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As the mobility being developed becomes more complex and numerous, it is becoming difficult and inefficient to apply current vehicle-test-based development. To overcome this, research on combining test and simulation models has been actively conducted to perform objective and subjective evaluations more accurately and efficiently in the advance stage without a vehicle over the years. At first, test models for various systems such as tire, suspension and body were made compatible with simulation models by using various methodologies such as blocked forces, FBS decoupling, and Virtual Point Transformation (VPT). The second step was to objectively estimate road noise by using FBS coupling with system models and to deeply analyze transfer paths and system’s sensitivity. The results were verified by comparing with what was measured and analyzed on vehicle.

During icing wind tunnel experiments, the calibration process of the spray nozzle and aerothermal systems introduces experimental uncertainty that can potentially compromise the reliability of the test results. Therefore, performing sensitivity analysis (SA) or uncertainty quantification (UQ) studies is not only essential to determine the influence of uncertainties on the ice shape and aerodynamic performance but also crucial to identify the most significant icing parameter uncertainty. However, given the wide range of icing envelopes, it is not practical to conduct SA and UQ by experimental method because a lot of evaluations are required for SA and UQ study. In this study, we addressed these challenges by using a deep learning-based reduced-order modeling technique.

Diagonalized alternating-direction implicit (DADI) method is implemented in the Eulerian hyperbolic droplet solver, ICEPAC, for efficient high-order accurate analysis of aircraft icing. Detailed techniques for implementing the DADI method considering hyperbolicity characteristics are discussed. For the Eulerian droplet equation system to be strictly hyperbolic, additional source terms regarding artificial droplet pressure are included. Validations of the present implicit solver are conducted using two- and three-dimensional steady benchmark tests: NACA0012 airfoil, NACA23012 airfoil, and a swept wing. Also, the oscillating airfoil SC2110 case was analyzed to verify the robustness and efficiency of the proposed solver. In addition, the computational cost of the current implicit solver is considerably lower than that of the explicit multi-stage solver.

This paper presents a stability monitoring algorithm with a combined slip tire model for maximized cornering speed of high-speed autonomous driving. It is crucial to utilize the maximum tire force with maintaining a grip driving condition in cornering situations. The model-free cruise controller has been designed to track the desired acceleration. The lateral motion has been regulated by the sliding mode controller formulated with the center of percussion. The controllers are suitable for minimizing the behavior errors. However, the high-level algorithm is necessary to check whether the intended motion is inside of the limit boundaries. In extreme diving conditions, the maximum tire force is limited by physical constraints. A combined slip tire model has been applied to monitor vehicle stability. In previous studies, vehicle stability was evaluated only by vehicle acceleration.

This paper presents a methodology of trajectory planning for the surrounding-aware lane change maneuver of autonomous vehicles based on a data-driven method. The lateral motion is planned by sampling candidate patterns which are defined based on quintic polynomial functions over time. Based on the cost evaluation among the sampled candidates, the optimal lateral motion pattern is selected as a reference and tracked by the controller. The longitudinal motion is planned and controlled using Model Predictive Control (MPC) which is an optimal control method designed considering the surrounding traffic information. To realize the lane change motion similar to the human driving behavior in the surrounding traffic situation, the human driving pattern is modeled in the form of motion parameters and considered in planning the lateral and longitudinal motion.

This paper describes a hierarchical motion planning and control framework for overtaking maneuvers under racing circumstances. Unlike urban or highway autonomous driving conditions, race track driving requires longer prediction and planning horizons in order to respond to upcoming corners at high speed. In addition, the subject vehicle should determine the optimal action among possible driving modes when opponent vehicles are present. In order to meet these requirements and secure real time performance, a hierarchical architecture for decision making, motion planning, and control for an autonomous racing vehicle is proposed. The supervisor determines whether the subject vehicle should stay behind the preceding vehicle or overtake, and its direction when overtaking. Next, a high level trajectory planner generates the desired path and velocity profile in a receding horizon fashion.

This paper presents an integrated control of in-wheel motor (IWM) and electronic limited slip differential (eLSD) to enhance the vehicle lateral stability and maneuverability. The two actuators are utilized in the proposed controller to achieve separate purposes. The IWM controller is designed to modify the understeer gradient for enhanced handling characteristic and maneuverability. The eLSD controller is devised to improve the lateral stability to prevent oversteer in a severe maneuver. The proposed controller consists of a supervisor, upper-level controller and lower-level controller. The supervisor determines a target motion based on a target understeer gradient for IWM control and a yaw rate reference for eLSD control. The upper-level controller generates a desired yaw moment for the target motion. In the lower-level controller, the desired yaw moment is converted to the control inputs for IWMs in the two front wheels and eLSD at the rear axle.

