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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.

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.

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.

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.

Monitoring driver thermal stress is an integral step for developing an automated climate control function. In this experimental study, various physiological measures for driver’s thermal stress were tracked while intentionally by altering thermal conditions of the seat with a seat air conditioning system (ACS) in summer and a seat heating system (HS) in winter. It was aimed to determine reliable physiological measures for identifying the changes in thermal status induced by the two seat climate control systems. In the first experiment, twenty experienced drivers drove a comfortable sedan for 60 minutes on a real highway while varying the intensity of the seat ACS every 10 minutes to incur ‘hot’ – ‘cool’ – ‘hot’ – ‘cool’ thermal stress. In the second experiment, a new group of eighteen drivers drove the same highway for 30 minutes while increasing the intensity of seat HS to incur ‘cold’ to ‘warm’ thermal stress.

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).

Tire tread pattern noise is a major source of road noise generated by motor vehicles. Recently, noise control technology has been developing, and low-noise motor vehicles, such as electric vehicles and hybrid vehicles, have been commercialized. The importance of low-noise tires has increased since regulations R117 for tire noise and R51.03 for motor vehicle noise have been strengthened. To evaluate the tire noise in the development stage of motor vehicles, finished products of tires are required; hence, financial and time costs should be invested. Therefore, it is highly useful to predict tire noise levels in the early stages. Recently, a technology to predict the tire pattern noise using a supervised training method of artificial neural network (ANN) has been developed. The tire tread depth is estimated using the shading of the full image of the actual tire, and the leading edge of the contact patch is calculated using tire contact patch images.

A physics-based model for SCR (Selective Catalytic Reduction) was developed based on five independent SGB (Synthetic Gas Bench) tests. There are NH3 adsorption & desorption test, NO oxidation test, NH3 oxidation test, SCR reaction (NOx & NH3) test and SV (Space Velocity) test. To validate the accuracy of SCR model’s prediction, transient reactor tests were conducted at four different input conditions. A newly developed SCR model showed more than 90% prediction accuracy in transient test conditions in view of cumulative NOx. Validation of SCR model was conducted on 1.6L light duty diesel vehicle in the WLTC (Worldwide Harmonized Light vehicles Test Cycle). Based upon this SCR model, vehicle level SCR calibrations used for urea dosing control were made and validated in the emission test cycles like WLTC.

In this study it will show, big data analysis and user survey of driving records were conducted to investigate frequency of use and ease of operation of the regen paddle to control one-pedal driving system in electric vehicle. According to 3.8 million driving record big data analysis result, it was found that the driver manipulates 3.31 times on average during a single trip, mainly during the early stages of driving. According to user observation research result in 41.8% of participants did not used or used less than 5 time of regen paddle during one single trip. Also 336 participants, which occupy 83%, responded that the regen paddle manipulation for one-pedal driving was inconvenient. In conclusion, because of the use frequency of the regen paddle is low and the operation of regen paddle is inconvenient. It seems necessary to change the design of the regen paddle.

Dashcam, which is considered essential parts of vehicles in Korea, are installed in most vehicles for proofs of accidents or threatened driving of other vehicles, and insurance premiums. Also global market is growing continuously. Aftermarket dashcams have been developed with many improvements such as higher resolution camera and a LCD, however still have technical limitations in usability and durability. The First limitation is that the dashcam which mounted on windshield can be separated and injure at an accident due to a collision impact, and the device obstructs the driver's vision. In addition, the connection of the power supply may cause a vehicle damages such as a fire due to a worker's mistake or a product defect. Secondly, in order to replay the recorded video, it is not easy to remove the SD card and check it on the computer. Moreover, since the LCD is so small, it is difficult to search and replay the wanted video from the list in many files.

Today, many automakers are using LED lamp sources in exterior lamps to establish brand awareness and introduce specialized lamp designs. These eye-catching LED lamp source solutions require many control functions as the lamp functions are diversified and advanced, and accordingly the requirements for standardization and optimization of controllers are increasing. In particular, our LED rear combination lamps have a variety of LED loads according to the design of the lamp model, the installation position, and the diagnostic regulations, so that the design complexity and the number of specifications of the controller are increased [4]. In recent years, more and more aesthetic designs and new technologies are used by various automakers to optimize their controllers in cooperation with global partners to optimize costs [1].

