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Reductions in powertrain noise have led to an increased proportion of road noise, prompting various studies aimed at mitigating it. Road excitation primarily traverses through the vehicle suspension system, necessitating careful optimization of the characteristics of bushings at connection points. However, optimizing at the vehicle assembly stage is both time-consuming and costly. Therefore, it is essential to proceed with optimization at the subsystem level using appropriate objective functions. In this study, the blocked force and energy-based index derived from complex power were used to optimize the NVH performance. Calculating the complex power in each bushing enables computing the power flow, thereby providing a basis for evaluating the NVH performance. Through stiffness injection, the frequency response functions (FRF) of the system can be predicted according to arbitrary changes in the bushing stiffness.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

It is an important factor in electric vehicles to show customers how much they can drive with the energy of the remaining battery. If the remaining mileage is not accurate, electric vehicle drivers will have no choice but have to feel anxious about the mileage. Additionally, the potential customers have range anxiety when they consider Electric Vehicles. If the remaining mileage to drive is wrong, drivers may not be able to get to the charging station and may not be able to drive because the battery runs out. It is important to show the remaining available driving range exactly for drivers. The previous study proposed an advanced model by predicting the remaining mileage based on actual driving data and based on reflecting the pattern of customers who drive regularly. The Bayesian linear regression model was right model in previous study.

After the COVID-19 pandemic, leisure activities and cultures have undergone significant transformations. Particularly, there has been an increased demand for outdoor camping. Consequently, the need for capabilities that allow vehicles to navigate not only paved roads but also unpaved and rugged terrains has arisen. In this study, we aim to address this demand by utilizing AI to introduce a 'Stuck Probability Estimation Algorithm' for vehicles on off-road. To estimate the 'Stuck Probability' of a vehicle, a mathematical model representing vehicle behavior is essential. The behavior of off-road driving vehicles can be characterized in two main aspects: firstly, the harshness of the terrain (how uneven and rugged it is), and secondly, the extent of wheel slip affecting the vehicle's traction.

One of the five major performances of vehicles, NVH(Noise, Vibration, Harshness), has recently emerged in electric vehicles, again. And, front loading NVH simulation is essential to respond nimbly to automotive industry these days. However, the two components of the simulation, mathematical sound absorption modeling equation, and the acoustic parameters, the input factor, is requiring improvement because of lack of robustness. In this study, we tried to strengthen, standardize, and refine the connectivity between micro (fine structure) and macro (acoustic parameter-related physical properties) characteristics, and improve the consistency with actual NVH performance. As a porous polymer material, polyurethane foam, which is widely used for the interior and exterior of automobiles, is treated as a target material.

The steering system is a critical component for controlling a vehicle's direction. In the context of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, where drivers may not always be actively holding the steering wheel, early detection of precursor noise signals is essential to prevent serious accidents resulting from the loss of steering system functionality. It is therefore imperative to develop a device capable of early detection and notification of steering system malfunctions. Therefore, the current study aimed to quantify the noise levels generated within the Column-based Electric Power Steering (C-EPS) system of a D-segment sedan. To this end, we measured the uniaxial acceleration in nine noise-generating areas while simultaneously collecting data from three Controller Area Network (CAN) sources that are directly related to steering operation.

In the early stages of vehicle development, it is critical to establish performance goals for the major systems. The fundamental modes of body and chassis frames are typically assessed using FE models that are discretized using shell elements. However, the use of the shell-based FE method is problematic in terms of fast analysis and quick decision-making, especially during the concept phase of a vehicle design because it takes much time and effort for detailed modeling. To overcome this weakness, a one-dimensional (1D) method based on beam elements has been extensively studied over several decades, but it was not successful because of low accuracy for thin-walled beam structures. This investigation proposes a 1D method based on thin-walled beam theory with comparable accuracy to shell models. Most body pillars and chassis frame members are composed of thin-walled beam structures because of the high stiffness-to-mass ratio of thin-walled cross sections.

