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In general, when a problem occurs in a component of powertrains, various phenomena appear, and abnormal noise is one of them. The service mechanics diagnose the noise through analysis by using their ears and equipment. However, depending on their experiences, analysis time and diagnostic accuracy vary greatly. To shorten the analysis time and improve the diagnostic accuracy, we have developed a technology to diagnose powertrain parts that cause abnormal noises. To create the best deep learning model for our diagnosis, we tried to collect many abnormal noises from various parts. The collected noise data was measured under idle and various operating conditions from our vehicles and test cells. This noise data is abnormal noises generated from engines, transmissions, drive system and PE (Power Electric) parts of eco-friendly vehicles. From the collected data, we distinguished good and bad data through detailed analysis in time and frequency domain.

In the brake system, unevenly distributed disc-pad contact pressure not only leads to a falling-off in braking feeling due to uneven wear of brake pads, but also a main cause of system instability which leads to squeal noise. For this reason there have been several attempts to measure contact pressure distribution. However, only static pressure distribution has been measured in order to estimate the actual pressure distribution. In this study a new test method is designed to quantitatively measure dynamic contact pressure distribution between disc and pad in vehicle testing. The characteristics of dynamic contact pressure distribution are analyzed for various driving conditions and pad shape. Based on those results, CAE model was updated and found to be better in detecting propensity of brake squeal.

A semi-empirical index to evaluate the noise propensity of brake friction materials is introduced. The noise propensity index (NPI) is based on the ratio of surface and matrix stiffness of the friction material, fraction of high-pressure contact plateaus on the sliding surface, and standard deviation of the surface stiffness of the friction material that affect the amplitude and frequency of the stick-slip oscillation. The correlation between noise occurrence and NPI was examined using various brake linings for commercial vehicles. The results obtained from reduced-scale noise dynamometer and vehicle tests indicated that NPI is well correlated with noise propensity. The analysis of the stick-slip profiles also indicated that the surface property affects the amplitude of friction oscillation, while the mechanical property of the friction material influences the propagation of friction oscillation after the onset of vibration.

Recently, upon customer’s needs for noise-free brake, carmakers are increasingly widely installing damping kits in their braking systems. However, an installation of the damping kits may excessively increase softness in the brake system, by loosening stroke feeling of a brake pedal and increasing compressibility after durability. To find a solution to alleviate this problem, we first conducted experiments to measure compressibility of shims by varying parameters such as adhesive shims (e.g., bonding spec., steel and rubber thickness), piston’s shapes (e.g., different contact areas to the shims), and the numbers of durability. Next, we installed a brake feeling measurement system extended from a brake pedal to caliper. We then compared experimental parameters with brake feeling in a vehicle. Finally, we obtained an optimized level of brake feeling by utilizing the Design for Six Sigma (DFSS).

Professional bus drivers are highly exposed to physical fatigue and work-related injuries because driving task includes complicated actions that require a variety of ability and cause extreme concentration or strain. For this reason, there has always been some sense of concern regarding driver fatigue, especially for drivers of commercial vehicles. In this study, we have tried to analyze quantitative fatigue degree of urban bus drivers by measuring their physiological signals. The investigation is made up of the following approaches: a traditional questionnaire survey and video-ethnographic method with 4-way cameras. The close-circuit cameras are installed to observe the upper and lower body of real drivers when they are in driving or even resting. This approach can help to understand urban bus drivers' behaviors and fatigue-related issues. Based on the video-ethnographic investigation results above, we have got certain patterns of drivers.

In order to reduce brake squeal noise, it is important to identify operational deflection shape (ODS) of brake disc while squeal arises. However, in the conventional modal analysis and optical measurement, it is only able to identify limited ODS because of the technical limits. This paper details the test method to identify ODS in radial and tangential as well as axial direction of a brake disc in driving condition. Vibrational signal of a rotating disc was obtained by triaxial accelerometer installed to solid type discs/cooling fins of ventilated type discs, then ODS of disc were analyzed through digital signal processing.

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.

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.

The high speed continuous braking distance assessment is the worst condition for thermal fades. This study was conducted to investigate the relationship between fade characteristic and friction materials & brake fluid amount for improving braking distance. So, we used the dynamometer to measure the friction coefficient, braking distance and required brake fluid amount. Through the measurements, the research was carried out as follows. First of all, we studied the influence of friction coefficient about different shapes (chamfer shape, area of the friction material, number of slots) on the same friction material. Secondly, we knew the effects of braking distance by the shape of the friction material. Through these two studies, the shape of the friction material favorable to the fade characteristics was derived. Finally, we measured the amount of required brake fluid in caliper after 10 consecutive braking cycles through Dynamometer.

In the latest works [12], we presented the guideline for reducing Metal pick up(MPU, the main component of disc scoring) by controlling the location of the roughness of disc, the brake pad friction coefficients and the disc slot's size. In this study, the previously studied iron transfer theory to 'Cu free' brake pad and the disc surface roughness controlling methods which are based on the mass production manufacturing process are applied. It is possible to suggest the ways to improve the scoring-free disc without reducing friction coefficient between the disc and pad, and any demerit such as increased wear and airplane noise like conventional slot discs [11].

A comparative study was carried out to investigate the DTV (disk thickness variation) behavior according to the types of brake disks (gray iron grade 250 and high-carbon gray iron grade 200, 170) with two typical friction materials (non-steel and low-steel friction materials). To evaluate DTV generation and recovery characteristics, a parasitic drag mode simulating highway driving (off-brake) and a normal braking mode simulating city traffic driving (on-brake) were used with an inertia brake dynamometer. Results showed that DTV and BTV were strongly affected by the microstructure, hardness level and distribution of the gray cast iron with the friction material types. The BTV was reduced in the friction two pairs using non-steel friction materials with high carbon grade disks and low-steel friction materials with high-carbon, low hardness disk. In particular, the pair of low-steel friction materials and high-carbon, low-hardness brake disks was more effective on DTV recovery.

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.

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.

Recently, regulations on automobile emission have been significantly strengthened to address climate change. The automobile industry is responding to these regulations by developing electric vehicles that use batteries and fuel-cells. Automobile emissions are environmentally harmful, especially in the case of vehicles equipped with high-temperature and high-pressure diesel engines using compression-ignition, the proportion of nitrogen oxides (NOx) emissions reaches as high as 85%. Additionally, air pollution caused by particulate matter (PM) is six to ten times higher compared to gasoline engines. Therefore, the electrification of commercial vehicles using diesel engines could potentially yield even greater environmental benefits. For commercial vehicles battery electric vehicles (BEVs) require a large number of batteries to secure a long driving range, which reduces their maximum payload capacity.

In recent years, the automotive industry has been making efforts to develop vehicles that satisfy customers’ emotions rather than malfunctions by improving the durability of vehicles. The durability and reliability of vehicles sold in the U.S. can be determined through the VDS (Vehicle Dependability Study) published by JD Power. The VDS is index which is the number of complaints per 100 units released by J.D. POWER in every year. It investigates customers who have used it for 3 years after purchasing a new car and consists of 177 specific problems grouped into 8 categories such as PT, ACEN, FCD, Exterior. The VDS-4 has been strengthened since the introduction of the new evaluation system VDS-5 in 2015. In order to improve the VDS index, it is important to gather various customer complaints such as internet data, warranty data, Enprecis data and clarify the problem and cause. Enprecis data is survey of customer complaints by on-line in terms of VDS.

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.