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Rollover is one of the significant life threatening factors in SUVs (Sports Utility Vehicles). By applying braking or steering, active roll stability controllers help prevent rollover accidents in SUVs. The performance of these controllers is very sensitive to vehicle inertial parameters such as vehicle mass and mass center height. In this paper, a unified estimation method for vehicle mass is proposed considering available driving conditions, where three estimation algorithms are developed based on longitudinal, lateral or vertical vehicle dynamics, respectively. The first algorithm is designed using the longitudinal vehicle dynamics and the recursive least square with the disturbance observer technique for longitudinal traveling case. The second algorithm is designed using the lateral vehicle dynamics where the lateral velocity is estimated with the kinematic vehicle model via the Kalman filter.

An efficient development environments such as Software-in-the-Loop Simulation (SILS) and Rapid Control Prototyping (RCP) have been widely used to reduce the development time and cost of real-time ECUs. However, conventional SILS does not consider temporal behaviors caused by computation time, task scheduling, network-induced delays, and so on. As a result, the control performance of ECU is likely to be degraded after implementation. To overcome this problem, SILS/RCP which considers the temporal behaviors was suggested in the previous research. In this study, we validated the proposed SILS/RCP environments which are used to design an Electronic Stability Control (ESC) system which is one of the hard real-time control systems. The proposed SILS/RCP environments make it possible to realize ECUs in the early design phase by considering temporal behaviors.

In this study, a Steer-by-Wire (SBW) controller was developed using the Hardware-In-the-Loop-Simulation (HILS) system. The mechanism of the HILS system consists of a hydraulic actuator for a lateral force on the front tires in a real vehicle. There are two motors in the SBW system controlled by one Electronic Control Unit (ECU). One motor in the steering wheel is to improve the driver's steering feel and the other motor in the steering linkage is to improve the vehicle maneuverability. The SBW controller's availability was verified through a number of simulations on the HILS system. The SBW fail-safe logic was tested through various simulations of the hazard environment on the HILS system. Consequently, the control logic of the SBW system was developed easily and safely in a laboratory.

Using quarter car model, an analytic method for performance estimation of a vehicle suspension system is presented and as the variables of performance estimation, frequency response, RMS response and performance index are considered. From frequency response function, compromise of response performance to the whole frequency range is verified and from RMS response and performance index, sensitivity of ride and handling characteristics are examined. Using full car model, sweet area (stable ride area) are determined and performance sensitivity is estimated according to the change of feedback gains. In order to estimate the output sensitivity, response curve is displayed using 3-dimensional and contour plot. Design data are suggested for optimal design parameter estimation, which maximize the performance of the given suspension system.

This paper presents a model based sensor fault detection and isolation algorithm for the vertical acceleration sensors of the Continuous Damping Control (CDC) system, installed on the sprung mass. Since sensor faults of CDC system have a critical influence on the ride performance as well as the vehicle stability, the sensor fault detection algorithm must be implemented into the overall CDC algorithm. In this paper, each vertical acceleration sensor installed on the sprung mass (two in the front corners and one in the rear) separately estimates the vertical acceleration of the center of gravity of the sprung mass. Then, the sensor fault is detected by cross-checking all three vertical acceleration estimates independently obtained by the each vertical acceleration sensor.

The paper presents a new offset compensation method of a yaw rate sensor and a lateral acceleration sensor. It is necessary to compensate the offsets of the analog sensors, such as the yaw rate sensor and the lateral acceleration sensor, to acquire accurate signals. This paper proposes two different offset compensation algorithms, the sequential compensation method and the model based compensation method. Both algorithms are combined with the algorithm map depending on the vehicle driving status. The proposed algorithm is verified by the computer simulations.

FlexRay is a deterministic and fault-tolerant in-vehicle network(IVN) protocol. It is expected to become a practical standard for automotive communication systems. According to the FlexRay protocol specifications, there are about 60 configurable parameters which should be determined in the design phases. The parameters increase the complexities of FlexRay-based control system development. In this study, we are suggesting a formal design process for FlexRay-based control systems, which is focused on network parameter optimization. We introduce design phases from functional system models to implementations. These phases present formal ways for task allocation, node assignment, network configuration, and implementations. In the network configuration phase, two FlexRay core parameters are selected to optimize network design. Optimal methods of the core parameters provide concise guide lines for optimal communication cycle length and optimal static slot length.

Under the present effort to decrease of air pollution, Electric Vehicles (EVs) are appeared and developed. EVs are running by using electrical energy resource by supporting of battery packs. The effect of air-conditioning has proven to be a serious problem to the point of battery depleting. Thus in the present study, effects of air conditioning (i.e., cooling and heating) on driving range were studied for various driving modes including UDDS, HWFET, and NEDC. The result shows that EV energy efficiency is opposing the usual trend of internal combustion engine vehicle's fuel consumption in highway driving mode than urban driving mode. In highway mode, EV energy efficiency and driving range also decease than urban driving mode. This status was influenced on motor characteristic which torque decrease in high speed rotating conditions and highway driving mode consist of constant speed velocity so it couldn't use the regenerative braking system effectively.

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.

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 performance of an integrated Vehicle Stability Control (VSC) system depends on not only control logic itself, but also the performance of state estimator and control threshold. In conventional VSCs, a control threshold is designed by vehicle characteristics and is centered on average drivers. A VSC algorithm with variable control threshold has been investigated in this study. The control threshold can be determined by phase plane analysis of side slip angle and angular velocity. Vehicle side slip angle estimator has been evaluated using test data. Estimated side slip angle has been used in the determination of the control threshold. The performance of the proposed VSC algorithm has been investigated by human-in-the-loop simulation using a vehicle simulator. The simulation results show that the control threshold has to be determined with respect to the driver steering characteristics.

The purpose of this paper is to develop and experimentally validate a practical and effective technique for the automatic regulation of a hydraulic semiactive vibration absorber (SAVA) for automobiles. The work relies on a consistent hydraulic model of the actuator dynamics that includes the effects of fluid compressibility and a nonlinear viscous loss characteristic. A bistate control algorithm is developed using a Lyapunov approach that seeks to dissipate the energy of the system. The performance of the proposed semiactive damper design on a quarter car model of an automobile suspension is established experimentally on a vibrating test stand. The work provides evidence that the inexpensive hardware design makes it possible to improve the ride and handling performance.