This paper presents the research related to the self-driving system that has been actively carried out recently. Previous studies have been limited to ensure the path following performance in linear and steady state-alike handling region with small lateral acceleration. However, in the high speed driving, the vehicle cornering response is extended to nonlinear region where tire grips are saturated. This requires a technology to create the driving path for minimum time maneuvering while grasping the tire grip limits of the vehicle in real time. The entire controller consists of three stages-hierarchy: The target motion is determined in the supervisor phase, and the target force to follow the target behavior is calculated in the upper stage controller. Finally, the lower stage controller calculates the actuator phase control input corresponding to the target force.

A spark-ignition engine commonly induces tumble flow because it generates high turbulence, which is a crucial factor in determining the flame propagation speed. Since tumble affects not only the flame propagation speed but also the various in-cylinder phenomena, it predominantly determines the performance of the engine. In that sense, many studies have been conducted to investigate tumble. Although various studies have revealed the characteristics of tumble numerically and experimentally, there has been no research to identify the physical mechanisms of these characteristics. Although some studies specified the mechanisms from an angular momentum perspective, the theory was insufficient to explain the entire phenomena of tumble. Hence, this study attempts to comprehend the fundamental causes of tumble phenomena such as ‘spinning up’ and ‘vortex breakdown’ from the perspective of kinetic energy.

An experimental study was conducted on a multi-cylinder engine to understand the feasibility of a six-stroke homogeneous charge compression ignition (HCCI) operation under stoichiometric conditions. State-of-the-art technologies such as continuously variable valve duration (CVVD) and high-pressure gasoline direct injection (GDI) were experimentally exploited to increase the degree of freedom of engine control. The motivation of six-stroke HCCI combustion is to remedy the load limitation and the cyclic variation in four-stroke HCCI combustion with two additional strokes: compression and expansion strokes. The six-stroke HCCI combustion occurs in the following order. First, hot residual gas is trapped by applying negative valve overlap (NVO). Next, fresh air enters, fuel is injected, and lean HCCI combustion occurs in the 1st power stroke (PS). Subsequently, additional fuel is injected, and the 2nd combustion occurs with the remaining oxygen in the two additional strokes.

This paper presents motion planning and control algorithm for urban automated driving using high-definition(HD) map. Many automakers have developed and commercialized advanced driver assistance system(ADAS) based on vision-only lane extraction in motorway environments. Compared to the motorway environments where the lane is continuous and clearly visible, however, in urban roads, degradation of the lane quality such as lane occlusion and lane loss occurs frequently. This leads to the poor quality of the local guide path for the autonomous vehicles with vision-only lane extraction. Global HD map is used to provide the lane information continuously instead of vision-only lane extraction. With the existence of global position of host vehicle and the HD map, the proposed sequential algorithm performs the lane keeping and lane changing decision and control with safety margin in multi-vehicle situation.

This paper describes a failsafe system of automated driving vehicles. The failsafe system consists of the following two parts: sliding mode observer-based environment sensor, chassis sensor fault detection, and emergency deceleration control. Two sliding mode observers are designed to reconstruct the fault of acceleration and environment sensor(Lidar) in a longitudinal direction. In the environment sensor's fault detection part, the longitudinal vehicle model receives clearance and relative velocity values. Therefore, failure diagnosis is possible regardless of environmental sensors, such as radar, lidar, and camera. This paper's sensor data is the failure of Delphi's Electronically Scanning Radar (ESR) and Ibeo's LUX Lidar installed in an autonomous vehicle. The emergency deceleration control algorithm employs the sliding mode control with adaptive convergence time. In the event of a failure, it is significant to control the vehicle within a short period safely.

This paper describes a lateral control of autonomous large size buses on road with large curvature. In the case of long and wide commercial vehicle such as large bus, applying centerline tracking controllers in constrained environments such as large curved road (e.g. turning at intersection) may cause some concerns. Two concerns are considered: inner lane crossing related to collisions with curb and opposite lane crossing related to threatening surrounding vehicles. Considering relations between width and curvature of the road and length and width of the large size bus, the curvature of road at which inner or outer lane crossing begin to occur was calculated when centerline tracking controller was applied. Thus, the proposed algorithm optimizes motion of the bus by using model predictive control (MPC) using road geometry as constraints.