As we move toward electrification in future mobility, weight and cost reduction continue to be priorities in vehicle development. This has led to continued interest in advanced molding processes and holistic design to enable polymer materials for demanding structural applications such as pickup truck beds. In addition to performance, it is necessary to continue to improve styling, functionality, quality, and sustainability to exceed customer expectations in a competitive market. To support development of a lightweight truck bed design, a cross-functional team objectively explored the latest materials and manufacturing technologies relevant to this application. In Phase 1 of this work, the team considered a variety of alternatives for each functional area of the bed, including thermoplastic and thermoset materials with a range of processing technologies.

Even if an optimal design is produced in the mid-to-late stages of development, when the maturity of development is increasing, it is already difficult to accept the proposal between the organizations and functions. In case the optimal proposal is made with a small amount of information in the preceding stage, it will be helpful for mutual decision-making. In addition, if all members have a system and development environment that enables access and utilization of necessary data in a timely manner, it is possible to produce quick results through collaboration. To implement such a system and development environment, “digital modeling" of tangible and intangible assets will be essential and to implement an "integrated IT environment" that can access and utilize digital models. Until now, Hyundai Motor Company has not yet fully established a digital development environment that all researchers can simultaneously utilize during the concept development stage.

In this study, a novel selective matching logic for a wheel/tire is proposed, to decrease the vehicle driving vibration caused by wheel/tire non-uniformity. The new logic was validated through matching simulation/in-line matching evaluation. A theoretical radial force variation model was established by considering the theoretical model of the existing references and the wheel/tire assembly mechanism. The model was validated with ZF’s high-speed uniformity equipment, which is standard in the tire industry. The validity of the new matching logic was verified through matching simulation and mass production in-line evaluation. In conclusion, the novel logic presented herein was demonstrated to effectively decrease the radial force variation caused by the wheel/tire.

The experimental control findings of increasing the handling performance so that the yaw motion of the vehicle is nimble and stable utilizing the upgraded rear wheel steering system equipped with dual-link actuators are shown in this work. In most automobiles, the steering axis is well defined in front suspension. However, unless the vehicle's rear suspension is a sort of double wishbone, the steering axis is not clearly defined in regular multi-link rear suspensions. As a result, most current automobiles have a suspension geometry feature in which the camber and toe angles change at the same time when the assist link is changed to steer the back wheels. To create lateral force from the rear tire while preserving maximum tire grip, the dual-link actuators control for modifying the strokes of suspension links must keep the camber angle constant and adjust only the toe angle.

This research addresses the pressing need for reducing vehicle aerodynamic resistance, with a specific focus on mitigating wheel and tire resistance, which constitutes approximately 25% of the overall vehicle drag. While the prevailing method for reducing resistance in mass production development involves wheel opening reduction, it inadvertently increases wheel weight and has adverse effects on brake cooling performance. To overcome these challenges, novel complementary resistance reduction methods that can be employed in conjunction with an appropriate degree of wheel opening reduction are imperative. In this study, we introduce symmetrical wheels with a fan-like shape as a solution. The fan configuration influences the surrounding flow by either drawing it in or pushing it out, depending on the direction of rotation. Application of these fan-type wheels to a vehicle's wheels results in the redirection of flow inwards or outwards during high-speed driving due to wheel rotation.

A need to develop a cooling method with high cooling performance like jet impingement is increased as high power of an inverter is required. Jet Impingement on the dimpled plate would increase thermal performance than that of flat plate. Many previous researchers have dealt with the multi jet impingement on flat plate and some results of the study on dimpled plate evaluate the effect on heat transfer coefficients on several limited cases, making it difficult to apply them to inverter designs. Therefore, in this paper, heat transfer performance, pressure drop, and robustness at micro-scale of jet impingement on the dimpled plate were investigated in detail and the correlations of each performance were proposed. Finally, the optimal design was presented. The cooling performance was influenced by the jet array and the effect of depth and width of the dimples.