The windshield is an integral part of almost every modern passenger car. Combined with current developments in the automotive industry such as electrification and the integration of lightweight material systems, the reduction of interior noise caused by stochastic and transient wind excitation is deemed to be an increasing challenge for future NVH measures. Active control systems have proven to be a viable alternative compared to traditional passive NVH measures in different areas. However, for windshield actuation there are neither comparative studies nor actually established actuation concepts available to the automotive industry. This paper illustrates a comparative conceptual study on windshield actuation for the active control of wind noise in a passenger car. Making use of an experimental modal analysis of the windshield installed in a medium-sized vehicle, a reduced order numerical simulation model is derived.

Current developments in the automotive industry such as electrification and consistent lightweight construction increasingly enable the application of active control systems for the further reduction of noise in vehicles. As different stochastic noise sources such as rolling and wind noise as well as noise radiated by the ventilation system are becoming more noticeable and as passive measures for NVH optimization tend to be heavy and construction-space intensive, current research activities focus on active reduction of noise caused by the latter mentioned sources. This paper illustrates the development, implementation and experimental investigation of an active noise control system integrated into the ventilation duct system of a passenger car.

Passenger fatigue during long distance driving is greatly influenced by the comfort performance of the seat. Seat comfort performance is determined by the appropriate contour of the seat and the appropriate pad with sufficient thickness. The height of vehicle has been lowered to enhance car styling, and battery for electric vehicle applied to the underbody of the vehicle, reducing the package space of the seat in the vehicle. These external factors eventually lead to a reduced pad thickness of the seat cushion and compromise one of the important components in the seat cushion compartment, creating an uncomfortable cushioning problem when driving long distances. To improve the cushion composition of the seat within a limited package, air bladders are applied to the underside of the cushion pad. In addition, the function to support the buttocks using the air bladders of the lower cushion, similar to lumbar support for the back, was implemented to improve cushion comfort performance.

The digital evaluation process of vehicle-seat dimensions is an efficient and cost-effective way to achieve better seating comfort and proper fit. The present study is intended to quantify the statistical relationships between seat dimensions (e.g., insert width and bolster height defined at SAE J2732) and subjective seat fit (e.g., too tight, right fit, or too wide). Subjective fit evaluations for 45 different vehicle seats and the corresponding vehicle seat dimensions at various cross-sectional planes were collected by seat engineers (experts). The best subset logistic regression analyses were applied to quantify the relationships between the collected expert evaluations and seat dimensions at each cross-sectional plane. As a result, significant seat dimensions on the seat fit were identified and their statistical relationships were quantified as regression coefficients.

Worldwide projections anticipate a fast-growing market share of the battery electric vehicles (BEVs) to meet stringent emissions regulations for global warming and climate change. One of the new challenges of BEVs is the effective and efficient thermal management of the BEV to minimize parasitic power consumption and to maximize driving range. Typically, the total efficiency of BEVs depends on the performance and power consumption of the thermal management system, which is highly affected by several factors, including driving environments (ambient temperature and traffic conditions) and driver's behavior (aggressiveness). Therefore, this paper investigates the influence of these factors on energy consumption by using a comprehensive BEV simulation integrated with a thermal management system model. The vehicle model was validated with experimental data, and a simulation study is performed by using the vehicle model over various traffic scenarios generated from a traffic simulator.

Electric Power Steering (EPS) needs to meet both functional and stability requirements, it plays significant role in controlling vehicle motion. In the meantime, customers emphasizes natural steering feel which can reflect vehicle motion and road surface information while isolate unwanted external disturbances. In general, conventional EPS control algorithms exert assist torque according to driver torque measured from torque sensor, while maintaining stability using stabilizing compensator. However, there exist significant trade-off between steering feel and stability, because the performances of assist torque control and stabilizing compensator are strongly coupled. In this paper a torque feedback control algorithm for EPS system is proposed in order to overcome the trade-off, and to achieve more natural, robust steering feel.