As global emission standards are becoming more stringent, it is necessary to increase thermal efficiency through the high compression ratio in spark-ignited engines. Various studies are being conducted to mitigate knocking caused by an increased compression ratio, which requires an understanding of the combustion phenomena inside the combustion chamber. In particular, the in-cylinder flow is a major factor affecting the entire combustion process from the generation to the propagation of flames. In the field of spark-ignited engine research, where interest in the concept of lean combustion and the expansion of the EGR supply is increasing, flow analysis is essential to ensure a rapid flame propagation speed and stable combustion process. In this study, the flow around the spark plug was measured by the Laser Doppler Velocimetry system, and the correlation with combustion in spark-ignited engines was analyzed.

Regulations regarding vehicles’ CO2 emissions are continuing to become stricter due to global warming. The CO2 regulations urge automobile manufacturers to develop gasoline engines with improved efficiency; however, the main obstacle to the improvement is the knock phenomenon in spark-ignition engines. If knock is predicted, the efficiency potential can be maximized in an engine by applying modest spark timing. Several research regarding knock prediction modeling have been conducted, and typically Livengood-Wu integral model is used to predict the knock occurrence. For the prediction, knock onset should be determined on a given pressure signal of given knock cycles for establishing the 0D ignition delay model. Several methodologies for knock onset determination have been developed because checking all the knock onset position by hand is impossible considering the breadth of data sets.

The recent development pace of the automotive technology is so rapid worldwide. Especially in a green car, hybrid electric vehicles (HEVs) have been studied a lot due to their significant effects on the urban driving. In the vehicle energy management strategy study, the driving speed is assumed to be known in advance, however the speed is not given in a real world. Accordingly, the prediction of vehicle speed is very important. In this study, we study the prediction methodology for the speed prediction using deep learning. Based on the vehicle driving speed data, the supervised deep learning has been used and the speed prediction accuracy using deep learning shows accurate results comparing to the actual speed. The supervised deep learning is used which is suitable for driving cycle database. As a result, the speed prediction after few seconds is feasible.

Since the amount of emitted CO2 is directly related to car fuel economy, attention is being drawn to DISI (Direct-Injection Spark-Ignition) engines, which have better fuel economy than conventional gasoline engines. However, it has been a problem that the rich air-fuel mixtures associated with fuel films during cold starts due to spray impingement produce particulate matter (PM). In predicting soot formation, it is important to predict the mixture field precisely. Thus, accurate spray and film models are a prerequisite of the soot model. The previous models were well matched with low-speed collision conditions, such as those of diesel engines, which have a relatively high ambient pressure and long traveling distances. Droplets colliding at low velocities have an order of magnitude of kinetic energy similar to that of the sum of the surface tension energy and the critical energy at which the splash occurs.

The combustion process using two fuels with different reactivity, known as dual-fuel combustion or RCCI is mainly studied to reduce emissions while maintaining thermal efficiency compared to the conventional diesel combustion. Many studies have proven that dual-fuel combustion has a positive prospect in future combustion to achieve ultra-low engine-out emissions with high indicated thermal efficiency. However, a limitation on high-load expansion due to the higher maximum in-cylinder pressure rise rate (mPRR) is a main problem. Thus, it is important to establish the operating strategy and study the effect of in-cylinder flow motion with dual-fuel combustion to achieve a low mPRR and emissions while maintaining high-efficiency. In this research, the characteristics of gasoline-diesel dual-fuel combustion on different hardware were studied to verify the effect of the in-cylinder flow motion on dual-fuel combustion.

This paper presents an Interacting Multiple Model (IMM) based side slip angle estimation method to estimate side slip angle under various road conditions for vehicle stability control. Knowledge of the side slip angle is essential enhancing vehicle handling and stability. For the estimation of the side slip angles in previous researches, prior knowledge of tire parameters and road conditions have been employed, and sometimes additional sensors have been needed. These prior knowledge and additional sensors, however, necessitates many efforts and make an application of the estimation algorithm difficult. In this paper, side slip angle has been estimated using on-board vehicle sensors such as yaw rate and lateral acceleration sensors. The proposed estimation algorithm integrates the estimates from multiple Kalman filters based on the multiple models with different parameter set.

In recent years, emission regulations have been getting increasingly strict. In the development of engines that comply with these regulations, in-cylinder pressure plays a fundamental role, as it is necessary to analyze combustion characteristics and control combustion-related parameters. The analysis of in-cylinder pressure data enables the modelling of exhaust emissions in which characteristic temperature can be derived from the in-cylinder pressure, and the pressure can be used for other investigations, such as optimizing efficiency and emissions through controlling combustion. Therefore, a piezoelectric pressure sensor to measure in-cylinder pressure is an essential element in the engine research field. However, it is difficult to practice the installation of this pressure sensor on all engines and on-road vehicles owing to cost issues.