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This paper describes a driving control algorithm based on skid steering for a Robotic Vehicle with Articulated Suspension (RVAS). The driving control algorithm consists of four parts; speed controller for tracking of the desired speeds, yaw rate controller which computes a yaw moment input to track desired yaw rates, longitudinal tire force distribution which determines an optimal desired longitudinal tire force and wheel torque controller which determines a wheel torque command at each wheel to keep slip ratio at each wheel below a limit value as well as track the desired tire force. Longitudinal and vertical tire force estimators are designed for optimal tire force distribution and wheel slip control. The dynamic model of RVAS for simulation study is validated using vehicle test data.

As fuel injection strategies in spark-ignition (SI) engines have been diversified, inhomogeneous mixing of the fuel-air mixture can occur to varying extents during mixture preparation. In this study, we analyzed the effect of inhomogeneous mixing on the knocking characteristics of iso-octane and air mixture under a standardized fuel testing condition for research octane number (RON), based on ASTM D2699. For this purpose, we assumed that both lean spots and rich spots existed in unburned gas during compression stroke and flame propagation and calculated the thermodynamic state of the spots by using an in-house multi-zone, zero-dimensional SI engine model. Then, the ignition delay was measured over the derived thermodynamic profiles by using rapid compression machine (RCM), and we calculated ξ, the ratio of sound speed to auto-ignition propagation speed, based on Zel’dovich and Bradley’s ξ − ε theory to estimate knock intensity.

A gasoline homogeneous charged compression ignition (HCCI) called the controlled auto ignition (CAI) engine is an alternative to conventional gasoline engines with higher efficiency and lower emission levels. However, noise and vibration are currently major problems in the CAI engine. The problems result from fast burning speeds during combustion, because in the CAI engine combustion is controlled by auto-ignition rather than the flame. Thus, the ignition delay of the local mixture has to vary according to the location in the combustion chamber to avoid noise and vibration. For making different ignition delays, stratification of temperature or mixing ratio was tested in this study. In charge stratification, which determines the difference between the start of combustion among charges with different properties, two kinds of mixtures with different properties flow into two intake ports.

This paper presents a Unified Chassis Control (UCC) strategy to improve vehicle stability and maneuverability by integrating Electronic Stability Control (ESC) and Active Front Steering (AFS). The UCC architecture consists of two parts: an estimator and a controller. The estimator is designed to estimate longitudinal and lateral tire forces and the controller is designed in two stages, namely, an upper level controller and a lower level controller. The upper level controller, provides the desired yaw moment for vehicle lateral stability by adopting a sliding control method. The lower level controller, provides the integration method of the AFS and ESC strategies for the desired yaw moment and is designed by optimal tire force coordination.

This paper presents a closed-loop evaluation of the Vehicle Stability Control (VSC) systems using a vehicle simulator. Human driver-VSC interactions have been investigated under realistic operating conditions in the laboratory. Braking control inputs for vehicle stability enhancement have been directly derived from the sliding control law based on vehicle planar motion equations with differential braking. A driving simulator which consists of a three-dimensional vehicle dynamic model, interface between human driver and vehicle simulator, three-dimensional animation program and a visual display has been validated using actual vehicle driving test data. Real-time human-in-the loop simulation results in realistic driving situations have shown that the proposed controller reduces driving effort and enhances vehicle stability.

The role of 1D simulation tool is growing as the engine system is becoming more complex with the adoption of a variety of new technologies. For the reliability of the 1D simulation results, it is necessary to improve the accuracy and applicability of the combustion model implemented in the 1D simulation tool. Since the combustion process in SI engine is mainly determined by the turbulence, many models have been concentrating on the prediction of the evolution of in-cylinder turbulence intensity. In this study, two turbulence models which can resemble the turbulence intensity close to that of 3D CFD tool were utilized. The first model is dedicated to predicting the evolution of turbulence intensity during intake and compression strokes so that the turbulence intensity at the spark timing can be estimated properly. The second model is responsible for predicting the turbulence intensity of burned and unburned zone during the combustion process.

This paper proposes a reference steering wheel torque map and a torque tracking algorithm via steer-by-wire to achieve the targeted steering feel. The reference steering wheel torque map is designed using the measurement data of rack force and steering characteristic of a target performance of the vehicle at transition steering test. Since the target performance of the vehicle is only tested in nominal road condition, various road conditions such as disturbances and tire-road friction are not considered. Hence, the measurement data of the rack force that reflects the road conditions in the reference steering wheel torque map have been used. The rack force is the net force which consists of tire aligning moment, road friction force and normal force on the tire kingpin axis. A motor and a magnetorheological damper are used as actuators to generate the desired steering feel using the torque tracking algorithm.

