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Simulators are essential part of the development process of vehicles and their advanced functionalities. The combination of virtual simulator and Hardware-in-the-loop technology accelerates the integration and functional validation of ECUs and mechanical components. The aim of this research is to investigate the benefits that can arise from the coupling of a steering Hardware-in-the-loop simulator and an advanced multi-contact tire model, as opposed to the conventional single-contact tire model. On-track tests were executed to collect data necessary for tire modelling using an experimental vehicle equipped with wheel force transducer, to measure force and moments acting on tire contact patch. The steering wheel was instrumented with a torque sensor, while tie-rod axial forces were quantified using loadcells. The same test set has been replicated using the Hardware-in-the-loop simulator using both the single-contact and multi-contact tire model.

The piston and piston ring are used in a severe contact environment in engine durability tests, which causes severe wear to the piston ring groove, leading to significant development costs for countermeasures. Conventionally, in order to ensure functional feasibility through wear on the piston top ring groove (hereinafter “ring groove”), only functional evaluations through actual engine durability testing were performed, and there was an issue in determining the limit value for the actual amount of wear itself. Because of this, the mechanism that may cause wear on the ring groove was clarified through past research, but this resulted in judgment criteria with some leeway from the perspective of functional assurance. To establish judgment criteria, it was necessary to understand both functional effect from ring groove wear and the mechanism behind it.

As part of the U.S. Environmental Protection Agency’s (EPA’s) continuing assessment of advanced light-duty automotive technologies in support of regulatory and compliance programs, a development project was started to study various test methods to benchmark Electric Drive Units (EDUs) consisting of an electric motor, inverter and a speed-reduction gearset. Several test methods were identified for consideration, including both in-vehicle testing of the complete EDU and stand-alone testing of the EDU and its subcomponents after removal from the vehicle. In all test methods explored, sweeps of speed and torque test points were conducted while collecting key EDU data required to determine efficiency, including motor torque and speed, direct current (DC) battery voltage and current into the inverter, and three-phase alternating current (AC) phase voltages and currents out of the inverter and into the electric motor.

The flight mechanisms of birds have long inspired efforts to develop bioinspired aerial vehicles. This study presents a computational framework to analyze a flapping mechanism's structural behavior and performance based on the Scotch yoke principle. A three-dimensional CAD model is developed and meshed for finite element analysis in ANSYS. Structural steel is chosen as the material. Static analysis is performed under simulated flapping loads to predict deformation, stresses, fatigue life, and failure points. Preliminary results identify regions of high-stress concentration requiring optimization. Topology optimization is conducted to determine an optimal material layout within defined constraints. Additional shape and compliance optimizations are employed. Comparison of initial and optimized designs significantly reduces maximum deformation and stresses throughout the structure. Fatigue life and safety factors are markedly improved.

By introducing the yield strength ratio λ of strengthened ridgeline to plate and the strengthening coefficient multiplier R, the theoretical prediction expression of maximum bending moment of thin-walled square tube with strengthened ridgelines under static cantilever bending condition is obtained. Then the software of Hypermesh 13. 0 was used to establish two quasi-static finite element simulation comparison models under the corresponding static cantilever conditions. One model was designed for the original material thin-walled square tube, and another one was designed for the thin-walled square tube with strengthened ridgelines. After that, the LS-DYNA 971 solver was introduced to perform the solution calculations. Through a series of simulation calculations and result analysis, the accuracy of the theoretical expression for the maximum bending moment of a thin-walled square tube with strengthened ridgelines was verified.

The National Highway Traffic Safety Administration (NHTSA) plays a crucial role in guiding the formulation of Corporate Average Fuel Economy (CAFE) standards, and at the forefront of this regulatory process stands Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) research institution, has developed Autonomie—an advanced and comprehensive full-vehicle simulation tool that has solidified its status as an industry standard for evaluating vehicle performance, energy consumption, and the effectiveness of various technologies. Under the purview of an Inter-Agency Agreement (IAA), the DOE Argonne Site Office (ASO) and Argonne have assumed the responsibility of conducting full-vehicle simulations to support NHTSA's CAFE rulemaking initiatives. This paper introduces an innovative approach that hinges on a large-scale simulation process, encompassing standard regulatory driving cycles tailored to various vehicle classes and spanning diverse timeframes.

To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against ground impact caused by road debris is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE model. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise direction, magnitude, and location of the force applied from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian process regression (GPR), to perform inverse analysis of the direction, magnitude, and location of vehicle-road contact forces during rough road conditions.

