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The application of 'Target Cascading' in the product development process is well known to be effective in various industry fields, and it is a methodology that is already actively applied in various forms in the field of vehicle Ride & Handling (R&H) performance development. This paper proposes a model-based vehicle performance benchmarking methodology and a technology for setting system characteristics to achieve handling performance objectives as a means of utilization for this. Rather than introducing already well-known target deployment techniques, a process is proposed for practical use in the vehicle development phase through the development of interfaces between low degrees of freedom vehicle model and optimization software. The methodology presented in this study is expected to be available in a timely manner as a practical vehicle performance development tool. [ABSTRACT FROM AUTHOR]

Background: Hydraulically interconnected suspension (HIS) system has drawn wide attention in vibration control fields. The pressure difference problem due to long time use of HIS system will cause unsafe driving of the entire vehicle. Purpose: To solve the pressure difference problem and improve the anti-pitch and ride comfort performance of the HIS system, the displacement-sensitive grooves are added to the oil cylinder wall of the HIS system. The displacement-sensitive HIS (DSHIS) system has different damping characteristics depending on the position of the piston. Methods: The DSHIS system and a seven-DOF vehicle are modeled to lay a foundation for the simulation analysis. Then, the damping characteristics of the displacement-sensitive cylinder are studied. Next, the influence of the parameters on the DSHIS system's performance is studied. Finally, a full car simulation is performed to compare the performance of the proposed DSHIS system to the original suspension and the HIS system. Results: In the brake simulation, the pitch angles of vehicle 3 and vehicle 2 are reduced by 56% and 62.3% compared with that of vehicle 1, respectively. In the random road simulation, the acceleration RMS values of vehicle 3 and vehicle 2 are decreased by 48.17% and 26.73% than the original suspension, respectively. In the loading shock simulation, the maximum suspension deflection of vehicle 3 is decreased by 17.39% and 3.37% than vehicle 1 and vehicle 2, respectively. The maximum dynamic tire load of the vehicle 3 is decreased by 4.35% and 1.32% than vehicle 1 and vehicle 2, respectively. Conclusion: The full vehicle simulations prove that the proposed DSHIS system can effectively improve the ride comfort and anti-pitch performance of the mining vehicle. Besides, the pressure difference problem can also be solved by the additional grooves on the cylinder wall. [ABSTRACT FROM AUTHOR]

In bridge structural health monitoring, the response of the bridge while the vehicle is on the bridge, is called a vehicle-bridge interaction (VBI) response. If the vehicle and the bridge are dynamically coupled, the VBI response depends on the bridge's and the vehicle's dynamic properties. Therefore, the damage detection techniques based on the bridge resonances become questionable due to the dynamic coupling between the bridge and the vehicle. This study investigates the influence of vehicle dynamics on the bridge's time-dependent resonances. Vehicle-Induced Delta Frequency (VIDF) represents the changes in the bridge's time-varying resonances resulting from the vehicle-bridge interaction, while Damage-Induced Delta Frequency (DIDF) accounts for the additional alterations caused by bridge damage. The dynamic interaction between vehicles and bridges (VBIs) is characterized by the frequency ratio between the vehicle (super-system) and the bridge (sub-system). The vehicle frequency is influenced by its dynamics, particularly the suspension systems. Two vehicle models, single suspension and dual suspension vehicles representing passenger trains and freight trains, respectively, are analyzed to assess the significance of vehicle dynamics on VIDF and DIDF. The results demonstrate that both vehicle models experience resonance, which magnifies the dynamic response to damage. However, not all types of vehicles possess the desired dynamic characteristics for effective bridge health monitoring. Trains with single suspension systems exhibit more pronounced changes in the bridge's frequency response. This characteristic makes them more suitable for effective bridge health monitoring and damage detection. [ABSTRACT FROM AUTHOR]

