evaporation. To prevent any leftover water droplets from entering the cabin, a polyester nonwoven filter has been integrated into the system. This invention not only makes the system robust, easy to install, and simple to maintain but also effectively controls the cabin's humidity level, providing comfort to the occupant. The prototype of the proposed concept is currently undergoing testing on a vehicle, and preliminary results show a temperature drop of 7.5°C concerning the ambient temperature without the use of any refrigerant that could harm the environment. In conclusion, it can be deduced that the proposed invention is more efficient in terms of cost, power consumption, space utilization, and provides superior cooling capabilities.
Characterization of Embedded Debris Particles on Crankshaft Bearings
Authors: Yang Jianghuai , Wang Qigui , Li Zhe
Published: April 2024

Crankshaft bearings function to maintain the lubrication oil films needed to support crankshaft journals in hydrodynamic regime of rotation. Discontinuous oil films will cause the journal-bearing couple to be in a mixed or boundary lubrication condition, or even a bearing seizure or a spun bearing. This condition may further force the crankshaft to break and an engine shutdown. Spun bearings have been identified to be one of the top reasons in field returned engines. Excessive investigations have found large, embedded hard debris particles on the bearings are inevitably the culprit of destroying continuity of the oil films. Those particles, in particular the suspicious steel residues, in the sizes of hundreds of micrometers, are large enough to cause oil film to break, but rather fine and challenging for materials engineers to characterize their metallurgical features. This article presents the methodology and steps of debris analyses on bearings at different stages of engine build. Common and advanced materials characterization techniques including optical microscopy (OM), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), have been employed to evaluate the appearance, size, composition, characteristic spectrum, and even microstructure, microhardness of the deleterious particles embedded on the failed bearings. The successful characterization of the troublesome particle material has made it practical in the relevant component manufacturing process for cleanliness improvement. Advantages and shortcomings of the analytical technique are also commentated.
Consumer-Oriented Energy Use and Range Metrics for Battery Electric Vehicles
Authors: Sluder C. Scott , Davis Stacy C. , Boundy Robert G.
Published: April 2024

The present study was motivated by a need to expand information for consumers offered through the FuelEconomy.Gov website. To that end, a power-based modeling approach has been used to examine the effect of steady-speed driving on estimated range for model year 2020 – 2023 battery electric vehicles (BEVs). This approach allowed rapid study of a broader range of BEV models than could be accomplished through vehicle tests. Publicly accessible certification test results and other data were used to perform a regression between cycle-average tractive power requirements and the resulting electrical power. This regression enabled estimation of electric power and energy use over a range of steady highway speeds. These analyses in turn allowed projection of vehicle range at differing speeds. The projections agree within 6% with available 65 MPH manufacturer test data. Analyses of vehicles from model years 2020 – 2023 show that the 5-cycle range and energy use values from the window stickers of new vehicles are reasonable values for steady-speed driving at 65 MPH. Range decreases by a median value of approximately 15% for each 10 MPH increase in speed. The 5-cycle energy use (in kW-Hr / 100 miles) and energy economy (in miles / kW-Hr) derived from the 5-cycle energy use are reasonable estimates for 65 MPH steady speed driving. Energy economy decreases by about 0.5 miles / kW-Hr for every 10 MPH increase in speed.
WITHDRAWN Powertrain Digital Twinning for Real-World Emissions Compliance
Authors: Roberts Phil , Mason Alex , Headley Aaron , Bates Luke , Tabata Kunio , Whelan Steve
Published: April 2024

The context for real-world emissions compliance has widened with the anticipated implementation of EU7 emissions regulations. The more stringent emissions limits and deeper real-world driving test fields of EU7 make compliance more challenging. While EU6 emissions legislation provided clear boundaries by which vehicle and powertrain Original Equipment Manufacturers (OEMs) could develop and calibrate against, EU7 creates additional challenges.

To ensure that emissions produced during any real-world driving comply with legal limits, physical testing conducted in-house and in-field to evaluate emissions compliance of a vehicle and powertrain will not be sufficient. Given this, OEMs will likely need to incorporate some type of virtual engineering to supplement physical testing. In this respect, the HORIBA Intelligent Lab virtual engineering toolset has been created and deployed to produce empirical digital twins of a modern light-duty electrified gasoline Internal Combustion Engine (ICE) and a commercial vehicle diesel ICE. The former was created with the specimen tested on an engine dynamometer with the latter ICE tested using chassis dynamometer methodology. Both powertrains were exercised across their entire operational ranges with the subsequent performance and emissions measurements used to generate corresponding transient empirical response models.

