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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.

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.

During the vehicle lifecycle, customers are able to directly perceive the outer panel stiffness of vehicles in various environmental conditions. The outer panel stiffness is an important factor for customers to perceive the robustness of the vehicle. In the real test of outer panel stiffness after prototype production, evaluators manually press the outer panel in advance to identify vulnerable areas to be tested and evaluate the performance only in those area. However, when developing the outer panel stiffness performance using FEA (Finite Element Analysis) before releasing the drawing, it is not possible to filter out these areas, so the entire outer panel must be evaluated. This requires a significant amount of computing resources and manpower. In this study, an approach utilizing artificial intelligence was proposed to streamline the outer panel stiffness analysis and improve development reliability.

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.

Recently, the environmental temperature of vehicles is changing due to the electrification of vehicles and improved internal combustion engine system to reduce carbon emissions. However, mechanical properties of plastic materials change very sensitively to environmental temperature changes, and mechanical properties decrease when exposed to high temperatures. Therefore, it is important to estimate lifespan estimation of plastic parts according to temperature changes. In this paper, reliability analysis process to estimate the maximum service temperature of plastic parts was developed using aging data of material properties, environmental condition data of automotive parts, and field driving condition data. Changes in the mechanical properties of plastic materials such as glass fiber reinforced polyamide materials were tested. The environmental exposure temperature of the vehicle and parts was measured, and the general driving pattern of the vehicle was analyzed.

In recent years, with the advent of the Fourth Industrial Revolution and the COVID-19 pandemic, people's lives worldwide have undergone significant changes. Additionally, the emergence of a new generation of consumers known as the millennial generation has led to a high demand for multipurpose family cars. The perspective is shifting towards choosing premium products that enhance the quality of life and pursue their own happiness and comfort through technology, rather than simply selecting a midsize SUV based on the increase in family size. We aim to meet the needs of these global customers by conducting research and developing various new features that were not previously available in midsize SUVs. In this study, we defined the actual target users for midsize SUVs and established UX concepts by analyzing their characteristics. Based on this, we employed an optimal design approach by analyzing the evaluation results by country for the various features implemented within the vehicle.

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.

An architecture virtual driving performance development process and strategy were established using the concept model. Driving performance concept models for each level and performance, that can be utilized in the architecture stage, were developed. Advanced concept models such as smart driver and comfort models were developed for reliable emergency handling and comfort performance prediction. System characteristic DB(DataBase) structure was designed and formed to utilize the concept model for major vehicle platforms and models. System characteristics can be configured by automatically extracting system characteristics from ADAMS model or SPMD(Suspension Parameters Measuring Device) DB. In addition, when the concept model is completed by updating the weight, specifications and tire characteristic of the new vehicle platform, handling and ride comfort performance can be analyzed.

As data science technologies are being widely applied on various industries, the importance of data itself increased. A typical manufacturer company has a vast data set of products as 2D&3D drawing formats, but a common problem was that building a database from the 2D&3D drawings costs much, and it is hard to update the database after it once built. Also, it is high-cost job when the new factor researched and necessary to investigate the new factors on previously fixed or uploaded drawings. As new products are developed with time, these problems are getting more difficult. In this paper, an automated database building method using CATIA introduced and future probabilities are suggested. An aluminum wheel part was used as an example. An automated logic used CATIA V5’s VBA functions and was handled by python programming language.

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.

The body stiffness plays a key role in vehicle performance, such as noise and vibration, ride and handling, durability and so on. In particular, a body D-pillar ring structure is the most sensitive affecting the body stiffness on vehicle with tail gate. Therefore, since D-pillar body ring structure for high stiffness and lightweight is required, an optimized design methodology that simultaneously satisfies the requirements was studied. It focused on a methodology that body engineering designers can optimize design parameters easily and quickly by themselves in the preceding stages of vehicle’s styling distribution and design conceptual planning. First, it is important to establish the body stiffness design strategy by predicting the body stiffness with the vehicle’s styling at early design stage. The methodology to predict body stiffness with the styling and body dimension specification parameters was introduced.