Optimizing fuel injection spray is essential to comply with stringent future emission regulations for hybrid vehicles and internal combustion engine vehicles, and the spray characteristics and geometry must be robust for various engine operating conditions. This study presents experimental and numerical assessments of spray for lateral-mounted gasoline direct injection (GDI) sprays with different plume arrangements to analyze collapse characteristics, which can significantly deteriorate the geometry and characteristics of fuel sprays. Novel spray characterization methods are applied to analyze complex spray collapse behaviors using LED-based diffusive back-illuminated extinction imaging (DBIEI) and 3D computed tomographic (CT) image reconstruction. High-fidelity computational fluid dynamics (CFD) simulations are performed to analyze the detailed spray characteristics besides experimental characterization.

This study presents a novel active vibration control (AVC) system on motor driven power steering (MDPS) to reduce interior noise reduction caused by operating the MDPS in an electric vehicle. MDPS is electronic power steering (EPS). The MDPS attached to the rack gear of power steering system is called R-MDPS. Operating of the R-MDPS generates a structural vibration of R-MDPS, and the vibration is transmitted to car body through mounts of car subframe. The vibrating body of car becomes a monopole and dipole sources of vibroacoustic noise generated inside car. This vibracoustic noise is a structure borne noise and makes passenger annoyance. To reduce interior noise inside a car directly, active noise control (ANC) has been used as active method and is a useful method for active cancellation of the low frequency noises less than 400Hz.

A technology was developed to recognize and predict the urgent degradation of the state of the rotating equipment installed in Hyundai-Kia factories. It is also being applied to activities to prevent equipment failures by establishing a monitoring system using this technology. Vibration data and artificial intelligence (AI) algorithms were used to predict conditions. It was developed and installed so that maintenance engineers could predict failures in advance. This is to improve preventive diagnosis of thousands of rotating equipment in the factory. And it has the advantage of allowing a small number of engineers to monitor exponentially increasing number of equipment. Vibration data including trends and alarms were collected along with the production schedule, and wavelet-based preprocessing DB9 (Daubechies 9) was performed to remove noise such as outliers. Two different AI algorithm models were developed to recognize and predict changes in equipment state.

Lithium-ion battery has advantages of high energy density and cost effectiveness than other types of batteries. However due to the low mechanical stability, their performance is strongly influenced by environmental conditions. Especially, external pressure on a cell surface is a crucial factor because an appropriate force can improve battery cycle life, but excessive force may cause structural failure. In addition, battery pack is composed of various components so that uncertainties in dimension and material properties of each component can cause a wide variance in initial pressure. Therefore, it is important to optimize structural design of battery pack to ensure initial pressure in an effective range. In this paper, target stiffness of module structure was determined based on cell level cycle life test, then structural design has been optimized for weight reduction. Cell cycling tests were performed under different stiffness conditions and analyzed with regression model.

This paper presents a stability monitoring algorithm with a combined slip tire model for maximized cornering speed of high-speed autonomous driving. It is crucial to utilize the maximum tire force with maintaining a grip driving condition in cornering situations. The model-free cruise controller has been designed to track the desired acceleration. The lateral motion has been regulated by the sliding mode controller formulated with the center of percussion. The controllers are suitable for minimizing the behavior errors. However, the high-level algorithm is necessary to check whether the intended motion is inside of the limit boundaries. In extreme diving conditions, the maximum tire force is limited by physical constraints. A combined slip tire model has been applied to monitor vehicle stability. In previous studies, vehicle stability was evaluated only by vehicle acceleration.

Expanding various future mobilities such as purpose built vehicle (PBV), urban air mobility (UAM), and robo-taxi, the application of autonomous driving system (ADS) technology is also spreading. The main point of ADS is to ensure safety by monitoring vehicle anomalies to prevent functional failure or accident. In this study, a model-based diagnosis and prognosis process was established using degradation data generated during autonomous driving simulation. A vehicle model was designed using Modelica/Dymola, and autonomous driving simulation was performed by integrating the lane keeping assistant (LKA) system with the vehicle model using Matlab/Simulink. Degradation data for the 3 components (a shock absorber damper, a suspension bush, and a tire) of the chassis system were input into the integrated simulation model. The degradation behavior was monitored with K-nearest neighbor (K-NN) and Gaussian mixture model (GMM).