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Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

Low-temperature heat release (LTHR) in spark-ignited internal combustion engines is a critical step toward the occurrence of auto-ignition, which can lead to an undesirable phenomenon known as engine knock. Hence, correct predictions of LTHR are of utmost importance to improve the understanding of knock and enable techniques aimed at controlling it. While LTHR is typically obscured by the deflagration following the spark ignition, extremely late ignition timings can lead to LTHR occurrence prior to the spark, i.e., pre-spark heat release (PSHR). In this research, PSHR in a boosted direct-injection SI engine was numerically investigated using three-dimensional computational fluid dynamics (CFD). A hybrid approach was used, based on the G-equation model for representing the turbulent flame front and the multi-zone well-stirred reactor model for tracking the chemical reactions within the unburnt region.

This paper presents on-going finite element modeling efforts of friction stir spot welding (FSSW) process using Abaqus/Explicit as a finite element solver. Three-dimensional coupled thermal-stress model was used to calculate thermo-mechanical response of FSSW process. Adaptive meshing and advection schemes, which makes it possible to maintain mesh quality under large deformations, is utilized to simulate the material flow and temperature distribution in FSSW process. The predicted overall deformation shape of the weld joint resembles that experimentally observed. Temperature and stress graphs in the radial direction as well as temperature-deformation distribution plots are presented.

Cabin heating of current electric vehicle (EV) designs is typically provided using electrical energy from the traction battery, since waste heat is not available from an engine as in the case of a conventional automobile. In very cold climatic conditions, the power required for space heating of an EV can be of a similar magnitude to that required for propulsion of the vehicle. As a result, its driving range can be reduced very significantly during the winter season, which limits consumer acceptance of EVs and results in increased battery costs to achieve a minimum range while ensuring comfort to the EV driver. To minimize the range penalty associated with EV cabin heating, a novel climate control system that includes thermal energy storage from an advanced phase change material (PCM) has been designed for use in EVs and plug-in hybrid electric vehicles (PHEVs).

Three dimensional fabrics have seen increasing use lately as composite reinforcements. Advantages over prepreg or chopped fiber processes can include cost, handling, consistent quality, impact behavior, and resistance to delamination [1]. To gain acceptance in the transportation industry it is imperative that properties including dynamic and fatigue behavior be designable. A Progressive Failure Analysis (PFA) was developed jointly by Alpha Star Corp and NASA to predict fatigue life of composites and determine their damage mechanisms so that the life could be extended. The title of this software package is GENOA™, and it was used to focus on the three dimensional fabric called 3WEAVE™ made by 3TEX, Inc. It was discovered through fatigue testing that void content greatly affected fatigue life for the 3D E-glass fabric reinforcing a polyurethane modified vinyl ester resin called Dion 9800 from Reichhold. This is a common characteristic for most structural materials.

Finite Element Analysis (FEA) has been successfully used in the simulation of sheet metal forming process. The accurate prediction of the springback is still a major challenge due to its sensitivity to the geometric modeling of the tools, strain hardening model, yield criterion, contact algorithm, loading pattern, element formulation, mesh size and number of through-thickness integration points, etc. The objective of this paper is to discuss the effect of numerical parameters on springback prediction using dynamic explicit FEA codes. The example used in the study is from the Auto/Steel Partnership High Strength Steel Rail Springback Project. The modeling techniques are discussed and the guidelines are provided for choosing numerical parameters, which influence the accuracy of the springback prediction and the computation cost.

Mechanical properties of as-rolled steels used in a vehicle vary with many parameters including gages, steel suppliers and manufacturing processes. The residual forming and strain rate effects of automotive components have been generally neglected in full vehicle crashworthiness analyses. Not having the above information has been considered as one of the reasons for the discrepancy between the results from computer simulation models and actual vehicle tests. The objective of this study is to choose the right material property for as-rolled steels for stamping and crash computer simulation, and investigate the effect of forming and strain rate on the results of full vehicle impact analyses. Major Body-in-White components which were in the crash load paths and whose material property would change in the forming process were selected in this study. The post-formed thickness and yield stress distributions on the components were estimated using One Step forming analyses.