The validated transient empirical performance and emissions models for the electrified ICE were then coupled with data extracted from virtual driving scenarios adopting a light-duty Sports Utility Vehicle (SUV); predictions of performance and emissions attributes for these scenarios were subsequently made. The diesel transient empirical models were coupled with data extracted from virtual driving scenarios utilising a commercial goods vehicle; again, predictions of performance and emissions attributes for these scenarios were subsequently made.

The predictions obtained by creating an empirical digital twin of a powertrain utilising engine or chassis dynamometer testing procedures can be utilised to quickly detect unfavourable powertrain operating conditions or problematic driving situations prior to product launch.
Numerical Analysis of Mixing of Bio-Hybrid Fuels in a Direct Injection Engine with a Pre-Chamber Ignition System
Authors: Wegmann Tim , Meinke Matthias , Fleischmann Maximilian , Pischinger Stefan , Schröder Wolfgang
Published: April 2024

Numerical analyses of the liquid fuel injection and subsequent fuel-air mixing for a high-tumble direct injection engine with an active pre-chamber ignition system at operation conditions of 2000 RPM are presented. The Navier-Stokes equations for compressible in-cylinder flow are solved numerically using a hierarchical Cartesian mesh based finite-volume method. To determine the fuel vapor before ignition large-eddy flow simulations are two-way coupled with the spray droplets in a Lagrangian Particle Tracking (LPT) formulation. The combined hierarchical Cartesian mesh ensures efficient usage of high performance computing systems through solution adaptive refinement and dynamic load balancing. Computational meshes with approximately 170 million cells and 1.0 million spray parcels are used for the simulations. The influence of a lateral ethanol injection on the tumble flow motion and the entrainment into the pre-chamber is analyzed for stoichiometric and lean fuel conditions for an early injection at 60 CAD. The injection into the main-chamber shows a strong impact of the fuel jets on the in-cylinder flow field with a sustained tumble-motion. For the stoichiometric condition and at 300 CAD, the fuel concentration in the pre-chamber lies 67% below the main-chamber fuel concentration. For the lean condition, the deviation from the target fuel concentration in the main-chamber is lowered by at least one order of magnitude compared to the stoichiometric fuel injection at the start of ignition.
Algorithm to Calibrate Catalytic Converter Simulation Light-Off Curve
Authors: Wilson John Parley , DelVescovo Dan
Published: April 2024

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money. However, calibration of these models can be challenging and requires significant time and effort. Catalytic converter models require the specification of input conditions (i.e. temperature, flowrate, and species concentrations). Then they calculate the predicted exhaust gas composition by simulating the chemical reactions occurring within the catalyst. These simulations can then be calibrated and validated against experimental measurements. The chemical reaction rates in the model utilize an Arrhenius expression which includes two tunable variables, the pre-exponential factor (A), which is a measure of collision frequency, and the activation energy (E), which is a threshold to overcome for molecules to react. Calibration of these values often requires many iterations, checking the results and adjusting to eventually identify the best values. In this work, an optimization algorithm was developed to automatically tune these parameters to best simulate catalyst light-off data. This algorithm is presented. It has the potential to significantly reduce time in calibrating catalyst models.
A Scenario-Based Test Selection and Scoring Methodology for Inclusion in a Safety Case Framework for Automated Vehicles
Authors: O'Malley Gavin , Wishart Jeffrey , Zhao Junfeng , Russo Brendan
Published: April 2024

Effectively determining automated driving system (ADS)-equipped vehicle (AV) safety without relying on testing an infeasibly large number of driving scenarios is a challenge with wide recognition in industry and academia. The following paper builds on previous work by the Institute of Automated Mobility (IAM) and Science Foundation Arizona (SFAz), and proposes a test selection and scoring methodology (TSSM) as part of a safety case-based framework being developed by the SFAz to ensure the safety of AVs while addressing the scenario testing challenge. The TSSM is an AV verification and validation (V&V) process that relies, in part, on iterative, partially random generation of AV driving scenarios. These scenarios are generated using an operational design domain (ODD) and behavioral competency portfolio, which expresses the vehicle ODD and behavioral competencies in terms of quantifiable amounts or intensities of discrete components. Once generated, these scenarios are subjected to filters based on their relevance to the AV ODD and behavioral competency portfolio that preserves the robustness of the generated test set; after filtration, scenarios are assigned to a test method and executed. Further, these scenarios may be generated entirely by the TSSM or may be drawn from a preexisting scenario database and subjected to the same filtration process. After the scenarios assembled by the TSSM are executed, the methodology aggregates their driving assessment (DA) scores into a single numerical value. We outline the overall safety case-based framework, the TSSM, including its role in the framework as well as planned future work, and outline two proofs of concept: (1) a demonstration of the ability of the TSSM to pare down the space of scenarios in a scenario database; and (2) a specification form which may be used to solicit a description of the AV ODD and behavioral competency portfolio from the AV developer.
CFD Analysis of the Battery Thermal Management System for a Heavy-Duty Truck
Authors: Makings Matthew , Maciocha Mateusz , Biggs Jonathan , Salek Farhad , Zare Ali , Resalati Shahaboddin , Short Thomas , Babaie Meisam
Published: April 2024

