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As environmental problems such as global warming are emerging, regulations on automobile exhaust gas are strengthened and various exhaust gas reduction technologies are being developed in various countries in order to satisfy exhaust emission regulations. Exhaust gas recirculation (EGR) technology is a very effective way to reduce nitrogen oxides (NOx) at high combustion temperatures by using EGR coolers to lower the combustion temperature. This EGR cooler has been mass-produced in stainless steel, but it is expensive and heavy. Recently, high efficiency and compactness are required for the EGR cooler to meet the new emission regulation. If aluminum material is applied to the EGR cooler, heat transfer efficiency and light weight can be improved due to high heat transfer coefficient of aluminum compared to conventional stainless steel, but durability is insufficient. Therefore, the aluminum EGR cooler has been developed to enhance performance and durability.

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 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.

A study of unsteady compressible flow for two types of exhaust manifold and CCC (Close-Coupled Catalyst) systems attached to a 4-cylinder DOHC gasoline engine was carried out to investigate the flow distribution of exhaust gases and finally to make the conversion efficiency of catalyst better. An experimental study was conducted, using LDV technique, to measure the velocity distributions inside exhaust manifolds and CCC under practical engine conditions. In this study, through experiment and calculation, the effects of geometric configuration of exhaust manifold on flow maldistribution in monolith were mainly investigated to understand the exhaust flow structure in terms of flow uniformity and to improve the conversion efficiency. As a result of this fundamental study, the modified exhaust manifold (Type B) was designed and manufactured. Full load performance tests and vehicle emission tests were performed to see the effects of flow characteristics on engine performance and emission.

In diesel engine development, the new technology is coming out to meet the stringent exhaust emission regulation. The regulation demands more eco-friendly vehicles. Euro6c demands to meet not only WLTP mode, but also RDE(Real Driving Emission). In order to satisfy RDE mode, the new technology to reduce emissions should cover all operating areas including High Load & High Speed. It is a big challenge to reduce NOx on the RDE mode and a lot of DeNOx technologies are being developed. So the new DeNOx technology is needed to cover widened operating area and strict acceleration / deacceleration. The existing LNT(Lean NOx Trap) and Urea SCR(Selective Catalytic Reduction) is necessary to meet the typical NEDC or WLTP, but the RDE mode demands the powerful DeNOx technology. Therefore, the LNT & Urea SCR on DPF was developed through this 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.

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.

A combined experimental and computational study of 3-D unsteady compressible flow in exhaust manifold and CCC system was performed to understand the flow characteristics and to improve the flow distribution of pulsating exhaust gases within monolith. An experimental study was carried out to measure the velocity distribution in production exhaust manifold and CCC under engine operating conditions using LDV (Laser Doppler Velocimetry) system. Velocity characteristics were measured at planes 25 mm away from the front surface of first monolith and between two monolithic bricks. To provide boundary conditions for the computational study, velocity fields according to crank angle were also measured at the entrance of exhaust manifold. The comparisons of exhaust gas flow patterns in the junction and mixing pipe between experimental and computational results were made.

The combustion characteristics of a HSDI diesel engine were analyzed numerically using a reduced chemical kinetics. The reaction mechanism consisting of 26 steps and 17 species including the Zel'dovich NOx mechanism for the higher hydrocarbon fuel was implemented in the KIVA-3V. The characteristic time scale model was adopted to account for the effects of turbulent mixing on the reaction rates. The soot formation and oxidation processes are represented by Hiroyasu's model and NSC's model. The validation cases include the homogenous fuel/air mixture and the spray combustion in a constant volume chamber. After the validation, the present approach was applied to the analysis of the spray combustion processes in a HSDI diesel engine. The present approach reasonably well predicts the ignition delay, combustion processes, and emission characteristics in the high-pressure turbulent spray flame-field encountered in the practical HSDI diesel engines.

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.

Improved Lean NOx Trap (LNT) catalysts with enhanced NH3 generation feature were developed for the small diesel engine. The next generation LNT system needs to perform good NOx conversions over the wide temperature range including below 200°C for urban driving and above 400°C for motorway of real road driving. However, the extended use of BaO, a component of LNT known to be very effective for high temperature NOx storage, results in the decrease of low temperature NOx conversion due to the degradation of NO oxidation associating with sulfur over time. The improvement of the low-temperature LNT performance is a key requirement for the real driving emission control as the best operation temperature for urea-SCR is above ~250°C. In this study, our next generation LNT with new washcoat architecture has demonstrated improved NOx removal efficiencies under the wider operation temperature window than the current production technology.