Rubber isolators are widely used under random vibrations. In order to predict their fatigue life, a study on the fatigue analysis methodology for rubber isolators is carried out in this paper. Firstly, taking a mount used for isolating air conditioning compressor vibrations as studying example, accelerations versus time of rubber isolator at both sides are acquired for a car under different running conditions. The acceleration in time domain is transformed to frequency domain using the Fourier transform, and the acceleration power spectral density (PSD) is the obtained. Using the PSD as input, fatigue test is carried for the rubber isolator in different temperature and constant humidity conditions. A finite element model of the rubber isolator using ABAQUS is established for estimating fatigue life, and model validity is verified through static characteristic testing. Dynamic responses of the rubber isolator at frequency domain are calculated if a unit load is applied.

Vibration from a mechanical system not only produces unwanted noises annoying to people around, but also runs a risk of fatigue failure that would actually hinder its functionality. There are several forms of vibration depending on the sources of excitation forms. Mechanical systems with rotating components can be subjected to sinusoidal excitation due to the fact the center of mass is not perfectly aligned with the rotating axis. If the rotating speed is strictly ramping up or ramping down, this can create an excitation whose frequency is changing with time in a frequency range corresponding to the speeds swept. Compared with a single sinusoidal excitation, the issue with fatigue at swept sinusoidal excitation, is that as it sweeps through a wide frequency range, some swept frequencies will definitely coincide with the natural frequencies of the system. Certainly, the stress response exactly at the resonant frequency becomes the highest and could account for a lot of fatigue damage.

Recently, topology optimization (TO) has seen increased usage in the automotive industry as a numerical tool, greatly enhancing the accessibility and production-readiness of optimal, lightweight solutions. By natural extension of classic single material TO (SMTO), a wealth of research has been completed in multi-material TO (MMTO), enabling simultaneous determination of material selection and existence. MMTO is effective for linear static analyses, making use of structural responses that are continuously differentiable, giving itself to efficient gradient-based optimization engines. A structural response that is inherently nonlinear and transient, thus providing difficulty to the mainstay MMTO process, is that of crashworthiness. This paper presents a multi-objective MMTO framework considering crashworthiness using the equivalent static load (ESL) method. The ESL method uses a series of linear static sub-models to approximate the transient crashworthiness model.

In recent years, an increase in vehicle weight due to the electrification of automobiles, specifically EVs, has increased the input loads on anti-vibration rubber parts. Moreover, the characteristics of these loads have also changed due to the rotational drive of electric motors, regenerative braking, and other factors. When designing a vehicle, in advance it is necessary to set specifications that take into account the spring characteristics and durability of the anti-vibration rubber parts in order to meet functional requirements. In this study, the hyperelastic and fatigue characteristics (S-N diagram and Haigh diagram) of Rubbers which is widely used for anti-vibration rubber parts, were experimentally obtained, and structural and fatigue analyses using FEM (Finite Element Method) were conducted in conjunction with spring and fatigue tests of anti-vibration rubber parts to determine the correlation between their spring and fatigue characteristics.

The fatigue prediction model of an air spring based on the crack initiation method is established in this study. Taking a rolling lobe air spring with an aluminum casing as the studying example, a finite element model for analyzing force versus displacement is developed. The static stiffness and dimensional parameters of limit positions are calculated and analyzed. The influence of different modeling methods of air springs bellow are compared and analyzed. Static stiffness measurement of an air spring is conducted, and the calculation results and the measured results of the static stiffness are compared. It is shown that the relative error of the measured stiffness and calculated stiffness is within 1%. The Abaqus post-processing stage is redeveloped in Python language.

This study deals with the fatigue life prediction methodology of welding simulation components involving arc welding. First, a method for deriving the cyclic deformation and fatigue properties of the weld metal (that is also called ER70S-3 in AWS, American Welding Standard) is explained using solid bar specimens. Then, welded tube specimens were used with two symmetric welds and subjected to axial, torsion, and combined in-phase and out-of-phase axial-torsion loads. In most previous studies the weld bead’s start/stop were arbitrarily removed by overlapping the starting and stop point. Because it can reduce fatigue data scatter. However, in this study make the two symmetric weld’s start/stops exposed to applying load. Because the shape of the weld bead generated after the welding process can act as a notch (Ex. root notch at weld start / Crater at weld stop) to an applied stress. Accordingly, they were intentionally designed to cause stress concentrations on start/stops.

