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With the advent of this new era of electric-driven automobiles, the simulation and virtual digital twin modeling world is now embarking on new sets of challenges. Getting key insights into electric motor behavior has a significant impact on the net output and range of electric vehicles. In this paper, a complete 3D CFD model of an Electric Motor is developed to understand its churning losses at different operating speeds. The simulation study details how the flow field develops inside this electric motor at different operating speeds and oil temperatures. The contributions of the crown and weld endrings, crown and weld end-windings, and airgap to the net churning loss are also analyzed. The oil distribution patterns on the end-windings show the effect of the centrifugal effect in scrapping oil from the inner structures at higher speeds. Also, the effect of the sump height with higher operating speeds are also analyzed.

The study focuses on understanding the air and oil flow characteristics within a ball bearing during high-speed rotation, with a particular emphasis on optimizing frictional heat dissipation and oil lubrication methods. Computational fluid dynamics (CFD) techniques are employed to analyze the intricate three-dimensional airflow and oil flow patterns induced by the motion of rotating and orbiting balls within the bearing. A significant challenge in conducting three-dimensional CFD studies lies in effectively resolving the extremely thin gaps existing between the balls, races, and cages within the bearing assembly. In this research, we adopt the ball-bearing structured meshing strategy offered by Simerics-MP+ to meticulously address these micron-level clearances, while also accommodating the rolling and rotation of individual balls. Furthermore, we investigate the impact of different designs of the lubrication ports to channel oil to other locations compared to the ball bearings.

To advance vehicle lightweighting, chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In this paper, a Representative Volume Element (RVE) approach is used to model the SMC microstructure. Two modeling methods, the Voronoi diagram-based method and the chip packing method, are developed to populate the RVE. The elastic moduli of the RVE are calculated and the two methods are compared with experimental tensile test conduct using Digital Image Correlation (DIC). Furthermore, the advantages and shortcomings of these two methods are discussed in terms of the required input information and the convenience of use in the integrated processing-microstructure-property analysis.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

This paper presents a computational fluid dynamics (CFD) model for predicting power losses associated with churning of oil by gears or other similar rotating components. The modeling approach and parameters are optimized to ensure the accuracy, robustness, and computational efficiency of these predictions. These studies include a look at two types of mesh and a turbulence model selection. The focus is on multiple reference frame (MRF) modeling technique for its computational efficiency advantage. Model predictions are compared to previously published experimental data [1] under varying operating conditions typical for an automotive transmission application. The model shows good agreement with the hardware both quantitatively and qualitatively, capturing the trends with speed and submersion level. The paper concludes with presenting some key lessons learned, and recommendation for future work to ultimately build a highly reliable tool as part of the virtual product development.

During a vehicle drive cycle, the oil in the engine oil pan sloshes very vigorously due to the acceleration of the vehicle. This can cause the pickup tube in the engine oil pan to become uncovered from oil and exposed to air, which affects the lubrication pump performance. Engine oil pan sloshing is inherently a 3D problem as the free oil surface is constantly changing. Multi-dimensional Computational Fluid Dynamics (CFD) methods are very useful to simulate such problems with high detail and accuracy but are computationally very expensive. Part of the engine lubrication system, such as the pump, can be modelled in 1D which can predict accurate results at relatively high computational speeds. By utilizing the advantages of both 1D and 3D CFD models, a coupled 1D-3D simulation approach has been developed to capture the detailed oil sloshing phenomenon in SimericsMP+ and the system level simulation is conducted in GT-SUITE where 3D spatial data is not required.

In recent years, with the increase of traffic accidents and traffic jams, lane change, as one of the most important and commonly automatic driving operations for autonomous vehicles, is receiving attention in academia. It is considered to be one of the important solutions that play an important role in improving road traffic safety and efficiency. However, most existing lane-changing models are rule-based lane-changing models. These models only assume a one-direction impact of surrounding vehicles on the lane-changing vehicle. In fact, lane change is a process of mutual interaction between vehicles due to the complexity and uncertainty of the traffic environment. Moreover, the safety and efficiency of existing lane-changing decision algorithms need to be improved. In this paper, we proposed a multivehicle cooperative control approach with a distributed control structure to control the model.