Large-eddy simulation (LES) applications for internal combustion engine (ICE) flows are constantly growing due to the increase of computing resources and the availability of suitable CFD codes, methods and practices. The LES superior capability for modeling spatial and temporal evolution of turbulent flow structures with reference to RANS makes it a promising tool for describing, and possibly motivating, ICE cycle-to-cycle variability (CCV) and cycle-resolved events such as knock and misfire. Despite the growing interest towards LES in the academic community, applications to ICE flows are still limited. One of the reasons for such discrepancy is the uncertainty in the estimation of the LES computational cost. This in turn is mainly dependent on grid density, the CFD domain extent, the time step size and the overall number of cycles to be run. Grid density is directly linked to the possibility of reducing modeling assumptions for sub-grid scales.

The goal of this study is to develop an actively translating rear diffuser device to reduce the aerodynamic drag experienced by passenger cars. The feature of this device is hidden under the rear bumper ordinarily not to ruin the external design of the car and slips out backward under the high-speed driving condition. By this study, a movable arc-shaped semi-diffuser device is designed to maintain the streamlined automobile rear underbody configuration. It's installed under the rear bumper of a passenger car. Seven types of rear diffuser devices whose positions, slid out lengths and widths are differing with the basic shape installed in the rear bumper section of a passenger car and performed Computational Fluid Dynamics (CFD) analyses under rotating wheel and moving ground conditions. The main purpose of this study is that explains the aerodynamic drag reduction mechanism of a passenger car via an actively translating rear diffuser device at a high speed driving condition.

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.

Improving fuel efficiency and emission characteristics are significant issues in engine research. Because the engine has complex systems and various operating parameters, the experimental research is limited by cost and time. One-dimensional (1D) simulation has attracted the attention of researchers because of its effectiveness and relatively high accuracy. In a 1D simulation, the applied model must be accurate for the reliability of the simulation results. Because in-cylinder turbulence mainly determines the combustion characteristics, and mean flow velocity affects the in-cylinder heat transfer and efficiency in a spark-ignited (SI) engine, a number of sophisticated models have been developed to predict in-cylinder turbulence and mean flow velocity. In particular, tumble is a significant factor of in-cylinder turbulence in SI engine.

Validation of a vehicle simulation model of the Chevrolet Volt 2016 was conducted. The Chevrolet Volt 2016 is equipped with the new “Voltec” extended-range propulsion system introduced into the market in 2016. The second generation Volt powertrain system operates in five modes, including two electric vehicle modes and three extended-range modes. Model development and validation were conducted using the test data performed on the chassis dynamometer set in a thermal chamber of Argonne National Laboratory’s Advanced Powertrain Research Facility. First, the components of the vehicle, such as the engine, motor, battery, wheels, and chassis, were modeled, including thermal aspects based on the test data. For example, engine efficiency changes dependent on the coolant temperature, or chassis heating or air-conditioning operations according to the ambient and cabin temperature, were applied.

Radiative heat transfer analysis in a finite cylindrical enclosure with non-gray media and internal heat generations have been conducted. Solutions are generated by a recently developed spherical harmonics method for a finite cylindrical configuration with the weighted sum of gray gases model. Numerical solutions are obtained for temperature and heat flux distributions with the variations of optical thickness and wall emissivity. The results show that with an increase in the absorption coefficient, the heat flux distribution along the lateral wall becomes symmetric regardless of the source distributions. The dependence of heat flux on the wall emissivity is reduced as well. The present solution technique seems to be easily extended to the coupled mode of heat transfer with convection in an engine cylinder.

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.

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.

The flame propagation characteristic is one of the greatest factor that determines the performance of spark ignition (SI) engines. The in-cylinder flow dynamics is very significant in terms of flame propagation because of its direct influence on the flame shape, turbulent flame speed, and the ignition quality. A number of different techniques are available to optimize the in-cylinder flow and maximize the utilization of turbulence for faster combustion, and tumble enhancement by intake port geometry is one of them. It requires excessive computational expenses to evaluate multiple designs under wide range of operating conditions by 3D-CFD, therefore, a low-dimensional model would be more competitive in such design optimization process. This work suggests a new modification approach for typical 0D turbulence model to take account for the tumble generation during the intake process as well as the turbulence characteristics associated with it.

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.

This paper proposes a rear-wheel steering control method that can modify and improve the vehicle lateral response without tire model and parameter. The proposed control algorithm is a combination of steady-state and transient control. The steady state control input is designed to modify steady-state yaw rate response of the vehicle, i.e. understeer gradient of the vehicle. The transient control input is a feedback control to improve the transient response when the vehicle lateral behavior builds up. The control algorithm has been investigated via computer simulations. Compared to classical control methods, the proposed algorithm shows good vehicle lateral response such as small overshoot and fast response. Specifically, the proposed algorithm can alleviate stair-shaped response of the lateral acceleration.