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This paper presents an automated driving control algorithm for the control of an autonomous vehicle. In order to develop a highly automated driving control algorithm, one of the research issues is to determine a safe driving envelope with the consideration of probable risks. While human drivers maneuver the vehicle, they determine appropriate steering angle and acceleration based on the predictable trajectories of the surrounding vehicles. Therefore, not only current states of surrounding vehicles but also predictable behaviors of that should be considered in determining a safe driving envelope. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, the safe driving envelope over a finite prediction horizon is defined in consideration of probabilistic prediction of future positions of surrounding vehicles.

This paper presents a tire slip-angle based speed control race driver model. In developing a chassis control system for enhancement of high-speed driving performance, analysis of the vehicle-driver interaction at limit handling is one of the main research issues. Thus, a driver model which represents driving characteristics in a racing situation is required to develop a chassis control system. Since a race driver drives a vehicle as fast as possible on a given racing line without losing control, the proposed driver model is developed to ensure a lateral stability. In racing situation, one of the reasons which cause the lateral instabilities is an excessive corner-entry speed. The lateral instability in that moment is hard to handle with only a steering control. To guarantee the lateral stability of the vehicle while maximizing a cornering speed, a desired speed is determined to retain a tire slip-angle that maximizes lateral tire forces without front tire saturation.

This paper describes an Integrated Chassis Control (ICC) strategy for improving high speed cornering performance by integration of Electronics Stability Control (ESC), Four Wheel Drive (4WD), and Active Roll Control System (ARS). In this study, an analysis of various chassis modules was conducted to prove the control strategies at the limits of handling. The analysis is focused to maximize the longitudinal velocity for minimum lap time and ensure the vehicle lateral stability in cornering. The proposed Integrated Chassis Control algorithm consists of a supervisor, vehicle motion control algorithms, and a coordinator. The supervisor monitors the vehicle status and determines desired vehicle motions such as a desired yaw rate, longitudinal acceleration and desired roll motion. The target longitudinal acceleration is determined based on the driver's intention and vehicle current state to ensure the vehicle lateral stability in high speed maneuvering.

This paper describes a lateral disturbance estimator for an application to a Motor Driven Power Steering (MDPS)-based driving assistant system. A vehicle motion can be disturbed laterally by wind force or load from bank angle acting on the vehicle in the lateral direction. An MDPS-based driving assistant system can be used to reduce steering effort of a human driver in a driving situation with lateral disturbance. In designing the MDPS-based driving assistant system, the lateral wind disturbance should be estimated to determine an assistant torque. An estimator for the vehicle lateral disturbance estimation has been developed. The proposed estimator consists of two parts: a tire self-aligning torque estimator and the lateral disturbance estimator. The lateral disturbance estimator has been designed on the basis of a 2-DOF bicycle model with available sensor signals from the MDPS module. A numerical simulation has been conducted in order to evaluate the proposed estimator.

Research on HEV (hybrid electric vehicle) intracity buses has become a topic of interest because the well-known service routes of intracity buses and the frequent stop/go pattern make the energy management of the vehicle straightforward. Thus, the energy flow and the energy management of the intracity bus have been studied extensively in order to improve fuel economy. However, the HEV buses that have been studied previously were equipped with a single traction motor or with dual motors with the same capacity for the convenience of the equipment without considering the motoring or generating efficiency of the traction motor. Therefore, the energy flow from the engine/generator unit to the traction motor that has been optimized by many kinds of energy distribution strategies could not be transferred to the wheels in the most efficient manner. This paper investigates this aspect of the energy flow.

A methodology to design a model free cruise control algorithm(MFCC) is presented in this paper. General cruise control algorithms require lots of vehicle parameters to control the power train and the brake system, that makes control system complicate. Moreover, when the target vehicle is changed, the vehicle parameters should be reinvestigated in order to apply the cruise control algorithm to the subject vehicle. To overcome these disadvantages of the conventional cruise control algorithm, MFCC algorithm has been developed. The algorithm directly determines the throttle, brake inputs based on the reference model parameters such as clearance, relative velocity, and subject vehicle acceleration. This simple structure facilitates human centered design of cruise controller and makes it easy to apply control algorithm to various vehicles without reinvestigation of vehicle parameters.

This paper describes a driving control algorithm based on a skid steering for a Robotic Vehicle with Articulated Suspension (RVAS). The RVAS is a kind of unmanned ground vehicle based on a skid steering using independent in-wheel drive at each wheel. The driving control algorithm consists of four parts: a speed controller for following a desired speed, a lateral motion controller that computes a yaw moment input to track a desired yaw rate or a desired trajectory according to the control mode, a longitudinal tire force distribution algorithm that determines an optimal desired longitudinal tire force and a wheel torque controller that determines a wheel torque command at each wheel in order to keep the slip ratio at each wheel below a limit value as well as to track the desired tire force. The longitudinal and vertical tire force estimators are required for the optimal tire force distribution and wheel slip control.

The increasing interest in the application of Large Eddy Simulation (LES) to Internal Combustion Engines (hereafter ICEs) flows is motivated by its capability to capture spatial and temporal evolution of turbulent flow structures. Furthermore, LES is universally recognized as capable of simulating highly unsteady and random phenomena driving cycle-to-cycle variability (CCV) and cycle-resolved events such as knock and misfire. Several quality criteria were proposed in the recent past to estimate LES uncertainty: however, definitive conclusions on LES quality criteria for ICEs are still far to be found. This paper describes the application of LES quality criteria to the TCC-III single-cylinder optical engine from University of Michigan and GM Global R&D; the analyses are carried out under motored condition.

