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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.

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.

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.

Finite element (FE) based simulations for fully trimmed bodies are a key tool in the automotive industry to predict and understand the Noise, Vibration and Harshness (NVH) behavior of a complete car. While structural and acoustic transfer functions are nowadays straight-forward to obtain from such models, the comprehensive understanding of the intrinsic behavior of the complete car is more complex to achieve, in particular when it comes to the contribution of each sub-part to the global response. This paper proposes a complete target cascading process, which first assesses which sub-part of the car is the most contributing to the interior noise, then decomposes the total structure-borne acoustic transfer function into several intermediate transfer functions, allowing to better understand the effect of local design changes.

This paper presents the novel active vibration control (AVC) system that controls vehicle body vibration to reduce the structural borne road noise. As a result of vehicle noise testing in an electric vehicle, the predominant frequency of vehicle body vibration that worsens interior noise is in the range of 150-250Hz. Such vibration in that frequency range, commonly masked in engine vibrations, are hard to neglect for electric vehicles. The vibration source of that frequency is the resonance of tire cavity mode. Resonator or absorption material has been applied inside the tire for the control of cavity noise as a passive method. They require an increment of weight and cost. Therefore, a novel method is necessary. The vibration amplified by resonance of cavity mode is transferred to the vehicle body throughout the suspension system. To reduce the vibration, AVC system is applied to the suspension mount.

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.

The recent surge in platforms like YouTube has facilitated greater access to information for consumers, and vehicles are no exception, so consumers are increasingly demanding of the quality of their vehicles. By the way, the door is composed of glass, moldings, and other parts that consumers can touch directly, and because it is a moving part, many quality issues arise. In particular, the door panel is assembled from all of the above-mentioned parts and thereby necessitates a robust structure. Therefore, this study focuses on the structural stiffness of the door inner panel module mounting area because the door module is closely to the glass raising and lowering, which is intrinsically linked to various quality issues.

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 electric-powertrains, noise and vibration can be generated by components such as gears and motors. Often a noise phenomenon known as rumble or droning noise can occur due to low shaft order excitation at the spline. In this study, we identified the excitation source for spline induced rumble noise and developed a novel analysis method. First, a detailed spline model, believed to be the key factor for rumble noise, has been developed and verified by comparison with Finite Element Method(FEM) analysis. In order to identify an excitation source, a typical electric-powertrain assembly model including the developed spline model was constructed and simulated. Results according to changes of key factors including spline pitch errors and shaft alignment errors were analyzed. Spline radial force has been identified as an excitation source of spline induced rumble noise. This was verified through comparison with the forced vibration analysis result and time domain analysis result.

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.

In the era of electric vehicles(EVs), the need for weight reduction of the vehicle body is increasing in order to maximize the driving distance of the EV. Accordingly, there is an increasing need for research to efficiently apply lightweight materials, such as aluminum and CFRP, to the EV body parts. In this study, design methodologies and optimization measures to increase lightweight efficiency when applying lightweight materials to EVs will be discussed. Based on theoretical basis and basic performance of each part of the EV, the “Material Substitution Method” of replacing existing parts of a steel body with aluminum materials will be defined, and the optimal design process on how to overcome performance trade-off caused by material characteristics will be addressed. In applying the “Material Substitution Method” to the actual EV body design process, it was possible to convert 93% of the components from steel to aluminum and reduce the overall weight of the body by 23%.

The shift towards electric vehicles is gaining pace to address carbon neutrality and environmental concerns. New technologies are being developed to cater to the unique features of EVs, such as the low indoor noise at low speeds, which require a low-noise ventilation system. A new dual-blower type system was developed to solve the problem of seat-bottom package caused by battery placement in the vehicle. This system uses two blowers, one for the cushion and one for the back, and reduces RPM to lower high-frequency noise. A new solution was introduced for temperature drop performance in the ventilation system. An integrated controller was also developed to control the seat warmer and ventilation system, with a smart control function added to respond to vehicle speed and ventilation time based on customer usage. As a result, this new ventilation system improves air volume, reduces noise, improves foot space, and reduces the number of parts compared to the previous system.