Ammonia has attracted the attention of a growing number of researchers in recent years. However, some properties of ammonia (e.g., low laminar burning velocity, high ignition energy, etc.) inhibit its direct application in engines. Several routes have been proposed to overcome these problems, such as oxygen enrichment, partial fuel cracking strategy and co-combustion with more reactive fuels. Improving the reactivity of ammonia from the oxidizer side is also practical. Ozone is a highly reactive oxidizer which can be easily and rapidly generated through electrical plasma and is an effective promoter applicable for a variety of fuels. The dissociation reaction of ozone increases the concentration of reactive radicals and promotes chain-propagating reactions. Thus, obtaining accurate rate constants of reactions related to ozone is necessary, especially at elevated to high pressure range which is closer to engine-relevant conditions.

Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models.

The prediction of agents' future trajectory is a crucial task in supporting advanced driver-assistance systems (ADAS) and plays a vital role in ensuring safe decisions for autonomous driving (AD). Currently, prevailing trajectory prediction methods heavily rely on high-definition maps (HD maps) as a source of prior knowledge. While HD maps enhance the accuracy of trajectory prediction by providing information about the surrounding environment, their widespread use is limited due to their high cost and legal restrictions. Furthermore, due to object occlusion, limited field of view, and other factors, the historical trajectory of the target agent is often incomplete This limitation significantly reduces the accuracy of trajectory prediction. Therefore, this paper proposes ETSA-Pred, a mapless trajectory prediction model that incorporates enhanced temporal modeling and spatial self-attention.

High temperature components in internal combustion engines and exhaust systems must withstand severe mechanical and thermal cyclic loads throughout their lifetime. The combination of thermal transients and mechanical load cycling results in a complex evolution of damage, leading to thermomechanical fatigue (TMF) of the material. Analytical tools are increasingly employed by designers and engineers for component durability assessment well before any hardware testing. The DTMF model for TMF life prediction, which assumes that micro-crack growth is the dominant damage mechanism, is capable of providing reliable predictions for a wide range of high-temperature components and materials in internal combustion engines. Thus far, the DTMF model has employed a local approach where surface stresses, strains, and temperatures are used to compute damage for estimating the number of cycles for a small initial defect or micro-crack to reach a critical length.

Precise trajectory prediction of surrounding vehicles is critical for decision-making of autonomous vehicles, and learning-based approaches are well recognized for the robustness. However, state-of-the-art learning-based methods ignore (1) the feasibility of the vehicle’s multimodal state information for prediction and (2) the mutually exclusive relationship between the global traffic scene receptive fields and the local position resolution when modeling vehicles’ interactions, which may influence prediction accuracy. Therefore, we propose a “vehicle descriptor”-based long short-term memory (LSTM) model with the dilated convolutional social pooling (VD+DCS-LSTM) to cope with the above issues.

Accurate and fast positioning of large aircraft component is of great importance for Automated Alignment System. The Ball joint is a widely-used mechanical device connecting the aircraft component and positioners. However, there are some shortcomings for the device in man-machine engineering, such as the entry state of the ball-head still needs to be confirmed by the workers and then switched to the locking state manually. To solve above problems, a new positioning mechanism is present in this paper, which consists of a ball-head and a ball-socket. The new device is equipped with a monocular vision system, in which a calibrated industrial camera is used to collect the images of the ball-head. And then, the 3-D coordinate of the ball-head center is calculated by a designed algorithm, guiding the positioner to capture the ball-head. Once the ball-head gets into the ball-socket, the pneumatic system will drive the pistons to move to the specified location.

Fuel chemistry plays a crucial role in the continued reduction of particulate emissions (PE) and cleaner air quality from vehicles and equipment powered by internal combustion engines (ICE). Over the past ten years, there have been great improvements in predictive particulate emissions indices (correlative mathematical models) based on the fuel’s composition. Examples of these particulate indices (PI) are the Honda Particulate Matter Index (PMI) and the General Motors Particulate Evaluation Index (PEI). However, the analytical chemistry lab methods used to generate data for these two PI indices are very time-consuming. Because gasoline can be mixtures of hundreds of hydrocarbon compounds, these lab methods typically include the use of the high resolution chromatographic separation techniques such as detailed hydrocarbon analysis (DHA), with 100m chromatography columns and long (3 - 4 hours) analysis times per sample.