Li-ion batteries (LIBs) optimum performance and lifetime depend on temperature, with the commonly suggested operating temperature being in the range of 25 to 40 °C. It's also crucial to keep the temperature difference between battery cells below 5°C. Operation at different temperature ranges can adversely affect or degrade the performance and lifetime of LIBs. A battery thermal management system (BTMS) is essential for keeping the battery temperature within the optimum range. This paper aims to develop and analyze the BTMS for an electric heavy-duty truck. To achieve this aim, battery cells and modules are modelled in ANSYS Fluent software. Validation with experimental results and mesh sensitivity studies are also performed to increase confidence in simulation data. The model is then analyzed for a specific cooling systems to investigate its effect on battery thermal performance during the operation. From the simulation results, the proposed efficient cooling system geometry is developed. Simulations based on discharge rates from a mathematical model of the truck are run with the optimum geometry design to identify optimal coolant mass flow rates and inlet temperatures at various ambient temperatures. It was found that the mass flow rate of 18 Lpm (36 Lpm pack flow rate) is optimal and the optimum inlet temperature is about 305.2K (32.2°C) for the ambient temperature of 313K (40°C).
48V Battery Pack Thermal Design Concept with Temperature Measurement on a Single Cell
Authors: Madhura Mangalath Hashim , Marikar Ismail
Published: April 2024

Mild hybrid topology with 48V battery packs offers a cost-effective solution with considerable improvement in fuel economy and performance over the conventional vehicles. Thermal management of the battery pack is of utmost importance to ensure a safe, reliable, and optimal operation over the target lifetime and under varying operating conditions. The battery management system needs to take into consideration the temperature of all the cells in the pack for estimating the maximum allowed current for charge/discharge. For example, at lower temperature the coldest cell in the pack would be more probable to lithium plating and hence will be the limiting case while at higher temperature the allowed current should be such that the hottest cell in the pack is taken care. Pack design with temperature sensor for each cell in the pack will increase the cost, hardware, and software complexity. An optimized pack design is necessary to keep the overall cost of the battery pack lower while meeting the design requirements. This paper discusses pack thermal design approach for 48V battery pack with 12S1P configuration considering a single cell temperature measurement instead of all the cells for a cost-effective solution. The various factors that could introduce temperature spread among the cells will be discussed in detail. A pack thermal model and simulation-based approach will be presented to derive the temperature margins relative to the monitored cell and how the margins can be considered for BMS calibration to ensure safe operation for all the pack components will be explained.
WITHDRAWN Investigation of URANS CFD Methods for Supersonic Hydrogen Jets
Authors: Kaczmarczyk Kacper Oskar , Liu Xinlei , Im Hong G. , Turner James W.G. , Yuan Hao , Akehurst Sam , Esposito Stefania
Published: April 2024

The urgent need to combat global warming has spurred legislative efforts within the transport sector to transition away from fossil fuels. Hydrogen is increasingly being utilised as a green energy vector, which can aid the decarbonisation of transport, including internal combustion engines. Computational fluid dynamics (CFD) is widely used as a tool to study and optimise combustion systems especially in combination with new fuels like hydrogen. Since the behaviour of the injection event significantly impacts combustion and emissions formation especially in direct injection applications, the accurate modelling of H2 injection is imperative for effective design of hydrogen combustion systems. This work aims to evaluate unsteady Reynolds-Averaged Navier Stokes (URANS) modelling of the advective transport process and related numerical methods. Measurements of H2 injection forming supersonic jets inside of constant volume chamber carried out at wide range of relevant conditions are utilised for validation. Investigations focused on aspects of simulated jet definition and its compatibility with Schlieren methods, cubic equations of state as well as probability of the relevant conditions inside the jet, use of adaptive mesh refinement (AMR), mesh dependency, convective flux and colocation methods, nozzle pressure ratio (NPR) effects and contribution of individual terms. The paper recommends applying molar fraction of H2 to define the jet as well as utilising Soave-Redlich-Kwong as equation of state. Mesh dependency is found to be strongly influenced by nozzle kinetic power (NKP), but not by selection of the turbulence model. Advanced convective flux schemes with flux/slope limiters and colocation generalisation can effectively reduce numerical diffusion and dispersion errors. Finally, comparison of the simulations against physical experiments has shown a good agreement across a wide range of NPRs, proving the reliability of modelling the advection.
Numerical Evaluation of Injection Parameters on Transient Heat Flux and Temperature Distribution of a Heavy-Duty Diesel Engine Piston
Authors: Ravikumar Avinash , Wright Stephen , Redmond Laura , Gingrich Eric , Korivi Vamshi , Tess Michael , Piehl Joshua , Lawler Benjamin
Published: April 2024