Bad weather conditions such as torrential rain, heavy snow, and thick fog frequently occur worldwide. Vehicle accidents in such bad weather conditions account for a significant portion of all vehicle accidents, and the level of damage is relatively severe compared to other accidents that occur in clear weather. This paper analyzes the driver's driving stability in bad weather conditions, which has such a significant meaning, in various ways through experiments on the inconvenience experienced by the driver. In this study, three levels of bad weather conditions were implemented in a driving simulator environment to evaluate driver inconvenience for six activities. Through driving experiment, quantitative bio-signals and vehicle signals were analyzed in each weather condition. The SD survey was used to assess the driver's inconvenience level for activities performed while driving and analyze the ranking of inconvenience.

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).

As the AVN display in the car interior becomes larger and located above the center fascia, the driver's visual visibility is becoming important. In addition, since an expensive touch sensor is installed, a transparent electrode cost reduction technology for a display touch sensor that can replace an indium material, which is an expensive rare metal, is required. In this paper, we developed new transparent electrode materials and manufacturing methods for the touch sensor film which light reflectance is low and flexible without a separate low-reflection multi-layer, so that the design freedom is high and the material cost is low. By optimizing the amount of fluorine doping ratio in tin oxide, excellent electrical conductivity and high optical transmittance are secured, and the surface reflectance is reduced by adjusting the diameter and length of the silver nanowire. As a result, it was shown that the AVN display image and font readability was improved.

Recently, increasing system complexity and various customer demands result in the need for highly efficient vehicle development processes. Once the brake torque is predicted accurately during the driving scenario in the earlier stage, it will be able to prevent the changing the vehicle or brake system design to satisfy the legal regulation and customer requirement. As brake torque performance target allocate brake pad friction coefficient level and characteristic, the accurate friction coefficient prediction should be preceded for accurate prediction for brake torque. Generally, the friction coefficient of the brake pad is known to vary nonlinearly depending on the physical properties of the disc and the pad, as well as the brake disc rotational speed, the disc temperature, and the hydraulic pressure. Furthermore, it varies depending on the driving scenario even when other conditions are the same. Therefore, it is necessary to apply new methods to solve these challenges.

With the spread of new trends such as autonomous driving and vehicle subscription service, drivers may pay less attention to the maintenance of the vehicle. Brake pads being safety critical components, the wear condition of all service brakes is required by regulation to be indicated by either acoustic of optical devices or a means of visually checking the degree of brake lining wear [1]. Current application of the wear indicator in the market uses either sound generating metal strip or wire harness based pad wear sensor. The former is not effective in generating clear alarm to the driver, and the latter is not cost effective, and there is a need for more effective and low cost solution. In this paper, a pad wear monitoring system using MOC(Motor On Caliper) EPB(Electric Parking Brake) ECU is proposed. An MOC EPB is equipped with a motor, geartrain and an ECU. The motor current when applying the parking brake is influenced by the mechanical load at the brake pad side of the system.

Automotive OEMs have introduced a new development paradigm, modular architecture development, to improve diversity quality and production efficiency. It needs solid fundamentals of system-based performance evaluation and development for each system level and single component level. When it comes to NVH development, it is challenging to realize the modular concept because noise and vibration should be transferred through various transfer path consisting of many parts and systems, which interact with each other. It is challenging for a single system of interest to be evaluated independently of the adjacent parts and environments. In this study, a new system-based development process for a vehicle suspension was investigated by applying blocked force theory and FRF-based dynamic substructuring. The objective is to determine the better dynamic stiffness distribution of many bushes installed in a suspension system in the frequency range corresponding to road noise.

Numerical simulations offer a wide range of benefits. Therefore, they are widely used in research and development. One of the biggest benefits is the possibility of automated parameter variation. This allows testing different scenarios very quickly. Nevertheless, physical experiments in the laboratory or on a test rig are still, and will remain, necessary. Physical experiments offer benefits, e.g., for very complex and/or nonlinear systems and are required for the validation of numerical models. To enhance the quality of experimental NVH investigations and to make use of the benefits of numerical simulation during experimental investigations at the same time, numerical models can be integrated into physical test rigs using the mechanical hardware-in-the-loop (mHIL) method (also referred to as real-time dynamic substructuring, hybrid testing or active control of impedance).