For vehicle state estimation, the conventional Kalman filter performs well under the Gaussian assumption, but in the real non-Gaussian situation (especially when the noise is non-Gaussian heavy-tailed), it shows poor accuracy and robustness. In this paper, an extended Kalman filter (EKF) algorithm based on the maximum correntropy criterion (MCC) is proposed (MCCEKF), and a lateral-longitudinal coupled vehicle model is established, while a state observer containing the yaw rate, vehicle sideslip angle, and longitudinal vehicle speed is designed using the easily available measurement information of on-board sensors. After analyzing the complexity of the proposed algorithm, the new algorithm is verified on the Simulink/CarSim simulation experimental platform by Double Lane Change and Sine Sweep Steer Torque Input maneuver. Experimental results show that the MCC-based EKF algorithm has stronger robustness and better estimation accuracy than the traditional EKF algorithm in the case of non-Gaussian noise, and the MCCEKF is more applicable for vehicle state estimation in practical situations. [ABSTRACT FROM AUTHOR]

Vehicle mass is an important parameter for motion control of intelligent vehicles, but is hard to directly measure using normal sensors. Therefore, accurate estimation of vehicle mass becomes crucial. In this paper, a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced. In machine learning method, a feedforward neural network (FFNN) is used to learn the relationship between vehicle mass and other state parameters, namely longitudinal speed and acceleration, driving or braking torque, and wheel angular speed. In dynamics-based method, recursive least square (RLS) with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass. According to the reliability of each method under different conditions, these two methods are fused using fuzzy logic. Simulation tests under New European Driving Cycle (NEDC) condition are carried out. The simulation results show that the estimation accuracy of the fusion method is around 97%, and that the fusion method performs better stability and robustness compared with each single method. [ABSTRACT FROM AUTHOR]

In the context of automated and connected vehicle technology, this paper proposes an environment-on-board predictive braking control system, regulating the vehicle velocity to a desired value, in order to enhance path-following performance and vehicle stability when a vehicle is driven on a path where the road friction coefficient changes suddenly on a curve. In this study, we assume that the vehicle enters a wet road from a dry road and moves on to a dry road, and that the forward road surface friction condition can be estimated. In such driving situation, the objective of the vehicle dynamics control system is to improve path-following performance and vehicle stability of automated vehicle, even under abnormal road conditions. To achieve this objective, the predictive braking control is designed to track a safe velocity which is determined based on a simplified linear two-degree-of-freedom bicycle and a 2nd order look-ahead driver model. The effectiveness of this control system is verified using a nonlinear four-degree-of-freedom four-wheel model under various conditions, with varying road friction coefficients and curvatures. Finally, the effectiveness is evaluated using a full vehicle model of the IPG CarMaker, which is similar to an actual vehicle. [ABSTRACT FROM AUTHOR]

In this research, recognition sensors are used for tracking the outside world along with a steering assistance model that reduces delays inherent in the human cognition, decision, and operation decision-making chain, thereby minimizing vehicle behavior delay with respect to the vehicle position and steering wheel angle based on a read-ahead effect. Additionally, the creation of an emergency avoidance assistance program based on obstacle detection and a cornering assistance program based on white line detection are reported. The effectiveness of these programs using a driving simulator and a model vehicle was investigated. It was found that when the emergency avoidance assist provided by the obstacle detection program was used, the automatic override function was capable of successfully intervening to prevent accidents in situations where it determined that manual steering operations by the driver would be too late. Additionally, in experiments involving cornering assistance by white line detection, it is found that smoother steering around curves was facilitated by the system's ability to set up a optimal approach earlier than could be expected by the curve recognition processes used by human drivers, and that the vehicle was more stable at the curve exit. [ABSTRACT FROM AUTHOR]

This research investigates a robust differential steering control system (DSCS) for an independent four wheel drive electric vehicle (EV). The DSCS will maneuver the independently actuated (IA) four wheel drive EV without the help of any conventional steering mechanism (CSM) via the input torque of the four wheels. The differential angular rotation speed between left and right wheels is used to generate the CSM effects. The DSCS is designed using the linear model of the vehicle with linear tire dynamics and is tested in simulations using a nonlinear vehicle model with nonlinear tire dynamics. The proposed DSCS is a combination of forward speed and yaw rate controllers, designed using the robust H∞ control methodology. The effectiveness of the proposed robust controller is analyzed by comparing the performance of the all-wheel drive (AWD), the rear wheel drive (RWD), and the front wheel drive (FWD) vehicles during simulations. The simulations results indicates that the proposed system can successfully maneuver the vehicle under different driving conditions by tracking the desired parameters without the use of any CSM. [ABSTRACT FROM AUTHOR]