A special spot weld element (SWE) is presented for simplified representation of spot joints in complex structures for structural durability evaluation using the mesh-insensitive structural stress method. The SWE is formulated using rigorous linear four-node Mindlin shell elements with consideration of weld region kinematic constraints and force/moments equilibrium conditions. The SWEs are capable of capturing all major deformation modes around weld region such that rather coarse finite element mesh can be used in durability modeling of complex vehicle structures without losing any accuracy. With the SWEs, all relevant traction structural stress components around a spot weld nugget can be fully captured in a mesh-insensitive manner for evaluation of multiaxial fatigue failure.

This paper presents deep learning-based prognostics and health management (PHM) for predicting fractures of an electric propulsion (eP) drivetrain system using real-time CAN signals. The deep learning algorithm, based on autoencoders, resamples time-series signals and converts them into 2D images using recurrence plots (RP). Subsequently, through unsupervised learning of DeepSVDD, it detects anomalies in the converted 2D images and predicts the failure of the system in real-time. Also, reliability analysis based on fracture mechanics was performed using the detected signals and big data. In particular, the severity of the eP drivetrain system is proportional to the maximum shear stress (τmax) in terms of linear elastic fracture mechanics (LEFM) and can be calculated by summarizing the relationship between cracks (a) and the stress intensity factor (KIII).

Fretting is a phenomenon in which a fatigue crack is initiated by a small relative slip between two objects, resulting in crack propagation and fracture at stresses far below the fatigue limit [1, 2]. Since the mechanism behind fretting is complex and covers multiple disciplines, it is not easy to develop a consistent evaluation method. In the field of engine development, fretting events can also pose an issue due to the complexity of the mechanism [3]. In particular, it has been a challenge to help predict changes in the presence and severity of fretting events, as the engine temperature fluctuates with operating conditions. As one method for evaluating fretting, Sato, et al. have made predictions using analytical models based on the finite element method (FEM) [4, 5]. However, their predictions did not take into account temperature fluctuations in the system, and they were unable to predict events in which the occurrence of fretting fatigue changed with temperature fluctuations.

An advanced multi-layer material model has been developed to simulate the complex behavior in case-carburized gears where hardness dependent strength and elastic-plastic behavior is characterized. Also, an advanced fatigue model has been calibrated to material fatigue tests over a wide range of conditions and implemented in FEMFAT software for root bending fatigue life prediction in differential gears. An FEA model of a differential is setup to simulate the rolling contact and transient stresses occurring within the differential gears. Gear root bending fatigue life is predicted using the calculated stresses and the FEMFAT fatigue model. A specialized rig test is set up and used to measure the fatigue life of the differential over a range of load conditions. Root bending fatigue life predictions are shown to correlate very well with the measured fatigue life in the rig test.

Optimizing the specifications of the parts that make up the vehicle is essential to develop a high performance and quality vehicle with price competitiveness. Optimizing parts specifications for quality and affordability means optimizing various factors such as engineering design specifications and manufacturing processes of parts. This optimization process must be carried out in the early stages of development to maximize its effectiveness. Therefore, in this paper, we studied the methodology of building a database for parts of already developed vehicles and optimizing them on a data basis. A methodology for collecting, standardizing, and analyzing data was studied to define information necessary for specification optimization. In addition, AI technology was used to derive optimization specifications based on the 3D shape of the parts. Through this study, body parts specification optimization system using AI technology was developed.

As a key component of in-vehicle intelligent voice technology, speech enhancement can extract clean speech signals contaminated by environmental noise to improve the perceptual quality and intelligibility of speech. It has extensive applications in the field of intelligent car cabins. Although some end-to-end speech enhancement methods based on time domain have been proposed, there is often limited consideration given to designing model architectures based on the characteristics of the speech signal. In this paper, we propose a new U-Net based speech enhancement framework that utilizes the temporal correlation of speech signals to reconstruct higher-quality and more intelligible clean speech.

The accurate prediction of engine performance maps can guide data-driven optimization of engine technologies to control fuel use and associated emissions. However, engine operational maps are scarcely reported in literature and often have missing data. Assessment of missing-data resilient algorithms in the context of engine data prediction could enable better processing of real-world driving cycles, where missing data is a more pervasive phenomenon. The goal of this study is, therefore, to determine the most effective technique to deal with missing data and employ it in prediction of engine performance characteristics. We assess the performance of two machine learning approaches, namely Artificial Neural Networks (ANNs) and the extreme tree boosting algorithm (XGBoost), in handling missing data.