Metal additive manufacturing has high potential to produce automobile parts, due to its shape flexibility and unique material properties. On the other hand, residual stress which is generated by rapid solidification causes deformation, cracks and failure under building process. To avoid these problems, understanding of internal residual stress distribution is necessary. However, from the view point of measureable area, conventional residual stress measurement methods such as strain gages and X-ray diffractometers, is limited to only the surface layer of the parts. Therefore, neutron which has a high penetration capability was chosen as a probe to measure internal residual stress in this research. By using time of flight neutron diffraction facility VULCAN at Oak Ridge National Laboratory, residual stress for mono-cylinder head, which were made of aluminum alloy, was measured non-distractively. From the result of precise measurement, interior stress distribution was visualized.

Modeling and simulation of batteries, in conjunction with theory and experiment, are important research tools that offer opportunities for advancement of technologies that are critical to electric motors. The development of data from the application of these tools can provide the basis for managerial and technical decision-making. Together, these will continue to transform batteries for electric vehicles.

The heat treatment of steel parts is an essential step in the manufacturing of high-performance components for a variety of commercial and military products. Distortion in the size and shape of parts resulting from the heat treatment process is a pervasive manufacturing problem that causes higher finishing costs, excessive scrap and rework, long delivery times, and negative environmental impact. To date, techniques that have been developed to reduce or eliminate heat treatment distortion are largely based on experience and have been limited to trial and error. This presentation describes the philosophy and results of an ongoing collaborative project to develop a methodology and computer simulation capability to predict ferrous alloy component response (distortion, residual stress, and microstructure) to industrial heat treatment processes for automotive, truck, bearing, and aerospace applications.

To accurately predict emissions as well as combustion phasing in a homogeneous charge compression ignition (HCCI) engine, detailed chemistry needs to be used in Computational Fluid Dynamics (CFD) modeling. In this work, CFD simulations of an Oak Ridge National Laboratory (ORNL) gasoline HCCI engine have been performed with full coupling to detailed chemistry. Engine experiments using an E30 gasoline surrogate blend were performed at ORNL, which included measurements of several trace species in the exhaust gas. CFD modeling using a detailed mechanism for the same fuel composition used in the experiments was also performed. Comparisons between data and model are made over a range of intake temperatures. The (experiment & model) surrogate blend consists of 33 wt % ethanol, 8.7 % n-heptane and 58.3 % iso-octane. The data and simulations involve timing sweeps using intake temperature to control combustion phasing at a constant fuel rate.

Reactivity Controlled Compression Ignition (RCCI) is an engine combustion strategy that produces low NO and PM emissions with high thermal efficiency. Previous RCCI research has been investigated in single-cylinder heavy-duty engines. The current study investigates RCCI operation in a light-duty multi-cylinder engine at 3 operating points. These operating points were chosen to cover a range of conditions seen in the US EPA light-duty FTP test. The operating points were chosen by the Ad Hoc working group to simulate operation in the FTP test. The fueling strategy for the engine experiments consisted of in-cylinder fuel blending using port fuel-injection (PFI) of gasoline and early-cycle, direct-injection (DI) of diesel fuel. At these 3 points, the stock engine configuration is compared to operation with both the original equipment manufacturer (OEM) and custom-machined pistons designed for RCCI operation.

In this study we identify components of a typical biodiesel fuel and estimate both their individual and mixed thermo-physical and transport properties. We then use the estimated mixture properties in computational simulations to gauge the extent to which combustion is modified when biodiesel is substituted for conventional diesel fuel. Our simulation studies included both conventional diesel combustion (DI) and premixed charge compression ignition (PCCI). Preliminary results indicate that biodiesel ignition is significantly delayed due to slower liquid evaporation, with the effects being more pronounced for DI than PCCI. The lower vapor pressure and higher liquid heat capacity of biodiesel are two key contributors to this slower rate of evaporation. Other physical properties are more similar between the two fuels, and their impacts are not clearly evident in the present study.