Rotating clutches play an important role in automatic transmissions (AT), dual-clutch transmissions (DCT) and hybrid transmissions. It is very important to continually improve the transmission systems in the areas such as simplifying actuator designs, reducing cost and increasing controllability. A new concept of electrical motor driven actuation using a wedge mechanism, a wedge clutch, demonstrates potential benefits. This wedge clutch has the characteristics of good mechanical advantage, self-reinforcement, and faster and more precise controllability using electrical motor. In this paper, a new rotating wedge clutch is proposed. It presents a challenge since the motor actuator has to be stationary while the clutch piston is rotating. A new mechanism to connect the motor to the wedge piston, including dual-plane bearings and two mechanical ramp linkages, is studied. The design and verification of the physical structure of the actuator are discussed in detail in the paper.

To achieve higher efficiencies and lower emissions, dual-fuel strategies have arisen as advanced engine technologies. In order to fully utilize engine fuels, understanding the combustion chemistry is urgently required. However, due to computation limitations, detailed kinetic models cannot be used in numerical engine simulations. As an alternative, approaches for developing reduced reaction mechanisms have been proposed. Nevertheless, existing simplified methods neglecting the real engine combustion processes, which is the ultimate goal of reduced mechanism. In this study, we propose a novel simplified approach based on fuel reactivity. The high-reactivity fuel undergoes pyrolysis first, followed by the pyrolysis and oxidation of the low-reactivity fuel. Therefore, the simplified mechanism consists of highly lumped reactions of high-reactivity fuel, radical reactions of low-reactivity fuel and C0-C2 core mechanisms.

Conventionally, the engines are calibrated under the assumption that engines will be made exactly to the prints, and all the engines from the same batch will be identical. However, engine-to-engine variations do exist which will affect the engine performances, and part-to-part variations, i.e., the tolerance, is an important factor leading to engine-to-engine variations. There are researches conducted on the influence of dimensional tolerances on engine performance, however, the impact of straightness, which is an important geometric tolerance, on lubrication is an unsolved issue. This study presents a systematic method to model the straightness and to analyze its effects on the friction loss. The bearing model is built based on elastohydrodynamic (EHD) theory. Meanwhile a novel modeling method to represent any form of straightness in three-dimensional space is proposed.

A novel normal measurement device for robotic drilling and countersinking has been developed. This device is mainly composed of three contact displacement sensors and a spherically compliant clamp pad. The compliance of the clamp pad allows it to be perpendicular to the part when the Multi-Function End Effector (MFEE) drives it to clamp the part surface prior to drilling, while the displacement sensors are used to measure the movement of the clamp pad relative to the MFEE. Once the sensors’ position is calibrated, the rotation angle of the clamp pad can be calculated by the displacement of the sensors. Then, the normal adjustment of MFEE is obtained, and the adjustment process can be achieved by the Rotation Tool Center Point (RTCP) function of robot. Thus, an innovative method based on laser tracker to identify the position of sensors is proposed.

The design and development of electric vehicles involves many unique challenges. One such challenge involves accurately predicting driveline abuse torque loads early in the design cycle to aid with sizing drive-unit and driveline components. Since electrified drivelines typically lack a torque-limiting “fuse” element such as a torque converter or slipping clutch, they can be vulnerable to sudden transient events involving high wheel acceleration or deceleration. Component sizing must account for the loads caused by such events, and these loads must be accurately quantified early on when vehicle parameters haven’t been finalized yet. Early load predictions can be made by completing abuse maneuver simulations where key parameters are varied to gauge their influence on simulated loads. Understanding how these parameters impact loads allows for better risk assessment during the design process, as these parameters will inevitably change until a final design is iterated upon.

Speaker performance in Acoustic Vehicle Alerting System (AVAS) plays a crucial role for pedestrian safety. Sound radiation from AVAS speaker has obvious directivity pattern. Considering this feature is critical for accurately simulating the exterior sound field of electrical vehicles. This paper proposes a new process to characterize the sound directivity pattern of AVAS speaker. The first step of the process is to perform an acoustic testing to measure the sound pressure radiated from the speaker at a certain number of microphone locations in a free field environment. Based on the geometry of a virtual speaker, the locations of each microphone and measured sound pressure data, an inverse method, namely the inverse pellicular analysis, is adopted to recover a set of vibration pattern of the virtual speaker surface. The recovered surface vibration pattern can then be incorporated in the full vehicle numerical model as an excitation for simulating the exterior sound field.