In order to align with net-zero CO2 ambitions, automotive OEMs have been developing increasingly sophisticated strategies to minimise the impact that combustion engines have on the environment. Intelligent thermal management systems to actively control coolant flow around the engine have a positive impact on friction generated in the power cylinder by improving the warmup rate of cylinder liners and heads. This increase in temperature results in an improved frictional performance and cycle averaged fuel consumption, but also increases the piston to liner clearances due to rapid warm up of the upper part of the cylinder head. These increased clearances can introduce piston slap noise and substantially degrade the NVH quality to unacceptable levels, particularly during warmup after soak at low ambient temperatures. Using analytical techniques, it is possible to model the thermo-structural and NVH response of the power cylinder with different warm up strategies.

In this paper, an application process is studied at which the insertion loss (IL) test data of sound insulating parts or noise control treatments are utilized for the sound transmission loss (STL) simulation of the trimmed dash structure. The considered sound barrier assemblies were composed of a felt layer, a mass layer, and a decoupler layer. Flat samples of sound barrier assemblies with several different thicknesses were prepared, and ILs of them were measured by using a sound transmission loss facility. Flat samples were assumed to have mass-spring-mass resonance frequencies. The mass was set as the area mass of the sound barrier layer of the felt layer and the mass layer. The spring constant of the decoupler layer was assumed as the multiplication of that of an air spring and a spring correction factor.

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.

NVH analysis based on numerical simulations before actual test vehicle is available becomes common process in the automotive industry. Furthermore, the latest work scope is extending even to conceptual study in the very early design stage, beyond traditional numerical simulations simply using 3-D CAD data. In case when reasonable information is provided at this very early vehicle development stage, a better decision on the design concept would be possible, and subsequent design process can be carried out in more efficient manner. The core of this trend is that it allows us to predict vehicle performance at the conceptual design stage without 3-D CAD data, and then, with this prediction, to suggest meaningful design directions for next stage. From this point of view, FRF-Based Substructuring (FBS) methodology has potential to be used as an appropriate tool for this purpose.

As autonomous driving vehicles are developed, automotive makers start focusing on implementing new door types, such as a falcon wing door or a B-pillarless dual sliding door, which could be one of the best-selling points. To make these doors electrically operate, applying advanced sensors like a RADAR or an Ultrasonic sensor is almost mandatory. Without these sensors, the door could be easily damaged or the customers could be seriously injured. Due to physical limitation, however, every sensor has a noise in nearby area and has a specification of the minimum detection range, which causes us not to be able to precisely detect the object in close area. If the controller cannot detect the precise distance of the object, the door could malfunction, since it could misidentify the obstacles. In this paper, we propose a method to reduce the minimum detection range by applying a prior estimation scheme.

In the path following problem, a commercial vehicle has a delay of a hydraulic steering actuator and slow steering response accordingly. In addition, there are disturbances due to the harsh driving conditions of commercial vehicles. These disturbances may include uncertainties about actuator dynamic delay, modeling error and steering angle sensor offset. Designing a lateral controller with good performance that can overcome this problem is the key to successfully carrying out autonomous driving of commercial vehicles. Usually, it is difficult to consider disturbances with uncertainties in the geometric based control methods. Therefore, this paper proposed a lateral controller using feedback augmented disturbance observer for the commercial vehicle. First, a dynamics was modeled which can describe delay of the hydraulic actuator of the commercial vehicle. After that, a lateral controller was designed based on this dynamics model.

The power trunk lid system is a device that automatically opens and closes the trunk lid by motor, for the purpose to improve user’s convenience. This technology was applied only to high-end large cars such as Equus and Genesis. But as preference for high convenience features increases, the scope of application is gradually expanding to semi-large and mid-sized cars. Therefore, the necessity of securing profitability through cost reduction was emerged, and it made us to develop the power trunk lid system by spindle drives. Compared to the conventional swing arm drive type, the spindle drive type may achieve cost savings, lightness and easy of assembly by optimizing the required motor specifications. However, since it uses a planetary gear with high gear ratio and the high rotation speed of the motor, operating noise is relatively large.

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.