We propose a procedure by which a two-zone thermodynamic model combined with a flame propagation sub-model can used for predicting the cycle-to-cycle variations of combustion in a spark ignition (SI) engine operating at very lean and high exhaust gas residual conditions. Under such conditions, the variations have been shown to consist of both deterministic and stochastic components. The deterministic component is inherent to the non-linear nature of the combustion efficiency variation with equivalence ratio (or dilution level) while the stochastic component results primarily from noise associated with the parameters (that are inevitable in a mechanical system) that affect combustion. Since the overall dynamics of the instabilities are driven by the low order deterministic component, if a model can be made to capture this component, the stochastic component is easily modeled by adding noise to the parameters.

We report experimental observations of cyclic combustion variability during the transition between propagating flame combustion and homogeneous charge compression ignition (HCCI) in a single-cylinder, stoichiometrically fueled, spark-assisted gasoline engine. The level of internal EGR was controlled with variable valve actuation (VVA), and HCCI combustion was achieved at high levels of internal EGR using the VVA system. Spark-ignition was used for conventional combustion and was optionally available during HCCI. The transition region between purely propagating combustion and HCCI was mapped at multiple engine speeds and loads by incrementally adjusting the internal EGR level and capturing data for 2800 sequential cycles. These measurements revealed a complex sequence of high COV, cyclic combustion variations when operating between the propagating flame and HCCI limits.

EGR coolers are mainly used on diesel engines to reduce intake charge temperature and thus reduce emissions of NOx and PM. Soot and hydrocarbon deposition in the EGR cooler reduces heat transfer efficiency of the cooler and increases emissions and pressure drop across the cooler. They may also be acidic and corrosive. Fouling has been always treated as an approximate factor in heat exchanger designs and it has not been modeled in detail. The aim of this paper is to look into fouling formation in an EGR cooler of a diesel engine. A 1-D model is developed to predict and calculate EGR cooler fouling amount and distribution across a concentric tube heat exchanger with a constant wall temperature. The model is compared to an experiment that is designed for correlation of the model. Effectiveness, mass deposition, and pressure drop are the parameters that have been compared. The results of the model are in a good agreement with the experimental data.

The computer-aided-design and analysis of a robust casting process requires the optimization of both mold filling and solidification. A number of commercial casting codes are available for modeling the fluid flow during mold filling and the heat transfer during solidification. The next generation casting process models will build on present capabilities to allow the prediction of microporosity and other defects and microstructure. This paper will discuss the issues involved in the development of next generation casting process models and present results from a computer model for microporosity prediction that is based on first principles, and will take into account alloy composition, alloy microstructure, the initial hydrogen content of the liquid alloy, and the resistance to inter-dendritic fluid flow to feed shrinkage.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

This paper deals with the effects of various approaches for modeling of strain rate effects for mild and high strength steels (HSS) on impact simulations. The material modeling is discussed in the context of the finite element method (FEM) modeling of progressive crush of energy absorbing automotive components. The characteristics of piecewise linear plasticity strain rate dependent material model are analyzed and various submodels for modeling of impact response of steel structures are investigated. The paper reports on the ranges of strains and strain rates that are calculated in typical FEM models for tube crush and their dependence on the material modeling approaches employed. The models are compared to the experimental results from drop tower tests.

Pickup box drum drop test is critical in vehicle development to determine the impact strength of the floor panels. Physical hardware tests on prototypes have been used to assess whether the performance of the future pickup box meets design requirements. In order to reduce costs and shorten development cycle, CAE methodology was developed to accurately model the drum drop test. In this paper, a CAE procedure for modeling the drum drop test is proposed. Dynamic explicit finite element code LS-Dyna was used to simulate the non-linear impact process of a drum onto the box floor. The permanent plastic damages on the floor panel were recorded in both simulation and experiments. Very good correlation between the simulation results and the physical hardware tests was achieved. It indicates that the methodology developed is an effective tool in evaluating the performances of the pickup box floor panels.