We developed new single coated Pd/Rh three-way catalysts (TWC). Several Pd/Rh single layered catalysts were prepared by changing the precious metal (PM) fixation method and adding new base metal oxides (BMO). These samples were compared with double-coated catalyst by using model gas activity test, BET test, XRD test and vehicle emission test. It is found that the performance of the single coated catalyst is as good as that of commercialized double-coated catalyst. The oxygen storage capacity of the single coated catalyst is better than that of double-coated catalyst. Moreover, manufacturing the single coated catalyst enables us to eliminate the unnecessary coating process which is essential to the conventional one. Our test results demonstrate that the developed catalyst has sufficient activity and durability of OSC to meet emission and OBD-II regulations.

Recently, the design of automotive headlamps has become diversified and complicated according to customer needs. Hence, structural complexity of the headlamps has also increased. Complex structure of the headlamps inevitably causes a disturbance in air circulation. For this reason, inadvertent micro-sized water droplets, called fogging, are condensed on the inner surface of headlamp lens due to temperature difference between the inner and outer lens surfaces. To circumvent fogging inside of the headlamp lens, an anti-fogging coating is indispensable. Conventionally, diverse surfactants have been adopted as substantial material for the anti-fogging coating. However, the usage of the surfactants causes undesirable side effect such as water mark arising from vapor condensation, which is an important issue that must be fully resolved. In this study, we developed an innovative anti-fogging coating material without using conventional surfactant.

Flow and wall guided gasoline direct injection engine was developed for decreasing fuel consumption in vehicle by operating engine in the stratified lean air fuel ratio and reducing Nitrogen Oxides by applying the system of exhaust gas recirculation in the part load. However, it needed an expensive lean NOx catalytic converter and low Sulfur gasoline. So, they are not successful because of higher cost of lean NOx catalytic converter and lower gain in fuel economy in the higher emission level. Yet, Homogeneous GDI engine fulfills demands for higher specific power output in full load over port injection gasoline engines by intake air cooling and shows the enhanced fuel economy by higher compression ratio and internal exhaust gas recirculation in part load. Also, Gasoline direct injection inside cylinder leads to the cost reduction of catalytic converter by reducing Hydro Carbon and NOx exhausted until catalyst light off from engine start and shortening light off time.

The integration of SCR catalyst into diesel-particulate filter (SDPF) may be one of most viable ways to meet upcoming stringent emission regulations with new test protocols such as Worldwide harmonized Light vehicles Test Cycles (WLTC) and Real Driving Emissions (RDE) requirements. The chabazite-structured SSZ-13-based catalysts enabled the wide implementation of urea-SCR technology for mobile applications due to their robust thermal stability up to 750°C compared to the thermally unstable ZSM-5-based technologies. However, the thermally stable Cu-SSZ-13 catalyst starts losing its initial activity with the increase of aging time at 850°C, where the SCR catalyst on SDPF can possibly be exposed during filter regeneration under a drop-to-idle (DTI) condition. Therefore, more durable SCR catalysts that survive under higher temperatures have been strongly desired in automotive industry. Recently, we found Cu-exchanged high silica LTA revealed an excellent hydrothermal stability.

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 author's new approach, diesel and gasoline dual fuel powered combustion system based on diesel CI triggered ignition control, provides not only how key ideas extracted from LTC concept could be established in a small bore HSDI turbocharged diesel engine but also which mechanism works to bring almost same benefits as we have experienced in both conventional diesel combustion and LTC based advanced combustion systems like HCCI, PCCI and PPCI combustions. The combustion system presented in the paper physically combines both mixing controlled diesel compression ignition combustion and gasoline premixed charge combustion in one power generation cycle. Gasoline fuel in the system is provided by the conventional gasoline PFI system firstly into the cylinder in which premixed charge spreads out. In compression stroke, the exact amount of diesel fuel is injected into the highly diluted EGR ambient with premixed gasoline charge.

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.

Diesel engines are the most commonly used power train of the freight and public transportations in the world. From the viewpoint of global warming restraint, however, reduction of exhaust emissions from the diesel engine is urgent demand. Stringent emission regulations are being proposed with growing concern on NOx, PM and CO2 emissions. Future emission regulations require advanced emission control technologies, such as SCR(Selective Catalytic Reduction), LNT(Lean NOx Trap) and EGR(Exhaust Gas Recirculation). The EGR is a commonly used technique to reduce emission. In this study, a LP-EGR(Low Pressure Exhaust Gas Recirculation) system was investigated to evaluate its potential on emission reduction and fuel economy improvement, especially for a passenger diesel engine. A 3.0ℓ diesel engine equipped with the LP-EGR system was tested using an in-house control algorithm.

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.