A major concern for a high-power density, heavy-duty engine is the durability of its components, which are subjected to high thermal loads from combustion. The thermal loads from combustion are unsteady and exhibit strong spatial gradients. Experimental techniques to characterize these thermal loads at high load conditions on a moving component such as the piston are challenging and expensive due to mechanical limitations. High performance computing has improved the capability of numerical techniques to predict these thermal loads with considerable accuracy. High-fidelity simulation techniques such as three-dimensional computational fluid dynamics and finite element thermal analysis were coupled offline and iterated by exchanging boundary conditions to predict the crank angle-resolved convective heat flux and surface temperature distribution on the piston of a heavy-duty diesel engine. A Bayesian calibration method was used to arrive at heat transfer coefficients for some of the impactful piston cooling surfaces.

This work provides insights about the potential use of injection parameters to manage piston thermal loads and maximize thermal efficiency at high load conditions. The influence of key injection parameters such as injection timing (start of injection) and pressure on the transient heat flux and surface temperature distribution on the piston surface were analyzed using the above-mentioned numerical technique. The results show that later injection timings result in lower piston surface temperatures, but also lower efficiencies. A reduced injection pressure at the earliest combustion phasing reduced the piston surface temperature with a slight drop in thermal efficiency. Significant spatial variations of heat flux and surface temperature were observed and quantified for the changes in injection parameters.
Objective and Perceptual Sound Quality Analysis of Internal Combustion Engine and Electric Vehicles
Authors: Dao Duy Loc , Baldwin Isabella , McGuire Andrew , Bray Wade , Baqersad Javad
Published: April 2024

The sound quality of automotive interiors is one of the critical factors regarding customer satisfaction. As electric vehicles (EVs) rapidly rise in popularity, the known literature on sound qualities of internal combustion engine (ICE) automotive interiors has become less relevant. Because of this, comparing and contrasting 'the sound qualities of EV and ICE vehicles is essential to have the proper foundation for studying automotive noise quality in the future. In this paper, we aim to benchmark the major differences between an EV and an ICE automobile regarding interior sound quality. This study seeks to understand basic sound engineering characteristics and how they differ between the two types of vehicles. We also analyzed the public's preferences when it comes to the two types of cars. To get as much data as possible in our time-constrained project, we tested both types of vehicles in two different environments: an uncontrolled road (Bluff Street in Flint, MI) and a controlled track (the GM Mobility Research Center - MRC - at Kettering University). We also tested three different positions in the car, including the driver's seat, passenger seat, and rear middle seat position. The interior sound was then recorded using the SQobold sound acquisition device and the HEAD acoustics Aachen HEAD as the microphone. Three recordings of every type of test were taken in order to confirm consistent and accurate results. We then compared and contrasted the data in Artemis SUITETM, a sound analysis software. We determined the major differences between the cars, particularly in loudness and sharpness. The final step was jury testing, in which the subjective samples compare well with our conclusions regarding sound quality metrics.
Dynamic Simulation of Steering Crimp Ring Assembly Process Using CAE and its Correlation with Testing
Authors: Song Gavin , Vlademar Michael , Venugopal Narayana
Published: April 2024

The process of assembling the bearing and crimp ring to the steering pinion shaft is intricate. The bearing is pressed into its position via the crimp ring, which is tipped inward and fully fitted into a groove on the pinion shaft. Only when the bearing is pressed to a low surface on the pinion shaft, the caulking force for the crimp ring is achieved. The final caulking distance for the crimp ring confirms the proper bearing position. Simulating this transient fitting process using CAE is a challenging topic. Key factors include controlling applied force, defining contact between bearing and pinion surface, and defining contact between crimp ring and bearing surface from full close to half open transition. The overall CAE process is validated through correlation with testing.