This paper presents an integrated chassis control method for vehicle stability under various road friction conditions without information on tire-road friction. For vehicle stability, vehicle with an integrated chassis control needs to cope with the various road friction conditions. One of the chassis control method under various road conditions is to determine and/or limit control inputs based on tire-road friction coefficient. The tire-road friction coefficient, however, is difficult to estimate and still a challenging task. The key idea for the proposed method without the estimation of the tire-road friction coefficient is to analyze and control vehicle states based on a tire slip angle - tire force phase plane, i.e. based on these vehicle responses: tire forces and tire slip angles of front/rear wheels.

This paper describes a driving motor torque distribution strategy of six-wheel-driven electric vehicles for optimized energy consumption. In this research, this strategy minimizes motoring power consumption and maximizes regenerative braking power under given required power condition. The torque distribution controller consists of total required motor torque calculation part, upper and optimal torque calculation part, lower level controller. The upper level controller determines total required torque of vehicle. And the torque is determined by acceleration pedal input of driver and vehicle velocity. The lower level controller calculates energy consumption in given condition and distributes motor torque to driving motor minimizing energy consumption. In distributing optimal motor torque, it is important to get accurate characteristics of driving motor and performance constraint.

This paper is concerned with the torque distribution problem including slip limitation and actuator fault tolerance to improve vehicle lateral stability and maneuverability of six-wheeled skid-steered vehicles. The torque distribution algorithm to distribute wheel torque to each wheel of a skid-steered vehicle consists of an upper level control layer, a lower level control layer and an estimation layer. The upper level control layer is designed to obtain longitudinal net force and desired yaw moment, while the lower level control layer determines distributed driving and braking torques to six wheels. The algorithm takes vehicle speed, slip ratio and tire load information from the estimation layer, as well as actuator fault information from each in-wheel motor controller unit.

Lithium-ion battery has advantages of high energy density and cost effectiveness than other types of batteries. However due to the low mechanical stability, their performance is strongly influenced by environmental conditions. Especially, external pressure on a cell surface is a crucial factor because an appropriate force can improve battery cycle life, but excessive force may cause structural failure. In addition, battery pack is composed of various components so that uncertainties in dimension and material properties of each component can cause a wide variance in initial pressure. Therefore, it is important to optimize structural design of battery pack to ensure initial pressure in an effective range. In this paper, target stiffness of module structure was determined based on cell level cycle life test, then structural design has been optimized for weight reduction. Cell cycling tests were performed under different stiffness conditions and analyzed with regression model.

The flamelet model is a widely used combustion model that demonstrates a good prediction of non-premixed combustion. In this model, the chemical time scales are considered to be smaller compared to those of the turbulence, which allows the heat and mass transfer equation to be decoupled from the flow equation. However, the model's dependency on the mixture fraction limits the combustion analysis to a single injection. To overcome this limitation, a two dimensional flamelet model, which uses two mixture fraction variables, was introduced to represent the non-premixed combustion of multiple injections. However, the model's computational time drastically increased due to the expansion of the solution domain. Thus, a modified 2-D flamelet model was introduced to reduce the computational time of the two dimensional flamelet model.

As an effective approach for the design, implementation and test of control systems, hardware-in-the-loop (HIL) test has been used in many research areas. This paper describes a real-time HIL simulation test for an automotive electronic control system. The HIL system proposed in this paper consists of three parts: real-time target hardware, electronic control unit (ECU) of the automotive electronic control systems and a signal-conditioning unit which regulates the voltage levels between real-time target and ECU. The HIL simulation evaluates mechanical and electronic behaviors in real time using off-line simulation models by interfacing real-target with electrical control units via interface box. The model has been developed by MATLAB/Simulink. The model is composed of mechanical part which predicts dynamic behaviors and electronic part to calculate the motor speeds, current and electronic loads under the various conditions.

This paper describes a robust Model Predictive Control (MPC) framework of lane change for automated driving vehicles. In order to develop a safe lane change for automated driving, the driving mode and lane change direction are determined considering environmental information, sensor uncertainties, and collision risks. The safety margin is calculated using predicted trajectories of surround and subject vehicles. The MPC based combined steering and longitudinal acceleration control law has been designed with extended bicycle model over a finite time horizon. A reachable set of vehicle state is calculated on-line to guarantee that MPC state and input constraints are satisfied in the presence of disturbances and uncertainties. The performance of the proposed algorithm has been conducted simulation studies.

This paper represents a parking lot occupancy detection and parking control algorithm for the autonomous valet parking system. The parking lot occupancy detection algorithm determine the occupancy of the parking space, using LiDAR sensors mounted at each side of front bumper. Euclidean minimum spanning tree (EMST) method is used to cluster that information. After that, a global parking map, which includes all parking lots and access road, is constructed offline to figure out which cluster is located in a parking space. By doing this, searching for available parking lots has been finished. The proposed parking control algorithm consists of a reference path generation, a path tracking controller, and a parking process controller. At first, route points of the reference path are determined under the consideration of the minimum turning radius and minimum safety margin with near parking.

Numerical simulations offer a wide range of benefits. Therefore, they are widely used in research and development. One of the biggest benefits is the possibility of automated parameter variation. This allows testing different scenarios very quickly. Nevertheless, physical experiments in the laboratory or on a test rig are still, and will remain, necessary. Physical experiments offer benefits, e.g., for very complex and/or nonlinear systems and are required for the validation of numerical models. To enhance the quality of experimental NVH investigations and to make use of the benefits of numerical simulation during experimental investigations at the same time, numerical models can be integrated into physical test rigs using the mechanical hardware-in-the-loop (mHIL) method (also referred to as real-time dynamic substructuring, hybrid testing or active control of impedance).